{ "schema_version": "1.0.0", "archived_at": "2026-10-09T15:48:11.467Z", "previous_sha256": "c48c77c851ada2c38d1448e60f9027f13de7385cdd7d42ff28153754c56c6139", "registry_sha256": "0b96fc5f7288ebf735223bdd0b8e2cf887a23d3dc87c3d6469212429ca869d06", "registry": { "dataset": { "name": "Strategic Signal — AI Access Registry", "ontology_version": "2.0.0", "generated_from": { "data_rights_version": "1.0.20", "capability_catalog_version": "1.0.63" }, "record_count": 81, "data_rights_qualified_count": 20, "governed_capability_count": 81, "human_reviewed_count": 76, "migration_notice": "Canonical v2 view over preserved immutable legacy release chains. Legacy IDs and approvals remain authoritative provenance." }, "records": [ { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-755376E1", "legacy_ids": [ "SS-CAP-20261009-46A846453804" ], "event_key": "caixabank|google cloud gemini enterprise agents|2026-10-09|financial repositories documents and internal workflows|governed synthesis classification summarization and automation", "announcement_date": "2026-10-09", "observed_status": "announced", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "CaixaBank" ], "ai_recipients": [ "Google Cloud Gemini Enterprise agents", "Authorized CaixaBank teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "CaixaBank's large financial information repositories, internal documents, classification and summarization tasks, decision-support context, and routine employee workflows within its private and cloud environments.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Specialized Gemini Enterprise agents will access governed CaixaBank repositories and documents to synthesize complex financial information.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The agents will classify and summarize complex content to support internal operational decisions.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The announced program will automate routine tasks and optimize daily workflows under CaixaBank governance.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: CaixaBank's expanded agreement through 2033 will connect Gemini Enterprise agents to governed financial repositories, documents, and employee workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-09-CaixaBank-Expands-Strategic-Collaboration-with-Google-Cloud-to-Accelerate-AI-and-Data-Capabilities", "source_type": "primary_syndicated", "checked_at": "2026-10-09T15:38:00Z", "summary": "The joint announcement extends the alliance through 2033 and says Gemini Enterprise agents will use large repositories for financial synthesis, document management, classification, summarization, and routine workflow automation under CaixaBank security, privacy, and responsible-AI governance." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:54.572Z", "revision_sha256": "304542cb8f537bc0edabf0e450b7483748f7649890f0718d23e600cb060f2316", "notes": "Autonomous publication under the active October 8 delegation: CaixaBank's expanded agreement through 2033 will connect Gemini Enterprise agents to governed financial repositories, documents, and employee workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-DDCABCB5", "legacy_ids": [ "SS-CAP-20261009-A0AB83784F4E" ], "event_key": "nextech specialty practices|assort health and eliseai patient access agents|2026-10-09|scheduling intake readiness communications and follow up workflows|integrated patient access execution", "announcement_date": "2026-10-09", "observed_status": "active", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "Nextech specialty practices and their patients", "Nextech" ], "ai_recipients": [ "Assort Health patient-access agents", "EliseAI patient-access agents", "Authorized specialty-practice staff" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Nextech-connected patient and appointment information plus specialty-practice scheduling, intake, readiness, communications, referrals, insurance-verification, chart-preparation, payments, medication-request, and follow-up workflows.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "other", "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Assort Health and EliseAI integrations can retrieve governed patient, appointment, and practice-workflow context from Nextech environments.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The agents apply specialty-specific rules involving visit history, provider subspecialty, insurance, referrals, location, urgency, and escalation.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "The agents handle patient-access tasks including scheduling and rescheduling, intake, referrals, communications, after-hours coverage, and follow-up through Nextech-connected workflows.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Nextech's multi-year Assort Health and EliseAI partnerships connect patient-access agents to specialty-practice scheduling, intake, readiness, communication, and follow-up workflows. Knowledge and Professional Workflow apply; Human Response remains unknown and strict data-rights qualification is false." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.globenewswire.com/news-release/2026/10/09/3378048/0/en/nextech-unveils-agentic-ai-capabilities-for-patient-experience-through-partnerships-with-assort-health-and-eliseai.html", "source_type": "primary_syndicated", "checked_at": "2026-10-09T15:38:00Z", "summary": "Nextech announces multi-year Assort Health and EliseAI partnerships connecting agentic patient-access capabilities to scheduling, intake, readiness, communication, and follow-up workflows through joint engineering and shared roadmaps." }, { "source_id": "E2", "url": "https://www.assorthealth.com/press/assort-health-and-nextech-to-codevelop-integrated-ai-agent-offering-for-ophthalmology-practices", "source_type": "primary", "checked_at": "2026-10-09T15:38:00Z", "summary": "Assort confirms an existing Nextech Practice+ integration for patient and appointment information, scheduling, and rescheduling, plus a shared API roadmap for deeper workflow access." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:54.775Z", "revision_sha256": "d4e880d2fb2ba6bd2d24055e97bf6cabd38881371d8f2b9865699f85ea5b266b", "notes": "Autonomous publication under the active October 8 delegation: Nextech's multi-year Assort Health and EliseAI partnerships connect patient-access agents to specialty-practice scheduling, intake, readiness, communication, and follow-up workflows. Knowledge and Professional Workflow apply; Human Response remains unknown and strict data-rights qualification is false." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-019B9384", "legacy_ids": [ "SS-CAP-20261009-EC9FE51CF43C" ], "event_key": "authorized dynamics 365 customers|microsoft copilot crm skills|2026-10-08|customer records case history verified knowledge and business actions|permissioned mcp retrieval proposals and authorized actions", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "Authorizing Microsoft Dynamics 365 customer organizations" ], "ai_recipients": [ "Microsoft Copilot Cowork", "Copilot Code and Autopilot experiences", "Customer-configured agents and authorized users" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Dynamics 365 customer records, sales opportunities, case histories, correspondence, verified knowledge, customer KPIs, consent and brand rules, business logic, and approved sales, service, and marketing actions.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration", "connector" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Thirty generally available CRM skills and more than 180 tools expose permissioned Dynamics 365 customer context and verified knowledge to supported Copilot experiences.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The skills analyze deal risk, case urgency, customer history, account strategy, readiness, and other sales, service, and marketing evidence.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "The skills prepare proposed CRM records, field updates, customer replies, account plans, and brand- or consent-aware outreach for review.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "MCP tools can expose approved business actions, but execution remains subject to authorization, review, tenant consent, licensing, and organizational policy.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Microsoft Dynamics 365 CRM skills expose permissioned customer records, case history, verified knowledge, business logic, proposals, and approved actions to Copilot and configured agents. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.microsoft.com/en-us/dynamics-365/blog/it-professional/2026/10/08/bring-microsoft-dynamics-365-into-the-flow-of-work-with-30-new-crm-skills/", "source_type": "primary", "checked_at": "2026-10-09T15:38:00Z", "summary": "Microsoft describes 30 generally available CRM skills and more than 180 tools that expose permissioned Dynamics customer data, verified knowledge, business logic, proposed changes, and approved actions to Copilot and custom agents under administrator, consent, licensing, and policy controls." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:54.984Z", "revision_sha256": "1823f11442f60eb13daf12d023bca7cb6773ac12c4d2ec4dde90744a936877b7", "notes": "Autonomous publication under the active October 8 delegation: Microsoft Dynamics 365 CRM skills expose permissioned customer records, case history, verified knowledge, business logic, proposals, and approved actions to Copilot and configured agents. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0BFB08C0", "legacy_ids": [ "SS-CAP-20261008-4418ABB5EED5" ], "event_key": "zip us|gemini enterprise product factory and zia|2026-10-08|customer research data product risk expertise and institutional knowledge|governed cross functional workflow execution and customer assistance", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Zip US" ], "ai_recipients": [ "Gemini Enterprise agents in Zip's AI-Native Product Factory", "Zip's Zia customer-facing AI assistant", "Authorized Zip employees and customers" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Zip's customer research and data, customer understanding, product and risk expertise, institutional knowledge, software and product-development workflows spanning research, design, engineering, risk, legal, compliance, and customer experience.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "other", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Gemini Enterprise agents will use Zip's customer data, institutional knowledge, risk expertise, and product context across the Product Factory.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "Specialized agents are designed to coordinate across product research, design, engineering, risk, legal, compliance, and customer experience.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The governed environment is intended to create, test, launch, and continuously improve products while Zia resolves increasingly complex customer requests.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "intended", "statement": "Zia is intended to understand customer needs and coordinate specialized agents to deliver the appropriate outcome.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Zip's planned Gemini Enterprise Product Factory connects proprietary customer, product, risk, legal, compliance, engineering, and service context in a governed agentic environment. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-Zip-US-Collaborates-with-Google-Cloud-to-Build-AI-Native-Product-Factory-with-Gemini-Enterprise", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "Zip selected Google Cloud to build a governed AI-Native Product Factory connecting customer research, product development, risk, legal, compliance, and customer experience. Gemini Enterprise will also underpin Zia's evolution toward coordinated customer-facing agents." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:25.660Z", "revision_sha256": "ba35adf753cbfb333b1c9a352da40eba990d31a13db4dca94fbb7e3b9b57f831", "notes": "Autonomous publication under the active October 8 delegation: Zip's planned Gemini Enterprise Product Factory connects proprietary customer, product, risk, legal, compliance, engineering, and service context in a governed agentic environment. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-23C92C10", "legacy_ids": [ "SS-CAP-20261008-6DF25715CAA0" ], "event_key": "balyasny asset management|google gemini models in bamagent|2026-10-08|80 internal financial databases market feeds research and proprietary strategies|private cloud retrieval and multimodal analysis", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Balyasny Asset Management" ], "ai_recipients": [ "Google Gemini models deployed within BAMAgent and Balyasny's proprietary research platforms", "More than 200 authorized Balyasny investment teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "More than 80 internal financial databases and enterprise tools, live market feeds, earnings transcripts, central-bank announcements, regulatory filings, financial charts, research, and proprietary trading data and strategies inside Balyasny's private cloud.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "BAMAgent uses Gemini for high-throughput retrieval across more than 80 internal databases, enterprise tools, and market feeds.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Gemini performs quantitative and multimodal analysis of financial charts, balance sheets, filings, and market signals.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "The research agents synthesize earnings transcripts, live market data, filings, and internal research for investment teams.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Balyasny deployed Gemini inside proprietary research platforms connected to more than 80 internal databases and market feeds, with private-cloud isolation. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-Balyasny-Asset-Management-Deploys-Gemini-Models-for-Agentic-Financial-Research", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "Balyasny deployed Gemini inside BAMAgent and proprietary research platforms connected to more than 80 internal databases and live market feeds. VPC Service Controls keep proprietary trading data and strategies inside its private cloud." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:25.819Z", "revision_sha256": "d71fa7dce67d177329127f0e09fffca0f4d612ae141f23276684275aad336e52", "notes": "Autonomous publication under the active October 8 delegation: Balyasny deployed Gemini inside proprietary research platforms connected to more than 80 internal databases and market feeds, with private-cloud isolation. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-32814414", "legacy_ids": [ "SS-CAP-20261008-D66F8C6BFA8F" ], "event_key": "orange spain|gemini enterprise agents|2026-10-08|network logs diagnostics corporate knowledge and service workflows|rag semantic search analysis and governed action across 1000 agents", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Orange Spain" ], "ai_recipients": [ "More than 1,000 custom Gemini Enterprise agents", "Authorized Orange Spain employees across HR, IT, sales, and customer service" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Orange Spain's network logs and diagnostics, millions of operational data points per second consolidated in BigQuery and Cloud Storage, verified corporate knowledge, incidents, internal requests, and customer-service workflows.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Agents use RAG and semantic search to retrieve verified Orange Spain corporate knowledge.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Agents analyze network logs and diagnostics for system root-cause analysis.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "More than 1,000 agents automate incident triage, request management, and daily workflows across major business areas.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Orange Spain deployed more than 1,000 governed Gemini Enterprise agents over corporate knowledge, network diagnostics, and service workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-Orange-Spain-Expands-Partnership-with-Google-Cloud-to-Industrialize-AI-Operations-with-Gemini-Enterprise", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "Orange Spain expanded its Google Cloud partnership into an enterprise AI Factory with more than 1,000 Gemini Enterprise agents. They use governed RAG and unified operational data for knowledge retrieval, incident triage, network root-cause analysis, and request management." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:26.720Z", "revision_sha256": "366fdbfeaa158aca6830dec243d18dbb4cef0ff2f7fec8651c7539760952e767", "notes": "Autonomous publication under the active October 8 delegation: Orange Spain deployed more than 1,000 governed Gemini Enterprise agents over corporate knowledge, network diagnostics, and service workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-3D90F9F9", "legacy_ids": [ "SS-CAP-20261008-7C2D593CFD8D" ], "event_key": "at&t legaledge|openai models and at&t legal agents|2026-10-08|institutional legal knowledge real matters and legal workflows|design build and evaluate governed attorney supervised workflows", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "AT&T LegalEdge and the AT&T Legal Department" ], "ai_recipients": [ "OpenAI models and AI agents used in AT&T legal workflows", "Authorized AT&T legal professionals" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "AT&T's century-scale institutional legal knowledge, experience from nearly 100 LegalEdge matters, legal service-delivery practices, knowledge-management methods, and attorney-supervised in-house legal workflows.", "asset_types": [ "content_corpus", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "other", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "The collaboration is designed to make AT&T institutional legal knowledge accessible to authorized teams and agents.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "Agents may gather and synthesize relevant information to support lawyers providing legal advice.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "AT&T and OpenAI will design, build, and evaluate practical legal workflows grounded in real matters and attorney oversight.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: AT&T LegalEdge and OpenAI will design and evaluate attorney-supervised legal workflows grounded in AT&T institutional knowledge and real operating experience. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://about.att.com/story/2026/att-legaledge.html", "source_type": "primary", "checked_at": "2026-10-08T15:36:00Z", "summary": "AT&T announced a flagship OpenAI collaboration built on LegalEdge to design, build, and evaluate AI-enabled legal workflows using AT&T institutional knowledge and real operational experience, with confidentiality, governance, attorney judgment, and human review in every workflow." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:25.982Z", "revision_sha256": "06f36ec2662791f3a370a51f07dc505b0a5812e1cd473c795e486f8bb2236c08", "notes": "Autonomous publication under the active October 8 delegation: AT&T LegalEdge and OpenAI will design and evaluate attorney-supervised legal workflows grounded in AT&T institutional knowledge and real operating experience. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-53FD9AAC", "legacy_ids": [ "SS-CAP-20261009-3968429BF22C" ], "event_key": "reinhausen|sap joule work|2026-10-08|sap s4hana business data and workflows|early adopter natural language retrieval and workflow assistance", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "Reinhausen" ], "ai_recipients": [ "SAP Joule Work", "Authorized Reinhausen employees" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Reinhausen's SAP S/4HANA business data, process context, and manual operating workflows accessed through the Joule Work desktop early-adopter program.", "asset_types": [ "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "provider_reported_demonstration", "statement": "Joule Work gives authorized Reinhausen teams natural-language access to business data across the company's SAP S/4HANA environment.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "provider_reported_demonstration", "statement": "The system answers business questions grounded in Reinhausen's SAP process and data context.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "provider_reported_demonstration", "statement": "A previously manual S/4HANA task taking up to an hour is reported as accessible in seconds through Joule Work.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Reinhausen's Joule Work early-adopter deployment gives authorized employees natural-language access to proprietary SAP S/4HANA data and workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://news.sap.com/2026/10/joule-work-sap-business-ai-platform-power-next-generation-business-ai/", "source_type": "primary", "checked_at": "2026-10-09T15:38:00Z", "summary": "SAP identifies Reinhausen as a Joule Work desktop early adopter using natural-language access across its SAP S/4HANA environment and reports that a manual task taking up to an hour is now accessible in seconds." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:54.354Z", "revision_sha256": "e01004cc90d78a0955f9d8b9314d28d04da6416f3706e8ee9084302c0e05e870", "notes": "Autonomous publication under the active October 8 delegation: Reinhausen's Joule Work early-adopter deployment gives authorized employees natural-language access to proprietary SAP S/4HANA data and workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-6E400421", "legacy_ids": [ "SS-CAP-20261008-A84F22E64638" ], "event_key": "state university of new york|google gemini enterprise and suny ai platform|2026-10-08|research datasets and scientific computing workflows across nine campuses|secure cloud research analysis and collaboration", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "State University of New York researchers and participating campuses" ], "ai_recipients": [ "Gemini Enterprise and other authorized models in the SUNY AI Platform", "Authorized SUNY researchers" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Research datasets and scientific-computing workflows across nine research-intensive SUNY campuses, including weather prediction, water-quality detection, cancer-treatment DNA sequencing, and high-resolution brain-scan analysis.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "software_execution_environment" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized researchers can use Gemini Enterprise and more than 200 trusted models within a common governed environment to analyze research data.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The platform supports scientific analysis across weather, water, genomics, cancer, and brain-imaging research.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "The platform combines storage, high-performance computing, data analysis, collaboration, and consistent governance across nine campuses.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: SUNY and Google Public Sector launched a governed AI and high-performance-computing environment across nine research campuses. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://cloud.google.com/blog/topics/public-sector/gps-suny-launch-ai-enabled-platform-to-accelerate-university-research", "source_type": "primary", "checked_at": "2026-10-08T15:36:00Z", "summary": "Google Public Sector and SUNY launched a secure AI and high-performance-computing platform across nine research campuses. Gemini Enterprise and more than 200 models can be used under common governance for weather, water, genomic, cancer, and brain-imaging research." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:26.548Z", "revision_sha256": "5bee8266ec064346579fa0540c58b7ad4954d19b41e66a7d7cc921b0ab8faa22", "notes": "Autonomous publication under the active October 8 delegation: SUNY and Google Public Sector launched a governed AI and high-performance-computing environment across nine research campuses. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-E608F104", "legacy_ids": [ "SS-CAP-20261009-EF31D6947CBF" ], "event_key": "cooley|google cloud gemini enterprise litigation redaction agent|2026-10-08|court filings pii technical and confidential information|attorney reviewed proposed redactions", "announcement_date": "2026-10-08", "observed_status": "announced", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "Cooley and its clients" ], "ai_recipients": [ "Google Cloud Gemini Enterprise litigation redaction agent", "Authorized Cooley litigators" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Court filings and the personally identifiable, technical, and other confidential information within them, together with Cooley's complex-litigation review and redaction workflow and litigators' ongoing product feedback.", "asset_types": [ "content_corpus", "expert_workflow" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "other", "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "The agent is designed to review court filings and identify personally identifiable, technical, and other potentially confidential material.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The agent is designed to distinguish material that may require targeted redaction in complex litigation.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "intended", "statement": "The agent will recommend proposed redactions for attorney review rather than make final filing decisions.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Cooley and Google are co-developing a Gemini Enterprise agent that reviews court filings and proposes targeted redactions while attorneys retain every final filing decision. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-Cooley-Collaborates-With-Google-Cloud-to-Develop-AI-Agent-for-Complex-Litigation", "source_type": "primary_syndicated", "checked_at": "2026-10-09T15:38:00Z", "summary": "Google Cloud's joint announcement says the in-development Gemini Enterprise agent will review court filings and recommend targeted redactions, with attorneys making every final redaction decision." }, { "source_id": "E2", "url": "https://www.cooley.com/news/coverage/2026/2026-10-08-cooley-collaborates-with-google-cloud-to-develop-ai-agent-for-complex-litigation", "source_type": "primary", "checked_at": "2026-10-09T15:38:00Z", "summary": "Cooley confirms it is launch partner, supplies product direction and ongoing litigator feedback, and retains attorney judgment over all court-filing redactions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:55.260Z", "revision_sha256": "1493e9e51e78e4f5c5bbf9786286b74544e497016215580aa62d4d13d907f4cd", "notes": "Autonomous publication under the active October 8 delegation: Cooley and Google are co-developing a Gemini Enterprise agent that reviews court filings and proposes targeted redactions while attorneys retain every final filing decision. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-E8199ACA", "legacy_ids": [ "SS-CAP-20261008-0E60E942F60C" ], "event_key": "ironclad customers|ironclad agent and contract knowledge graph|2026-10-08|contracts clauses obligations precedents negotiation decisions and workflows|permissioned retrieval reasoning and contract workflow action", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Ironclad customers and contract counterparties" ], "ai_recipients": [ "Ironclad Agent and specialized Ironclad contracting agents", "Authorized legal, procurement, and sales teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer contracts, clauses, relationships, obligations, precedents, approved positions, counterparty histories, internal benchmarks, negotiation decisions, past deals, and contracting workflows organized by Ironclad's Contract Knowledge Graph.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Ironclad Agent lets authorized teams search and understand contracts and workflows through a conversational interface.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The Contract Knowledge Graph connects clauses, obligations, relationships, positions, and past decisions to surface patterns and risks.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Ironclad Agent orchestrates specialized agents across stages of the contracting process.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Authorized teams can use natural language to act on contracts and workflows while agents remain grounded in company commercial context.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Ironclad Agent and Contract Knowledge Graph provide permissioned access to customer contracts, commercial context, and contracting workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/ironclad-launches-new-ai-agents-grounded-in-commercial-context-helping-teams-negotiate-better-and-capture-more-value-from-every-deal-302900338.html", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "Ironclad launched Ironclad Agent and Contract Knowledge Graph, allowing authorized teams to search, understand, and act on contracts and workflows using context from clauses, obligations, precedents, approved positions, negotiations, and past decisions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:25.303Z", "revision_sha256": "1916655ec3a5b9e552060f4a38a18bfe602c6e8706a4bddd7aab8bf22bbf8df2", "notes": "Autonomous publication under the active October 8 delegation: Ironclad Agent and Contract Knowledge Graph provide permissioned access to customer contracts, commercial context, and contracting workflows. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FB900159", "legacy_ids": [ "SS-CAP-20261008-3AE6860FA0E2" ], "event_key": "the realreal|ask trr powered by gemini enterprise|2026-10-08|1.5 million one of one luxury items 15 years customer and product context|personalized conversational retrieval and recommendations", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "The RealReal and its marketplace members" ], "ai_recipients": [ "Ask TRR powered by Google Cloud Gemini Enterprise", "Authorized The RealReal shoppers" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "The RealReal's continuously changing inventory of more than 1.5 million one-of-one luxury items, fifteen years of proprietary luxury and customer context, and shopper-stated intent and preferences used for conversational product discovery.", "asset_types": [ "content_corpus", "structured_dataset" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Ask TRR retrieves matching products from a continuously changing catalog of more than 1.5 million unique items.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "The agent uses stated shopper intent and preferences plus The RealReal's proprietary context to tailor recommendations.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "Ask TRR responds to natural-language shopping questions with contextual product guidance.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Ask TRR uses The RealReal's live one-of-one inventory, proprietary luxury context, and shopper intent for personalized recommendations. Knowledge applies; Human Response remains unknown because no linked outcome loop is disclosed. Strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-The-RealReal-Expands-Ask-TRR-AI-Shopping-Agent-With-Google-Clouds-Gemini-Enterprise", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "The RealReal expanded Ask TRR to all 45 million members. The Gemini Enterprise agent searches more than 1.5 million unique items and uses proprietary luxury expertise plus shopper intent and preferences to generate tailored recommendations." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:25.481Z", "revision_sha256": "6e15e997f02ee02b3194f28feab2df981eca0c97769da01a10a0a66c44c7a1e6", "notes": "Autonomous publication under the active October 8 delegation: Ask TRR uses The RealReal's live one-of-one inventory, proprietary luxury context, and shopper intent for personalized recommendations. Knowledge applies; Human Response remains unknown because no linked outcome loop is disclosed. Strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FE4F7130", "legacy_ids": [ "SS-CAP-20261008-8C640E7049D4" ], "event_key": "london stock exchange and databp|customer ai agents and assistants|2026-10-08|licensing entitlements usage terms invoices and billing history|authenticated entitlement scoped rest and mcp retrieval and reconciliation", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "London Stock Exchange", "DataBP", "Authorized London Stock Exchange market-data customers" ], "ai_recipients": [ "Customer in-house AI agents and assistants connected to DataBP Licensing Ledger" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "The authoritative structured record governing each customer's London Stock Exchange market-data relationship: licensing inventories, entitlements, permissions, usage terms, invoices, billing history, and invoice line items.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized systems and AI agents can query entitlement-scoped licensing, permissions, usage, invoice, and billing records through REST and MCP.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "Agents can answer licensing and billing questions grounded in the authoritative records governing the commercial relationship.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "Agents can reconcile licensed entitlements against invoices and flag discrepancies for human review.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: LSE and DataBP provide authenticated, entitlement-scoped REST and MCP access to authoritative licensing, usage, invoice, and billing records for agent-assisted reconciliation. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.lseg.com/en/about-us/new-at-lseg/making-market-data-administration-simpler-with-ai-ready-licensing-intelligence", "source_type": "primary", "checked_at": "2026-10-08T15:36:00Z", "summary": "LSE and DataBP are exposing customer-specific licensing, entitlement, usage, invoice, and billing records through authenticated REST and MCP interfaces so customer agents can answer questions, reconcile invoices, and flag discrepancies." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:26.365Z", "revision_sha256": "ac0b17e5f5aa4f17372c4a5bd186844e4b9ca5239dc5e9ea1c3d5834d93be041", "notes": "Autonomous publication under the active October 8 delegation: LSE and DataBP provide authenticated, entitlement-scoped REST and MCP access to authoritative licensing, usage, invoice, and billing records for agent-assisted reconciliation. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FF767464", "legacy_ids": [ "SS-CAP-20261008-05AD7A96914A" ], "event_key": "on|google cloud migration agents and gemini enterprise|2026-10-08|24 core services 20 code repositories infrastructure configurations and internal work context|bounded multi agent migration and workforce workflow execution", "announcement_date": "2026-10-08", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "On" ], "ai_recipients": [ "Google Cloud migration agents", "Gemini Enterprise agents", "Authorized On engineers and employees" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "On's 24 core services, more than 20 code repositories, deeply integrated microservices, deployment patterns, infrastructure configurations, data-replication requirements, validation environments, and broader internal work context.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "execution_traces", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "provider_reported_demonstration", "statement": "Migration agents analyzed complex deployment patterns across more than 20 On code repositories.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "provider_reported_demonstration", "statement": "A coordinated multi-agent system divided migrations into bounded specialist tasks with explicit checks.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "provider_reported_demonstration", "statement": "Agents generated infrastructure configurations and translation pipelines under engineer review.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "provider_reported_demonstration", "statement": "Agents supported codebase analysis, configuration, data replication, testing, and validation for 24 services; engineers authorized production cutovers.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: On's governed Google Cloud migration agents accessed proprietary code, configurations, and infrastructure workflows under engineer review. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.googlecloudpresscorner.com/2026-10-08-On-Establishes-Google-Cloud-as-Enterprise-AI-Backbone%2C-Beginning-with-Agent-Led-Cloud-Migration", "source_type": "primary_syndicated", "checked_at": "2026-10-08T15:36:00Z", "summary": "On used coordinated Google Cloud agents to migrate 24 core services, including analysis of more than 20 repositories, infrastructure generation, data replication, testing, and validation. Engineers retained control of code review, infrastructure changes, and production cutovers." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:24.998Z", "revision_sha256": "6325f84a17bef1a12c6078da906fe650f3e5bd009d4b3fff7f896fe2c660447c", "notes": "Autonomous publication under the active October 8 delegation: On's governed Google Cloud migration agents accessed proprietary code, configurations, and infrastructure workflows under engineer review. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-184696DE", "legacy_ids": [ "SS-CAP-20261008-DEA3494E5A55" ], "event_key": "intuit|intuit marketing agents via apollo graphos agent services|2026-10-07|real time marketing spend results and enterprise apis|field level identity policy credential brokering and audit", "announcement_date": "2026-10-07", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Intuit and its enterprise system and API owners", "Apollo GraphQL" ], "ai_recipients": [ "Intuit marketing agents using GraphOS Agent Services" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Intuit's real-time marketing spend and campaign-results data plus enterprise APIs exposed through an Apollo domain model with agent identity, field-level policy, credential brokering, and audit trails.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration", "connector" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Intuit's pilot agents can access only the real-time marketing and enterprise API fields authorized through GraphOS.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "provider_reported_demonstration", "statement": "The agents analyze marketing spend against campaign results in real time.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The pilot agents can propose campaign changes while GraphOS governs identity, permitted fields, credentials, actions, and audit.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Intuit is piloting Apollo GraphOS Agent Services so marketing agents can analyze live spend and results under field-level identity, policy, credential, and audit controls. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.apollographql.com/newsroom/press-releases/apollo-graphql-introduces-graphos-agent-services", "source_type": "primary", "checked_at": "2026-10-08T15:36:00Z", "summary": "Apollo launched GraphOS Agent Services and identified Intuit as a preview pilot. Intuit agents analyze live marketing spend against results and propose changes through field-level identity, policy, credential, and audit controls." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:26.909Z", "revision_sha256": "f954a739e30085e017134e047d1e9189c8e74db2dec4d73623ddcb1afe9a62a2", "notes": "Autonomous publication under the active October 8 delegation: Intuit is piloting Apollo GraphOS Agent Services so marketing agents can analyze live spend and results under field-level identity, policy, credential, and audit controls. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-92EAD77C", "legacy_ids": [ "SS-AIDT-2026-0018" ], "event_key": "legacy|ss-aidt-2026-0018", "announcement_date": "2026-10-07", "observed_status": "announced_funded_program", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Biohub", "U.S. Department of Energy", "National Institutes of Health", "Participating scientific organizations" ], "ai_recipients": [ "Meta", "Google DeepMind", "Isomorphic Labs", "Biohub predictive biology models" ], "strategic_buyer_or_operator": [ "Meta", "Google DeepMind", "Isomorphic Labs", "Biohub predictive biology models" ] }, "asset": { "description": "New, coordinated measurements of how cells and tissues respond across conditions, including spatial transcriptomics and environmental-response screens, combined with standardized NIH and DOE biomedical resources. Commercially funded data is initially embargoed for participating funders and later released as an open scientific resource; government-funded work has no comparable restriction.", "asset_types": [ "Large-scale cellular response measurements", "Spatial transcriptomics and intact-tissue molecular maps", "Standardized federal biomedical datasets and repositories", "AI-ready biology training data" ], "industry": "Biomedical research, life sciences, drug discovery, scientific AI, and public research infrastructure", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "not_disclosed", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "unknown" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training and evaluation of predictive biology and virtual-cell models for digital experiments, disease understanding and drug-development prioritization.", "source_ids": [ "E1", "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Canonical Knowledge is true and Professional Workflow is true for scientific measurement and virtual experimentation. Human Response is not applicable because the modeled response subject is cellular or tissue biology, not repeated human behavior-response episodes. Strict data-rights qualification is true only for the separable commercial-funded tranche with a one-year embargo and explicit model training; government-funded open work is part of the same arrangement but is not itself scored as a private rights transfer." }, "economics": { "transaction_structure": "Strategic Model Co-Development", "disclosed_consideration": { "status": "aggregate_commitments_disclosed_allocation_partial", "amount": 1800000000, "currency": "USD", "description": "The initiative totals $1.8 billion in funding, data, compute and measurement technology: $300 million jointly from Meta, Google DeepMind and Isomorphic Labs, more than $500 million from DOE over five years, more than $500 million in prior NIH-funded resources, and Biohub’s earlier $500 million commitment." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "Only the $300 million commercial contribution is directly associated with the time-limited embargo advantage; the disclosed $1.8 billion total also includes public and philanthropic investments and in-kind resources." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 94, "coverage_pct": 100, "rationale": "The coordinated cell-response data is exceptionally scarce, grounded, renewable and useful for predictive-model learning; exact historical depth and post-embargo rights remain less clear.", "factor_ratings": { "uniqueness_scarcity": 5, "historical_depth": 3, "decision_outcome_richness": 5, "decision_value_density": 5, "real_world_grounding": 5, "domain_value": 5, "refreshability": 5, "proprietary_advantage": 4, "model_learning_usefulness": 5, "non_replicability": 5, "rights_usability": 4 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for uniqueness scarcity under methodology v1.0.0." }, "historical_depth": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 3 of 5 rating for historical depth under methodology v1.0.0." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for decision outcome richness under methodology v1.0.0." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for decision value density under methodology v1.0.0." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for real world grounding under methodology v1.0.0." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for domain value under methodology v1.0.0." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for refreshability under methodology v1.0.0." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4 of 5 rating for proprietary advantage under methodology v1.0.0." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for model learning usefulness under methodology v1.0.0." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for non replicability under methodology v1.0.0." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4 of 5 rating for rights usability under methodology v1.0.0." } } }, "transaction_signal": { "score": 84, "coverage_pct": 85, "rationale": "The disclosed commercial funding and one-year access advantage strongly signal standalone data value, while price allocation, competition and independent uplift are not disclosed.", "factor_ratings": { "disclosed_economics": 4.5, "clean_price_discovery": 2.5, "data_consideration_separability": 4.5, "explicit_model_use": 5, "rights_clarity": 4, "competing_bids": null, "strategic_buyer_quality": 5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for disclosed economics under methodology v1.0.0." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 2.5 of 5 rating for clean price discovery under methodology v1.0.0." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for data consideration separability under methodology v1.0.0." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for explicit model use under methodology v1.0.0." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4 of 5 rating for rights clarity under methodology v1.0.0." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for strategic buyer quality under methodology v1.0.0." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for precedent value under methodology v1.0.0." } } } }, "governance": { "governed_access": "Commercially funded data is subject to a one-year embargo, while government-funded data carries no such restriction and the long-term resource is intended to be open.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The initiative concerns cellular and tissue data; human-subject privacy, consent and re-identification controls are not detailed in the reviewed sources.", "trade_secret": "The embargo creates a temporary proprietary advantage; later public release limits permanent exclusivity.", "de_identification": "not_disclosed", "employee_customer_data": "not_applicable", "access_structure": "Biohub coordinates data generation and standardization with DOE and NIH. Meta, Google DeepMind and Isomorphic Labs jointly invest $300 million and receive a one-year commercial embargo period for data they help develop, enabling early model training and experimentation before public release." }, "evidence": [ { "source_id": "E1", "url": "https://www.eurekalert.org/news-releases/1146765", "source_type": "primary_syndicated", "publisher": "Biohub via EurekAlert", "title": "International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models" }, { "source_id": "E2", "url": "https://www.reuters.com/business/healthcare-pharmaceuticals/us-government-google-join-zuckerberg-backed-biohub-18-billion-push-ai-biology-2026-10-07/", "source_type": "secondary", "publisher": "Reuters", "title": "US government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data" }, { "source_id": "E3", "url": "https://www.axios.com/2026/10/07/zuckerberg-biohub-ai-biology-data", "source_type": "secondary", "publisher": "Axios", "title": "Zuckerberg teams with Google, U.S. in push to map cells" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-10-07T16:31:20.531Z", "decision_id": "conversation-20261007-26D1A0A725B3", "packet_sha256": "32194e93d8bd8652fcdd9fff90cb88e0bc7ddb5b2628c9f28e1b867797b67118", "record_sha256": "dec3313484a6e685f8931fef4e7804b35609a4914c42530eaa8dd2dc7a8ed008" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0018/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-AA192A93", "legacy_ids": [ "SS-CAP-20261009-2875822B6917" ], "event_key": "qlik customers|qlik answers and amazon bedrock|2026-10-07|knowledge bases live analytics apps glossaries documents and automation context|tenant governed grounded retrieval and reasoning", "announcement_date": "2026-10-07", "observed_status": "active", "first_added_date": "2026-10-09", "last_reviewed_date": "2026-10-09", "parties": { "providers": [ "Authorizing Qlik Cloud customers" ], "ai_recipients": [ "Qlik Answers specialist agents", "Amazon Bedrock foundation models", "Authorized customer employees" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer knowledge bases, live analytics applications, structured app metadata, glossary definitions, automation context, documents, real-time operations data, and compliance guidance connected through Qlik Cloud.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "platform_integration", "connector" ], "modes": [ "retrieval", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Qlik Answers retrieves tenant-authorized content from knowledge bases, documents, glossaries, and live analytics applications.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "provider_reported_demonstration", "statement": "Specialist agents route and decompose questions, analyze structured and unstructured sources, and validate answers against grounding content.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "The system generates sourced natural-language answers and summaries with citations and Bedrock Guardrails checks.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: Qlik Answers routes authorized customer knowledge bases, analytics, metadata, glossaries, documents, and automation context through tenant-governed specialist agents and Amazon Bedrock. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://aws.amazon.com/blogs/machine-learning/how-qlik-built-grounded-enterprise-scale-ai-with-amazon-bedrock/", "source_type": "primary", "checked_at": "2026-10-09T15:38:00Z", "summary": "AWS and Qlik describe production Qlik Answers routing customer knowledge bases, analytics, metadata, glossaries, automation context, and documents through specialist agents, OpenSearch, and Bedrock with tenant flags, sovereignty controls, and grounding validation." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-09T15:47:53.933Z", "revision_sha256": "ac808e9c14f9887971ed4ca8fa1daba4d08eb62afb7d7f6482edf0845d0b0cd2", "notes": "Autonomous publication under the active October 8 delegation: Qlik Answers routes authorized customer knowledge bases, analytics, metadata, glossaries, documents, and automation context through tenant-governed specialist agents and Amazon Bedrock. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-048CF86D", "legacy_ids": [ "SS-CAP-20261007-60E3FBEDDD3C" ], "event_key": "atlassian and customer organizations|openai gpt chatgpt codex|2026-10-06|teamwork graph jira confluence loom bitbucket people projects documents decisions|permission aware api plugin and platform integration", "announcement_date": "2026-10-06", "observed_status": "active", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Atlassian", "Authorizing Atlassian customers" ], "ai_recipients": [ "OpenAI GPT models", "ChatGPT", "Codex", "Atlassian Rovo" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Permission-aware Atlassian Teamwork Graph context connecting people, Jira work items, Confluence documents, Loom recordings, Bitbucket pull requests, projects and organizational decisions, together with the planning, development and review workflows built on those systems.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "execution_traces", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "prohibited", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "OpenAI models, ChatGPT, Codex and Rovo can retrieve permission-aware project information, documentation, people and development context.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Rovo can combine Jira, Confluence and discussion context to assess launch readiness, identify blockers and surface unresolved decisions.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The partners are exploring deeper Jira integrations for assigning work to agents, tracking progress, capturing decisions and reviewing results.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Atlassian plugins surface each authorized user’s assigned work, recent Looms, projects and Bitbucket pull requests.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the OpenAI × Atlassian Teamwork Graph canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized users receive permission-aware retrieval and planned workflow execution across Jira, Confluence, Loom, Bitbucket and organizational context; Atlassian states customer Rovo data is not shared with third-party providers for model training." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://openai.com/index/atlassian-partnership/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "OpenAI describes the expanded agreement, Teamwork Graph grounding, permission-aware ChatGPT and Codex plugins, Rovo uses and planned Jira agent workflows." }, { "source_id": "E2", "url": "https://www.atlassian.com/blog/company-news/atlassian-openai-strategic-partnership", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "Atlassian confirms that OpenAI frontier intelligence is being combined with its permission-aware, continuously updated Teamwork Graph and enterprise workflows." }, { "source_id": "E3", "url": "https://support.atlassian.com/organization-administration/docs/atlassian-hosted-llms/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "Atlassian documents that Rovo data is not shared with third-party AI providers for training, fine-tuning or improving their models." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:20.616Z", "revision_sha256": "2228b40d803bf5d54feb7e9d9aaf697d187349386e3700048c7671e40d038878", "notes": "Mike Ye approved the OpenAI × Atlassian Teamwork Graph canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized users receive permission-aware retrieval and planned workflow execution across Jira, Confluence, Loom, Bitbucket and organizational context; Atlassian states customer Rovo data is not shared with third-party providers for model training." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0E9373F0", "legacy_ids": [ "SS-CAP-20261006-76E0A27891B4" ], "event_key": "techwolf|sap and joule|2026-10-06|proprietary context graph for work skills work and labor market intelligence|pending acquisition and governed grounding", "announcement_date": "2026-10-06", "observed_status": "signed", "first_added_date": "2026-10-06", "last_reviewed_date": "2026-10-06", "parties": { "providers": [ "TechWolf", "Authorizing TechWolf enterprise customers" ], "ai_recipients": [ "SAP", "SAP Joule", "SAP SuccessFactors agents" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "TechWolf's proprietary context graph for work, continuously updated skills, task and external labor-market intelligence derived from customer-connected HR and business systems, together with TechWolf AI models and applied-AI research capabilities, to be integrated into SAP SuccessFactors and used to ground SAP Joule workforce-agent queries after closing.", "asset_types": [ "structured_dataset", "expert_workflow", "longitudinal_response_history" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "acquisition", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "conditional", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "SAP Joule and SuccessFactors agents are intended to retrieve customer-authorized skills, task, work and labor-market context from TechWolf's context graph.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The context graph is intended to ground workforce-agent reasoning for skills-based hiring, workforce planning, reskilling and role redesign.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "SAP and TechWolf plan AI-powered workforce and skills optimization products that help leaders plan development, deployment, internal mobility and hiring.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the SAP × TechWolf canonical AI Access event as Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. SAP is acquiring TechWolf's proprietary workforce context graph, AI models and research capabilities for future Joule and SuccessFactors grounding, while customer source-data ownership and separately material customer data rights are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://news.sap.com/2026/10/sap-to-acquire-techwolf-evidence-based-work-age-of-ai/", "source_type": "primary", "checked_at": "2026-10-06T15:03:00.000Z", "summary": "SAP announces a signed agreement to acquire TechWolf. The proprietary context graph for work, TechWolf AI models and research team are to become strategic SAP assets and ground Joule and SuccessFactors workforce agents in skills, task, work, labor-market and business-strategy context after closing." }, { "source_id": "E2", "url": "https://www.techwolf.ai/resources/blog/a-new-chapter-sap-to-acquire-techwolf", "source_type": "primary", "checked_at": "2026-10-06T15:04:00.000Z", "summary": "TechWolf confirms the definitive acquisition agreement and describes its AI models as helping large enterprises understand their people and the work they do." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-06T15:25:50.826Z", "revision_sha256": "3f3c733b74585f577fae11a9afe57cc0128c8087cc264c3472e92247011637d0", "notes": "Mike Ye approved the SAP × TechWolf canonical AI Access event as Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. SAP is acquiring TechWolf's proprietary workforce context graph, AI models and research capabilities for future Joule and SuccessFactors grounding, while customer source-data ownership and separately material customer data rights are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0F463E60", "legacy_ids": [ "SS-CAP-20261007-7157F6A8CE9F" ], "event_key": "gitlab and authorizing enterprise customers|anthropic claude mythos and gitlab duo agents|2026-10-06|software lifecycle repositories security findings pipelines approvals and evidence chain|governed platform integration and security remediation", "announcement_date": "2026-10-06", "observed_status": "announced", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "GitLab", "Authorizing GitLab customers" ], "ai_recipients": [ "GitLab Duo Agent Platform", "Anthropic Claude Mythos 5 and 5.1" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-governed software lifecycle context spanning source repositories, issues, goals, CI/CD pipelines, packages, security findings, credentials, policies, approvals and an evidence chain that records how changes move from intent to production.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "execution_traces", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Claude Mythos and GitLab Duo flows will retrieve authorized software context and security findings inside GitLab.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The models will analyze vulnerabilities using repository, pipeline, policy and evidence-chain context.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "New security flows are intended to identify findings, propose fixes and verify remediation under GitLab identity, policy and approvals.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the GitLab Duo × Claude Mythos canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Planned security agents receive authorized software-lifecycle context across repositories, findings, pipelines, policies, approvals and evidence chains, with production-action boundaries remaining governed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://about.gitlab.com/press/releases/2026-10-06-gitlab-announces-the-foundation-for-the-governed-software-factory/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "GitLab announces Claude Mythos-powered security flows, governed agent orchestration, software-lifecycle context, policy controls and evidence-chain tracking." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:21.366Z", "revision_sha256": "68193c9e64c105d8eda8146a72d4dfe7bd3c163d0d928789d06f07fca017bb3f", "notes": "Mike Ye approved the GitLab Duo × Claude Mythos canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Planned security agents receive authorized software-lifecycle context across repositories, findings, pipelines, policies, approvals and evidence chains, with production-action boundaries remaining governed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-33C1A8E4", "legacy_ids": [ "SS-CAP-20261007-B2AF7A34E623" ], "event_key": "precisely and authorizing enterprise customers|mcp compatible ai agents|2026-10-06|governed data management communications master data sap and mainframe workflows|subscription mcp retrieval and action execution", "announcement_date": "2026-10-06", "observed_status": "active", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Precisely", "Authorizing Precisely customers" ], "ai_recipients": [ "MCP-compatible AI agents", "ChatGPT", "Claude", "Microsoft Copilot" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-authorized Precisely data management and communications assets and workflows across Automate, Data360, EngageOne, EnterWorks, Spectrum, Trillium and Syncsort, including SAP processes, dataflows, master data, engagement histories, addresses, geocoding, business rules and mainframe knowledge.", "asset_types": [ "structured_dataset", "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "license", "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Agents can search master data, inspect communications activity and engagement history, retrieve documentation and analyze authorized dataflows.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Precisely tools can cleanse, match, validate, geocode and enrich records using governed business rules.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Agents can initiate SAP automation, create or run dataflows, trigger customer communications and manage jobs through product-specific MCP tools.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Precisely MCP portfolio canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Subscription MCP servers expose governed enterprise data, tools and workflow actions under existing customer security controls; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.precisely.com/press-release/precisely-core-mcp/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "Precisely identifies the supported products, callable data and workflow capabilities, subscription availability, existing-control inheritance and audit logging for customer communications." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:21.189Z", "revision_sha256": "cde2a6d85af433bd17cf47c6855131b113bcc5da8a3972d7bd1e4cd62066359f", "notes": "Mike Ye approved the Precisely MCP portfolio canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Subscription MCP servers expose governed enterprise data, tools and workflow actions under existing customer security controls; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-93AF7160", "legacy_ids": [ "SS-CAP-20261007-4E942E14981B" ], "event_key": "moodys and sap ariba customers|sap joule agents|2026-10-06|supplier financial operational regulatory and risk monitoring data|continuous embedded feed and agent risk workflows", "announcement_date": "2026-10-06", "observed_status": "announced", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Moody's Corporation", "Authorizing SAP Ariba customers" ], "ai_recipients": [ "SAP Joule Agents", "SAP Ariba Supplier Lifecycle and Performance" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Continuously updated Moody’s supplier financial-health, operational-risk, regulatory-status, ownership, entity and multi-domain risk signals embedded into SAP Ariba supplier lifecycle and performance workflows.", "asset_types": [ "structured_dataset", "content_corpus", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Joule Agents and procurement teams will retrieve continuously updated Moody’s supplier risk signals inside SAP Ariba.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "Agents will interpret supplier financial, operational and regulatory risk in the context of procurement decisions.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "Joule Agents are intended to flag emerging supplier risks and surface alternatives before disruption affects the business.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the SAP Ariba × Moody's canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Continuously updated supplier-risk intelligence is embedded into procurement workflows for risk detection and alternative-supplier recommendations, without separately material model-development data rights." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://news.sap.com/2026/10/sap-connect-sap-puts-autonomous-enterprise-to-work/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "SAP announces the Moody’s partnership, the supplier-risk categories, continuous monitoring and intended Joule Agent use inside SAP Ariba." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:20.949Z", "revision_sha256": "9fcb0ef9e05b5a2727f6d3ea578641afc9b2d3fcefb8ef7b1b07c8799c210b67", "notes": "Mike Ye approved the SAP Ariba × Moody's canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Continuously updated supplier-risk intelligence is embedded into procurement workflows for risk detection and alternative-supplier recommendations, without separately material model-development data rights." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-99A57352", "legacy_ids": [ "SS-CAP-20261006-F9E26EF88D09" ], "event_key": "robin powered and authorizing customers|anthropic claude|2026-10-06|workplace people booking attendance utilization floorplan and policy context|oauth mcp retrieval planning and approved action execution", "announcement_date": "2026-10-06", "observed_status": "active", "first_added_date": "2026-10-06", "last_reviewed_date": "2026-10-06", "parties": { "providers": [ "Robin Powered", "Authorizing Robin customers" ], "ai_recipients": [ "Anthropic Claude" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-authorized Robin workplace context including locations, floors, desks, rooms, building hours, people, reservations, attendance and utilization patterns, floor plans, capacity, department allocations, booking policies and planning scenarios, exposed to Claude through an OAuth-authenticated MCP connector with user- and role-bound permissions.", "asset_types": [ "structured_dataset", "longitudinal_response_history", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Claude can retrieve authorized workplace locations, floor and desk availability, people, reservations, booking patterns, floor plans, utilization and policy context.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Claude can compare proposed allocations with capacity and explain policy reasons when a booking is unavailable.", "source_ids": [ "E2", "E3" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Claude can draft stack plans, model seat-assignment scenarios and show the booking changes a proposed floor plan would cause.", "source_ids": [ "E2", "E3" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "With confirmation and the signed-in user's permissions, Claude can book, extend, move or cancel desks and publish reviewed floor-plan changes.", "source_ids": [ "E2", "E3" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Claude can surface a user's upcoming bookings, usual desk and normal office-attendance days.", "source_ids": [ "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Robin Workplace × Claude canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude receives OAuth- and role-bound workplace context for retrieval, planning, personalization and confirmed booking or reviewed floor-plan actions; intervention-outcome linkage sufficient for H2/H3 is not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/robin-moves-workplace-management-from-operations-to-orchestration-launches-workplace-management-connector-for-claude-302899437.html", "source_type": "primary", "checked_at": "2026-10-06T15:06:00.000Z", "summary": "Robin announces its Claude connector and an agentic workplace-orchestration direction using customer workplace, people and office context for planning and action." }, { "source_id": "E2", "url": "https://claude.com/marketplace/connectors/robin-powered", "source_type": "primary", "checked_at": "2026-10-06T15:07:00.000Z", "summary": "Anthropic's verified connector listing documents retrieval of workplaces, desks, people, bookings and patterns; stack and scenario planning; booking actions; user-permission enforcement; confirmations; and draft-before-publish safeguards." }, { "source_id": "E3", "url": "https://support.robinpowered.com/hc/en-us/articles/49332437441933-Connect-Robin-to-Claude", "source_type": "primary", "checked_at": "2026-10-06T15:08:00.000Z", "summary": "Robin's setup documentation confirms OAuth authentication, individual account authorization, organization-admin setup, permission equivalence with Robin, user confirmation before actions and draft-only floor-plan changes until review." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-06T15:25:51.038Z", "revision_sha256": "d80f5c012f54f26e8b60a379d1fad9317b4d43980db1e5d0712e0aee3740920d", "notes": "Mike Ye approved the Robin Workplace × Claude canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude receives OAuth- and role-bound workplace context for retrieval, planning, personalization and confirmed booking or reviewed floor-plan actions; intervention-outcome linkage sufficient for H2/H3 is not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-E8A53297", "legacy_ids": [ "SS-AIDT-2026-0017" ], "event_key": "legacy|ss-aidt-2026-0017", "announcement_date": "2026-10-06", "observed_status": "active_research_collaboration", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Ironclad" ], "ai_recipients": [ "OpenAI", "GPT-6 Astra" ], "strategic_buyer_or_operator": [ "OpenAI", "GPT-6 Astra" ] }, "asset": { "description": "Eleven high-value contracting tasks selected with Ironclad experts across legal, commercial and procurement work, each evaluated against detailed rubrics and practiced in hosted Ironclad product environments. Training inputs were synthetic and based on filtered public SEC contracts; nonpublic customer contracts were excluded.", "asset_types": [ "Expert contracting workflows and rubrics", "Hosted Ironclad software environments", "Synthetic legal, commercial, and procurement tasks" ], "industry": "Legal technology, enterprise contracting, procurement, and frontier AI", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Frontier-model training, reinforcement learning and evaluation for complex computer-use agents performing legal, procurement and commercial workflows.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Canonical Knowledge and Professional Workflow facets are true. Human Response is not applicable. The strict data-rights derived flag is true because Ironclad grants identifiable proprietary workflow and software-environment access for training and evaluation outside ordinary deployment. Training is explicit; customer contracts are excluded; ownership, sublicensing and retention are not inferred." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "No cash consideration or commercial rights allocation is disclosed; the material consideration is expert workflow design, hosted product access and model-development collaboration." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "The announcement does not allocate value among expert time, hosted environments, synthetic task creation, evaluation design or prospective product integration." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 93, "coverage_pct": 92, "rationale": "Ironclad provides scarce expert workflow structure, executable environments and rubric-based feedback with direct model-learning value; historical depth is intentionally excluded.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": null, "decision_outcome_richness": 5, "decision_value_density": 4.5, "real_world_grounding": 5, "domain_value": 4.5, "refreshability": 4, "proprietary_advantage": 4.5, "model_learning_usefulness": 5, "non_replicability": 4.5, "rights_usability": 4.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for uniqueness scarcity under methodology v1.0.0." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for decision outcome richness under methodology v1.0.0." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for decision value density under methodology v1.0.0." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for real world grounding under methodology v1.0.0." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for domain value under methodology v1.0.0." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4 of 5 rating for refreshability under methodology v1.0.0." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for proprietary advantage under methodology v1.0.0." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for model learning usefulness under methodology v1.0.0." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for non replicability under methodology v1.0.0." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for rights usability under methodology v1.0.0." } } }, "transaction_signal": { "score": 63, "coverage_pct": 85, "rationale": "The arrangement clearly grants model-development access and sets a strong vertical-software precedent, but no price, competitive process or independent uplift evidence is disclosed.", "factor_ratings": { "disclosed_economics": 1, "clean_price_discovery": 1, "data_consideration_separability": 3.5, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 1 of 5 rating for disclosed economics under methodology v1.0.0." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 1 of 5 rating for clean price discovery under methodology v1.0.0." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 3.5 of 5 rating for data consideration separability under methodology v1.0.0." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for explicit model use under methodology v1.0.0." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 4.5 of 5 rating for rights clarity under methodology v1.0.0." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for strategic buyer quality under methodology v1.0.0." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Strategic Signal analysis: reviewed evidence supports a 5 of 5 rating for precedent value under methodology v1.0.0." } } } }, "governance": { "governed_access": "Research used hosted Ironclad environments, expert-defined tasks and detailed rubrics; production customer access is not established.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "OpenAI states tasks were created from filtered public SEC contracts with personal-information removal.", "trade_secret": "Ironclad retains its product and workflow know-how; detailed confidentiality terms are not disclosed.", "de_identification": "Synthetic tasks and public-contract filters were used instead of nonpublic customer contracts.", "employee_customer_data": "OpenAI states it did not use OpenAI customer data, OpenAI internal contracts, or nonpublic Ironclad customer data or contracts.", "access_structure": "OpenAI researchers worked with Ironclad employees and experienced users to define tasks and success criteria, received hosted Ironclad software environments for practice, created synthetic training tasks and applied reinforcement learning and evaluation to GPT-6 Astra." }, "evidence": [ { "source_id": "E1", "url": "https://openai.com/index/advancing-computer-use-with-ironclad/", "source_type": "primary", "publisher": "OpenAI", "title": "Advancing computer use with Ironclad" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-10-07T16:31:20.553Z", "decision_id": "conversation-20261007-5332CD80C5A8", "packet_sha256": "25ef205031d894d3cf255e175595c7dd0dcac5947126991e7d9622f768a28677", "record_sha256": "7dd356d0763c504ba5ebe2128ad2c6fdc153e628731223874a25c5c649fd790a" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0017/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FA3D18AF", "legacy_ids": [ "SS-CAP-20261007-E3FF519716B5" ], "event_key": "hawthorn football club|google cloud gemini enterprise and intelia|2026-10-06|player gps match medical scouting and coaching data|unified bigquery platform and planned conversational data agents", "announcement_date": "2026-10-06", "observed_status": "active", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Hawthorn Football Club", "intelia" ], "ai_recipients": [ "Google Cloud Gemini Enterprise", "Hawthorn coaching and analytics agents" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Hawthorn Football Club’s unified player and team intelligence spanning repeated GPS tracking, Champion Data match statistics, medical logs, handwritten scouting notes and qualitative coaching observations organized in Google BigQuery for analysis and planned conversational agents.", "asset_types": [ "structured_dataset", "content_corpus", "sensor_history", "longitudinal_response_history", "expert_workflow" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized analysts can query unified match, GPS, medical and scouting data in Google Cloud rather than reconciling siloed systems.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The platform supports faster identification of performance trends across quantitative tracking and qualitative coaching context.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "intended", "statement": "Planned Gemini Enterprise conversational agents will let coaches ask questions directly against the club’s authorized data.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Hawthorn Football Club × Google Cloud/intelia canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0 only. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. The unified environment combines repeated GPS, match, medical, scouting and coaching data, but no specific intervention-outcome linkage supports H2/H3, recovery or adaptation assertions." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://blog.google/intl/en-au/company-news/technology/hawthorn-football-club-drafts-google-cloud-to-improve-club-analytics-with-ai/", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "Google describes the joint implementation, the source systems unified in BigQuery, current analytics use and planned Gemini Enterprise conversational agents." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:21.493Z", "revision_sha256": "9a822775d5691488cd792a6aacab96e30fd6e2f2516d6920a90cd0f290048f0a", "notes": "Mike Ye approved the Hawthorn Football Club × Google Cloud/intelia canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0 only. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. The unified environment combines repeated GPS, match, medical, scouting and coaching data, but no specific intervention-outcome linkage supports H2/H3, recovery or adaptation assertions." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FBCFF2E0", "legacy_ids": [ "SS-CAP-20261007-068B08027487" ], "event_key": "verified security organizations|anthropic claude cvp|2026-10-06|authorized code systems malware vulnerability and critical infrastructure security environments|tiered verified model access with reduced safeguards and monitoring", "announcement_date": "2026-10-06", "observed_status": "active", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Verified security organizations and authorized system owners" ], "ai_recipients": [ "Anthropic Claude", "Claude Mythos", "Claude Opus", "Claude Sonnet" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Owned or explicitly authorized source code, malware samples, vulnerability evidence, security alerts, red-team targets and critical-infrastructure test environments supplied by verified organizations through Anthropic’s tiered Cyber Verification Program.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "software_execution_environment", "execution_traces", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "license", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Verified defenders can use Claude to inspect owned source code, malware, security alerts and vulnerability evidence.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Claude can analyze and validate vulnerabilities, reverse-engineer malware and plan authorized defensive or red-team work.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Tiered access supports defensive remediation, authorized penetration testing and specialized testing of high-risk systems under program controls.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Anthropic Cyber Verification Program canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Verified organizations can authorize Claude to access code, malware, vulnerabilities, security alerts, red-team targets and critical systems under tiered controls; undisclosed rights remain explicitly unknown." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.anthropic.com/news/cyber-verification-program", "source_type": "primary", "checked_at": "2026-10-07T15:50:00.000Z", "summary": "Anthropic defines three verification tiers, authorized-use boundaries, model access, monitoring and retention requirements, and reports verified vulnerability outcomes from the predecessor program." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T16:31:20.803Z", "revision_sha256": "8b066a8878d511bc1021bdc66b98858045224c7c537c6eb153bc4bd35440eb04", "notes": "Mike Ye approved the Anthropic Cyber Verification Program canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Verified organizations can authorize Claude to access code, malware, vulnerabilities, security alerts, red-team targets and critical systems under tiered controls; undisclosed rights remain explicitly unknown." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-06A4DA4F", "legacy_ids": [ "SS-CAP-20261006-CBB10701B65C" ], "event_key": "wood mackenzie|microsoft copilot and authorized subscriber assistants|2026-10-05|proprietary energy research datasets analytical models and methodologies|subscription mcp retrieval and analytical use", "announcement_date": "2026-10-05", "observed_status": "active", "first_added_date": "2026-10-06", "last_reviewed_date": "2026-10-06", "parties": { "providers": [ "Wood Mackenzie", "Authorizing Wood Mackenzie subscribers" ], "ai_recipients": [ "Microsoft Copilot", "Authorized customer AI assistants" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Wood Mackenzie's proprietary energy and natural-resources research, datasets, methodologies, analytical models and analyst expertise made available to authorized subscribers inside Microsoft Copilot through a live MCP connector, with access constrained by relevant Wood Mackenzie subscriptions and Copilot eligibility.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "license", "connector", "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized Copilot users can discover and retrieve Wood Mackenzie research, datasets, supporting evidence and analysis through natural-language prompts.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Wood Mackenzie analytical models, methodologies and domain intelligence can support screening, benchmarking, strategic analysis and decision preparation inside Copilot.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Wood Mackenzie × Microsoft Copilot canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized subscribers receive entitlement-controlled Copilot retrieval of proprietary research, datasets, methodologies and analytical models; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.woodmac.com/press-releases/wood-mackenzie-connects-trusted-energy-intelligence-with-microsoft-copilot/", "source_type": "primary", "checked_at": "2026-10-06T15:05:00.000Z", "summary": "Wood Mackenzie announces a live MCP connector that gives authorized subscribers access inside Microsoft Copilot to proprietary energy and natural-resources research, datasets, methodologies, analytical models and supporting evidence for screening, benchmarking, strategic analysis and decision preparation." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-06T15:25:50.941Z", "revision_sha256": "a668aa0899c149592789787cba21c9827ee92666a58914e21a8f928bf1ea6899", "notes": "Mike Ye approved the Wood Mackenzie × Microsoft Copilot canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized subscribers receive entitlement-controlled Copilot retrieval of proprietary research, datasets, methodologies and analytical models; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-A1E29C18", "legacy_ids": [ "SS-CAP-20261008-87CD2B6D21D1" ], "event_key": "pwc client organizations|cohere north and enterprise models|2026-10-05|trusted enterprise data knowledge systems and regulated workflows|private cloud on premises or air gapped retrieval decision support and automation", "announcement_date": "2026-10-05", "observed_status": "active", "first_added_date": "2026-10-08", "last_reviewed_date": "2026-10-08", "parties": { "providers": [ "Participating PwC client organizations and enterprise system owners", "PwC" ], "ai_recipients": [ "Cohere North, Cohere enterprise models, and authorized client users" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Participating clients' trusted enterprise data, institutional knowledge, core business systems, and regulated workflows connected under PwC-designed governance in private-cloud, on-premises, or air-gapped environments.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "other", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Cohere North and enterprise models will support secure enterprise search, knowledge access, and deep research over client-controlled data.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The alliance will apply governed organizational context to decision support in regulated environments.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "PwC and Cohere will redesign and automate client workflows with defined human judgment, accountability, and controls.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Autonomous publication under the active October 8 delegation: PwC and Cohere's alliance is designed to connect Cohere North to participating clients' trusted data, systems, and regulated workflows through private-cloud, on-premises, or air-gapped deployments. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.pwc.com/gx/en/news-room/press-releases/2026/accelerate-secure-enterprise-ai-adoption.html", "source_type": "primary", "checked_at": "2026-10-08T15:36:00Z", "summary": "PwC and Cohere announced a global alliance, starting in Canada, to connect Cohere North and enterprise models to participating clients' trusted data, knowledge, systems, and workflows through private-cloud, on-premises, or air-gapped deployments." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-08T15:55:26.160Z", "revision_sha256": "1f0f2a60d3f79e9720efd141a81827fe98d41fd4a3d58c618841f57f9de137dc", "notes": "Autonomous publication under the active October 8 delegation: PwC and Cohere's alliance is designed to connect Cohere North to participating clients' trusted data, systems, and regulated workflows through private-cloud, on-premises, or air-gapped deployments. Knowledge and Professional Workflow apply; strict data-rights qualification is false and scores are not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-656AB54A", "legacy_ids": [ "SS-CAP-20261002-23345411F63F" ], "event_key": "drivecentric dealerships and customers|drivecentric acquire agent|2026-10-01|dealership customer records valuations conversations and operating process|personalized outreach appointment workflow and manager coaching", "announcement_date": "2026-10-01", "observed_status": "active", "first_added_date": "2026-10-02", "last_reviewed_date": "2026-10-02", "parties": { "providers": [ "DriveCentric dealerships", "Dealership customers submitting trade valuations" ], "ai_recipients": [ "DriveCentric Acquire Agent", "Authorized dealership teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Dealer-controlled customer records, trade valuations, conversations and interactions, permissions, workflows, and manager-coached dealership process context used by Acquire Agent for personalized vehicle-acquisition outreach and appointment handling.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Acquire Agent operates from the dealership's existing customer data, permissions, conversations, and workflow context.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "The agent initiates outreach after a trade valuation, holds a customer conversation, and works toward a dealership appointment inside the existing process.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Outreach is personalized using the customer's valuation and dealership-controlled context.", "source_ids": [ "E1" ] }, { "type": "intervention_selection", "evidence_level": "enabled", "statement": "The agent selects immediate conversational follow-up within manager-defined dealership processes and permissions.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the DriveCentric Acquire Agent canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Dealership-controlled customer records, valuations, conversations, permissions and workflow context support personalized outreach and execution; measured outcomes and repeated Human Response linkage are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/drivecentric-introduces-acquire-agent-for-real-time-vehicle-acquisition-302895499.html", "source_type": "primary_syndicated", "checked_at": "2026-10-02T15:14:00.000Z", "summary": "DriveCentric announces Acquire Agent, which runs on dealership data, workflows and permissions, initiates personalized outreach after a trade valuation, converses with the customer, and works toward an appointment under manager-defined processes." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-02T15:36:38.695Z", "revision_sha256": "1016621c5b0e82aa51d96d0f5435a93b2e1831fccdef83e87754c6463f445068", "notes": "Mike Ye approved the DriveCentric Acquire Agent canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Dealership-controlled customer records, valuations, conversations, permissions and workflow context support personalized outreach and execution; measured outcomes and repeated Human Response linkage are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-71FEDC0A", "legacy_ids": [ "SS-CAP-20261002-94594D160E6B" ], "event_key": "mixpanel customers and their product users|mixpanel agent intelligence and product teams|2026-10-01|agent conversations tool calls customer behavior and business outcomes|identity-linked analysis experimentation and improvement", "announcement_date": "2026-10-01", "observed_status": "active", "first_added_date": "2026-10-02", "last_reviewed_date": "2026-10-02", "parties": { "providers": [ "Mixpanel customers", "Product users interacting with customer AI agents" ], "ai_recipients": [ "Mixpanel Agent Intelligence", "Authorized product and analytics teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Identity-linked records of agent conversations, tool calls and results, customer behavior, funnels, replays, cohorts, retention, experimentation results, and downstream business outcomes inside customer Mixpanel projects.", "asset_types": [ "execution_traces", "longitudinal_response_history", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "conditional", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Teams can inspect reconstructed agent conversations turn by turn, including tool calls and results, alongside the user's broader product journey.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Mixpanel Agent and root-cause analysis identify performance changes, customer impact, rising costs, and opportunities for improvement.", "source_ids": [ "E1" ] }, { "type": "response_prediction", "evidence_level": "enabled", "statement": "The system relates agent interactions to user behavior, funnel movement, cohorts, retention, and business outcomes.", "source_ids": [ "E1" ] }, { "type": "intervention_selection", "evidence_level": "enabled", "statement": "Experimentation and feature flags let teams compare prompts, tool configurations, and models against measured customer and business outcomes.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "mixed", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Mixpanel Agent Intelligence canonical AI Access event as governed Knowledge + Professional Workflow + Human Response access at H2. Strict data-rights qualification does not apply and scores are not applicable. Identity-linked agent conversations, tool calls, product behavior and business outcomes establish repeated linked observations; H3, recovery and adaptation are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/mixpanel-introduces-agent-intelligence-helping-product-teams-build-and-measure-more-effective-ai-agents-302895602.html", "source_type": "primary_syndicated", "checked_at": "2026-10-02T15:13:00.000Z", "summary": "Mixpanel announces Agent Intelligence, which reconstructs conversations and tool traces, ties them to the same user identity as product events, surfaces funnels, replays, cohorts and retention, links interactions to customer and business outcomes, and supports experiments comparing prompts, tools, and models." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-02T15:36:38.575Z", "revision_sha256": "0633566eeb75c957565d01bf0818811cd5f774e656061863963a4704e80c14a3", "notes": "Mike Ye approved the Mixpanel Agent Intelligence canonical AI Access event as governed Knowledge + Professional Workflow + Human Response access at H2. Strict data-rights qualification does not apply and scores are not applicable. Identity-linked agent conversations, tool calls, product behavior and business outcomes establish repeated linked observations; H3, recovery and adaptation are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-B97517D2", "legacy_ids": [ "SS-CAP-20261001-57D37B6C055A" ], "event_key": "enterprise content and connected application owners|progress agentic rag smart agent and authorized enterprise users|2026-10-01|governed knowledge boxes live mcp applications web sources and synchronized wordpress content|permission-aware retrieval planning synthesis and citation", "announcement_date": "2026-10-01", "observed_status": "active", "first_added_date": "2026-10-07", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Progress Agentic RAG customers and connected content or system owners" ], "ai_recipients": [ "Progress Smart Agent", "Authorized enterprise users through the Progress Agentic RAG application and Microsoft Teams" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-governed Knowledge Boxes containing enterprise documents and synchronized WordPress content, plus live data retrieved from MCP-connected applications and selected web sources under existing enterprise permissions.", "asset_types": [ "content_corpus", "structured_dataset" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration", "connector" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Smart Agent retrieves from governed Knowledge Boxes, connected enterprise applications through MCP, and selected web sources while respecting existing permissions.", "source_ids": [ "E1", "E2" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "The agent plans research, decomposes questions into subqueries, evaluates sources, and assembles cited answers.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The system synthesizes retrieved enterprise and external information into contextual answers with traceable citations.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Progress Agentic RAG wording correction. The canonical event remains Knowledge-only governed access. Retrieval, agent planning, synthesis, and MCP connectivity do not establish Professional Workflow access or domain-action authority. Strict data-rights qualification remains false and methodology v1.0.0 scores remain not applicable." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.globenewswire.com/news-release/2026/10/01/3158857/0/en/Progress-Introduces-Agentic-RAG-to-Transform-Enterprise-Knowledge-into-Actionable-Intelligence.html", "source_type": "primary_syndicated", "checked_at": "2026-10-01T14:50:00.000Z", "summary": "Progress announces general availability of Agentic RAG, describing Smart Agent retrieval over governed Knowledge Boxes, live MCP applications and web sources, multi-step planning, source evaluation, citations, Microsoft Teams access, and existing-permission enforcement." }, { "source_id": "E2", "url": "https://www.progress.com/agentic-rag", "source_type": "primary", "checked_at": "2026-10-01T14:51:00.000Z", "summary": "The current Progress product page details governed Knowledge Boxes, live connectors, WordPress synchronization, permission-aware access, planning, retrieval, synthesis, and cited enterprise answers." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-07T18:10:22.432Z", "revision_sha256": "3445156132d47eeec85385ab25ccd9ba5414323d53b4c94fe593fac9c3582b11", "notes": "Mike Ye approved the Progress Agentic RAG wording correction. The canonical event remains Knowledge-only governed access. Retrieval, agent planning, synthesis, and MCP connectivity do not establish Professional Workflow access or domain-action authority. Strict data-rights qualification remains false and methodology v1.0.0 scores remain not applicable." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-D2452B12", "legacy_ids": [ "SS-CAP-20261004-35C353ECFCA3" ], "event_key": "barclays|anthropic claude|2026-10-01|internal service knowledge global markets emails software engineering and legacy systems|retrieval email routing and workflow execution", "announcement_date": "2026-10-01", "observed_status": "active", "first_added_date": "2026-10-04", "last_reviewed_date": "2026-10-04", "parties": { "providers": [ "Barclays" ], "ai_recipients": [ "Anthropic Claude", "Claude Code" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Barclays internal customer-service knowledge used by a retrieval-augmented Colleague Knowledge Assistant; incoming Global Markets client emails used for classification, enrichment and routing; and Barclays software-development, cybersecurity and legacy-system workflows made available to Claude and Claude Code under enterprise governance.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "permitted", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Claude powers a retrieval-augmented assistant that finds Barclays internal information for employees supporting more than 20 million UK retail customers.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Claude classifies and enriches incoming Global Markets client emails and determines an operational processing route before colleagues act.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Claude is embedded in customer-support, email-triage, software-development, testing, security and legacy-modernization workflows across Barclays.", "source_ids": [ "E1", "E2" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "Claude Code is being deployed to Barclays developers to accelerate software development, modernize legacy platforms and improve software quality.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Barclays × Anthropic Claude canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude is used for retrieval over Barclays internal service knowledge, high-volume client-email classification/enrichment/routing, and software-development, security and legacy-modernization workflows. Human Response H2/H3 is not established. Retrieval is permitted; action execution is conditional; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "permitted", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.anthropic.com/news/barclays-scales-claude", "source_type": "primary", "checked_at": "2026-10-04T15:54:00.000Z", "summary": "Anthropic and Barclays announce an expanded active collaboration: a Claude-powered retrieval assistant has handled more than one million searches for 16,000 employees, Claude processes about 120,000 Global Markets emails daily, and Claude Code is expanding into software, security and legacy-modernization workflows under Barclays governance and human oversight." }, { "source_id": "E2", "url": "https://www.bloomberg.com/news/articles/2026-10-01/barclays-expands-use-of-anthropic-s-claude-in-efficiency-push", "source_type": "secondary", "checked_at": "2026-10-04T15:57:00.000Z", "summary": "Bloomberg independently reports that Barclays is widening Claude use for legacy modernization and operational efficiency, expects half of developers to use Claude Code by year-end, and uses Anthropic models to process roughly 120,000 client emails daily." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-04T16:19:21.822Z", "revision_sha256": "93a3032f3125725c403dd164392d494f53e195366d4ace281c1f8ab92f604993", "notes": "Mike Ye approved the Barclays × Anthropic Claude canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude is used for retrieval over Barclays internal service knowledge, high-volume client-email classification/enrichment/routing, and software-development, security and legacy-modernization workflows. Human Response H2/H3 is not established. Retrieval is permitted; action execution is conditional; training, retention, ownership, sublicensing and derived-model rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-E14A22EA", "legacy_ids": [ "SS-CAP-20261002-289AE6AC7EEE" ], "event_key": "snowflake joint customers|uipath agents robots and snowflake coco|2026-10-01|governed snowflake enterprise data and operational context|zero-copy retrieval orchestration and action execution", "announcement_date": "2026-10-01", "observed_status": "active", "first_added_date": "2026-10-02", "last_reviewed_date": "2026-10-02", "parties": { "providers": [ "Snowflake joint customers", "Snowflake" ], "ai_recipients": [ "UiPath agents and automations", "Snowflake CoCo" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Joint customers' governed Snowflake enterprise data and operational context, accessed in place through UiPath Data Fabric and Cortex connectivity, plus UiPath agentic skills invoked from Snowflake CoCo.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration", "connector" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "UiPath Data Fabric reads and models customer data directly in Snowflake without copying it, under existing Snowflake controls and policies.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Snowflake CoCo can invoke UiPath agentic skills and trigger UiPath automations while execution remains governed and auditable.", "source_ids": [ "E1", "E2" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "UiPath Maestro orchestrates agents, robots, people, data context, and operational actions across the connected platforms.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the UiPath × Snowflake canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Zero-copy retrieval of governed Snowflake enterprise data and bidirectional invocation of UiPath skills/automations are evidenced; training, ownership, retention and sublicensing rights are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.hpcwire.com/bigdatawire/this-just-in/uipath-expands-snowflake-partnership-to-connect-ai-agents-with-governed-data", "source_type": "primary_syndicated", "checked_at": "2026-10-02T15:10:00.000Z", "summary": "UiPath announces an expanded Snowflake partnership providing two-way, zero-copy access between governed Snowflake data and UiPath orchestration. Data stays in Snowflake under existing controls, while UiPath automations can be triggered from Snowflake and CoCo can call UiPath agentic skills." }, { "source_id": "E2", "url": "https://www.uipath.com/", "source_type": "primary", "checked_at": "2026-10-02T15:11:00.000Z", "summary": "UiPath's current platform page describes governed orchestration across agents, robots, people and systems, including data protection, controlled agency, auditable actions, and execution through APIs and systems of record." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-02T15:36:38.455Z", "revision_sha256": "06261f9b75335adcb568e6748b4e6bffbfb845c9c1160662cfd601522ed053c0", "notes": "Mike Ye approved the UiPath × Snowflake canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Zero-copy retrieval of governed Snowflake enterprise data and bidirectional invocation of UiPath skills/automations are evidenced; training, ownership, retention and sublicensing rights are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-6A5387B2", "legacy_ids": [ "SS-AIDT-2026-0016" ], "event_key": "legacy|ss-aidt-2026-0016", "announcement_date": "2026-09-30", "observed_status": "active_multi_year_preferred_partnership", "first_added_date": "2026-10-01", "last_reviewed_date": "2026-10-07", "parties": { "providers": [ "Synopsys" ], "ai_recipients": [ "OpenAI", "GPT-Synopsys" ], "strategic_buyer_or_operator": [ "OpenAI", "GPT-Synopsys" ] }, "asset": { "description": "Synopsys' proprietary EDA toolchain and expert engineering workflows, including the execution behavior, outputs, constraints, and iterative optimization patterns needed to design and verify chips. The reviewed sources do not identify a delivered static corpus, and customer data is excluded from training.", "asset_types": [ "Proprietary electronic design automation tools", "Verified chip-design workflows and tool outputs", "Synopsys engineering expertise" ], "industry": "Semiconductor design, electronic design automation, and frontier AI", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "not_disclosed", "evaluation": "unknown", "inference_retrieval": "not_disclosed", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Domain-model training and post-training for chip-design planning, tool orchestration, output interpretation, iterative optimization, and engineering assistance.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Canonical Knowledge is true. Professional Workflow remains unknown in the canonical registry because the strict legacy adapter has no separately approved capability-flow classification for this arrangement; executable tool access is not converted into that facet by narrative inference. Human Response is likewise not separately classified in this strict record. The strict data-rights derived flag remains true because a named proprietary operating asset is licensed for model development, the training subscription makes those rights separately material, model use is explicit, and the arrangement is not ordinary enterprise deployment. Customer data is excluded from training. Ownership, raw data delivery, retention, sublicensing, and model-IP allocation are not inferred." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "economic_structure_disclosed_amount_not_disclosed", "amount": null, "currency": null, "description": "Reuters reports that OpenAI pays Synopsys a training subscription fee and that the parties share downstream GPT-Synopsys revenue. Dollar amounts, revenue-share percentages, minimum commitments, and term-by-term allocation are not disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "The reported training subscription covers development access, while downstream revenue sharing covers commercial use. The reviewed sources do not allocate value among tools, expertise, workflow access, model hosting, or go-to-market rights." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 94, "coverage_pct": 92, "rationale": "Strategic Signal analysis. Synopsys' proprietary EDA environment and verified expert workflows are scarce, economically consequential, highly grounded, and explicitly useful for model learning; historical depth is not disclosed.", "factor_ratings": { "uniqueness_scarcity": 5, "historical_depth": null, "decision_outcome_richness": 4, "decision_value_density": 5, "real_world_grounding": 4.5, "domain_value": 5, "refreshability": 4, "proprietary_advantage": 5, "model_learning_usefulness": 5, "non_replicability": 5, "rights_usability": 4.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Synopsys' production EDA toolchain, engineering expertise, and verified design workflows are difficult to substitute with public chip-design data." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Tool execution and iterative optimization expose relationships between design choices, tool outputs, constraints, and improved chip designs." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Semiconductor design decisions carry extremely high engineering, tape-out, manufacturing, schedule, and product-value consequences." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "GPT-Synopsys is trained to operate production engineering tools and interpret their outputs, grounding learning in real design processes." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Electronic design automation is strategic infrastructure for the semiconductor industry and directly shapes costly chip development." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "A multi-year preferred partnership can expose continuing tool and workflow evolution, although no specific data-refresh cadence is disclosed." }, "proprietary_advantage": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The licensed asset is Synopsys' proprietary EDA environment and engineering expertise, not a readily available public corpus." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The tools and workflows are licensed expressly so the model can learn to run tools, interpret outputs, and optimize designs." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Recreating the full toolchain, verification behavior, and accumulated engineering workflow knowledge would require exceptional time, capital, and domain access." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The multi-year license and hosted product path support model development and commercial deployment, while detailed retention and derivative-right terms remain undisclosed." } } }, "transaction_signal": { "score": 74, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The training subscription, downstream revenue share, explicit model-development license, and strong counterparties create a high-value signal, while amounts, bid dynamics, and independent uplift remain undisclosed.", "factor_ratings": { "disclosed_economics": 2.5, "clean_price_discovery": 2, "data_consideration_separability": 4, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E2" ], "rationale": "Reuters reports a training subscription fee and downstream revenue sharing, but no dollar amount, percentage, or minimum commitment." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The bilateral multi-year partnership reveals an economic structure but not a public price, competitive process, or asset valuation." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Reuters identifies a training subscription distinct from downstream revenue sharing, making model-development access partly separable from product commercialization." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The agreement expressly licenses Synopsys tools for GPT-Synopsys development and training to execute and optimize chip-design workflows." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The licensed development purpose, OpenAI hosting, preferred-partner structure, customer-data training prohibition, subscription, and revenue share are disclosed; retention and sublicensing are not." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "OpenAI is a leading frontier-model developer and Synopsys is a leading electronic-design-automation provider." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "A domain-software provider licensing expert tools and workflows for frontier-model training with subscription and revenue share is a repeatable vertical-AI template." } } } }, "governance": { "governed_access": "OpenAI receives licensed tool access for specified model development; GPT-Synopsys is OpenAI-hosted and customer data is explicitly not used for training.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The disclosed asset is engineering software and workflow knowledge. Personal data is not identified.", "trade_secret": "Synopsys retains ownership of its tools; detailed safeguards for proprietary algorithms, tool outputs, and workflow traces are not disclosed.", "de_identification": "not_applicable_to_disclosed_engineering_assets", "employee_customer_data": "Synopsys and OpenAI state that customer data will not be used to train GPT-Synopsys.", "access_structure": "Under a multi-year preferred partnership, OpenAI licenses Synopsys EDA tools to develop and train the OpenAI-hosted GPT-Synopsys model to run expert tools, interpret results, and iteratively optimize designs. Synopsys contributes engineering expertise; customer data is not used for training." }, "evidence": [ { "source_id": "E1", "url": "https://investor.synopsys.com/news/news-details/2026/OpenAI-and-Synopsys-Announce-GPT-Synopsys-Frontier-Intelligence-to-Revolutionize-Chip-Design", "source_type": "primary", "publisher": "Synopsys and OpenAI", "title": "OpenAI and Synopsys Announce GPT-Synopsys, Frontier Intelligence to Revolutionize Chip Design" }, { "source_id": "E2", "url": "https://www.reuters.com/business/synopsys-openai-strike-deal-develop-ai-model-chip-design-work-2026-09-30/", "source_type": "secondary", "publisher": "Reuters", "title": "Synopsys, OpenAI strike deal to develop AI model for chip design work" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-10-07T18:10:22.357Z", "decision_id": "conversation-20261007-6C2EB1F82CB3", "packet_sha256": "dc08a78d4b713c345b02753095ca2d3602418e64ff7c76ac043c057fa0dfb7aa", "record_sha256": "b77584df90656b3d44bc2277aea8702327497348bc2cfdc73dd5fd58b02fd465" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0016/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-88582C6B", "legacy_ids": [ "SS-AIDT-2026-0014" ], "event_key": "legacy|ss-aidt-2026-0014", "announcement_date": "2026-09-30", "observed_status": "active_multiyear_data_license", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "OneMedNet" ], "ai_recipients": [ "Undisclosed commercially scaled AI-enabled precision-medicine company" ], "strategic_buyer_or_operator": [ "Undisclosed commercially scaled AI-enabled precision-medicine company" ] }, "asset": { "description": "De-identified, multimodal, longitudinal clinical real-world data sourced directly through OneMedNet's network of more than 2,300 healthcare partner sites, including medical imaging and longitudinal clinical journeys, licensed for AI model training and incorporation into a commercial derivative dataset.", "asset_types": [ "De-identified multimodal clinical real-world data", "Medical imaging", "Longitudinal clinical records and patient journeys" ], "industry": "Healthcare, precision medicine and clinical AI", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "unknown", "derived_model": "unknown", "sublicensing": "conditional", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training clinical and diagnostic AI models and constructing a commercial derivative dataset that combines OneMedNet imaging and longitudinal clinical journeys with the customer's molecular datasets.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The issuer release establishes a seven-figure multiyear license, explicit AI-model training, derivative-dataset commercialization, recurring downstream sublicensing revenue, retained source-data rights, de-identification, more than 2,300 healthcare partner sites, and an option to expand volume and value. The counterparty name, exact contract value, annual payment schedule, sublicensing rate, licensed subset size, historical years, exclusivity, retention, post-training and evaluation rights, geographic processing terms, patient consent details, model ownership and independent model-uplift evidence are not disclosed. Longitudinal patient journeys do not by themselves establish Human Response H2/H3 linkage. Currency correction: the reviewed issuer announcement uses 'seven figure' without explicitly identifying a currency or settlement form. The earlier USD field is withdrawn in this successor record; no numeric range, cash realization or currency conversion is inferred." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "reported_range_only", "amount": null, "currency": null, "description": "Seven-figure multiyear agreement, plus incremental annual sublicensing revenue to OneMedNet for each downstream end-customer license; exact contract value, currency, payment form, annual schedule and per-license revenue share are not disclosed in the reviewed issuer announcement." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "The disclosed seven-figure amount applies to the multiyear data license. OneMedNet separately states that it receives recurring sublicensing revenue on each downstream license of the derivative product and that the agreement includes an option to expand data volume and contract value; exact values are not disclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 94, "coverage_pct": 100, "rationale": "Strategic Signal analysis. OneMedNet's direct-from-source multimodal clinical corpus is large, longitudinal, renewable, real-world and explicitly useful for AI training, with strong scarcity and derivative-product utility; exact licensed-subset depth and intervention-to-outcome linkage remain undisclosed.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": 4, "decision_outcome_richness": 4, "decision_value_density": 5, "real_world_grounding": 5, "domain_value": 5, "refreshability": 5, "proprietary_advantage": 5, "model_learning_usefulness": 5, "non_replicability": 4.5, "rights_usability": 4.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The direct-from-source network, multimodal imaging plus clinical journeys, and scale across more than 2,300 healthcare sites support a high scarcity rating while the exact licensed subset remains undisclosed." }, "historical_depth": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Longitudinal clinical journeys and the broader platform scale support substantial history, but the announcement does not disclose the licensed subset's exact years of coverage." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The corpus includes longitudinal clinical journeys and imaging relevant to diagnosis, but the disclosure does not establish treatment-intervention and outcome linkage for canonical Human Response staging." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Clinical and diagnostic model development operates in a high-consequence healthcare domain where the licensed data can influence valuable model-development decisions." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "OneMedNet states the data are first-party, direct-from-source real-world clinical data drawn from a live network of more than 2,300 healthcare partner sites." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Precision medicine, clinical diagnostics and life-sciences data products are economically valuable applications, supporting a maximum domain-value rating." }, "refreshability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The multiyear agreement, live provider network, expansion option and recurring downstream licenses establish a renewable data relationship rather than a one-time corpus." }, "proprietary_advantage": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "OneMedNet retains source-data rights while licensing a direct-from-source corpus that the customer will combine with proprietary molecular data into a derivative product." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The customer explicitly intends to use the licensed data to train AI-enabled clinical and diagnostic models." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Replicating a de-identified multimodal corpus across more than 2,300 direct healthcare sites would require substantial access, integration and compliance infrastructure." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Training and derivative commercialization are expressly permitted and downstream sublicensing economics are defined, although retention, exclusivity and model-ownership details remain undisclosed." } } }, "transaction_signal": { "score": 78, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The transaction discloses a seven-figure multiyear data license, explicit model-training use, cleanly separable data consideration, derivative commercialization and recurring sublicensing economics. The exact price, counterparty identity, competitive process and independent model-uplift evidence remain undisclosed.", "factor_ratings": { "disclosed_economics": 3, "clean_price_discovery": 2, "data_consideration_separability": 5, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 3.5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer discloses a seven-figure multiyear data license plus recurring annual sublicensing revenue, but not an exact contract amount or payment schedule." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The disclosure isolates the data-license economics from the derivative sublicensing stream, but gives only a seven-figure range and no competitive bidding evidence." }, "data_consideration_separability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announced consideration is explicitly for the multiyear data license, with a separately described downstream sublicensing revenue stream." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The customer explicitly intends to train AI-enabled clinical and diagnostic models using the licensed OneMedNet data." }, "rights_clarity": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announcement clearly establishes training, derivative commercialization, retained source-data rights and downstream restrictions, while several secondary rights remain undisclosed." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The counterparty is unnamed but described as a commercially scaled AI-enabled precision-medicine company with an established life-sciences customer base." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "A multiyear training license plus recurring participation in derivative-data sublicensing provides a repeatable precedent for source-data owners monetizing downstream AI data products." } } } }, "governance": { "governed_access": "Data are delivered under a multiyear commercial license through OneMedNet's iRWD platform; OneMedNet retains source-data rights and derivative end customers may not relicense or resell the underlying OneMedNet data.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The licensed clinical data are disclosed as de-identified; the release does not disclose patient-level consent terms or the detailed privacy architecture.", "trade_secret": "The source corpus and derivative commercialization rights are governed by the commercial license; additional confidentiality terms are not disclosed.", "de_identification": "OneMedNet states that data are de-identified at source and designed to meet standards for AI training and life-sciences commercial use.", "employee_customer_data": "The asset consists of de-identified healthcare patient data and clinical records rather than employee or ordinary commercial customer data.", "access_structure": "Under a seven-figure multiyear agreement, OneMedNet will deliver de-identified multimodal longitudinal clinical RWD through its iRWD platform to an undisclosed commercially scaled AI-enabled precision-medicine company. The customer intends to train clinical and diagnostic AI models and create a licensable derivative dataset combining its molecular data with OneMedNet imaging and longitudinal clinical journeys. OneMedNet retains source-data rights and receives incremental annual sublicensing revenue when the derivative solution is licensed to end customers." }, "evidence": [ { "source_id": "E1", "url": "https://www.globenewswire.com/news-release/2026/09/30/3371902/0/en/onemednet-signs-seven-figure-multiyear-agreement-to-power-a-partner-sold-derivative-data-solution.html", "source_type": "primary_syndicated", "publisher": "OneMedNet", "title": "OneMedNet Signs Seven Figure Multiyear Agreement to Power a Partner Sold Derivative Data Solution" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-30T19:34:37.164Z", "decision_id": "conversation-20260930-1BE00DE10512", "packet_sha256": "ab15069afc2980e4e693ed1554c3ee7c9091af7fc4fa43e2021a8c482500df80", "record_sha256": "780e019ff810f2e9758e4fa33c443b9018cd250c1647c418e50dc08d66ce1c57" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0014/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-A951156C", "legacy_ids": [ "SS-CAP-20260930-13C7C0BF7CA0" ], "event_key": "amazon engineering teams|synopsys ai powered eda simulation and custom agents|2026-09-30|custom silicon designs engineering context and silicon to system workflows|analysis optimization validation and agentic execution", "announcement_date": "2026-09-30", "observed_status": "announced", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Amazon engineering teams" ], "ai_recipients": [ "Synopsys AI-powered EDA, simulation and analysis systems", "Custom Synopsys-Amazon agentic AI capabilities" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Amazon's custom-silicon and AWS-infrastructure engineering context, complex chip and system designs, and silicon-to-system design, analysis, optimization and validation workflows, made available to deployed Synopsys AI-powered EDA, physics simulation and jointly developed agentic capabilities.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "license", "partnership", "other", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "domain_reasoning", "evidence_level": "intended", "statement": "Synopsys AI-powered EDA and physics-based simulation will analyze and optimize Amazon's complex chips and systems.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "The parties plan custom agentic capabilities to coordinate silicon-to-system engineering work for Amazon teams.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "Synopsys and Amazon plan to apply AI across design, analysis, optimization and validation workflows.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Synopsys × Amazon canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Amazon engineering context and workflows are available to Synopsys AI-powered design, simulation and planned agentic systems, but no separately material data-rights grant is established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://news.synopsys.com/2026-09-30-Synopsys-and-Amazon-Announce-Strategic%2C-Multi-year-IP-Agreement-for-Custom-Silicon-Collaboration-Also-Extends-to-Cloud-and-AI-Powered-Engineering", "source_type": "primary", "checked_at": "2026-09-30T15:35:00Z", "summary": "Synopsys discloses a multi-year $1 billion-plus strategic agreement expanding Amazon's use of Synopsys IP, EDA, simulation and agentic AI, with planned custom agents spanning complex chip and system design, analysis, optimization and validation." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:05.049Z", "revision_sha256": "6950fed7ea31f74e673305eadc49a4f348cb502dc1ad87d0ddec4979354493ac", "notes": "Mike Ye approved the Synopsys × Amazon canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Amazon engineering context and workflows are available to Synopsys AI-powered design, simulation and planned agentic systems, but no separately material data-rights grant is established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-EAE98277", "legacy_ids": [ "SS-CAP-20261001-590A3D2CF9E0" ], "event_key": "predactiv and partner data sources|predactiv people model and authorized customer ai tools|2026-09-30|pseudonymous open web behavioral signals group psychographic intelligence and first party audience data|training retrieval audience modeling and activation", "announcement_date": "2026-09-30", "observed_status": "active", "first_added_date": "2026-10-01", "last_reviewed_date": "2026-10-01", "parties": { "providers": [ "Predactiv", "Predactiv data and partner sources" ], "ai_recipients": [ "Predactiv People Model", "Authorized customer AI tools connected through Predactiv MCP Server or Platform API" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Predactiv's pseudonymous open-web behavioral graph spans more than three million domains, roughly 65 billion monthly events, and more than one billion users, with group-level psychographic attributes and optional customer first-party audience data.", "asset_types": [ "structured_dataset", "content_corpus", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized AI tools can query the People Model for group-level audience intelligence through MCP or the Platform API.", "source_ids": [ "E2", "E3" ] }, { "type": "response_prediction", "evidence_level": "enabled", "statement": "The service builds and sizes audiences by predicting group-level interests, intent, and psychographic characteristics from behavioral signals.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Connected tools can analyze, build, and activate audiences subject to customer authorization and organizational entitlements.", "source_ids": [ "E1", "E3" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Customers can combine first-party audience data with the People Model to produce audience-specific planning and activation outputs.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Predactiv People Model canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Behavioral and psychographic audience intelligence is available through governed MCP/API retrieval and workflow use; Human Response H2/H3 is not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://predactiv.com/news/predactiv-introduces-the-people-model", "source_type": "primary", "checked_at": "2026-10-01T14:35:00.000Z", "summary": "Predactiv announces the People Model, describes more than three million domains, 65 billion monthly events and more than one billion users, and says customers can build, analyze and activate audiences with first-party data and privacy controls." }, { "source_id": "E2", "url": "https://predactiv.com/people-model", "source_type": "primary", "checked_at": "2026-10-01T14:36:00.000Z", "summary": "The People Model product page describes the proprietary pseudonymous behavior graph, group-level psychographics, governed data practices, and audience intelligence available to customers." }, { "source_id": "E3", "url": "https://predactiv.com/mcp", "source_type": "primary", "checked_at": "2026-10-01T14:37:00.000Z", "summary": "Predactiv documents authenticated MCP and API access for AI tools, including OAuth, organization entitlements, audience analysis and activation functions, and privacy-aware controls." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-01T15:40:21.209Z", "revision_sha256": "cd7390134e7a054c94c745060d02b79cc501663ff7d1b7978b1be6523e1bc0b0", "notes": "Mike Ye approved the Predactiv People Model canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and scores are not applicable. Behavioral and psychographic audience intelligence is available through governed MCP/API retrieval and workflow use; Human Response H2/H3 is not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-ED1045F2", "legacy_ids": [ "SS-CAP-20260930-06BC81AFAF68" ], "event_key": "ennoble care|ennoble clinical ai agents on coreweave|2026-09-30|proprietary electronic medical record and home based clinical workflows|inference and workflow execution", "announcement_date": "2026-09-30", "observed_status": "active", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Ennoble Care" ], "ai_recipients": [ "Ennoble Care clinical and back-office AI agents hosted on CoreWeave Cloud" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Ennoble Care's proprietary ONC-certified electronic medical record and home-based primary, palliative and hospice care workflows, made available to internally developed AI agents for clinical summarization, documentation, decision support and back-office automation on dedicated CoreWeave infrastructure.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "software_execution_environment" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Ennoble Care runs clinical AI inference against the context available in its proprietary electronic medical record and care-delivery environment.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Clinical agents support summarization, documentation and clinical decision support for home-based care workflows.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Ennoble Care is scaling AI agents embedded in care-delivery and back-office workflows on reserved CoreWeave capacity.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Ennoble Care × CoreWeave canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Clinical and operational agents access Ennoble's proprietary EMR and workflows in a controlled inference deployment; Human Response H2/H3 linkage remains unestablished." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.businesswire.com/news/home/20260930542844/en/", "source_type": "primary_syndicated", "checked_at": "2026-09-30T15:34:00Z", "summary": "CoreWeave and Ennoble disclose dedicated infrastructure for clinical inference, hardware isolation and zero-trust controls, plus multiple AI agents built on Ennoble's proprietary ONC-certified EMR for summarization, documentation, decision support and back-office automation." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:04.991Z", "revision_sha256": "3e7844e3ee45d7034a0d8d9e37c24837b1ddc7a8db3cc7ff6c3ddf061549d7aa", "notes": "Mike Ye approved the Ennoble Care × CoreWeave canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Clinical and operational agents access Ennoble's proprietary EMR and workflows in a controlled inference deployment; Human Response H2/H3 linkage remains unestablished." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0580C24B", "legacy_ids": [ "SS-AIDT-2026-0015" ], "event_key": "legacy|ss-aidt-2026-0015", "announcement_date": "2026-09-29", "observed_status": "active_consortium_initial_dataset_expected_early_2027", "first_added_date": "2026-10-01", "last_reviewed_date": "2026-10-01", "parties": { "providers": [ "AbbVie", "argenx", "Lundbeck", "Takeda" ], "ai_recipients": [ "Ginkgo Datapoints", "Apheris", "AbbVie", "argenx", "Lundbeck", "Takeda" ], "strategic_buyer_or_operator": [ "Ginkgo Datapoints", "Apheris", "AbbVie", "argenx", "Lundbeck", "Takeda" ] }, "asset": { "description": "A planned standardized dataset targeting 10,000 antibodies, combining proprietary antibody sequences contributed by AbbVie, argenx, Lundbeck, and Takeda with Ginkgo-generated developability assay measurements. Raw member sequences remain protected through Apheris federated infrastructure.", "asset_types": [ "Proprietary antibody sequences", "Standardized antibody developability assay data", "Federated member model outputs" ], "industry": "Biopharmaceutical research, antibody engineering, and scientific AI", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "federated_access" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "not_disclosed", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "unknown", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Foundation-model training, federated member training, benchmarking, refinement, and member-local fine-tuning for antibody developability prediction and biologics R&D.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Canonical Knowledge facet is true. Professional Workflow is false because the arrangement supports scientific learning but does not grant execution authority over a member operating workflow. Human Response is false and response subject is scientific/biological nonhuman. The strict data-rights derived flag is true because named proprietary corporate corpora, separately material federated learning rights, explicit model training, and a non-ordinary consortium structure are all disclosed. No ownership, sublicensing, exclusivity, economics, or raw-sequence access is inferred beyond the announcement." }, "economics": { "transaction_structure": "Federated / Controlled Learning Rights", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "No membership fee, cash consideration, revenue share, minimum commitment, or asset valuation is disclosed. Members contribute proprietary sequences and receive governed access to consortium data, models, and derivatives for internal use." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "Member contributions, assay generation, model-development services, and internal-use benefits are disclosed; transaction value and the allocation of consideration among data, laboratory work, infrastructure, and models are not disclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 90, "coverage_pct": 92, "rationale": "Strategic Signal analysis. The pooled proprietary sequences, standardized experimental outcomes, cross-company scope, and explicit model-learning rights create an unusually strong scientific asset; historical depth and continuing refresh cadence remain partly undisclosed.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": null, "decision_outcome_richness": 4, "decision_value_density": 4.5, "real_world_grounding": 5, "domain_value": 5, "refreshability": 3, "proprietary_advantage": 5, "model_learning_usefulness": 5, "non_replicability": 4.5, "rights_usability": 4.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The consortium pools otherwise siloed proprietary antibody sequences and harmonized assay outputs across four large drug developers." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The standardized assays connect antibody sequences to measured developability properties that guide candidate-selection decisions." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Each experimentally measured sequence can inform costly and high-value biologic discovery and development decisions." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Ginkgo will generate standardized physical assay data rather than relying only on synthetic or literature-derived examples." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Antibody developability is central to biopharmaceutical R&D, where better early selection can avoid expensive late-stage failure." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The initial target is 10,000 antibodies with a first dataset expected in early 2027; a continuing refresh cadence is not disclosed." }, "proprietary_advantage": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Each pharmaceutical member contributes proprietary sequences while federated infrastructure prevents exposure of raw member data." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Ginkgo will train a foundation model and members may train, benchmark, refine and fine-tune models against the consortium data." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Reproducing the pooled sequences and standardized assays would require access to multiple proprietary portfolios and substantial laboratory work." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Federated training and member-local fine-tuning make the asset usable for learning while preserving raw-sequence boundaries." } } }, "transaction_signal": { "score": 51, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The consortium provides explicit and well-governed training rights with strong precedent value, but no disclosed economics or clean price discovery.", "factor_ratings": { "disclosed_economics": 0, "clean_price_discovery": 0, "data_consideration_separability": 2.5, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 4.5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announcement does not disclose membership fees, cash consideration, revenue share, or minimum commitments." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "No transaction value, auction, bid process, or separable market price for the contributed rights is disclosed." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Members expressly contribute proprietary sequences and receive model and dataset-use benefits, but monetary consideration is not separated." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Foundation-model training, benchmarking, refinement, and member-local fine-tuning are all expressly disclosed." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Contribution ownership, raw-data non-exposure, federated access, internal model use, and local fine-tuning are described, while sublicensing remains undisclosed." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "AbbVie, argenx, Lundbeck, and Takeda are major strategic biopharma participants working with Ginkgo and Apheris." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The federated consortium structure is a repeatable template for pooling competitively sensitive scientific data without central raw-data transfer." } } } }, "governance": { "governed_access": "Apheris provides privacy-preserving federated infrastructure so members can train, benchmark, and refine models without exposing raw proprietary sequences.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The disclosed assets are antibody sequences and assay data; patient-level or personal data is not identified.", "trade_secret": "Raw proprietary member sequences are not exposed to other members, and ownership remains with the contributing member.", "de_identification": "not_applicable_to_disclosed_antibody_assets", "employee_customer_data": "No employee or ordinary customer data is identified; the external assets are proprietary scientific research data.", "access_structure": "Members contribute proprietary sequences; Ginkgo generates standardized assay data and trains a foundation model. Apheris federated infrastructure lets members train, benchmark, and refine models across consortium data without exposing raw proprietary sequences, and members can fine-tune models in their own environments." }, "evidence": [ { "source_id": "E1", "url": "https://www.businesswire.com/news/home/20260929556449/en/Ginkgo-Datapoints-and-Apheris-Welcome-AbbVie-argenx-Lundbeck-and-Takeda-as-Founding-Members-of-the-Antibody-Developability-Consortium", "source_type": "primary_syndicated", "publisher": "Ginkgo Datapoints and Apheris via Business Wire", "title": "Ginkgo Datapoints and Apheris Welcome AbbVie, argenx, Lundbeck and Takeda as Founding Members of the Antibody Developability Consortium" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-10-01T15:39:17.459Z", "decision_id": "conversation-20261001-D4406416282E", "packet_sha256": "330c0cd6e2b7b761bddc7a509c54ad9a2d86c2519f50cf3c2efe6d9412bdcfeb", "record_sha256": "355e673232bf563ed8484247753d67ff0f4c353fdafa2850263693087b157fc1" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0015/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0BE0E3B4", "legacy_ids": [ "SS-CAP-20261005-789E151A8FA3" ], "event_key": "authorizing adobe enterprise customers and adobe|chatgpt and cx enterprise coworker|2026-09-29|experience platform audience journey asset and workfront workflow context|retrieval planning and governed action execution", "announcement_date": "2026-09-29", "observed_status": "active", "first_added_date": "2026-10-05", "last_reviewed_date": "2026-10-05", "parties": { "providers": [ "Adobe", "Authorizing Adobe enterprise customers" ], "ai_recipients": [ "Adobe CX Enterprise Coworker", "OpenAI ChatGPT agents" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer Experience Platform audience and journey data, conversions and engagement observations, approved marketing assets, brand and compliance context, and Workfront projects, budgets, tasks and approval workflows made available to CX Enterprise Coworker in ChatGPT and to authorized ChatGPT agents in Workfront.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "execution_traces", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "CX Enterprise Coworker retrieves governed Adobe Experience Platform journey, audience and asset context inside ChatGPT.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Coworker analyzes conversion changes, journey drop-off, audience overlap, engagement and brand or compliance alignment.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Coworker plans campaign workspaces, budgets, tasks, sequencing and multi-stage approval chains across Adobe systems.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Coworker saves audience segments and creates Workfront projects and approval workflows within enterprise policy boundaries.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "across_subjects", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Adobe CX Enterprise Coworker × ChatGPT canonical AI Access event as governed Knowledge + Professional Workflow + Human Response H2 access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Governed journey, audience, conversion, engagement, asset and Workfront context supports repeated marketing context-intervention-outcome observations; H3, recovery and adaptation are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://business.adobe.com/blog/cx-enterprise-coworker-in-chatgpt", "source_type": "primary", "checked_at": "2026-10-05T15:50:00.000Z", "summary": "Adobe announces the live CX Enterprise Coworker plugin in ChatGPT, grounded in Experience Platform audience and journey data and able to analyze conversions, drop-off and engagement, retrieve governed assets, save segments, and create Workfront projects and approvals within policy boundaries; ChatGPT agents are also planned for Workfront through OpenAI's Agents API." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-05T16:25:56.903Z", "revision_sha256": "071aab4de270081e517a8cb74f7a4726e869f0ad74e80d4c640a2ac9288c857b", "notes": "Mike Ye approved the Adobe CX Enterprise Coworker × ChatGPT canonical AI Access event as governed Knowledge + Professional Workflow + Human Response H2 access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Governed journey, audience, conversion, engagement, asset and Workfront context supports repeated marketing context-intervention-outcome observations; H3, recovery and adaptation are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-1EEC0FFD", "legacy_ids": [ "SS-CAP-20260930-E602EBAA6321" ], "event_key": "authorizing chatgpt users and workspaces|openai dots|2026-09-29|connected apps files email calendars local computers memories and ongoing tasks|retrieval personalization and action execution", "announcement_date": "2026-09-29", "observed_status": "active", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Authorizing ChatGPT users and business workspaces", "Connected application and computer providers" ], "ai_recipients": [ "OpenAI Dots" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "User- and administrator-authorized information and workflows from connected applications, email, calendars, Slack, files, local computers, Codex environments, saved memories, scheduled tasks and ongoing conversations, made available to an always-on OpenAI dot for proactive retrieval, personalization and action execution.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration", "connector", "other" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "conditional", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "A dot can retrieve authorized information from connected apps, email, calendars, Slack, attached files and an optionally connected local computer.", "source_ids": [ "E1", "E2" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "A dot can conduct background research, schedule recurring work and proactively suggest or pursue work without a new message, subject to user and workspace controls.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "A dot can use its cloud computer, optionally use a connected local computer, create Codex or Work tasks and take permitted application actions under configurable confirmation rules.", "source_ids": [ "E1", "E2" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "A dot can create memories from conversations and connected applications and use that context in ongoing work.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the OpenAI Dots canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Permissioned retrieval, memory, planning and action execution across authorized apps, files and computers are evidenced; no Human Response H2/H3 loop is established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://help.openai.com/en/articles/20001530-getting-started-with-your-dot", "source_type": "primary", "checked_at": "2026-09-30T15:28:00Z", "summary": "OpenAI's launch documentation describes Dots availability, connected apps, proactive review, memory, scheduled tasks, cloud and optional local-computer access, Codex tasks, action rules, pausing and reset deletion." }, { "source_id": "E2", "url": "https://help.openai.com/en/articles/20001554-manage-dots-in-chatgpt-workspaces", "source_type": "primary", "checked_at": "2026-09-30T15:29:00Z", "summary": "OpenAI's Enterprise administrator documentation says Dots are off by default, app access remains bounded by plugin controls and provider authorization, and Slack or Teams connections require separate permissions and setup." }, { "source_id": "E3", "url": "https://openai.com/business-data/", "source_type": "primary", "checked_at": "2026-09-30T15:30:00Z", "summary": "OpenAI states that business, Enterprise, Edu and Healthcare inputs and outputs are not used for training by default and describes encryption, retention, residency and access controls." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:04.664Z", "revision_sha256": "b8ff5f757945434640dc42f8296ee47286ca3f012e29f85b3edb1a03cfa6e345", "notes": "Mike Ye approved the OpenAI Dots canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Permissioned retrieval, memory, planning and action execution across authorized apps, files and computers are evidenced; no Human Response H2/H3 loop is established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-911A49BF", "legacy_ids": [ "SS-CAP-20260930-3AEF146D9F1C" ], "event_key": "enterprise customers and connected systems|authorized enterprise ai agents via cdata connect ai gateway|2026-09-29|live enterprise records schemas semantics tools and business actions|governed mcp retrieval and execution", "announcement_date": "2026-09-29", "observed_status": "active", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Enterprise customers and their connected systems", "CData Software" ], "ai_recipients": [ "Authorized enterprise AI agents and models using CData Connect AI Gateway" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-authorized live enterprise records, schemas, relationships, metric definitions, business terminology, semantic models and permitted actions across SAP, Salesforce, NetSuite, databases, warehouses, on-premises and legacy systems, exposed through a governed MCP gateway.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions", "software_execution_environment" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized agents can query live records and semantic context across hundreds of connected enterprise systems under source-level entitlements.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The gateway supplies schemas, relationships, metric definitions, terminology and approved corrections so models can reason consistently across systems.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "provider_reported_demonstration", "statement": "Agents can take validated actions in source systems; Adobe reports a test-automation agent operating inside SAP under configured permissions.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the CData Connect AI Gateway canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Entitlement-governed retrieval, semantic context and validated actions across live enterprise systems are evidenced; separate learning, ownership or reuse rights are not." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/cdata-launches-connect-ai-gateway-one-control-point-between-ai-and-the-systems-that-run-the-business-302891531.html", "source_type": "primary_syndicated", "checked_at": "2026-09-30T15:32:00Z", "summary": "CData's issuer release launches Connect AI Gateway with governed MCP access to live enterprise systems, record-level identity and entitlements, source-side filtering, validated actions, auditability and an Adobe SAP automation example." }, { "source_id": "E2", "url": "https://www.cdata.com/ai/mcp-gateway/", "source_type": "primary", "checked_at": "2026-09-30T15:33:00Z", "summary": "CData's product page describes agent-specific toolkits, team workspaces, connected first- and third-party MCP servers, hundreds of schema-aware sources and customer-controlled governance." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:04.865Z", "revision_sha256": "ca5e5f3b997575af0cd6fee84a3ff5007d09dfc1a44e0213ddb994c1868e340a", "notes": "Mike Ye approved the CData Connect AI Gateway canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Entitlement-governed retrieval, semantic context and validated actions across live enterprise systems are evidenced; separate learning, ownership or reuse rights are not." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-BF27494A", "legacy_ids": [ "SS-CAP-20260929-92F337B317BB" ], "event_key": "nasdaq calypso clients|nasdaq-hosted calypso agents|2026-09-29|client trading risk collateral data documentation and workflows|contained retrieval and planned workflow execution", "announcement_date": "2026-09-29", "observed_status": "announced", "first_added_date": "2026-09-29", "last_reviewed_date": "2026-09-29", "parties": { "providers": [ "Nasdaq Calypso clients", "Nasdaq" ], "ai_recipients": [ "Nasdaq-hosted Calypso agents and client-connected proprietary agents" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Financial institutions' Nasdaq Calypso system-of-record trading, risk, collateral, positions, cashflow and operational data, documentation and front-to-back capital-markets workflows made available to governed Nasdaq-hosted and client-connected agents within each institution's perimeter.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration", "connector" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "prohibited", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "The launched natural-language assistant lets authorized users and agents query Calypso system-of-record data, documentation and other information inside a governed institutional environment.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Agents can analyze consistent trading, risk, collateral and operational records using Calypso's governed data models, semantic context, lineage and controls.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "Nasdaq plans governed agentic workers that operate across capital-markets and treasury workflows while institutions retain oversight and control.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "The MCP-based orchestration layer is designed to connect Nasdaq and proprietary agents to real data, controls and multi-stage trade-lifecycle workflows.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Nasdaq Calypso canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed retrieval and planned workflow execution over institution-controlled Calypso data are evidenced; external retention is prohibited, while training, post-training, evaluation, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.globenewswire.com/news-release/2026/09/29/3370427/0/en/nasdaq-calypso-launches-agentic-capabilities-to-scale-ai-adoption-across-the-trade-lifecycle.html", "source_type": "primary_syndicated", "checked_at": "2026-09-29T15:57:00Z", "summary": "Nasdaq's issuer release announces a contained MCP-based environment in which Nasdaq-hosted and client-connected agents can operate on client Calypso trading, risk and collateral records; the launched assistant queries data and documentation, while future workers remain governed by sandboxing, live oversight, no external retention and client control." }, { "source_id": "E2", "url": "https://www.nasdaq.com/products/fintech/calypso/intelligence-platform", "source_type": "primary", "checked_at": "2026-09-29T15:58:00Z", "summary": "Nasdaq documents Calypso's governed data foundation across trading, risk and operations, including raw and standardized data, positions, exposures and cashflows, semantic structure, lineage, access controls, audit trails and integration with client or third-party tools." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-29T16:14:52.205Z", "revision_sha256": "d79512b04305df6bfade3763c94d653a2c934b4086a8995657a60d1ca5565f21", "notes": "Mike Ye approved the Nasdaq Calypso canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed retrieval and planned workflow execution over institution-controlled Calypso data are evidenced; external retention is prohibited, while training, post-training, evaluation, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-D1026BEB", "legacy_ids": [ "SS-CAP-20260929-538A8637DB46" ], "event_key": "bloomberg|client-controlled enterprise ai agents|2026-09-29|licensed bloomberg financial data metadata and workflow skills|permissioned mcp retrieval", "announcement_date": "2026-09-29", "observed_status": "active", "first_added_date": "2026-09-29", "last_reviewed_date": "2026-09-29", "parties": { "providers": [ "Bloomberg" ], "ai_recipients": [ "Enterprise clients' AI agents and applications" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Licensed Bloomberg Data License Plus financial content across more than 100 million securities and 50,000 fields, with AI-ready metadata, semantic context, entity resolution and workflow Skills for research, portfolio, risk and operations, exposed to client-controlled AI agents through Bloomberg Enterprise MCP.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "license", "connector", "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Entitled client agents can discover, understand and retrieve licensed Bloomberg pricing, reference, fundamentals, economics and alternative data through the Enterprise MCP interface.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Agents receive field definitions, calculation context, qualifiers, semantic search and entity resolution so financial results can be interpreted against Bloomberg-defined meaning.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "Bloomberg workflow Skills are designed to support point-in-time universe retrieval, corporate-action adjustment, revision history, outlier review, trade-price deviation review and sanctions assessment.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Bloomberg Enterprise MCP canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Entitlement-gated retrieval of licensed Bloomberg financial data and workflow semantics is evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/bloomberg-launches-enterprise-mcp-to-seamlessly-connect-bloomberg-data-with-clients-enterprise-ai-applications-302891331.html", "source_type": "primary_syndicated", "checked_at": "2026-09-29T15:55:00Z", "summary": "Bloomberg's issuer release announces live Enterprise MCP access for client-controlled AI agents to licensed DL+ financial data, metadata, semantic interfaces and workflow Skills, with entitlement checks, per-call limits and Bloomberg-hosted infrastructure." }, { "source_id": "E2", "url": "https://professional.bloomberg.com/products/data/data-license/", "source_type": "primary", "checked_at": "2026-09-29T15:56:00Z", "summary": "Bloomberg's Data License documentation describes billions of daily data points, more than 100 million financial instruments, tens of thousands of fields, long historical coverage and DL+ delivery through Enterprise MCP under licensed access." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-29T16:14:52.134Z", "revision_sha256": "1ca0c0188523d5e93153c0aa1fe94a8668da0779e3c42ba6f3b3279d97e84004", "notes": "Mike Ye approved the Bloomberg Enterprise MCP canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Entitlement-gated retrieval of licensed Bloomberg financial data and workflow semantics is evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-EC07805C", "legacy_ids": [ "SS-CAP-20260929-DA9FDCBBA313" ], "event_key": "joint mongodb cognition customers|cognition devin and mongodb amp|2026-09-29|legacy application code business logic data access layers and migration tooling|workflow execution", "announcement_date": "2026-09-29", "observed_status": "active", "first_added_date": "2026-09-29", "last_reviewed_date": "2026-09-29", "parties": { "providers": [ "Joint MongoDB and Cognition customers", "MongoDB Application Modernization Platform" ], "ai_recipients": [ "Cognition Devin" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Enterprise customers' legacy application code, queries, stored procedures, business logic, data access layers, schemas and live MongoDB operational context, plus MongoDB AMP migration and validation tooling, made accessible to Devin for governed modernization planning, code transformation, testing and migration orchestration.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Devin can inspect customer legacy code and data access layers and connect to MongoDB Atlas context using customer credentials and access controls.", "source_ids": [ "E1", "E2" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Devin plans modernization work and coordinates code transformation with AMP's deterministic data-migration and validation tooling.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "provider_reported_demonstration", "statement": "Devin rewrites business logic, queries and data access layers while MongoDB AMP moves and validates records in the target environment.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "provider_reported_demonstration", "statement": "The integrated offering orchestrates migration planning, code changes, data movement, validation and testing, with early joint work reportedly reduced from five or six hours to just over one hour.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the MongoDB × Cognition Devin canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed access to customer legacy code, data-access layers and MongoDB operational context for modernization is evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.prnewswire.com/news-releases/cognition-and-mongodb-partner-to-modernize-enterprise-infrastructure-in-months-not-years-302892846.html", "source_type": "primary_syndicated", "checked_at": "2026-09-29T16:00:00Z", "summary": "MongoDB's issuer release launches Devin for MongoDB Modernizations, connecting Devin to AMP so it can plan and rewrite customer legacy code and data access layers while AMP moves and validates data; engineers retain target-model and cutover control, and early testing reports a substantial time reduction." }, { "source_id": "E2", "url": "https://www.mongodb.com/company/newsroom/press-releases/mongodb-brings-live-operational-data-to-the-agentic-coding-stack", "source_type": "primary", "checked_at": "2026-09-29T16:01:00Z", "summary": "MongoDB's August managed-MCP disclosure documents direct Devin and coding-agent access to live Atlas operational data using existing customer credentials and access controls, including schema inspection, queries and permissioned data-management actions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-29T16:14:52.275Z", "revision_sha256": "47df5e04d7e8b5298d7bfc13f23730e8eb2074e6e06d285a493f08371755799b", "notes": "Mike Ye approved the MongoDB × Cognition Devin canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed access to customer legacy code, data-access layers and MongoDB operational context for modernization is evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing and exclusivity remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-8D155030", "legacy_ids": [ "SS-AIDT-2026-0013" ], "event_key": "legacy|ss-aidt-2026-0013", "announcement_date": "2026-09-28", "observed_status": "active_renewed_exclusive_data_agreement", "first_added_date": "2026-09-29", "last_reviewed_date": "2026-09-29", "parties": { "providers": [ "vRad (Virtual Radiologic)" ], "ai_recipients": [ "GuideAI Health" ], "strategic_buyer_or_operator": [ "GuideAI Health" ] }, "asset": { "description": "De-identified imaging data generated across vRad's national network of more than 2,100 U.S. hospitals and healthcare facilities, made exclusively available to GuideAI for development, training and validation of AI models for vascular-disease detection and characterization.", "asset_types": [ "De-identified radiology imaging data", "Routine CT imaging", "Vascular disease imaging corpus" ], "industry": "Healthcare, medical imaging and clinical AI", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "not_disclosed", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "not_disclosed", "sublicensing": "prohibited", "exclusivity": "unknown" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training, validating and expanding GuideAI models for detection and characterization of peripheral arterial and other vascular diseases from routine CT imaging.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The issuer release establishes the renewed exclusive rights, de-identified imaging scope, more than 2,100-hospital network, approximately 500-radiologist workflow, and training/validation purpose. Economics, term length, exact corpus size, delivery architecture, retention, derived-model ownership, sublicensing, geographic processing controls and independent model-uplift evidence are not disclosed." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "The renewed agreement's financial consideration, minimum commitments and pricing formula are not disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "No agreement value, per-study pricing, revenue share, equity component or other economic terms are disclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 93, "coverage_pct": 100, "rationale": "Strategic Signal analysis. The exclusive national imaging corpus is large, real-world, renewable and directly useful for clinical-model training, though exact longitudinal depth and patient-level outcome linkage are not disclosed.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": 4, "decision_outcome_richness": 4, "decision_value_density": 5, "real_world_grounding": 5, "domain_value": 5, "refreshability": 5, "proprietary_advantage": 5, "model_learning_usefulness": 5, "non_replicability": 4.5, "rights_usability": 4 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate uniqueness scarcity for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "historical_depth": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate historical depth for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate decision outcome richness for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate decision value density for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate real world grounding for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate domain value for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "refreshability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate refreshability for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "proprietary_advantage": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate proprietary advantage for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate model learning usefulness for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate non replicability for the exclusive vRad imaging corpus while preserving undisclosed limits." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The issuer disclosure provides sufficient evidence to rate rights usability for the exclusive vRad imaging corpus while preserving undisclosed limits." } } }, "transaction_signal": { "score": 55, "coverage_pct": 85, "rationale": "Strategic Signal analysis. Exclusive model-development rights are unusually explicit, but economics and market price discovery are absent, materially limiting the transaction signal despite strong AI-use and rights clarity.", "factor_ratings": { "disclosed_economics": 0, "clean_price_discovery": 0, "data_consideration_separability": 4, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 3.5, "precedent_value": 4.5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate disclosed economics for this exclusive AI-model development data-rights arrangement." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate clean price discovery for this exclusive AI-model development data-rights arrangement." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate data consideration separability for this exclusive AI-model development data-rights arrangement." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate explicit model use for this exclusive AI-model development data-rights arrangement." }, "rights_clarity": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate rights clarity for this exclusive AI-model development data-rights arrangement." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate strategic buyer quality for this exclusive AI-model development data-rights arrangement." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The renewed agreement provides sufficient evidence to rate precedent value for this exclusive AI-model development data-rights arrangement." } } } }, "governance": { "governed_access": "Access is limited to the renewed vRad data partnership and AI-model development scope; detailed technical access controls are not disclosed.", "geographic_sovereignty": "The disclosed source network consists of U.S. hospitals and healthcare facilities; processing-location restrictions are not disclosed.", "privacy_pii": "The licensed imaging data are disclosed as de-identified, with healthcare privacy and data-protection compliance identified as relevant risks.", "trade_secret": "The dataset is treated as an exclusive commercial data asset; technical confidentiality controls are not disclosed.", "de_identification": "The announcement expressly describes the imaging data as de-identified.", "employee_customer_data": "The asset consists of de-identified patient imaging generated in clinical care rather than employee or ordinary commercial customer data.", "access_structure": "Under the renewed data exclusivity agreement, GuideAI retains exclusive rights to use de-identified imaging generated across vRad's network to develop AI models. The issuer states that the dataset provides the foundation to train and validate GuideAI algorithms; detailed delivery, retention and downstream model-rights mechanics are not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://www.globenewswire.com/news-release/2026/09/28/3369827/0/en/guideai-health-corp-and-vrad-renew-data-exclusivity-agreement-to-develop-ai-models-for-the-detection-and-characterization-of-multiple-vascular-diseases.html", "source_type": "primary_syndicated", "publisher": "GuideAI Health", "title": "GuideAI Health Corp. and vRad Renew Data Exclusivity Agreement to Develop AI Models" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-29T18:02:23.716Z", "decision_id": "conversation-20260929-22F00D6C2ABE", "packet_sha256": "8534eef99edbad6cbffa7a0a9b29177561506eb77207ffc61f8d68ac30f125f4", "record_sha256": "498d385cb70c4250f97bb7a0856be2a422c066b9f4d49cd94f96f875fa453cc3" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0013/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-C62D0871", "legacy_ids": [ "SS-CAP-20260930-B4467D37D033" ], "event_key": "health plan customers|cognizant trizetto workflow agents and customer ai agents|2026-09-28|claims data standard operating procedures member provider context and adjudication workflows|retrieval and claims execution", "announcement_date": "2026-09-28", "observed_status": "active", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Health plan customers using TriZetto Facets and QNXT", "Cognizant TriZetto" ], "ai_recipients": [ "Cognizant Workflow Agentic Processing agents", "Authorized health-plan AI agents using TriZetto MCP servers" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Health plans' proprietary claims, eligibility, prior-authorization, payment-integrity, member and provider context, plus each plan's standard operating procedures and adjudication workflows, made available to governed TriZetto agents and customer AI initiatives for retrieval and routine claims execution.", "asset_types": [ "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration", "connector" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "A library of more than 100 MCP tools lets authorized health-plan agents access and summarize Facets and QNXT data context.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Workflow Agentic Processing follows each health plan's operating procedures and routes denials, exceptions and complex cases to human reviewers.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Agents can automatically clear eligible routine pended claims while maintaining visibility, auditability and human oversight.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Cognizant TriZetto canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed agents access claims, member and provider context and plan procedures for claims workflows; claims volume does not establish Human Response H2/H3 linkage." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://news.cognizant.com/2026-09-28-Cognizant-Brings-Agentic-AI-and-MCP-tool-library-to-Core-Claims-Operations-with-Workflow-Agentic-Processing-for-TriZetto", "source_type": "primary", "checked_at": "2026-09-30T15:36:00Z", "summary": "Cognizant announces general availability of agentic processing and 100-plus MCP tools for Facets and QNXT, enabling plan agents to access data context and automatically clear eligible pended claims under plan procedures, guardrails, auditability and human exception review." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:05.108Z", "revision_sha256": "6b1649921492391a0a627edf68b9d21d3593213411214133369b9aecdad72517", "notes": "Mike Ye approved the Cognizant TriZetto canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed agents access claims, member and provider context and plan procedures for claims workflows; claims volume does not establish Human Response H2/H3 linkage." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-FF640A29", "legacy_ids": [ "SS-CAP-20260928-3EF37DE8EAC2" ], "event_key": "dv01|claude chatgpt client agents|2026-09-28|loan-level structured-finance data and analytics|retrieval and workflow execution", "announcement_date": "2026-09-28", "observed_status": "active", "first_added_date": "2026-09-28", "last_reviewed_date": "2026-09-28", "parties": { "providers": [ "dv01" ], "ai_recipients": [ "Claude", "ChatGPT", "Client-built AI applications and internal agents", "dv01 AI Assistants" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Licensed dv01 loan-level structured-finance data, semantic definitions, documented calculations, analytical tools and professional credit workflows—including borrower and loan attributes, payment history, delinquency, loss, prepayment and cashflow modeling—made available to Claude, ChatGPT, client-built agents and dv01 AI Assistants through permissioned MCP and in-platform access.", "asset_types": [ "structured_dataset", "expert_workflow", "execution_traces", "domain_actions", "longitudinal_response_history" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "license", "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authenticated Claude, ChatGPT and internal agents can query the licensed deals, portfolios and loan-level data available to a dv01 customer, with structured results and provenance.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "MCP-connected agents can analyze collateral performance and borrower- and loan-level attributes using dv01's semantic layer, standardized definitions and documented calculations.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Agents can run cashflow projections and stress tests, structure deals, configure facilities and prepare source-linked reports subject to permissioned access and human review.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "response_prediction", "evidence_level": "enabled", "statement": "Agents can model prepayment, default, pricing and rate assumptions against longitudinal loan performance and analyze resulting effects across collateral, bonds and tranches.", "source_ids": [ "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "mixed", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the dv01 canonical AI Access event as Knowledge + Professional Workflow + Human Response H2 access for the explicitly accessible consumer-loan subset. Strict data-rights qualification does not apply; scores are not applicable. Permissioned retrieval and workflow execution against licensed loan-level data are evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing, exclusivity, recovery, adaptation, and an H3 outcome-informed next-decision loop remain undisclosed or not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.dv01.co/resources/blog/dv01-unveils-agentic-infrastructure-to-move-structured-finance-beyond-ai-experimentation/", "source_type": "primary", "checked_at": "2026-09-28T15:16:00Z", "summary": "dv01 announces live MCP access for Claude, ChatGPT and client-built agents plus built-in AI Assistants, enabling collateral analysis, cashflow projections, facility configuration, deal structuring and reporting against loan-level data and dv01 analytical capabilities." }, { "source_id": "E2", "url": "https://www.dv01.co/artificial-intelligence/mcp/", "source_type": "primary", "checked_at": "2026-09-28T15:20:00Z", "summary": "dv01 documents authenticated, permissioned MCP access to licensed deals, portfolios and loan-level data; agents can use borrower and loan attributes, payment history, performance across time, delinquency, loss and prepayment measures, and scenario-based cashflow analytics with source traceability and human review." }, { "source_id": "E3", "url": "https://www.dv01.co/artificial-intelligence/overview/", "source_type": "primary", "checked_at": "2026-09-28T15:20:00Z", "summary": "dv01 describes the broader governed agent infrastructure: standardized loan-level data, semantic definitions, workflow logic, permissioned access, source traceability, editable outputs and human approval for collateral, cashflow, deal and facility workflows." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-28T16:47:16.918Z", "revision_sha256": "dfbf76c4bdbe915db0c7a198aebe2b96a1190ad3e5c374e2d5c9f133fccc6c79", "notes": "Mike Ye approved the dv01 canonical AI Access event as Knowledge + Professional Workflow + Human Response H2 access for the explicitly accessible consumer-loan subset. Strict data-rights qualification does not apply; scores are not applicable. Permissioned retrieval and workflow execution against licensed loan-level data are evidenced, while training, post-training, evaluation, retention, ownership, derived-model, sublicensing, exclusivity, recovery, adaptation, and an H3 outcome-informed next-decision loop remain undisclosed or not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-D6DBEA99", "legacy_ids": [ "SS-CAP-20260927-1111040F4978" ], "event_key": "bnp paribas|google cloud gemini|2026-09-24|bank data and cib workflows|retrieval and workflow execution", "announcement_date": "2026-09-24", "observed_status": "announced", "first_added_date": "2026-09-27", "last_reviewed_date": "2026-09-27", "parties": { "providers": [ "BNP Paribas" ], "ai_recipients": [ "Google Cloud Gemini models and BNP Paribas-deployed Gemini agents" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "BNP Paribas information-system resources, internal banking knowledge and governed professional workflows—including corporate-credit-memo preparation and planned applications across sales, trading, research and structuring—made conditionally accessible to Gemini models and purpose-built agents within the bank's security and data-governance framework.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Gemini models integrated into LLM@CIB and purpose-built agents can access only the BNP Paribas resources assigned to their banking use cases, including information used to prepare corporate credit memos.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "Authenticated Gemini agents are planned for corporate-credit-memo preparation and additional sales, trading, research and structuring workflows, with resource access and agent interactions monitored and controlled by BNP Paribas.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The five-year arrangement is intended to support controlled banking-domain reasoning inside BNP Paribas's internal assistant and purpose-built CIB agents.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the BNP Paribas × Google Cloud Gemini canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Authenticated, resource-scoped retrieval and governed workflow execution are evidenced, while training, post-training, evaluation, retention, ownership, derived-model, reuse, sublicensing, and exclusivity remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://group.bnpparibas/en/press-release/bnp-paribas-and-google-cloud-announce-new-partnership-on-agentic-ai-and-cloud-innovation", "source_type": "primary", "checked_at": "2026-09-27T15:08:00Z", "summary": "BNP Paribas and Google Cloud disclose a five-year partnership to integrate Gemini into LLM@CIB and deploy authenticated, resource-scoped agents for credit-memo preparation and planned sales, trading, research and structuring workflows; agent access and interactions will be monitored and controlled, and some data categories will remain off public cloud." }, { "source_id": "E2", "url": "https://www.reuters.com/technology/bnp-paribas-keep-sensitive-data-off-public-cloud-despite-google-deal-2026-09-24/", "source_type": "secondary", "checked_at": "2026-09-27T15:09:00Z", "summary": "Reuters independently reports that the five-year agreement covers Gemini agents for internal credit-memo, sales, trading, research and structuring work, while BNP Paribas will keep some sensitive customer data and critical operations off public cloud infrastructure." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-27T17:05:43.972Z", "revision_sha256": "72c6504f04e35f640451a6bf29aaf518909429bb9941c65572361d9ba0126a44", "notes": "Mike Ye approved the BNP Paribas × Google Cloud Gemini canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Authenticated, resource-scoped retrieval and governed workflow execution are evidenced, while training, post-training, evaluation, retention, ownership, derived-model, reuse, sublicensing, and exclusivity remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-578D243B", "legacy_ids": [ "SS-CAP-20260925-CB08C9F12F21" ], "event_key": "paypal|meta muse|2026-09-23|paypal merchant network checkout|execution", "announcement_date": "2026-09-23", "observed_status": "announced", "first_added_date": "2026-09-25", "last_reviewed_date": "2026-09-25", "parties": { "providers": [ "PayPal" ], "ai_recipients": [ "Meta Muse" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "PayPal's worldwide merchant network and checkout rails, made available to Meta Muse so authorized PayPal customers can shop and complete checkout across participating PayPal merchants from the personal AI agent.", "asset_types": [ "structured_dataset", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Muse is intended to access the merchant and checkout context required to shop across PayPal's worldwide merchant network.", "source_ids": [ "E1", "E3" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "PayPal customers will be able to complete checkout through Muse across PayPal merchants worldwide, subject to user approval.", "source_ids": [ "E1", "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the PayPal × Meta Muse canonical AI Access event as Knowledge Access with conditional merchant and transaction-state retrieval and conditional checkout execution. Strict data-rights qualification does not apply; scores are not applicable. Exact field access, training, post-training, evaluation, personalization, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.linkedin.com/posts/paypal_were-excited-to-partner-with-meta-to-enable-activity-7508181692109565952-OEuG", "source_type": "primary", "checked_at": "2026-09-25T15:22:00Z", "summary": "PayPal says it is partnering with Meta so PayPal customers can shop and check out using Muse across PayPal merchants worldwide." }, { "source_id": "E2", "url": "https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/", "source_type": "primary", "checked_at": "2026-09-25T15:22:00Z", "summary": "Meta says Muse can make purchases, uses secure storage for credentials and payment methods, requests approval before purchases, and lets users choose and revoke connected-app access." }, { "source_id": "E3", "url": "https://www.barrons.com/articles/paypal-meta-muse-ai-agent-stock-34ed47ca", "source_type": "secondary", "checked_at": "2026-09-25T15:22:00Z", "summary": "Barron's independently reports that PayPal will integrate Muse for shopping and checkout across PayPal merchants, corroborating the parties, arrangement, and intended transaction-execution function." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-25T15:45:13.576Z", "revision_sha256": "862b0d815998c2c86e5f94027e6b2af6b119ce9b55b42d12b042d49da298497d", "notes": "Mike Ye approved the PayPal × Meta Muse canonical AI Access event as Knowledge Access with conditional merchant and transaction-state retrieval and conditional checkout execution. Strict data-rights qualification does not apply; scores are not applicable. Exact field access, training, post-training, evaluation, personalization, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-8854E68E", "legacy_ids": [ "SS-CAP-20260924-7D32DF2DC412" ], "event_key": "government-of-ukraine|openai-daybreak|2026-09-23|civilian-infrastructure-software-cyber-defense|retrieval-evaluation-execution", "announcement_date": "2026-09-23", "observed_status": "announced", "first_added_date": "2026-09-24", "last_reviewed_date": "2026-09-24", "parties": { "providers": [ "Government of Ukraine", "Authorized Ukrainian civilian-infrastructure defenders" ], "ai_recipients": [ "OpenAI Daybreak cyber-defense models and tools" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Authorized Ukrainian civilian-infrastructure security context, including legacy software, suspicious activity, vulnerability candidates, fixes, and connected cyber-defense workflows across hospitals, energy, telecommunications, and other essential services, made available to OpenAI Daybreak for review, investigation, validation, and fix testing.", "asset_types": [ "content_corpus", "software_execution_environment", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "conditional", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Daybreak tools will inspect authorized software and suspicious activity associated with Ukrainian civilian infrastructure.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "OpenAI cyber models will identify and validate software vulnerabilities and investigate suspicious activity for Ukrainian defenders.", "source_ids": [ "E1", "E2" ] }, { "type": "generation", "evidence_level": "intended", "statement": "Daybreak will help Ukrainian teams develop fixes for vulnerabilities found in authorized software and systems.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The program will support testing fixes and moving findings through an authorized discover-validate-remediate cyber-defense workflow.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the OpenAI Daybreak × Ukraine canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Authorized retrieval, evaluation, vulnerability validation, fix generation, and guarded workflow execution are evidenced, while training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://openai.com/index/openai-extends-cyber-access-to-ukraine-for-civilian-defense/", "source_type": "primary", "checked_at": "2026-09-24T15:19:00Z", "summary": "OpenAI says it will provide Ukrainian government teams with Daybreak access for civilian-infrastructure defense, including reviewing older software, investigating suspicious activity, identifying and validating vulnerabilities, and developing and testing fixes. It names hospitals, energy, and telecommunications as affected sectors and describes prior European public-sector vulnerability work." }, { "source_id": "E2", "url": "https://openai.com/daybreak/", "source_type": "primary", "checked_at": "2026-09-24T15:19:00Z", "summary": "OpenAI describes Daybreak as a governed cyber-defense stack combining frontier models, Codex tooling, trusted workflows, and partners in an inventory-discover-validate-assign-remediate-prove loop, with trusted access and action kept under human control." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-24T15:18:43.491Z", "revision_sha256": "acf7319511c6803342abb339ca1fed5ca13932247f68eb3193810aa54f719067", "notes": "Mike Ye approved the OpenAI Daybreak × Ukraine canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Authorized retrieval, evaluation, vulnerability validation, fix generation, and guarded workflow execution are evidenced, while training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-89D9B184", "legacy_ids": [ "SS-CAP-20260924-027B74AE5F30" ], "event_key": "amazon-selling-partner|anthropic-claude-amazon-quick|2026-09-23|seller-data-seller-assistant-workflows|retrieval-execution-personalization", "announcement_date": "2026-09-23", "observed_status": "active", "first_added_date": "2026-09-24", "last_reviewed_date": "2026-09-24", "parties": { "providers": [ "Amazon Selling Partner Services", "Authorizing Amazon sellers" ], "ai_recipients": [ "Amazon Quick", "Anthropic Claude" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Seller-authorized Amazon business data and Seller Assistant capabilities: listing contributions, real-time performance metrics, inventory levels, sales analytics, pricing patterns, inventory cycles, growth goals, recommendations, and governed actions on listings, prices, restocking, advertising, account health, and other Seller Central workflows.", "asset_types": [ "structured_dataset", "expert_workflow", "execution_traces", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "permitted", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Quick and Claude can retrieve an authorizing seller's listings, performance metrics, inventory levels, sales analytics, and Seller Assistant insights.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Connected agents can reason across pricing, inventory, advertising, listings, account health, supplier, accounting, and other business context.", "source_ids": [ "E1", "E2" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Agents can monitor seller-defined conditions and prepare recommendations or response plans within seller guardrails.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "The plugin lets connected agents adjust prices, update listings, refresh listings, and carry out other Seller Assistant actions after human approval.", "source_ids": [ "E1", "E2" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Persistent Seller Assistant memory uses each seller's pricing patterns, inventory cycles, preferences, and growth goals to sharpen recommendations across tools over time.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Amazon Selling Partner Plugin × Amazon Quick / Anthropic Claude canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Seller-authorized retrieval, personalization, planning, and guarded execution are evidenced, while training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.aboutamazon.com/news/innovation-at-amazon/seller-assistant-plugin-amazon-quick-claude", "source_type": "primary", "checked_at": "2026-09-24T15:18:00Z", "summary": "Amazon says its new Selling Partner plugin is live in Quick and beta in Claude for U.S. sellers. Authorized agents can use listings, real-time performance metrics, inventory, sales analytics, and Seller Assistant's persistent memory and can perform seller-approved actions under access boundaries and audit trails. Amazon says seller data remains inside Amazon infrastructure." }, { "source_id": "E2", "url": "https://www.geekwire.com/2026/amazon-opens-its-seller-tools-to-outside-ai-agents-starting-with-anthropics-claude/", "source_type": "secondary", "checked_at": "2026-09-24T15:18:00Z", "summary": "GeekWire independently reports that the plugin pulls seller listings, inventory, sales analytics, and performance metrics into Claude or Quick, lets agents act on Seller Central accounts, gives sellers field-level access choices, and requires approval before actions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-24T15:18:43.449Z", "revision_sha256": "198b9d12b09725d797e2d9decaab7f60fd6b2c5f880b4e225ef71bb7b871dfd9", "notes": "Mike Ye approved the Amazon Selling Partner Plugin × Amazon Quick / Anthropic Claude canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Seller-authorized retrieval, personalization, planning, and guarded execution are evidenced, while training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-13D52D17", "legacy_ids": [ "SS-CAP-20260923-EDB908A502BC" ], "event_key": "instacart|meta muse|2026-09-22|grocery catalog inventory orders personalization|connector inference execution", "announcement_date": "2026-09-22", "observed_status": "announced", "first_added_date": "2026-09-23", "last_reviewed_date": "2026-09-23", "parties": { "providers": [ "Instacart" ], "ai_recipients": [ "Meta Muse" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Instacart's grocery infrastructure, including a catalog of more than 2 billion product instances, roughly 10 million daily inventory signals across nearly 100,000 stores, active promotions, item availability, and preference-grounded shopping context, made available through a connector so Meta Muse can turn open-ended grocery requests into ready-to-buy carts.", "asset_types": [ "structured_dataset", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "permitted", "action_execution": "conditional", "personalization": "permitted", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "intended", "statement": "Muse will use Instacart's live catalog, inventory, promotions, item availability, and shopping context to ground grocery results.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The connector is intended to translate open-ended meal and grocery requests into products that are actually available at the user's chosen store.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "intended", "statement": "Muse is intended to present personalized, ready-to-buy grocery carts assembled through Instacart's infrastructure.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The announced integration will give Muse users a path to create an Instacart account and place an order from the AI experience.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "intended", "statement": "Results and carts are intended to reflect Instacart preferences, active promotions, and local item availability.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Instacart × Meta Muse classification as Knowledge access with intended personalization and shopping execution. Retrieval and conditional action execution are evidenced; training, post-training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "permitted", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://company.instacart.com/updates/instacart-to-bring-personalized-grocery-shopping-to-muse-from-meta", "source_type": "primary", "checked_at": "2026-09-23T15:03:00Z", "summary": "Instacart CTO Anirban Kundu announced that Meta Muse will join Instacart's external connector program. The connector will use Instacart's grocery infrastructure—more than 2 billion product instances, about 10 million daily inventory signals across nearly 100,000 stores, promotions, item availability, and preference context—to translate open-ended requests into personalized, ready-to-buy carts and provide a path to account creation and ordering." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-23T15:19:47.450Z", "revision_sha256": "40a0ffe33d0eb91c4ae12f6a06766ae4c7728f6a78feefda9bf32788093f4a13", "notes": "Mike Ye approved the Instacart × Meta Muse classification as Knowledge access with intended personalization and shopping execution. Retrieval and conditional action execution are evidenced; training, post-training, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-C4BE236C", "legacy_ids": [ "SS-CAP-20260922-2BCDD20B8D7C" ], "event_key": "enterprise-customers-unit42|claude-mythos-gpt-5.6-cyber|2026-09-22|live-security-estates-and-source-code|continuous-offensive-testing", "announcement_date": "2026-09-22", "observed_status": "active", "first_added_date": "2026-09-22", "last_reviewed_date": "2026-09-22", "parties": { "providers": [ "Palo Alto Networks Unit 42", "Subscribing enterprise customers" ], "ai_recipients": [ "Anthropic Claude Mythos 5", "OpenAI GPT-5.6-Cyber", "Open-weight cyber models selected through the Unit 42 harness" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer-authorized enterprise security estates—including first- and third-party web applications, APIs, cloud infrastructure, source-code repositories and network assets—combined with Unit 42 threat intelligence and offensive-security expertise for continuous AI-assisted discovery, exploit validation, attack-path mapping, remediation guidance and re-testing.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "execution_traces", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "The multi-model harness can inspect the subscribed customer's authorized web applications, APIs, cloud infrastructure, source-code repositories and network assets.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Cyber-specialized models identify vulnerabilities, validate exploitability and map end-to-end attack paths using customer-estate context and Unit 42 threat intelligence.", "source_ids": [ "E1", "E2" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "The service generates prioritized remediation guidance, including code-level fixes and virtual-patching options, for identified security gaps.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Within an agreed testing scope, the service executes adversary simulations to validate real-world exploitability and automatically re-tests as customer environments change.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "The harness routes security tasks to specialized models and chains findings into end-to-end attack paths and ordered remediation priorities.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Palo Alto Networks Unit 42 Continuous Frontier AI Defense classification as Knowledge + Professional Workflow access. Retrieval and adversary-simulation execution are scoped to the enterprise security service; training, ownership, retention and broader downstream rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://investors.paloaltonetworks.com/news-releases/news-release-details/palo-alto-networks-delivers-anthropics-mythos-and-openais-gpt-56", "source_type": "primary", "checked_at": "2026-09-22T15:55:00Z", "summary": "Palo Alto Networks says Unit 42 Continuous Frontier AI Defense is a worldwide annual-subscription service using Claude Mythos 5, GPT-5.6-Cyber and open-weight models in a proprietary harness. It performs a baseline and always-on testing across web applications, APIs, cloud infrastructure, source-code repositories and network assets; validates exploitability and attack paths; combines Unit 42 threat intelligence and expertise; provides remediation guidance; and automatically re-tests as environments change." }, { "source_id": "E2", "url": "https://www.reuters.com/technology/palo-alto-networks-unveils-ai-powered-cybersecurity-service-using-claude-gpt-2026-09-22/", "source_type": "secondary", "checked_at": "2026-09-22T15:55:00Z", "summary": "Reuters independently reports that Palo Alto Networks launched the service using Anthropic, OpenAI and open-weight models to continuously test enterprise web applications, APIs and cloud infrastructure, identify vulnerabilities and attack paths, and provide code-level fixes and virtual-patching options through annual subscriptions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-22T16:13:07.027Z", "revision_sha256": "76f06e5bad2bbf663f8bc1a9b444a6112cf5f90925ed68a4ae7404c134acc1a7", "notes": "Mike Ye approved the Palo Alto Networks Unit 42 Continuous Frontier AI Defense classification as Knowledge + Professional Workflow access. Retrieval and adversary-simulation execution are scoped to the enterprise security service; training, ownership, retention and broader downstream rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-DEF95246", "legacy_ids": [ "SS-CAP-20260920-18759FE9B4C1" ], "event_key": "usa-rare-earth|pasqal-riven-systems|2026-09-17|rare-earth-separation-experiments|model-training-evaluation", "announcement_date": "2026-09-17", "observed_status": "announced", "first_added_date": "2026-09-20", "last_reviewed_date": "2026-09-20", "parties": { "providers": [ "Riven Systems", "USA Rare Earth" ], "ai_recipients": [ "Pasqal", "Riven Systems" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "A planned renewable corpus from thousands of automated rare-earth separation experiments in Riven Systems' self-driving laboratory, tailored to USA Rare Earth's feedstocks and linking candidate extractants to measured selectivity results for classical and quantum machine-learning training and benchmarking.", "asset_types": [ "structured_dataset", "expert_workflow", "execution_traces" ], "industry": null, "subject_type": "industrial_system", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "other" ], "modes": [ "corpus_delivery", "ongoing_feed" ] }, "rights": { "training": "permitted", "post_training": "not_disclosed", "evaluation": "permitted", "inference_retrieval": "not_disclosed", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "domain_reasoning", "evidence_level": "intended", "statement": "Classical and quantum machine-learning models will learn extractant selectivity for rare-earth elements from real automated-lab experiment data.", "source_ids": [ "E1", "E2" ] }, { "type": "response_prediction", "evidence_level": "intended", "statement": "The models are intended to predict which extractant molecules will bind selectively to USA Rare Earth's target materials and feedstocks.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "intended", "statement": "The envisioned discovery loop links model-selected extractants to autonomous laboratory testing and subsequent validation in USA Rare Earth's R&D facility.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "The planned end-to-end system would use quantum machine learning to identify high-potential extractants for testing and validation.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the USA Rare Earth × Pasqal × Riven Systems capability-access classification as Knowledge + Professional Workflow. Training and evaluation use are evidenced; ownership, retention, reuse and exclusivity remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "not_disclosed", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.pasqal.com/newsroom/usa-rare-earth-pasqal-and-riven-systems-partner-to-advance-next-generation-technologies-for-critical-mineral-production/", "source_type": "primary", "checked_at": "2026-09-20T15:32:00Z", "summary": "The joint announcement says Riven will conduct thousands of automated experiments to generate training data for classical and quantum machine-learning models of rare-earth extractant selectivity. Pasqal will benchmark the models, work will be tailored to USA Rare Earth's feedstocks, and the parties envision a loop from model-selected molecules through autonomous testing and USA Rare Earth R&D validation. Contract economics, corpus ownership, retention, derived-model rights, reuse, and exclusivity are not disclosed." }, { "source_id": "E2", "url": "https://www.barrons.com/articles/pasqal-stock-quantum-ai-usa-rare-earth-acb88736", "source_type": "secondary", "checked_at": "2026-09-20T15:32:00Z", "summary": "Barron's independently reports the partnership and says Riven plans thousands of automated experiments to create model-training data that will be used to compare quantum and classical approaches for rare-earth separation chemistry." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-20T15:35:57.473Z", "revision_sha256": "397df63359c58073a7ef18756dff0438062b60000b552f50251a393cafd096ed", "notes": "Mike Ye approved the USA Rare Earth × Pasqal × Riven Systems capability-access classification as Knowledge + Professional Workflow. Training and evaluation use are evidenced; ownership, retention, reuse and exclusivity remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-F671BFCA", "legacy_ids": [ "SS-CAP-20260918-3C1D557A0E96" ], "event_key": "openai|astra-for-law|2026-09-17", "announcement_date": "2026-09-17", "observed_status": "active", "first_added_date": "2026-09-19", "last_reviewed_date": "2026-09-19", "parties": { "providers": [ "OpenAI" ], "ai_recipients": [ "GPT-6 Astra Law" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "A dedicated Legal Search Index of U.S. case law, statutes, regulations, court rules and administrative decisions, refreshed with new sources daily and made available to GPT-6 Astra Law for cited legal research and analysis.", "asset_types": [ "content_corpus" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "permitted", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Astra for Law can search a dedicated, daily refreshed index of U.S. legal authorities and return answers grounded in cited sources.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The product is tailored with custom instructions for legal analysis and can develop arguments, deal terms and objections from client facts.", "source_ids": [ "E1" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "Astra for Law is expressly offered for legal analysis and writing grounded in the indexed legal authorities.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the OpenAI Astra for Law capability-access classification as Knowledge access. Retrieval to the dedicated Legal Search Index is evidenced; training, ownership and broader downstream rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "permitted", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://help.openai.com/en/articles/20001528-astra-for-law", "source_type": "primary", "checked_at": "2026-09-18T15:58:00Z", "summary": "OpenAI documents GPT-6 Astra Law, the dedicated Legal Search Index, daily source refresh, the covered categories of U.S. legal authority, and enabled legal research, analysis and writing uses. The same page separately mentions partner-built plugins, which are not treated here as rights to the index." }, { "source_id": "E2", "url": "https://www.reuters.com/legal/litigation/openai-launches-legal-focused-ai-platform-escalating-race-law-firm-users-2026-09-17", "source_type": "secondary", "checked_at": "2026-09-18T15:58:00Z", "summary": "Reuters independently reports the September 17 launch, the legal source categories, selected-firm access, and integrations with legal software providers. It does not establish training or ownership rights." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-19T20:34:09.547Z", "revision_sha256": "f18e49159c386f507149c5f508236a5d324596de35e9d46ba95adf7156d5b57b", "notes": "Mike Ye approved the OpenAI Astra for Law capability-access classification as Knowledge access. Retrieval to the dedicated Legal Search Index is evidenced; training, ownership and broader downstream rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-44C2DD8A", "legacy_ids": [ "SS-AIDT-2026-0011", "SS-CAP-20260926-73A63A01605C" ], "event_key": "legacy|ss-aidt-2026-0011", "announcement_date": "2026-09-15", "observed_status": "active_multi_year_controlled_learning_platform", "first_added_date": "2026-09-26", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "A-Alpha Bio", "Adaptive Biotechnologies" ], "ai_recipients": [ "Authorized Harell Data model builders and scientific research teams" ], "strategic_buyer_or_operator": [ "Authorized Harell Data model builders and scientific research teams" ] }, "asset": { "description": "Proprietary scientific datasets hosted in Harell Data's controlled environment, initially including A-Alpha Bio antibody-antigen affinity and structural data and Adaptive Biotechnologies T-cell-receptor binding data, plus hidden validation questions retained by data partners.", "asset_types": [ "Antibody-antigen affinity and structural data", "T-cell-receptor binding data", "Hidden scientific validation datasets" ], "industry": "Biotechnology, scientific AI, and AI infrastructure", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "federated_access", "license", "partnership", "platform_integration", "other" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "conditional", "post_training": "not_disclosed", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "not_disclosed", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training, fine-tuning, validating, and serving protein-language, structure-prediction, antibody-engineering, and immune-receptor models without transferring raw proprietary records.", "source_ids": [ "E1", "E2", "E3", "E4" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Authorized builders can train, fine-tune, validate, and run inference for scientific models against proprietary biology datasets while the raw records remain inside Harell's controlled environment.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "Training runs produce models and model intellectual property that builders retain and may list for downstream inference through Harell's marketplace.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The named data partners, initial dataset categories, controlled in-platform training and inference, raw-data non-extraction, builder model ownership, multi-year infrastructure agreement, and per-run data-owner revenue share are disclosed. Exact corpus sizes, dataset-specific update cadence, access prices, revenue-share percentages, exclusivity, geography, retention periods, patient-level content, downstream memorization controls, and measured model uplift are not disclosed. Chronology correction: the founder post is dated September 15, 2026 and already describes the same named datasets and learning-rights architecture; the September 23 release concerns supporting infrastructure. The first contract execution date, original provider agreement dates and historical page publication/version timestamps are not independently established. This record is excluded from the September 17, 2026 forecast cohort under the conservative first-disclosure treatment; the correction does not create or remove a registry event." }, "economics": { "transaction_structure": "Federated / Controlled Learning Rights", "disclosed_consideration": { "status": "disclosed_revenue_share_structure_amount_not_disclosed", "amount": null, "currency": null, "description": "Harell states that data owners receive a revenue share on every training job executed against their dataset; the percentage, minimum commitment, and cash amount are not disclosed. Model builders can also collect a fee when users run marketplace-listed models." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "Per-training-run data-owner revenue share and usage fees for downstream Models as a Service; CoreWeave contract value and exact revenue-share percentages are not disclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 84, "coverage_pct": 92, "rationale": "Strategic Signal analysis. The named scientific corpora combine scarce proprietary experimental measurements with high-value biological outcomes and direct model-training utility; exact historical depth and corpus scale remain partly undisclosed.", "factor_ratings": { "uniqueness_scarcity": 4, "historical_depth": null, "decision_outcome_richness": 4, "decision_value_density": 4, "real_world_grounding": 5, "domain_value": 5, "refreshability": 3, "proprietary_advantage": 4.5, "model_learning_usefulness": 4.5, "non_replicability": 4, "rights_usability": 4 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "The platform exposes named proprietary antibody and immune-receptor datasets, and Harell states that A-Alpha Bio's scale exceeds publicly available alternatives." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Binding-affinity and receptor-binding measurements connect biological inputs to experimentally observed outcomes suitable for learning and blinded validation." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Individual experimental outcomes can shape high-value protein engineering, immune medicine, and drug-discovery decisions." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E2", "E3" ], "rationale": "The assets are experimentally generated affinity, structural, and immune-receptor data rather than synthetic-only benchmarks." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1", "E3" ], "rationale": "Protein engineering and immune-driven medicine are economically consequential domains with direct scientific and therapeutic applications." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1", "E3" ], "rationale": "Harell describes a growing ecosystem and future datasets, but does not disclose a contractual refresh cadence for the initial assets." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Named data partners control experimental assets that builders cannot view or extract and that exceed public alternatives in scale or specificity." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E1", "E3" ], "rationale": "The arrangement expressly supports model training, fine-tuning, inference, and blinded validation on the proprietary scientific data." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Reproducing the precise experimental measurements would require comparable biological platforms, samples, protocols, time, and cost." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Controlled in-platform execution makes the data usable for training while preserving raw-data boundaries, though detailed contract terms remain undisclosed." } } }, "transaction_signal": { "score": 71, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The arrangement cleanly separates controlled data use from raw-data transfer and ties data-owner compensation to training runs, but provides no dollar value, share percentage, bid process, or independent uplift result.", "factor_ratings": { "disclosed_economics": 2.5, "clean_price_discovery": 1.5, "data_consideration_separability": 4.5, "explicit_model_use": 5, "rights_clarity": 4.5, "competing_bids": null, "strategic_buyer_quality": 3, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1", "E4" ], "rationale": "A per-training-run revenue share and downstream model fees are disclosed, but no dollar amount, percentage, or minimum commitment is provided." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Metered runs create an attributable usage base, but the private CoreWeave agreement and undisclosed share terms do not reveal a clean market price." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1", "E4" ], "rationale": "Data-owner compensation is expressly linked to each training job against a dataset, making the data consideration unusually separable from general software subscriptions." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Training, fine-tuning, validation, and inference are all expressly identified as supported workloads." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Raw-data non-extraction, controlled execution, builder model ownership, and marketplace use are clear; retention, sublicensing, and exclusivity remain undisclosed." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1", "E4" ], "rationale": "The platform targets serious scientific model builders and runs on a scaled AI cloud, but no named frontier model developer is disclosed as a customer." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1", "E4" ], "rationale": "Per-run compensation for in-place proprietary data use is a repeatable template for scientific and other high-value data markets." } } } }, "governance": { "governed_access": "Training, fine-tuning, validation, and inference execute inside Harell's controlled platform; raw datasets cannot be viewed or extracted, and compute is attributable and metered per run.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The disclosed initial assets are scientific binding datasets. Patient-level content, consent terms, and privacy controls are not disclosed in the reviewed sources.", "trade_secret": "Raw proprietary datasets remain inside the platform and are not delivered to model builders; contractual remedies and technical exfiltration testing are not disclosed.", "de_identification": "not_disclosed", "employee_customer_data": "No employee or ordinary customer-operational corpus is identified; the assets are proprietary experimental scientific datasets from named data partners.", "access_structure": "Authorized model builders bring models into Harell Data's CoreWeave-powered environment for training, fine-tuning, validation, and inference. Raw datasets remain in place and cannot be viewed or extracted. Builders receive trained models and model IP; data owners receive revenue share from each metered training run, while downstream models may be listed as Models as a Service on Harell's marketplace." }, "evidence": [ { "source_id": "E1", "url": "https://investors.coreweave.com/news/news-details/2026/Harell-Data-Selects-CoreWeave-to-Power-Its-Secure-Platform-for-AI-Model-Training/default.aspx", "source_type": "primary", "publisher": "CoreWeave and Harell Data", "title": "Harell Data Selects CoreWeave to Power Its Secure Platform for AI Model Training" }, { "source_id": "E2", "url": "https://harelldata.com/data-partners/", "source_type": "primary", "publisher": "Harell Data", "title": "Data Partners" }, { "source_id": "E3", "url": "https://harelldata.com/why-we-started-harell-data/", "source_type": "primary", "publisher": "Harell Data", "title": "Why we started Harell Data" }, { "source_id": "E4", "url": "https://www.geekwire.com/2026/adaptive-biotech-co-founder-raises-15m-for-new-startup-to-rethink-how-ai-trains-on-science/", "source_type": "secondary", "publisher": "GeekWire", "title": "Adaptive Biotech co-founder raises $15M for new startup to rethink how AI trains on science" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-30T19:34:37.142Z", "decision_id": "conversation-20260930-2AEBFCD969B7", "packet_sha256": "56a04ca0b550881fe5f41794246d96f8b36ed91f5268a6fc2d7e7ba0c2ba3724", "record_sha256": "d4ef8da3e3de774ff279956f7058b3a294d0b60b6726fdc91d8baee8a3799611" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0011/", "source_series": "merged_legacy_views" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-AA084737", "legacy_ids": [ "SS-CAP-20260920-B82ABD1AAB3C" ], "event_key": "google-home|third-party-ai-agents|2026-09-15|home-telemetry-history-device-control|retrieval-execution", "announcement_date": "2026-09-15", "observed_status": "active", "first_added_date": "2026-09-20", "last_reviewed_date": "2026-09-20", "parties": { "providers": [ "Google Home", "Authorizing Google Home users" ], "ai_recipients": [ "MCP-compatible AI agents, including Google Antigravity, Claude Cowork, and OpenClaw" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "User-authorized Google Home structures, device and area metadata, real-time device telemetry, historical state changes and event logs, plus parameterized device-control commands exposed to MCP-compatible AI agents.", "asset_types": [ "structured_dataset", "execution_traces", "domain_actions", "sensor_history" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized AI clients can inspect home structures and resources, query real-time device states, and retrieve historical state changes and event logs.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "AI assistants can ground answers and analysis in the connected home's current telemetry and event history.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Authorized AI clients can issue parameterized control commands to supported devices, subject to Google Home safety restrictions and tool permissions.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "intended", "statement": "Google describes agents using household history and current state to troubleshoot devices, identify patterns, and act on the user's behalf.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Google Home MCP capability-access classification as Knowledge access. User-authorized retrieval and supported device execution are evidenced; broader downstream rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://developers.home.google.com/mcp/home", "source_type": "primary", "checked_at": "2026-09-20T15:18:00Z", "summary": "Google's Early Access Home MCP documentation says MCP-compatible assistants can inspect home structures and device metadata, query real-time telemetry, retrieve historical state changes and event logs, and execute supported device actions. Access requires OAuth and a Premium Advanced subscription, can be revoked, blocks sensitive actions such as door unlocking, and requires separate consent for familiar-face data." }, { "source_id": "E2", "url": "https://www.theverge.com/tech/996310/google-home-mcp-integration-agentic-ai-smart-home-price-release-date", "source_type": "secondary", "checked_at": "2026-09-20T15:18:00Z", "summary": "The Verge independently reports that Google Home MCP lets agents including Claude and OpenClaw analyze household data, control devices, reason across event history, and build dashboards, while noting launch availability, rate limits, sensitive-action restrictions, and security risks." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-20T15:35:57.453Z", "revision_sha256": "46bacb35a92c6236ea824f53117233e222c816e1af5e4b176f11ff5e13db6a25", "notes": "Mike Ye approved the Google Home MCP capability-access classification as Knowledge access. User-authorized retrieval and supported device execution are evidenced; broader downstream rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-B61B9B1C", "legacy_ids": [ "SS-AIDT-2026-0007" ], "event_key": "legacy|ss-aidt-2026-0007", "announcement_date": "2026-09-15", "observed_status": "announced_three_year_program", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-16", "parties": { "providers": [ "TotalEnergies" ], "ai_recipients": [ "Mistral AI" ], "strategic_buyer_or_operator": [ "Mistral AI" ] }, "asset": { "description": "Nearly a century of TotalEnergies geoscience data, knowledge, and reservoir-engineering expertise used in a joint laboratory to develop frontier and agentic AI models for exploration and reservoir decisions.", "asset_types": [ "Geological / subsurface data", "Reservoir engineering", "Decision histories", "Scientific data" ], "industry": "Energy & geoscience", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership" ], "modes": [ "training" ] }, "rights": { "training": "permitted", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Develop frontier and agentic models that generate exploration scenarios, interpret subsurface data, optimize reservoirs, and support expert decisions.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Program duration, investment lower bound, joint lab and geoscience history are disclosed. Scores are qualitative analysis, not measured uplift or a valuation of the dataset. Ownership, retention and derived-model rights are not disclosed." }, "economics": { "transaction_structure": "Strategic Model Co-Development", "disclosed_consideration": { "amount": 100000000, "amount_qualifier": "greater_than", "currency": "EUR", "description": "More than €100 million over three years; explicitly a program-wide investment, not a dataset price.", "status": "disclosed_program_commitment" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "bundled_research_compute_people_and_implementation" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 88, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "Analysis: operational context suggests useful decision relationships; missing outcome-label evidence limits the rating.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Analysis: mistakes in this domain can have major economic consequences. This is domain judgment, not a disclosed corpus valuation.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Analysis: the disclosed domain supports economically consequential enterprise activities; this is not a model-uplift result.", "source_ids": [ "E1" ] }, "historical_depth": { "basis": "analysis", "rationale": "Analysis: the announced near-century geoscience history suggests considerable temporal depth; uniform record completeness is not demonstrated.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "Analysis: the announcement directly links this corpus to model development; improvement magnitude is unmeasured.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Analysis: reproducing owner-specific operating history would require comparable activity and time.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Analysis: member-owned operational or scientific data offers context beyond ordinary public web corpora.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "Analysis: the described corporate operational or scientific records are grounded in real activity rather than synthetic-only tasks.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Analysis: access to this owner-contributed domain corpus is difficult to reproduce; exclusivity is not established.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3.5, "decision_value_density": 4.5, "domain_value": 5, "historical_depth": 4.5, "model_learning_usefulness": 4, "non_replicability": 4, "proprietary_advantage": 4, "real_world_grounding": 4.5, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": 4.5 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 85 }, "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Analysis: no separately priced corporate dataset is identified; the announcement offers little clean data-price discovery.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified consideration for data is disclosed. Zero measures disclosure weakness, not zero data value.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "A program investment lower bound is disclosed, but its allocation to data is not disclosed.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "The company explicitly connects proprietary corporate data to developing the named AI models.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "Analysis: this is a reusable model-co-development structure, but repeatable standalone pricing is not established.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Analysis: the announced model-development purpose is clear, while detailed transfer, retention and licensing terms remain unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Analysis: an identified model developer with a specific domain program makes the observation strategically relevant.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 1, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 4.5, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 2.5, "strategic_buyer_quality": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 56 } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_applicable_or_not_disclosed", "geographic_sovereignty": "European strategic-technology context; binding geographic terms not disclosed.", "governed_access": "Joint laboratory; technical access controls and contractual rights allocation are not disclosed.", "privacy_pii": "not_applicable_or_not_disclosed", "trade_secret": "Announcement emphasizes protecting intellectual property; specific binding provisions are not disclosed.", "access_structure": "Three-year joint scientific laboratory to develop frontier models using TotalEnergies geoscience data and expertise. Specific licenses, data custody and allocation of resulting model rights are not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://totalenergies.com/newsroom/totalenergies-annonce-un-partenariat-avec-mistral-en-vue-de-developper-des-modeles-de-frontiere-dintelligence-artificielle-dedies-a-lexploration-et-a-lingenierie-des-reservoirs-498514", "source_type": "primary", "publisher": "TotalEnergies", "title": "TotalEnergies Announces a Partnership with Mistral to Develop Frontier AI Models for Reservoir Exploration and Engineering" } ], "publication_review": { "approval_channel": "owner_conversation", "decision_id": "conversation-20260916-5768A8330103", "packet_sha256": "656620707ca856653b6dd4b75854d841b3c4106e4e89e51e1088de26891e85e2", "record_sha256": "f40e71b98171603e1825f3e02d1584b2c8e0822e8f4d59cc9e102b8ea534dd6a", "reviewed_at": "2026-09-16T21:17:28.112Z", "reviewer": "Mike Ye", "status": "human_reviewed" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0007/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-098F28D8", "legacy_ids": [ "SS-AIDT-2026-0008" ], "event_key": "legacy|ss-aidt-2026-0008", "announcement_date": "2026-09-14", "observed_status": "announced_preview_training_window", "first_added_date": "2026-09-17", "last_reviewed_date": "2026-09-17", "parties": { "providers": [ "Bolt / StackBlitz" ], "ai_recipients": [ "Arcee AI" ], "strategic_buyer_or_operator": [ "Arcee AI" ] }, "asset": { "description": "Opted-in Bolt Forge sessions containing developer prompts, code, project files and configuration, tool calls, edit histories and fix traces from individual Pro users during the September 14–October 14, 2026 preview window.", "asset_types": [ "Source code and project files", "Developer prompts", "Tool-call traces", "Edit histories", "Error and fix traces" ], "industry": "Software development & AI training data", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training open-weight coding and software-building models to complete real application-development workflows in fewer attempts.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The data-license structure, corpus fields, consent controls, initial Arcee recipient and model-training purpose are disclosed by Bolt. Cash economics, raw-data retention, derived-weight ownership, Arcee sublicensing and measured model uplift are not disclosed." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "disclosed_non_cash_user_consideration", "amount": null, "currency": null, "description": "Individual Pro users receive up to 50× additional Forge usage during the preview; Bolt describes the allocation as payment for sharing. Bolt may also receive cash for licensed datasets, but no amount is disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "In-kind usage allocation to contributing users; any Arcee-to-Bolt license payment is undisclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 72, "coverage_pct": 100, "rationale": "Strategic Signal analysis. The workflow traces are unusually useful for software-agent learning, but the corpus is new, excludes enterprise workspaces and has no independent uplift evidence.", "factor_ratings": { "uniqueness_scarcity": 4, "historical_depth": 0.5, "decision_outcome_richness": 4, "decision_value_density": 2.5, "real_world_grounding": 4, "domain_value": 4, "refreshability": 4.5, "proprietary_advantage": 4, "model_learning_usefulness": 4.5, "non_replicability": 3.5, "rights_usability": 3.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Static code is common, but consented end-to-end build, error and fix traces are difficult to scrape or reconstruct." }, "historical_depth": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The disclosed corpus begins with a one-month preview window in September 2026, so historical depth is presently limited." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Edit histories, tool calls and fix traces expose repeated action-and-result sequences within software-building workflows." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Individual coding decisions are usually lower-value than clinical or capital decisions, though successful repairs carry useful technical information." }, "real_world_grounding": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The sessions arise from real user projects and tool interactions rather than a synthetic-only benchmark corpus." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Coding-agent capability has broad economic value across software development, even though the disclosed users are not enterprise workspaces." }, "refreshability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Forge can continue collecting new opted-in sessions as users build, subject to the stated program and geography limits." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Bolt controls the platform telemetry and de-identification pipeline that create a differentiated corpus beyond public repositories." }, "model_learning_usefulness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Bolt directly links the sessions to training a new model and explains that repair traces can reduce the number of attempts required." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Another builder could collect similar telemetry, but reproducing Bolt's exact user interactions and repair sequences would require comparable scale and consent." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Opt-in collection, de-identification and an explicit data license support usability, while retention and downstream model terms remain undisclosed." } } }, "transaction_signal": { "score": 66, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The arrangement cleanly proves training use and non-cash contributor consideration, but provides weak cash price discovery and no measured uplift.", "factor_ratings": { "disclosed_economics": 3, "clean_price_discovery": 1, "data_consideration_separability": 3.5, "explicit_model_use": 5, "rights_clarity": 4, "competing_bids": null, "strategic_buyer_quality": 3.5, "precedent_value": 4.5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Bolt discloses up to 50× usage as contributor payment and says it may receive payment, but gives no cash value for the dataset license." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The usage benefit is bundled with a preview program and no buyer payment amount is disclosed, limiting clean price discovery." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Bolt explicitly describes the usage allocation as payment for sharing, but no cash-equivalent valuation is provided." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announcement directly states that shared sessions feed Arcee's first training run for a trillion-parameter-class model." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Training, opt-in, exclusions and Bolt's ability to license other developers are clear; retention and derived-model allocation are not." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Arcee is a named model developer with a concrete disclosed training run, though it is not a frontier lab of the largest scale." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The data-for-usage exchange is a replicable template for software platforms monetizing consented workflow traces." } } } }, "governance": { "governed_access": "One-tap opt-in is required before collection; switching away from Forge stops new collection.", "geographic_sovereignty": "Sessions from the EEA, United Kingdom and Switzerland are excluded from training and dataset licensing.", "privacy_pii": "Bolt says personal information is stripped and the de-identification pipeline is tested with seeded data.", "trade_secret": "Bolt says secrets and sensitive data are stripped before material leaves its infrastructure; the effectiveness and contractual remedies are not independently verified.", "de_identification": "Explicitly required and performed by Bolt before transfer.", "employee_customer_data": "Team and Enterprise workspaces are excluded; the disclosed corpus comes from opted-in individual Pro users.", "access_structure": "Bolt de-identifies opted-in Forge sessions inside its infrastructure and licenses resulting datasets to AI developers under a data license agreement. Arcee AI is the first named recipient; Bolt says the datasets may also be licensed to other AI developers." }, "evidence": [ { "source_id": "E1", "url": "https://bolt.new/blog/what-is-bolt-forge", "source_type": "primary", "publisher": "Bolt", "title": "What is Bolt Forge?" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "github_workflow", "reviewer": "Trailgenic", "reviewed_at": "2026-09-17T17:36:14.674Z", "decision_id": "35253728428-1", "packet_sha256": "e1891b409964b996e4b464fd5436cf7ef8ae43599ca7f8153171c06be5ce1ff9", "record_sha256": "c5a10eeece7f3e720d635e409999286246ebbd48d54636a21ffa50017c641d5e" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0008/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-DF6DFF33", "legacy_ids": [ "SS-AIDT-2026-0006" ], "event_key": "legacy|ss-aidt-2026-0006", "announcement_date": "2026-09-10", "observed_status": "launched_active", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-15", "parties": { "providers": [ "LSEG", "PitchBook", "Daloopa", "Crunchbase", "Quartr", "LSEG News" ], "ai_recipients": [ "OpenAI" ], "strategic_buyer_or_operator": [ "OpenAI" ] }, "asset": { "description": "Continuously updated proprietary financial datasets integrated as built-in, cited data sources in ChatGPT for Financial Services. Customer-connected subscription sources are excluded from this record.", "asset_types": [ "Financial datasets", "Private-market data", "Fundamental company data", "Earnings and filings" ], "industry": "Financial information services", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "retrieval" ] }, "rights": { "training": "prohibited", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "permitted", "action_execution": "unknown", "personalization": "unknown", "retention": "unknown", "ownership_transfer": "prohibited", "derived_model": "prohibited", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Ground inference, financial analysis, retrieval, and citations; no training rights are claimed.", "source_ids": [ "E1", "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Built-in providers and retrieval/citation use are disclosed. Training, post-training, ownership, and pricing are not claimed." }, "economics": { "transaction_structure": "Proprietary Corpus License", "disclosed_consideration": { "amount": null, "currency": null, "description": "No provider-specific consideration disclosed.", "status": "not_disclosed" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "not_disclosed" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "governance": { "de_identification": "not_applicable_or_not_disclosed", "employee_customer_data": "Customer-connected datasets are explicitly excluded from this record.", "geographic_sovereignty": "not_disclosed", "governed_access": "Product and provider controls apply; contract details are not public.", "privacy_pii": "not_disclosed", "trade_secret": "Provider-license restrictions not disclosed.", "access_structure": "Built-in licensed/indexed data available to the financial-services product for grounded analysis and citations; specific provider contracts are not public." }, "evidence": [ { "source_id": "E1", "url": "https://help.openai.com/en/articles/12608093-chatgpt-for-financial-services", "source_type": "primary", "publisher": "OpenAI", "title": "ChatGPT for Financial Services" }, { "source_id": "E2", "url": "https://www.reuters.com/business/openai-launches-chatgpt-financial-services-industry-2026-09-10/", "source_type": "secondary", "publisher": "Reuters", "title": "OpenAI launches ChatGPT for financial services industry" }, { "source_id": "E3", "url": "https://daloopa.com/", "source_type": "primary", "publisher": "Daloopa", "title": "Daloopa data infrastructure" } ], "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "The cited pages could not be retrieved for this audit. Source-specific rights and the boundary between built-in access and customer connectors remain unresolved.", "status": "provisional" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0006/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-6F032C18", "legacy_ids": [ "SS-AIDT-2026-0005" ], "event_key": "legacy|ss-aidt-2026-0005", "announcement_date": "2026-09-09", "observed_status": "announced_active", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-15", "parties": { "providers": [ "Samsung Electronics" ], "ai_recipients": [ "Mistral AI" ], "strategic_buyer_or_operator": [ "Mistral AI" ] }, "asset": { "description": "Sensitive semiconductor operational and manufacturing data used inside Samsung boundaries to customize on-premise models for defect detection, equipment optimization, and engineering workflows.", "asset_types": [ "Manufacturing history", "Equipment and process data", "Engineering knowledge" ], "industry": "Semiconductors", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership" ], "modes": [ "training", "post_training", "retrieval" ] }, "rights": { "training": "permitted", "post_training": "permitted", "evaluation": "unknown", "inference_retrieval": "permitted", "action_execution": "unknown", "personalization": "unknown", "retention": "unknown", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Customize models for semiconductor defect detection, equipment optimization, technical support, and engineering decision workflows.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "On-premise controls, target use cases, and strategic equity relationship are disclosed. Model ownership, exclusivity, sublicensing, and data-specific consideration are not." }, "economics": { "transaction_structure": "Proprietary Data + Equity Partnership", "disclosed_consideration": { "amount": null, "currency": null, "description": "Partnership economics and Samsung's individual equity investment were not disclosed separately.", "status": "not_disclosed" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "bundled_with_strategic_equity_relationship" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "On-premise / sovereign control", "governed_access": "On-premise deployment; sensitive data stays inside Samsung boundaries.", "privacy_pii": "not_disclosed", "trade_secret": "Explicit protection of sensitive semiconductor technology and operational data.", "access_structure": "Customized on-premise model development; Samsung says sensitive technology and operational data remain entirely within its boundaries. Samsung also participated in Mistral's financing round." }, "evidence": [ { "source_id": "E1", "url": "https://news.samsung.com/global/samsung-and-mistral-ai-announce-strategic-partnership-for-intelligence-driven-semiconductor-infrastructure", "source_type": "primary", "publisher": "Samsung", "title": "Samsung and Mistral AI Announce Strategic Partnership for Intelligence-Driven Semiconductor Infrastructure" }, { "source_id": "E2", "url": "https://www.reuters.com/world/europe/french-ai-company-mistral-hits-24-billion-valuation-funding-round-2026-09-08/", "source_type": "secondary", "publisher": "Reuters", "title": "French AI company Mistral hits $24 billion valuation in funding round" } ], "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Samsung announces customized on-premise models and a strategic equity stake. Processing data within Samsung boundaries does not establish what learning rights Mistral receives.", "status": "provisional" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0005/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-224556B9", "legacy_ids": [ "SS-CAP-20261003-F7F8075647D8" ], "event_key": "authorizing muse users and connected services|meta muse|2026-09-08|connected apps inboxes calendars computer data conversations memories and credentials|retrieval personalization planning and action execution", "announcement_date": "2026-09-08", "observed_status": "active", "first_added_date": "2026-10-03", "last_reviewed_date": "2026-10-03", "parties": { "providers": [ "Authorizing Muse users", "Connected application, device, and service providers" ], "ai_recipients": [ "Meta Muse", "Muse Secure VM" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "User-authorized information and workflows from connected email, calendars, payments, shopping, health, smart-home and other applications; browser and computer context; conversations, goals and persistent memories; and credentials held in secure storage, made available to Meta Muse for retrieval, planning, personalization and action execution.", "asset_types": [ "content_corpus", "expert_workflow", "software_execution_environment", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "connector", "platform_integration", "other" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "conditional", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "conditional", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Muse can retrieve authorized information from connected apps, browser sessions, email, calendars, messages and a user's computer environment.", "source_ids": [ "E1", "E2", "E4", "E5" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Muse turns user goals into plans, keeps working in the background and resumes when conditions change or approval is required.", "source_ids": [ "E1", "E2" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Muse can browse, fill forms, send email, book travel, sell items, negotiate, shop and make authorized payments across connected services.", "source_ids": [ "E1", "E2", "E4" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Muse uses conversations and persistent memories to make proactive suggestions and act on details the user previously shared.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Meta Muse canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Muse receives user-authorized access to connected applications, device/browser context, conversations, goals, memories and credentials for retrieval, planning, personalization and action execution. Human Response H2/H3 is not established. Training over Muse interactions is conditional on user controls; broader connected-asset training, ownership, sublicensing, derived-model and exclusivity rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/", "source_type": "primary", "checked_at": "2026-10-03T15:12:00.000Z", "summary": "Meta announces Muse as a permissioned personal agent that stores connected-service data and credentials in a dedicated VM, remembers user context, plans and acts across applications, uses confirmations and audit trails, permits disconnection and memory deletion, and lets users opt out of interaction training." }, { "source_id": "E2", "url": "https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse", "source_type": "primary", "checked_at": "2026-10-03T15:13:00.000Z", "summary": "Meta describes Muse's access to inboxes, calendars and a shell, background and multi-agent operation, secure VM architecture, Sentinel enforcement and the training focus on tool use, long trajectories and agent security." }, { "source_id": "E3", "url": "https://developer.apple.com/news/?id=p6zjojqw", "source_type": "primary", "checked_at": "2026-10-03T15:14:00.000Z", "summary": "Apple says Full Disk Access can expose files, mail, messages and browsing history without full understanding and announces additional explicit-action controls because autonomous-agent risk is increasing; Apple does not name Muse or provide an implementation date." }, { "source_id": "E4", "url": "https://www.reuters.com/business/meta-launches-ai-agent-that-can-access-other-apps-send-emails-make-payments-2026-09-08/", "source_type": "secondary", "checked_at": "2026-10-03T15:15:00.000Z", "summary": "Reuters reports the September 8 launch, app categories including email, calendars, payments, health, shopping and smart home, active US availability, subscription tiers and user-controlled permissions." }, { "source_id": "E5", "url": "https://www.reuters.com/business/retail-consumer/apple-says-it-will-flag-ai-requests-mac-data-after-metas-muse-draws-complaints-2026-10-02/", "source_type": "secondary", "checked_at": "2026-10-03T15:16:00.000Z", "summary": "Reuters connects Apple's October 2 Full Disk Access announcement to Muse complaints and reports Meta's statement that Messages access requires both user-enabled Full Disk Access and a Messages connector; disputed unauthorized-access allegations remain unresolved." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-03T15:38:17.617Z", "revision_sha256": "e5f9d75518626222b0a747ee0609d7c464ffe7f4105917bac2af01ef3afea1d5", "notes": "Mike Ye approved the Meta Muse canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Muse receives user-authorized access to connected applications, device/browser context, conversations, goals, memories and credentials for retrieval, planning, personalization and action execution. Human Response H2/H3 is not established. Training over Muse interactions is conditional on user controls; broader connected-asset training, ownership, sublicensing, derived-model and exclusivity rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-7B000D3D", "legacy_ids": [ "SS-CAP-20260919-3B792B6F72A4" ], "event_key": "healthex|meta-muse|2026-09-08|personal-health-history|retrieval-execution", "announcement_date": "2026-09-08", "observed_status": "active", "first_added_date": "2026-09-19", "last_reviewed_date": "2026-09-19", "parties": { "providers": [ "HealthEx", "Consenting Muse users" ], "ai_recipients": [ "Meta Muse" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "User-authorized longitudinal medical records, including diagnoses, lab results, medications, care history and prior clinician recommendations, made available to Meta's Muse personal AI agent for grounded health guidance and follow-through.", "asset_types": [ "structured_dataset", "longitudinal_response_history" ], "industry": null, "subject_type": "human", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Muse can draw on a user's shared longitudinal health history instead of relying only on general information.", "source_ids": [ "E1" ] }, { "type": "agent_planning", "evidence_level": "enabled", "statement": "Muse can assemble visit summaries, surface unfinished follow-ups and plan next steps around a user's health goals and clinical context.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "With user confirmation, Muse can draft questions or messages and set refill or follow-up reminders based on the shared record.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "Muse provides guidance grounded in the individual's own records and tracks progress as the person's context changes.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Meta Muse × HealthEx capability-access classification as Knowledge access. Personalized guidance and approved actions are supported; measured intervention outcomes are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.healthex.io/press/healthex-muse-partnership", "source_type": "primary", "checked_at": "2026-09-19T15:03:00Z", "summary": "HealthEx says U.S. users can connect selected longitudinal medical records to Meta's Muse personal AI agent. Muse can ground guidance in those records, assemble visit summaries, surface follow-ups, draft questions or messages and set reminders, with explicit consent, auditable permissions and user confirmation before actions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-19T20:34:09.630Z", "revision_sha256": "84e40a9c0ec1600ba14b512d788af4fe2778c63978e9571d31c163202cf44264", "notes": "Mike Ye approved the Meta Muse × HealthEx capability-access classification as Knowledge access. Personalized guidance and approved actions are supported; measured intervention outcomes are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-C0445F96", "legacy_ids": [ "SS-CAP-20260925-5622225A4FF8" ], "event_key": "shopify|meta muse|2026-09-08|shopify catalog shop pay agentic storefronts|retrieval execution", "announcement_date": "2026-09-08", "observed_status": "active", "first_added_date": "2026-09-25", "last_reviewed_date": "2026-09-25", "parties": { "providers": [ "Shopify" ], "ai_recipients": [ "Meta Muse" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Shopify's merchant catalog and Agentic Storefronts commerce layer, including products searchable for best matches and Shop Pay checkout, made available to Meta Muse so shoppers can discover products and complete purchases inside the Muse experience.", "asset_types": [ "structured_dataset", "domain_actions" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "permitted", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Muse can search Shopify Catalog for products that best match a shopper's request.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Muse can translate a shopping request into product matches drawn from Shopify's catalog across participating merchants.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Direct Shop Pay checkout is live inside Muse, subject to the shopper's approval before purchase.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "unknown", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Shopify × Meta Muse canonical AI Access event as Knowledge Access with governed product retrieval and conditional Shop Pay transaction execution. Strict data-rights qualification does not apply; scores are not applicable. Training, post-training, evaluation, personalization, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "permitted", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://www.linkedin.com/company/shopify", "source_type": "primary", "checked_at": "2026-09-25T15:21:00Z", "summary": "Shopify states that products from Shopify merchants are live for Muse, that Muse searches Shopify Catalog for best matches, that the Meta channel is live in Agentic Storefronts, and that direct Shop Pay checkout is live inside Muse." }, { "source_id": "E2", "url": "https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/", "source_type": "primary", "checked_at": "2026-09-25T15:21:00Z", "summary": "Meta's Muse launch announcement identifies Shop Pay as an intended payment option, states that Muse can make purchases, and says the agent requests user approval before purchases while users control connected-app access." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-25T15:45:13.529Z", "revision_sha256": "63c83dc098d7c25b727eaecffdc2d0285936b23dfd9e3f99a44ff7d68f59659e", "notes": "Mike Ye approved the Shopify × Meta Muse canonical AI Access event as Knowledge Access with governed product retrieval and conditional Shop Pay transaction execution. Strict data-rights qualification does not apply; scores are not applicable. Training, post-training, evaluation, personalization, retention, ownership, derived-model, sublicensing, and broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-4B562460", "legacy_ids": [ "SS-AIDT-2026-0009" ], "event_key": "legacy|ss-aidt-2026-0009", "announcement_date": "2026-09-07", "observed_status": "contract_signed", "first_added_date": "2026-09-17", "last_reviewed_date": "2026-09-17", "parties": { "providers": [ "Silencio" ], "ai_recipients": [ "Undisclosed leading voice-AI company" ], "strategic_buyer_or_operator": [ "Undisclosed leading voice-AI company" ] }, "asset": { "description": "A contracted corpus of native-speaker Tagalog and Cebuano single- and multi-speaker voice recordings assembled through Silencio's distributed contributor network for a leading voice-AI company.", "asset_types": [ "Human speech recordings", "Low-resource language data", "Single-speaker voice data", "Multi-speaker voice data" ], "industry": "Voice AI & training data", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "stranded_separable", "renewal": "finite" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "not_disclosed", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "prohibited", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training or improving speech recognition, multilingual voice generation or voice-agent performance in Tagalog and Cebuano.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Contract value, languages, speaker formats and buyer type are disclosed by an investor in Silencio. Buyer identity, dataset size, ownership, exclusivity, retention, sublicensing, derived-model rights and independent model uplift are not disclosed." }, "economics": { "transaction_structure": "Proprietary Corpus License", "disclosed_consideration": { "status": "disclosed_contract_value", "amount": 250000, "currency": "USD", "description": "Dataset-specific contract value disclosed as $250,000; the buyer is unnamed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "Specific Tagalog and Cebuano single- and multi-speaker voice-data contract." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 63, "coverage_pct": 100, "rationale": "Strategic Signal analysis. The recordings are scarce, consent-oriented and useful for model coverage, but they contain little decision-outcome structure and have limited disclosed historical depth.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": 1, "decision_outcome_richness": 0.5, "decision_value_density": 1, "real_world_grounding": 5, "domain_value": 4, "refreshability": 4, "proprietary_advantage": 4.5, "model_learning_usefulness": 4.5, "non_replicability": 4, "rights_usability": 3.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Native-speaker Tagalog and Cebuano recordings cannot be assembled reliably from the open web at the same coverage and consent standard." }, "historical_depth": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Silencio says collection began in November 2025, so the disclosed business has limited temporal depth relative to longitudinal operating datasets." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Speech recordings contain linguistic observations but not a disclosed sequence of economically consequential decisions and outcomes." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The corpus improves language coverage but does not encode high-value underwriting, clinical, industrial or capital-allocation decisions." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2" ], "rationale": "The recordings are contributed by native speakers in relevant markets rather than described as synthetic speech." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Multilingual speech capability is commercially valuable for voice agents and speech systems, especially in underserved languages." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Silencio's distributed network can collect follow-on speech and related languages, though this contract is a finite delivery." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Silencio controls consented contributor relationships and collection operations that are unavailable through ordinary web scraping." }, "model_learning_usefulness": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The corpus directly addresses model coverage in languages with little public training data; the magnitude of improvement is unmeasured." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "A competitor could recruit speakers, but reproducing geographic coverage, consent and collection at comparable speed requires substantial network infrastructure." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The data is marketed as consent-cleared and licensing-ready, while contract-specific retention, exclusivity and downstream rights remain unknown." } } }, "transaction_signal": { "score": 89, "coverage_pct": 79, "rationale": "Strategic Signal analysis. The contract supplies unusually clean dataset-specific cash economics for a defined language corpus, but detailed rights and the buyer identity remain undisclosed.", "factor_ratings": { "disclosed_economics": 5, "clean_price_discovery": 4.5, "data_consideration_separability": 5, "explicit_model_use": 4.5, "rights_clarity": 3, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": 4, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The issuer-responsible announcement states a $250,000 contract value for the defined Tagalog and Cebuano corpus." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The cash amount is tied to a specific corpus, although undisclosed volume and contract term prevent a per-hour benchmark." }, "data_consideration_separability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announcement presents the $250,000 amount as a voice-data contract rather than a bundled software deployment." }, "explicit_model_use": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The buyer is identified as a leading voice-AI company and Silencio says AI labs train on its network data; exact training language is not quoted from the contract." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "A data contract and consent-oriented supply are clear, but ownership, exclusivity, retention and sublicensing are undisclosed." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "A dataset-specific cash price for named low-resource languages can inform future speech-data transactions, subject to missing volume normalization." } } } }, "governance": { "governed_access": "Procurement, legal and security review are disclosed; binding access controls are not.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "Voice recordings can carry biometric and identity risk; contract-specific privacy terms are not disclosed.", "trade_secret": "not_disclosed", "de_identification": "not_disclosed", "employee_customer_data": "not_applicable; contributors record data for compensation rather than supplying enterprise employee or customer records.", "access_structure": "Dataset contract and delivery following procurement, legal, security and sample evaluation. The buyer, delivery volume, term, exclusivity and detailed license language are not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://www.eqs-news.com/news/corporate/advanced-blockchain-ag-portfolio-company-silencio-signs-record-voice-data-contract-as-ai-labs-turn-to-low-resource-languages/87e56ca5-9883-4f82-8b2e-d265764a5dc7_en", "source_type": "primary_syndicated", "publisher": "Advanced Blockchain AG via EQS News", "title": "Portfolio company Silencio signs record voice-data contract as AI labs turn to low-resource languages" }, { "source_id": "E2", "url": "https://www.silencio.network/", "source_type": "primary", "publisher": "Silencio", "title": "Silencio AI" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "github_workflow", "reviewer": "Trailgenic", "reviewed_at": "2026-09-17T17:37:52.520Z", "decision_id": "35253885258-1", "packet_sha256": "b084f0f7a0ae807c847525e517d07eaafda15abc1df076c4c07397f0a2558baf", "record_sha256": "165b8c0418f97c60c4332558ccb0a13968fb6c5ecb635a68041e380d662f75a0" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0009/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-9CF1426E", "legacy_ids": [ "SS-AIDT-2026-0010" ], "event_key": "legacy|ss-aidt-2026-0010", "announcement_date": "2026-09-07", "observed_status": "license_signed", "first_added_date": "2026-09-17", "last_reviewed_date": "2026-09-17", "parties": { "providers": [ "Silencio" ], "ai_recipients": [ "Undisclosed leading AI developer" ], "strategic_buyer_or_operator": [ "Undisclosed leading AI developer" ] }, "asset": { "description": "Native-speaker voice datasets covering Swahili, Amharic, Hausa, Wolof and Yoruba, assembled through Silencio's distributed contributor network under a separate license to an unnamed leading AI developer.", "asset_types": [ "Human speech recordings", "Low-resource language data", "African language datasets", "Transcription-ready voice data" ], "industry": "Voice AI & training data", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "stranded_separable", "renewal": "finite" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "not_disclosed", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "prohibited", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training or improving speech recognition, multilingual voice generation or voice-agent performance across five underrepresented African languages.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The separate license, five initial languages and buyer type are disclosed by an investor in Silencio. Buyer identity, consideration, dataset size, ownership, exclusivity, retention, sublicensing, derived-model rights and independent uplift are not disclosed." }, "economics": { "transaction_structure": "Proprietary Corpus License", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "The announcement discloses the license but not its consideration." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "Separate license for five African-language datasets; no transaction economics are disclosed." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 63, "coverage_pct": 100, "rationale": "Strategic Signal analysis. The recordings are scarce, consent-oriented and useful for model coverage, but they contain little decision-outcome structure and have limited disclosed historical depth.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": 1, "decision_outcome_richness": 0.5, "decision_value_density": 1, "real_world_grounding": 5, "domain_value": 4, "refreshability": 4, "proprietary_advantage": 4.5, "model_learning_usefulness": 4.5, "non_replicability": 4, "rights_usability": 3.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Native-speaker recordings across the five named languages are difficult to source at scale from public web material." }, "historical_depth": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "Silencio says collection began in November 2025, so the disclosed business has limited temporal depth relative to longitudinal operating datasets." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "Speech recordings contain linguistic observations but not a disclosed sequence of economically consequential decisions and outcomes." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The corpus improves language coverage but does not encode high-value underwriting, clinical, industrial or capital-allocation decisions." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2" ], "rationale": "The recordings are contributed by speakers in relevant markets rather than described as synthetic speech." }, "domain_value": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Multilingual speech capability is commercially valuable for voice agents and speech systems, especially in underserved languages." }, "refreshability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The network can collect follow-on speech and additional languages even though the initial five-language delivery is finite." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "Silencio controls consented contributor relationships and collection operations that are unavailable through ordinary web scraping." }, "model_learning_usefulness": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The datasets directly address model coverage in languages with little public training data; improvement magnitude is unmeasured." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "A competitor could recruit speakers, but reproducing multi-country coverage, consent and collection speed requires substantial network infrastructure." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The data is marketed as consent-cleared and licensing-ready, while contract-specific retention, exclusivity and downstream rights remain unknown." } } }, "transaction_signal": { "score": null, "coverage_pct": 47, "rationale": "Strategic Signal analysis. A separable AI-data license is explicit, but undisclosed economics, buyer identity and most contract rights leave evidence coverage below the methodology's 70% threshold.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": 4.5, "explicit_model_use": 4.5, "rights_clarity": 3, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": 4, "independent_model_uplift": null }, "factor_evidence": { "data_consideration_separability": { "basis": "disclosed_fact", "source_ids": [ "E1" ], "rationale": "The announcement explicitly describes a separate data licensing agreement for five named language datasets." }, "explicit_model_use": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The counterparty is described as a leading AI developer and Silencio says AI labs train on its network data; contract wording is not public." }, "rights_clarity": { "basis": "analysis", "source_ids": [ "E1", "E2" ], "rationale": "The existence and scope languages of the license are clear, but ownership, exclusivity, retention and sublicensing are undisclosed." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1" ], "rationale": "The agreement demonstrates repeat multi-language licensing from a renewable contributor network, even though it offers no price benchmark." } } } }, "governance": { "governed_access": "A commercial data license is disclosed; binding access controls are not.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "Voice recordings can carry biometric and identity risk; contract-specific privacy terms are not disclosed.", "trade_secret": "not_disclosed", "de_identification": "not_disclosed", "employee_customer_data": "not_applicable; contributors record data for compensation rather than supplying enterprise employee or customer records.", "access_structure": "A separate data licensing agreement for five named African-language datasets. Economics, volume, delivery term, exclusivity and detailed license language are not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://www.eqs-news.com/news/corporate/advanced-blockchain-ag-portfolio-company-silencio-signs-record-voice-data-contract-as-ai-labs-turn-to-low-resource-languages/87e56ca5-9883-4f82-8b2e-d265764a5dc7_en", "source_type": "primary_syndicated", "publisher": "Advanced Blockchain AG via EQS News", "title": "Portfolio company Silencio signs record voice-data contract as AI labs turn to low-resource languages" }, { "source_id": "E2", "url": "https://www.silencio.network/", "source_type": "primary", "publisher": "Silencio", "title": "Silencio AI" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "github_workflow", "reviewer": "Trailgenic", "reviewed_at": "2026-09-17T17:38:43.226Z", "decision_id": "35253967579-1", "packet_sha256": "edd54770424ddcc1d311f83df2a2a5fce9d23adc3996636c30b8222479b3b11b", "record_sha256": "4b81ae9d51db5e8e069e1517003afc4e36600684cd8c67cc4e403d839067a698" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0010/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-A1462677", "legacy_ids": [ "SS-CAP-20260930-39F057FD87F1" ], "event_key": "docusign customers|docusign iris and authorized third party ai agents|2026-09-04|agreements negotiation history clauses policies and contract workflows|mcp retrieval and action execution", "announcement_date": "2026-09-04", "observed_status": "active", "first_added_date": "2026-09-30", "last_reviewed_date": "2026-09-30", "parties": { "providers": [ "Docusign", "Authorizing Docusign customers" ], "ai_recipients": [ "Docusign Iris", "Claude", "ChatGPT", "Gemini", "Copilot", "Salesforce Agentforce", "Other authorized MCP clients" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer agreements and their professional context—including past negotiations, accepted terms, clauses, company policy and contract-lifecycle workflows—made available through Docusign Iris and a globally available MCP server for governed analysis, drafting, sending, tracking and downstream actions.", "asset_types": [ "content_corpus", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Authorized agents can retrieve and analyze agreement terms, clauses, negotiation history and company policy through Docusign Iris and MCP.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Docusign Iris supplies agreement intelligence and policy context so agents can interpret terms inside contract-management workflows.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Agents can perform governed agreement analysis, drafting, sending and tracking, including actions embedded in Slack, Salesforce and other work environments.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Docusign MCP canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed retrieval and action over customer agreements, clauses, policies and contract workflows are evidenced; training and other broader reuse rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://investor.docusign.com/news-and-events/press-releases/news-details/2026/Docusign-Agreement-Layer-for-the-Agentic-Enterprise-Coming-to-Every-Agent/default.aspx", "source_type": "primary", "checked_at": "2026-09-30T15:31:00Z", "summary": "Docusign's issuer release states that its MCP server becomes globally available September 30, gives authorized agents agreement intelligence and governed actions, uses account-level controls, and exposes negotiations, terms, clauses, policies and CLM workflows through Iris." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-30T16:15:04.751Z", "revision_sha256": "cbc6c81faee0d86f64cd60798f2399887e160537f1402c09ab32d984a80e85d6", "notes": "Mike Ye approved the Docusign MCP canonical AI Access event as Knowledge + Professional Workflow access. Strict data-rights qualification does not apply; scores are not applicable. Governed retrieval and action over customer agreements, clauses, policies and contract workflows are evidenced; training and other broader reuse rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-89BE2570", "legacy_ids": [ "SS-AIDT-2026-0004" ], "event_key": "legacy|ss-aidt-2026-0004", "announcement_date": "2026-09-03", "observed_status": "announced_development", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-16", "parties": { "providers": [ "NAVER Cloud", "LG CNS", "KEPCO KDN", "Korea Hydro & Nuclear Power", "Financial Security Institute", "KISTI", "LG Uplus", "Other consortium members" ], "ai_recipients": [ "NAVER Cloud consortium", "NAVER Cloud", "LG AI Research" ], "strategic_buyer_or_operator": [ "NAVER Cloud consortium", "NAVER Cloud", "LG AI Research" ] }, "asset": { "description": "Approximately 830 TB of high-quality real-world operational and security data from consortium members across power, finance, telecommunications, science, semiconductors, defense, and aerospace.", "asset_types": [ "Cybersecurity records", "Critical-infrastructure operations", "Industrial systems data" ], "industry": "Cybersecurity & critical infrastructure", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership" ], "modes": [ "training", "retrieval" ] }, "rights": { "training": "permitted", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "permitted", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Train defensive and offensive cybersecurity foundation models and validate them in seven critical-industry domains.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The 830 TB planned corpus, contributors and training purpose are company disclosures. Scores are qualitative Strategic Signal analysis, not measured model performance. Federated topology and contract terms are unknown." }, "economics": { "transaction_structure": "Strategic Model Co-Development", "disclosed_consideration": { "amount": null, "currency": null, "description": "Government GPU support is disclosed, alongside consortium computing resources. Dataset-specific cash consideration is not disclosed.", "status": "in_kind_and_government_compute" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "bundled_program_support" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 88, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "Analysis: operational context suggests useful decision relationships; missing outcome-label evidence limits the rating.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Analysis: mistakes in this domain can have major economic consequences. This is domain judgment, not a disclosed corpus valuation.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Analysis: the disclosed domain supports economically consequential enterprise activities; this is not a model-uplift result.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "Analysis: the announcement directly links this corpus to model development; improvement magnitude is unmeasured.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Analysis: reproducing owner-specific operating history would require comparable activity and time.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Analysis: member-owned operational or scientific data offers context beyond ordinary public web corpora.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "Analysis: the described corporate operational or scientific records are grounded in real activity rather than synthetic-only tasks.", "source_ids": [ "E1" ] }, "refreshability": { "basis": "analysis", "rationale": "Analysis: planned field trials and model refinement indicate recurring operational input; no perpetual refresh entitlement is claimed.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Analysis: access to this owner-contributed domain corpus is difficult to reproduce; exclusivity is not established.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3, "decision_value_density": 4, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 4, "proprietary_advantage": 4.5, "real_world_grounding": 4.5, "refreshability": 4, "rights_usability": null, "uniqueness_scarcity": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 81 }, "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Analysis: no separately priced corporate dataset is identified; the announcement offers little clean data-price discovery.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified consideration for data is disclosed. Zero measures disclosure weakness, not zero data value.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "No dataset-specific cash amount is disclosed. This rating measures public price disclosure, not whether payment exists.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "The company explicitly connects proprietary corporate data to developing the named AI models.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "Analysis: this is a reusable model-co-development structure, but repeatable standalone pricing is not established.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Analysis: the announced model-development purpose is clear, while detailed transfer, retention and licensing terms remain unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Analysis: an identified model developer with a specific domain program makes the observation strategically relevant.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 0, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 0, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 2.5, "strategic_buyer_quality": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 35 } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "Korean program; contractual geographic restrictions not_disclosed", "governed_access": "Consortium/government program; detailed member-level controls are not disclosed.", "privacy_pii": "not_disclosed", "trade_secret": "Operational security and critical-infrastructure data; controls not disclosed.", "access_structure": "A 33-organization consortium is developing cybersecurity foundation models using member operational and security data. Custody and member-level licensing terms are not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://navercorp.com/en/media/pressReleasesDetail?seq=10034653", "source_type": "primary", "publisher": "NAVER", "title": "NAVER Cloud Consortium to Develop AI Models Tailored for Cybersecurity" } ], "publication_review": { "approval_channel": "owner_conversation", "decision_id": "conversation-20260916-2D6377E44A57", "packet_sha256": "0d3511e1d7c9a9ce6cd876440fe1b414e967551ce11a7404834f16cf82849ba7", "record_sha256": "9e66511ee6acde56b1620a2e82c7786e746dd09abcef983c751844558ad77bff", "reviewed_at": "2026-09-16T21:17:28.097Z", "reviewer": "Mike Ye", "status": "human_reviewed" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0004/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-AFF15D0F", "legacy_ids": [ "SS-CAP-20260919-FB11DD6F788E" ], "event_key": "healthex|eight-sleep|2026-09-03|medical-records-biometric-foundation-model|inference", "announcement_date": "2026-09-03", "observed_status": "active", "first_added_date": "2026-09-19", "last_reviewed_date": "2026-09-19", "parties": { "providers": [ "HealthEx", "Consenting Eight Sleep members" ], "ai_recipients": [ "Eight Sleep Biological Age health foundation model" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Member-authorized longitudinal medical records, including conditions, medications and lab results, combined with nightly Pod biometrics, daytime activity and recovery history for a personalized Biological Age and Speed of Aging estimate.", "asset_types": [ "structured_dataset", "longitudinal_response_history", "sensor_history" ], "industry": null, "subject_type": "human", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "response_prediction", "evidence_level": "enabled", "statement": "The Biological Age feature estimates speed of aging and a biological-age measure from continuously updated longitudinal health signals.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "The model considers an individual's own history and baseline alongside peer patterns and connected clinical context.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "within_subject", "recovery_observed": true, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Eight Sleep × HealthEx capability-access classification as Knowledge + Human Response (H2). The classification does not infer training rights to HealthEx-connected records." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.healthex.io/press/healthex-eightsleep-partnership", "source_type": "primary", "checked_at": "2026-09-19T15:03:00Z", "summary": "The joint release says Eight Sleep's Biological Age feature combines nightly Pod biometrics, connected fitness data and opt-in HealthEx medical records. The feature is built on a health foundation model, considers individual history, updates weekly, and exposes factors including sleep, recovery, cardiovascular, respiratory and activity signals." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-19T20:34:09.619Z", "revision_sha256": "ab5d03560c9e7fb392e94c273392dd9c13be804fab5fd6695b23fcee5f38007f", "notes": "Mike Ye approved the Eight Sleep × HealthEx capability-access classification as Knowledge + Human Response (H2). The classification does not infer training rights to HealthEx-connected records." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-0F028A87", "legacy_ids": [ "SS-AIDT-2026-0003" ], "event_key": "legacy|ss-aidt-2026-0003", "announcement_date": "2026-09-02", "observed_status": "announced_active", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-15", "parties": { "providers": [ "Caterpillar" ], "ai_recipients": [ "FieldAI" ], "strategic_buyer_or_operator": [ "FieldAI" ] }, "asset": { "description": "Caterpillar operational data, engineering capability, and industry expertise combined with FieldAI robot foundation models for complex jobsites and manufacturing environments.", "asset_types": [ "Industrial operational data", "Sensor and robotics data", "Engineering knowledge" ], "industry": "Industrial equipment & construction", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership" ], "modes": [ "retrieval" ] }, "rights": { "training": "unknown", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "permitted", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Improve robotic autonomy, inspection, situational awareness, and digital-twin systems for industrial environments.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The announced combination of operational data and robot foundation models is fact. Training, retention, exclusivity, and derived-model rights are unknown." }, "economics": { "transaction_structure": "Strategic Model Co-Development", "disclosed_consideration": { "amount": null, "currency": null, "description": "No consideration disclosed.", "status": "not_disclosed" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "not_disclosed" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "Engineering and operational data are described but controls are not disclosed.", "access_structure": "Collaborative physical-AI development; detailed data-access boundaries and model-rights allocation were not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://www.caterpillar.com/en/news/corporate-press-releases/h/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation.html", "source_type": "primary", "publisher": "Caterpillar", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation" }, { "source_id": "E2", "url": "https://www.prnewswire.com/news-releases/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation-302866862.html", "source_type": "primary_syndicated", "publisher": "PR Newswire", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation" } ], "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Caterpillar explicitly mentions operational data and FieldAI foundation models; separately material learning rights are not disclosed. Qualification remains unresolved.", "status": "provisional" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0003/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-1B35A4CA", "legacy_ids": [ "SS-CAP-20261005-92CB8396CA2D" ], "event_key": "authorizing healthcare organizations and epic customers|chatgpt for healthcare|2026-09-01|authorized epic patient records and clinical workflow context|read only retrieval summarization and clinical workflow assistance", "announcement_date": "2026-09-01", "observed_status": "active", "first_added_date": "2026-10-05", "last_reviewed_date": "2026-10-05", "parties": { "providers": [ "Authorizing healthcare organizations using Epic" ], "ai_recipients": [ "ChatGPT for Healthcare", "Codex" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Authorized Epic patient records including appointment notes, laboratory results, medications, specialist documentation, clinical timelines, referrals and unresolved issues, exposed read-only to ChatGPT for Healthcare and eligible Codex workspaces under organization administration, individual Epic sign-in and existing chart permissions.", "asset_types": [ "structured_dataset", "longitudinal_response_history", "expert_workflow" ], "industry": null, "subject_type": "human", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "connector", "platform_integration" ], "modes": [ "retrieval" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "prohibited", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "ChatGPT retrieves authorized Epic chart information and cites supporting patient-record context under existing chart permissions.", "source_ids": [ "E1", "E2" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "ChatGPT synthesizes clinical timelines, medication and laboratory changes, referrals and unresolved issues for clinician review.", "source_ids": [ "E1", "E2" ] }, { "type": "generation", "evidence_level": "enabled", "statement": "The integration prepares pre-visit reviews, clinical timelines, medication reviews and handoff summaries for clinicians.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "H0", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Epic EHR × ChatGPT for Healthcare canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0 only. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized Epic chart data is available read-only for retrieval and clinical workflow synthesis; intervention-to-outcome linkage is not established and write actions are prohibited." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "prohibited" }, "evidence": [ { "source_id": "E1", "url": "https://openai.com/index/chatgpt-connects-health-records-and-healthcare-sources/", "source_type": "primary", "checked_at": "2026-10-05T15:48:00.000Z", "summary": "OpenAI announces active Epic EHR integration for ChatGPT for Healthcare, enabling authorized patient-history retrieval and clinical workflow assistance with role-based access, SSO, audit logs and applicable BAA controls." }, { "source_id": "E2", "url": "https://help.openai.com/en/articles/10128477-chatgpt-enterprise-and-edu-release-notes", "source_type": "primary", "checked_at": "2026-10-05T15:49:00.000Z", "summary": "OpenAI release notes state that the Epic plugin is read-only, works in eligible ChatGPT for Healthcare and HIPAA-enabled enterprise workspaces, and requires administrator configuration, individual Epic sign-in and existing chart permissions." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-05T16:25:57.065Z", "revision_sha256": "17621748739829fcd6b35b764b2c6908d4c3167ec8f5687714ab6f7977924c95", "notes": "Mike Ye approved the Epic EHR × ChatGPT for Healthcare canonical AI Access event as governed Knowledge + Professional Workflow access with Human Response H0 only. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Authorized Epic chart data is available read-only for retrieval and clinical workflow synthesis; intervention-to-outcome linkage is not established and write actions are prohibited." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-8D55666B", "legacy_ids": [ "SS-CAP-20261005-0164F8004739" ], "event_key": "participating physicians across 60 countries|openai chatgpt healthcare models|2026-09-01|expert physician reviews and safety accuracy ratings across clinical use cases|evaluation feedback and model behavior improvement", "announcement_date": "2026-09-01", "observed_status": "active", "first_added_date": "2026-10-05", "last_reviewed_date": "2026-10-05", "parties": { "providers": [ "Participating physicians across 60 countries" ], "ai_recipients": [ "OpenAI", "ChatGPT for Healthcare models and tools" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "More than 700,000 expert physician reviews of model responses across real-world healthcare questions, plus 4,363 safety ratings across 27 clinical use cases and additional accuracy ratings against large U.S. healthcare datasets, provided to OpenAI for evaluation and improvement of healthcare model behavior and tools.", "asset_types": [ "structured_dataset", "expert_workflow", "execution_traces" ], "industry": null, "subject_type": "human", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "other" ], "modes": [ "corpus_delivery", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "permitted", "inference_retrieval": "not_disclosed", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "generation", "evidence_level": "enabled", "statement": "Physician feedback is used to improve model behavior and strengthen healthcare-specific ChatGPT tools and generated clinical responses.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The evaluation program measures model responses across clinical timelines, medication review, handoffs and nuanced healthcare questions.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "across_subjects", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the OpenAI physician evaluation canonical AI Access event as Knowledge + Human Response H2 access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. More than 700,000 physician reviews and thousands of safety ratings provide repeated context-linked evaluation outcomes used to improve healthcare model behavior and tools; H3, recovery and adaptation are not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "not_disclosed", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://openai.com/index/chatgpt-connects-health-records-and-healthcare-sources/", "source_type": "primary", "checked_at": "2026-10-05T15:48:00.000Z", "summary": "OpenAI says hundreds of physicians across 60 countries, 49 languages and 26 specialties have reviewed more than 700,000 model responses; physicians also provided 4,363 safety ratings across 27 clinical use cases and accuracy ratings on healthcare-dataset questions, and OpenAI states this feedback improves model behavior and healthcare-specific tools." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-05T16:25:56.618Z", "revision_sha256": "a42508e927055d3138a84a3ebaa7861c48293b2d9d6b0676227209c07be47fa9", "notes": "Mike Ye approved the OpenAI physician evaluation canonical AI Access event as Knowledge + Human Response H2 access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. More than 700,000 physician reviews and thousands of safety ratings provide repeated context-linked evaluation outcomes used to improve healthcare model behavior and tools; H3, recovery and adaptation are not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-F212B48B", "legacy_ids": [ "SS-CAP-20261005-4292A43CDFF1" ], "event_key": "authorizing salesforce customers and salesforce|anthropic claude and agentforce|2026-08-26|customer information revenue context business logic workflows and slack|retrieval reasoning and governed action execution", "announcement_date": "2026-08-26", "observed_status": "active", "first_added_date": "2026-10-05", "last_reviewed_date": "2026-10-05", "parties": { "providers": [ "Authorizing Salesforce customers", "Salesforce" ], "ai_recipients": [ "Anthropic Claude", "Salesforce Agentforce", "Salesforce Slackbot" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Customer information, live revenue context, accounts, pipelines, Salesforce business logic, workflows, actions and Slack collaboration context made available to Claude and Claude-powered Salesforce agents under customer authentication, permissions, business rules and the Salesforce Trust Boundary.", "asset_types": [ "content_corpus", "structured_dataset", "expert_workflow", "domain_actions" ], "industry": null, "subject_type": "unknown", "proprietary_status": "proprietary", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "connector", "platform_integration" ], "modes": [ "ongoing_feed", "retrieval", "action_execution" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "conditional", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "retrieval", "evidence_level": "enabled", "statement": "Salesforce in Claude reads authorized customer information, live revenue context, accounts, pipeline and connected Slack context.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "Claude reasons across the seller revenue cycle for meeting preparation, deal-health review, pipeline review and enterprise questions.", "source_ids": [ "E1" ] }, { "type": "workflow_execution", "evidence_level": "enabled", "statement": "Claude and Salesforce agents can update pipelines and execute governed Salesforce actions routed through customer business rules.", "source_ids": [ "E1" ] }, { "type": "personalization", "evidence_level": "enabled", "statement": "The onboarding flow reads each seller's authorized enterprise context to generate a tailored account and pipeline dashboard.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": true, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Salesforce Claudeforce × Anthropic canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude receives permission-governed CRM, revenue, pipeline, business-rule and Slack context for retrieval, reasoning, personalization and governed Salesforce actions; Human Response H2/H3 is not established." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "conditional" }, "evidence": [ { "source_id": "E1", "url": "https://investor.salesforce.com/news/news-details/2026/Salesforce-and-Anthropic-Announce-Claudeforce-The-1-AI-Meets-the-1-AI-CRM/default.aspx", "source_type": "primary", "checked_at": "2026-10-05T15:52:00.000Z", "summary": "Salesforce and Anthropic announce Claudeforce: Salesforce in Claude gives pilot customers 37 sales skills grounded in live CRM and Slack context, with centrally managed permissions and actions routed through Salesforce business rules; Claude also powers Agentforce and Slack experiences inside the Salesforce Trust Boundary." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-05T16:25:56.765Z", "revision_sha256": "9c79e8ddaf392d4ce22acd8f2748a06375e904606e3316334617ad1b2205cb35", "notes": "Mike Ye approved the Salesforce Claudeforce × Anthropic canonical AI Access event as governed Knowledge + Professional Workflow access. Strict data-rights qualification does not apply and methodology v1.0.0 scores are not applicable. Claude receives permission-governed CRM, revenue, pipeline, business-rule and Slack context for retrieval, reasoning, personalization and governed Salesforce actions; Human Response H2/H3 is not established." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-2D081B8A", "legacy_ids": [ "SS-AIDT-2026-0002" ], "event_key": "legacy|ss-aidt-2026-0002", "announcement_date": "2026-08-14", "observed_status": "pending_court_approval", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-15", "parties": { "providers": [ "Spirit Aviation Holdings / Spirit Airlines" ], "ai_recipients": [ "Google LLC (selected bidder; challenged)" ], "strategic_buyer_or_operator": [ "Google LLC (selected bidder; challenged)" ] }, "asset": { "description": "A deidentified archive spanning roughly 34 years, reported to include about 100 million emails, 500 million Teams messages, 30 million lines of source code, operational data, documents, and internally developed software. Customer databases are outside scope.", "asset_types": [ "Internal communications", "Source code and software", "Operational records", "Financial and workforce records" ], "industry": "Aviation", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "stranded_separable", "renewal": "finite" }, "access": { "mechanisms": [ "acquisition" ], "modes": [ "training", "post_training" ] }, "rights": { "training": "permitted", "post_training": "permitted", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "permitted", "derived_model": "permitted", "sublicensing": "prohibited", "exclusivity": "unknown" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Train and improve AI systems and productivity products on real enterprise work, software, communication, and operational sequences.", "source_ids": [ "E1", "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Bid amounts, selected/alternate bidders, asset scope, and pending status are sourced facts. Model-use and valuation implications beyond disclosed purpose are Strategic Signal analysis." }, "economics": { "transaction_structure": "Data Asset Acquisition", "disclosed_consideration": { "amount": 10000000, "currency": "USD", "description": "Google selected bid; court approval pending. Later micro1 notice proposed $12.5 million.", "status": "disclosed_bid_not_closed" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "dataset_and_related_internal_software_specific" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "governance": { "de_identification": "required_before_transfer", "employee_customer_data": "Customer datasets excluded; treatment of employee and labor data remains contested.", "geographic_sovereignty": "not_disclosed_for_google_bid", "governed_access": "Independent third-party deidentification contemplated before delivery; final restrictions remain subject to court approval.", "privacy_pii": "Customer databases excluded; incidental consumer data to be deidentified. Employee-data objections remain unresolved.", "trade_secret": "Contract-counterparty and labor objections remain on file.", "access_structure": "Bankruptcy asset auction. Google was selected at $10 million; Mercor was alternate at $7.5 million. micro1 later noticed a $12.5 million competing bid. No sale order had been entered as of the review date." }, "evidence": [ { "source_id": "E1", "url": "https://chapter11cases.com/blogs/news/the-spirit-airlines-deidentified-data-sale-auction-results-objections-and-the-september-30-hearing", "source_type": "secondary", "publisher": "Research Suite by Stretto", "title": "Spirit deidentified-data auction results and docket chronology" }, { "source_id": "E2", "url": "https://www.reuters.com/legal/litigation/google-buy-spirit-airlines-business-data-10-million-2026-08-17/", "source_type": "secondary", "publisher": "Reuters", "title": "Google to buy Spirit Airlines business data for $10 million" }, { "source_id": "E3", "url": "https://www.wired.com/story/spirit-airlines-wants-to-sell-its-data-to-google-former-flight-attendants-are-freaked-out", "source_type": "secondary", "publisher": "WIRED", "title": "Spirit Airlines Wants to Sell Its Data to Google" } ], "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Stretto reports an auction and pending approval through September 11. Treat this as secondary reporting. Underlying filing and current status remain to be verified.", "status": "provisional" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0002/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-32287802", "legacy_ids": [ "SS-CAP-20260919-33EA22DA9F78" ], "event_key": "healthex|whoop|2026-06-17|medical-records-biometric-context|personalization", "announcement_date": "2026-06-17", "observed_status": "active", "first_added_date": "2026-09-19", "last_reviewed_date": "2026-09-19", "parties": { "providers": [ "HealthEx", "Consenting WHOOP members" ], "ai_recipients": [ "WHOOP AI", "WHOOP My Memory" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Member-authorized electronic health records, including chronic conditions, recent procedures and medication changes, linked inside WHOOP with continuous recovery, strain, sleep and performance history for contextual AI guidance.", "asset_types": [ "structured_dataset", "longitudinal_response_history", "sensor_history" ], "industry": null, "subject_type": "human", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "renewable" }, "access": { "mechanisms": [ "partnership", "platform_integration" ], "modes": [ "retrieval", "ongoing_feed" ] }, "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "conditional", "action_execution": "not_disclosed", "personalization": "conditional", "retention": "not_disclosed", "ownership_transfer": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "not_disclosed" }, "capabilities": [ { "type": "personalization", "evidence_level": "enabled", "statement": "WHOOP AI and My Memory can consider clinical history alongside continuous recovery, strain, sleep and performance data to deliver more contextual guidance and personalized recommendations.", "source_ids": [ "E1" ] }, { "type": "domain_reasoning", "evidence_level": "enabled", "statement": "The integrated experience can interpret chronic conditions, procedures and medication changes in the context of a member's biometric history.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": true, "human_response_stage": "H2", "repetition_scope": "within_subject", "recovery_observed": true, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the WHOOP × HealthEx capability-access classification as Knowledge + Human Response (H2). Access is user-authorized; training, ownership and broader downstream rights remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "conditional", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.healthex.io/press/healthex-whoop-launch", "source_type": "primary", "checked_at": "2026-09-19T15:03:00Z", "summary": "The joint announcement says WHOOP members can connect EHRs through HealthEx; WHOOP AI and My Memory can consider conditions, procedures and medication changes alongside continuous recovery, strain, sleep and performance data for contextual guidance. Members opt in, can inspect sharing and can disconnect." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-09-19T20:34:09.602Z", "revision_sha256": "92af6be05369d9b4f7310bed1ae2b477eabcdbf6640e881d47c531066eeaeff4", "notes": "Mike Ye approved the WHOOP × HealthEx capability-access classification as Knowledge + Human Response (H2). Access is user-authorized; training, ownership and broader downstream rights remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-486B19C8", "legacy_ids": [ "SS-AIDT-2026-0001" ], "event_key": "legacy|ss-aidt-2026-0001", "announcement_date": "2026-04-16", "observed_status": "reported_completed", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-15", "parties": { "providers": [ "Cielo24" ], "ai_recipients": [ "Undisclosed AI buyer" ], "strategic_buyer_or_operator": [ "Undisclosed AI buyer" ] }, "asset": { "description": "Thirteen years of the shuttered company's Slack messages, internal email, and Jira tickets, reported sold as AI training data.", "asset_types": [ "Internal communications", "Product / engineering workflow", "Institutional memory" ], "industry": "Media technology", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "stranded_separable", "renewal": "finite" }, "access": { "mechanisms": [ "acquisition" ], "modes": [ "training" ] }, "rights": { "training": "permitted", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "unknown", "derived_model": "prohibited", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training AI systems to navigate realistic workplace communication, engineering, and project-management tasks.", "source_ids": [ "E1", "E2" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "The corpus and consideration description are attributed to the former CEO through reporting. Buyer, contract rights, and privacy controls remain unknown." }, "economics": { "transaction_structure": "Data Asset Acquisition", "disclosed_consideration": { "amount": null, "currency": "USD", "description": "Seller described proceeds as hundreds of thousands of dollars; no exact figure was disclosed.", "status": "reported_range_only" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "reported_dataset_specific" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "not_disclosed", "access_structure": "Reported transfer of a finite archive; detailed contract terms and buyer identity were not disclosed." }, "evidence": [ { "source_id": "E1", "url": "https://www.forbes.com/sites/annatong/2026/04/16/ais-new-training-data-your-old-work-slacks-and-emails/", "source_type": "secondary", "publisher": "Forbes", "title": "AI's New Training Data: Your Old Work Slacks And Emails" }, { "source_id": "E2", "url": "https://www.fastcompany.com/91528808/shuttered-startups-are-selling-old-slack-chats-and-emails-to-ai-companies", "source_type": "secondary", "publisher": "Fast Company", "title": "Shuttered startups are selling old Slack chats and emails to AI companies" } ], "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Sale reporting is supported by indexed Forbes excerpts, but full text and an independent corroborating source remain to be captured. Detailed contract rights are not verified.", "status": "provisional" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0001/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2025-68A52FB7", "legacy_ids": [ "SS-AIDT-2025-0001" ], "event_key": "legacy|ss-aidt-2025-0001", "announcement_date": "2025-10-01", "observed_status": "active", "first_added_date": "2026-09-15", "last_reviewed_date": "2026-09-16", "parties": { "providers": [ "AbbVie", "Johnson & Johnson", "Bristol Myers Squibb", "Takeda", "Astex Pharmaceuticals" ], "ai_recipients": [ "AISB Federated OpenFold3 Initiative", "AlQuraishi Lab", "Apheris" ], "strategic_buyer_or_operator": [ "AISB Federated OpenFold3 Initiative", "AlQuraishi Lab", "Apheris" ] }, "asset": { "description": "Proprietary experimentally determined protein–small molecule structures supplied for federated fine-tuning. This observation records the October 2025 consortium expansion.", "asset_types": [ "Scientific / pharmaceutical", "Protein–small molecule structures" ], "industry": "Pharmaceuticals & biotechnology", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "federated_access" ], "modes": [ "training", "post_training" ] }, "rights": { "training": "permitted", "post_training": "permitted", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "unknown", "ownership_transfer": "prohibited", "derived_model": "permitted", "sublicensing": "prohibited", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Fine-tune OpenFold3 for protein–ligand prediction.", "source_ids": [ "E1", "E2", "E3", "E4" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "March 27, 2025 founding is context, not a second transaction here. September 14, 2026 performance evidence is a later update. Uplift is consortium-reported, not independently replicated; no price is disclosed." }, "economics": { "transaction_structure": "Federated / Controlled Learning Rights", "disclosed_consideration": { "amount": null, "currency": null, "description": "No data-specific consideration disclosed.", "status": "not_disclosed" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "not_disclosed" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 84, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "A structure records an experimental result, but does not establish downstream commercial or clinical outcomes.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Better molecular prioritization can influence expensive research choices; a qualitative domain judgment.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Drug-discovery research makes predictive accuracy economically consequential.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "The learning objective is closely matched to the described scientific observations.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Reproducing experimental observations requires laboratory effort; cost is not quantified.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Combining otherwise separate private archives offers access unavailable from public corpora alone.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "The described observations originate in physical experiments rather than generated scenarios.", "source_ids": [ "E1" ] }, "rights_usability": { "basis": "analysis", "rationale": "Local learning is explicitly enabled, while undisclosed downstream terms limit certainty.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Nonpublic experimental observations are hard to substitute with generic text.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3.5, "decision_value_density": 4.5, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 4, "proprietary_advantage": 4.5, "real_world_grounding": 5, "refreshability": null, "rights_usability": 4, "uniqueness_scarcity": 4.5 }, "rationale": "Strong experimental grounding and learning fit. Structural results are not full decision histories. Unknown archive age and contribution cadence remain unscored.", "score": 87 }, "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "No observable price or auction is provided by this announcement.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified data consideration is available.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "The reviewed announcement supplies no monetary amount usable for valuation.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "Fine-tuning is the stated purpose, not an inference from product deployment.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "A multi-owner structure offers a repeatable pattern for otherwise inaccessible scientific corpora.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "The permitted learning method is clear; the full contractual allocation remains unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Established research participants support execution relevance, not evidence of a market-clearing price.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 0, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 0, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4.5, "rights_clarity": 4, "strategic_buyer_quality": 4 }, "rationale": "Clear learning arrangement, weak price discovery. Consortium-reported uplift is recorded separately and earns no independent-replication points.", "score": 40 } }, "governance": { "de_identification": "not_disclosed", "employee_customer_data": "not_applicable", "geographic_sovereignty": "not_disclosed", "governed_access": "Local computation and contributor-defined access policies.", "privacy_pii": "Confidential scientific data; no patient-data entitlement inferred.", "trade_secret": "Raw structures stay under contributor control.", "access_structure": "Astex, Bristol Myers Squibb and Takeda joined the existing AbbVie and Johnson & Johnson initiative. Local computation permits model learning without pooling the underlying records." }, "evidence": [ { "source_id": "E1", "url": "https://www.apheris.com/resources/aisb-network-expands-federated-openfold3-initiative-with-three-new-pharma-contributors", "source_type": "primary", "publisher": "Apheris", "title": "AISB initiative expansion — October 1, 2025" }, { "source_id": "E2", "url": "https://www.apheris.com/resources/alquraishi-lab-s-openfold3-to-be-fine-tuned-with-pharma-industry-data-in-a-secure-ai-collaboration", "source_type": "primary", "publisher": "Apheris", "title": "Founding initiative — March 27, 2025" }, { "source_id": "E3", "url": "https://www.apheris.com/resources/federated-training-dramatically-improves-the-accuracy-of-protein-ligand-co-folding-on-private-pharma-structures", "source_type": "primary", "publisher": "Apheris", "title": "Consortium performance report — September 14, 2026" }, { "source_id": "E4", "url": "https://www.apheris.com/networks/aisb", "source_type": "primary", "publisher": "Apheris", "title": "AISB network governance — current description" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-16T21:38:20.361Z", "decision_id": "conversation-20260916-81BF2CAD028B", "packet_sha256": "4e1f58d72f53b3ad5140e1c8b01fbb7347a8af99dc3f26fc29a5d28f52422173", "record_sha256": "0e4eef8e3f50a13a1e520e21f1545515918d9e29722134c414ceab9f6c02dc16" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2025-0001/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2025-96B3850D", "legacy_ids": [ "SS-AIDT-2025-0002" ], "event_key": "legacy|ss-aidt-2025-0002", "announcement_date": "2025-04-23", "observed_status": "agreement_disclosed; current payment performance not verified", "first_added_date": "2026-09-16", "last_reviewed_date": "2026-09-16", "parties": { "providers": [ "Tempus AI" ], "ai_recipients": [ "Pathos AI" ], "strategic_buyer_or_operator": [ "Pathos AI" ] }, "asset": { "description": "A de-identified multimodal oncology dataset licensed for a foundation-model development program.", "asset_types": [ "Scientific / pharmaceutical", "De-identified multimodal oncology data" ], "industry": "Pharmaceuticals & biotechnology", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "unknown" }, "access": { "mechanisms": [ "license" ], "modes": [ "training" ] }, "rights": { "training": "permitted", "post_training": "prohibited", "evaluation": "unknown", "inference_retrieval": "prohibited", "action_execution": "unknown", "personalization": "unknown", "retention": "prohibited", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "unknown", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Develop and train an oncology foundation model.", "source_ids": [ "E1" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "This observation covers the data-license leg only. Payment completion, corpus renewal and many contractual restrictions remain unknown. Related fees and financing are context, not additional data transactions." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "amount": 200000000, "currency": "USD", "description": "$200m data-license fees over three years, including $50m upfront payable. Up to 50% may be settled in Pathos preferred shares. Separate $35m flows run from AstraZeneca to Tempus and Tempus to Pathos.", "status": "disclosed_contractual_fees_not_verified_cash_receipts" }, "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "economics_scope": "contractually_identified_data_license_fees_within_broader_arrangement" }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "coverage_pct": 71, "factor_evidence": { "decision_value_density": { "basis": "analysis", "rationale": "Oncology research choices can be consequential; the rating does not assert actual patient-outcome improvement.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "The use case lies in economically significant scientific research.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "The agreement explicitly links the dataset to the model training task.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Reassembly may require specialized data collection; no reproduction-cost estimate is available.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "The purpose-specific private license offers access beyond generic public material.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "A de-identified domain dataset provides real-world grounding; field detail is limited.", "source_ids": [ "E1" ] }, "rights_usability": { "basis": "analysis", "rationale": "Specified training permission is useful, but purpose limits and undisclosed retention constrain reuse.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "The licensed private corpus is differentiated, but exact scarcity cannot be quantified.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": 4, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 3.5, "proprietary_advantage": 4, "real_world_grounding": 4, "refreshability": null, "rights_usability": 3.5, "uniqueness_scarcity": 4 }, "rationale": "Potentially valuable domain training corpus; uncertainty about contents, age and renewal materially limits coverage. Score is strategic analysis, not measured utility.", "score": 82 }, "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Linked financing and reciprocal obligations weaken the isolated market-price interpretation.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "The filing identifies a data-license payment leg even though it sits within a broader arrangement.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "Contractual fees and payment form are disclosed, but realized proceeds are not verified.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "Model development and training are the express permitted purposes.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "The contract structure offers a useful reference if future comparisons preserve its restrictions.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Purpose and some model rights are visible; exclusivity, retention and data sublicensing are not.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "A specified specialist model developer provides credible use, without implying open-market bidding.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 2, "competing_bids": null, "data_consideration_separability": 4, "disclosed_economics": 4.5, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 3.5, "strategic_buyer_quality": 3 }, "rationale": "Explicit fee allocation is valuable pricing evidence. Equity settlement, reciprocal commitments and missing comparable bids prevent treatment as a clean cash market price.", "score": 75 } }, "governance": { "de_identification": "explicit", "employee_customer_data": "Detailed consent and patient-data terms not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "License purpose is development and training of the specified model.", "privacy_pii": "de_identified_dataset", "trade_secret": "not_disclosed", "access_structure": "Purpose-limited data license to Pathos within a three-party arrangement involving AstraZeneca." }, "evidence": [ { "source_id": "E1", "url": "https://www.sec.gov/Archives/edgar/data/1717115/000119312525090062/d944168d8k.htm", "source_type": "primary", "publisher": "Tempus AI / SEC", "title": "Form 8-K filed April 23, 2025, Item 8.01" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-16T21:38:20.346Z", "decision_id": "conversation-20260916-CD20DA31F0CF", "packet_sha256": "401f767bb940538d1a214a8130fd811dfa4275b939fcba6f15377ce87c2ac9b2", "record_sha256": "12bdd18b6c5a16d4e4612db7460712415283174fb14daa888dd8dd3ebb6a945b" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2025-0002/", "source_series": "legacy_data_rights" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-B29DEA30", "legacy_ids": [ "SS-CAP-20261002-FB83A6E6B677" ], "event_key": "university of oxford bodleian libraries|openai models|2025-03-04|public-domain historical dissertations ballads and other digitized collections|non-exclusive model training and public digitization", "announcement_date": "2025-03-04", "observed_status": "active", "first_added_date": "2026-10-02", "last_reviewed_date": "2026-10-02", "parties": { "providers": [ "University of Oxford", "Bodleian Libraries" ], "ai_recipients": [ "OpenAI models" ], "strategic_buyer_or_operator": [] }, "asset": { "description": "Digitized public-domain or out-of-copyright historical collections from the Bodleian Libraries, including approximately 125,000 images shared by June 2025, dissertations, 10,000 ballads, and other materials selected for preservation and model training.", "asset_types": [ "content_corpus" ], "industry": null, "subject_type": "unknown", "proprietary_status": "unknown", "asset_disposition": "not_applicable", "renewal": "unknown" }, "access": { "mechanisms": [ "partnership", "other" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "permitted", "post_training": "not_disclosed", "evaluation": "not_disclosed", "inference_retrieval": "not_disclosed", "action_execution": "not_disclosed", "personalization": "not_disclosed", "retention": "not_disclosed", "ownership_transfer": "prohibited", "derived_model": "not_disclosed", "sublicensing": "not_disclosed", "exclusivity": "prohibited" }, "capabilities": [ { "type": "domain_reasoning", "evidence_level": "intended", "statement": "The collaboration is intended to broaden model knowledge of historical sources, languages, and cultures represented in the collections.", "source_ids": [ "E1", "E2", "E3" ] }, { "type": "generation", "evidence_level": "intended", "statement": "The digitized materials are disclosed as inputs to OpenAI model training, supporting subsequent generative model capabilities.", "source_ids": [ "E2", "E3" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": false, "human_response_access": false, "human_response_stage": "not_applicable", "repetition_scope": "not_applicable", "recovery_observed": false, "adaptation_observed": false }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": false, "qualification_version": "1.0.0", "rationale": "Mike Ye approved the Oxford/Bodleian × OpenAI canonical AI Access event as governed Knowledge access with permitted non-exclusive training use. Strict data-rights qualification does not apply because the underlying corpus is public-domain or out-of-copyright, and scores are not applicable. Ownership transfer and exclusivity are not granted; post-training, retention, derived-model and sublicensing terms remain undisclosed." }, "economics": { "transaction_structure": null, "disclosed_consideration": null, "estimated_economics": null, "economics_scope": null }, "scores": { "applicability": "not_applicable", "methodology_version": null, "asset": null, "transaction_signal": null }, "governance": { "governed_access": true, "inference_retrieval_control": "not_disclosed", "action_execution_control": "not_disclosed" }, "evidence": [ { "source_id": "E1", "url": "https://www.ox.ac.uk/news/2025-03-04-oxford-and-openai-launch-collaboration-advance-research-and-education", "source_type": "primary", "checked_at": "2026-10-02T15:10:00.000Z", "summary": "Oxford announces a collaboration with OpenAI to digitize public-domain Bodleian collections, broaden access, and preserve historically significant materials." }, { "source_id": "E2", "url": "https://www.theguardian.com/technology/2026/sep/26/oxford-university-bodleian-library-open-ai-chat-gpt", "source_type": "secondary", "checked_at": "2026-10-02T15:11:00.000Z", "summary": "The Guardian reports from Oxford documents that approximately 125,000 images, dissertations and ballads were digitized and supplied for OpenAI training under non-exclusive terms, while Bodleian retained scan rights and planned public release." }, { "source_id": "E3", "url": "https://thenextweb.com/news/oxford-bodleian-openai-training-data", "source_type": "secondary", "checked_at": "2026-10-02T15:12:00.000Z", "summary": "TNW independently reports that Bodleian digitized historical holdings for OpenAI training, with non-exclusive use, retained scan rights, and planned public availability." } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "recorded_at": "2026-10-02T15:36:38.822Z", "revision_sha256": "1c591656983e10f7900fa2635a894df9ec5a140c6b03c6ce94a37110903658d5", "notes": "Mike Ye approved the Oxford/Bodleian × OpenAI canonical AI Access event as governed Knowledge access with permitted non-exclusive training use. Strict data-rights qualification does not apply because the underlying corpus is public-domain or out-of-copyright, and scores are not applicable. Ownership transfer and exclusivity are not granted; post-training, retention, derived-model and sublicensing terms remain undisclosed." }, "canonical_path": null, "source_series": "legacy_capability_access" }, { "ontology_version": "2.0.0", "canonical_record_id": "SS-AI-2026-A9E5E7B5", "legacy_ids": [ "SS-AIDT-2026-0012" ], "event_key": "legacy|ss-aidt-2026-0012", "announcement_date": "2023-11-03", "observed_status": "active_amended_six_year_license", "first_added_date": "2026-09-29", "last_reviewed_date": "2026-09-29", "parties": { "providers": [ "Tempus AI" ], "ai_recipients": [ "Recursion Pharmaceuticals" ], "strategic_buyer_or_operator": [ "Recursion Pharmaceuticals" ] }, "asset": { "description": "Tempus's proprietary de-identified patient-centric multimodal oncology database of clinical and molecular records, originally described by Recursion as more than 20 petabytes, licensed for therapeutic-development AI/ML use subject to unique-record caps and retention limits.", "asset_types": [ "De-identified clinical oncology data", "Molecular and genomic oncology data", "Patient-centric multimodal records" ], "industry": "Healthcare, oncology, biotechnology and AI drug discovery", "subject_type": "organization", "proprietary_status": "proprietary", "asset_disposition": "embedded_continuing", "renewal": "renewable" }, "access": { "mechanisms": [ "license" ], "modes": [ "corpus_delivery" ] }, "rights": { "training": "unknown", "post_training": "unknown", "evaluation": "unknown", "inference_retrieval": "unknown", "action_execution": "unknown", "personalization": "unknown", "retention": "unknown", "ownership_transfer": "prohibited", "derived_model": "unknown", "sublicensing": "unknown", "exclusivity": "prohibited" }, "capabilities": [ { "type": "model_use", "evidence_level": "enabled", "statement": "Training, improving, modifying and creating derivative works of Recursion causal AI/ML models for therapeutic product development, plus biomarker and patient-stratification work.", "source_ids": [ "E1", "E2", "E3", "E4" ] } ], "flows": { "knowledge_access": true, "professional_workflow_access": null, "human_response_access": null, "human_response_stage": "unknown", "repetition_scope": "unknown", "recovery_observed": null, "adaptation_observed": null }, "qualification": { "capability_access_qualified": true, "data_rights_qualified": true, "qualification_version": "strict-v1.0.0", "rationale": "Primary SEC filings establish the proprietary corpus, explicit AI/ML training and derivative-work rights, original and amended economics, six-year term, removal of convenience termination, lower aggregate unique-record cap, ownership boundaries and de-identification. The amended exact record cap, exclusivity, current refresh cadence, geographic restrictions and independent model-uplift evidence are not disclosed." }, "economics": { "transaction_structure": "Training Rights License", "disclosed_consideration": { "status": "disclosed_contractual_license_fees", "amount": 42000000, "currency": "USD", "description": "The 2026 amendment sets $14 million annual license fees on each of the third, fourth and fifth anniversaries, at least $4 million of each payable in cash and the remainder in cash and/or Recursion shares. The original 2023 deal disclosed up to $160 million over five years." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "$42 million of amended third-through-fifth anniversary license fees is explicitly disclosed; amounts already paid under the original schedule and the economic effect of the reduced unique-record cap are not restated here." }, "scores": { "applicability": "applicable", "methodology_version": "1.0.0", "asset": { "score": 94, "coverage_pct": 100, "rationale": "Strategic Signal analysis. Tempus's large proprietary multimodal oncology corpus combines real-world clinical and molecular observations with explicit AI-training utility and strong therapeutic decision relevance.", "factor_ratings": { "uniqueness_scarcity": 4.5, "historical_depth": 4.5, "decision_outcome_richness": 4.5, "decision_value_density": 5, "real_world_grounding": 5, "domain_value": 5, "refreshability": 4.5, "proprietary_advantage": 4.5, "model_learning_usefulness": 5, "non_replicability": 4, "rights_usability": 4.5 }, "factor_evidence": { "uniqueness_scarcity": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports uniqueness scarcity as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "historical_depth": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports historical depth as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "decision_outcome_richness": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports decision outcome richness as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "decision_value_density": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports decision value density as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "real_world_grounding": { "basis": "disclosed_fact", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports real world grounding as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "domain_value": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports domain value as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "refreshability": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports refreshability as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "proprietary_advantage": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports proprietary advantage as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "model_learning_usefulness": { "basis": "disclosed_fact", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports model learning usefulness as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "non_replicability": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports non replicability as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." }, "rights_usability": { "basis": "analysis", "source_ids": [ "E2", "E3" ], "rationale": "Evidence supports rights usability as a material characteristic of the Tempus oncology corpus and its AI-training use, with remaining limits preserved explicitly." } } }, "transaction_signal": { "score": 96, "coverage_pct": 85, "rationale": "Strategic Signal analysis. The agreement has unusually explicit model-use rights and separately disclosed multi-year license economics, strengthened by a filed 2026 amendment; no competitive process or independent uplift study is disclosed.", "factor_ratings": { "disclosed_economics": 5, "clean_price_discovery": 4, "data_consideration_separability": 5, "explicit_model_use": 5, "rights_clarity": 5, "competing_bids": null, "strategic_buyer_quality": 4.5, "precedent_value": 5, "independent_model_uplift": null }, "factor_evidence": { "disclosed_economics": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate disclosed economics for this separately priced AI data-rights arrangement." }, "clean_price_discovery": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate clean price discovery for this separately priced AI data-rights arrangement." }, "data_consideration_separability": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate data consideration separability for this separately priced AI data-rights arrangement." }, "explicit_model_use": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate explicit model use for this separately priced AI data-rights arrangement." }, "rights_clarity": { "basis": "disclosed_fact", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate rights clarity for this separately priced AI data-rights arrangement." }, "strategic_buyer_quality": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate strategic buyer quality for this separately priced AI data-rights arrangement." }, "precedent_value": { "basis": "analysis", "source_ids": [ "E1", "E2", "E3" ], "rationale": "The filed agreement and amendment provide sufficient evidence to rate precedent value for this separately priced AI data-rights arrangement." } } } }, "governance": { "governed_access": "Use is restricted to therapeutic product development within a secure Recursion environment and subject to download, aggregate unique-record and retention controls.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "The licensed corpus is disclosed as de-identified clinical and molecular data and governed by healthcare-data restrictions.", "trade_secret": "Tempus retains ownership of its proprietary database and related materials; permitted uses do not authorize unrestricted reproduction or transfer.", "de_identification": "Licensed records are de-identified; the agreement references HIPAA-aligned de-identification treatment for relevant data.", "employee_customer_data": "The asset contains patient-centric clinical and molecular oncology records rather than ordinary employee or customer-operational data.", "access_structure": "Recursion receives limited licensed access to Tempus's proprietary de-identified clinical and molecular data for therapeutic product development, including model training, improvement, modification and derivative works. Downloaded records are subject to per-time and aggregate unique-record caps and historically a 180-day retention limit. The September 2026 amendment extends the term to six years, removes convenience termination and lowers the remaining unique-record cap." }, "evidence": [ { "source_id": "E1", "url": "https://www.sec.gov/Archives/edgar/data/1601830/000160183026000112/rxrx-20260915.htm", "source_type": "primary", "publisher": "Recursion Pharmaceuticals / SEC", "title": "September 15, 2026 Form 8-K — Tempus Master Agreement Amendment" }, { "source_id": "E2", "url": "https://www.sec.gov/Archives/edgar/data/1601830/000160183023000068/rxrx-20231109.htm", "source_type": "primary", "publisher": "Recursion Pharmaceuticals / SEC", "title": "November 9, 2023 Form 8-K — Tempus Master Agreement" }, { "source_id": "E3", "url": "https://www.sec.gov/Archives/edgar/data/1601830/000160183023000069/exhibit104-masteragreement.htm", "source_type": "primary", "publisher": "Recursion Pharmaceuticals / SEC", "title": "Tempus Master Agreement Exhibit" }, { "source_id": "E4", "url": "https://www.sec.gov/Archives/edgar/data/1601830/000160183023000068/exhibit992-q0323.htm", "source_type": "primary", "publisher": "Recursion Pharmaceuticals", "title": "Recursion Announces Data Collaboration Deal with Tempus" } ], "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-29T18:02:23.696Z", "decision_id": "conversation-20260929-1C674D15A8B4", "packet_sha256": "23a5d5b381767ab928eeb336bd5bad568134f4a95c5968247b856aa36360be0e", "record_sha256": "c4b83271b0cdc8d5f6cbf96f6f12176eaaba7ff0ca039c4dd314f7dd504b3461" }, "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0012/", "source_series": "legacy_data_rights" } ] } }