{ "contract_version": "machine_relations_index_public_view_v2.0", "generated": "2026-09-04", "methodology": "The Machine Relations Index measures how often AI answer engines cite each source domain when people ask real buying and research questions. It observes six engines — Perplexity, ChatGPT, Gemini, Claude, Google AI Mode, and Google AI Overviews — across a fixed basket of 24 measured subject categories plus a legacy news-topic bucket (25 taxonomy nodes in all). Findings are grouped into strata, where a stratum is one subject category paired with one question type. A stratum publishes a citation rate only after it clears the evidence floor of at least 10 observed runs across at least 7 distinct dates; below that line it is marked collecting rather than scored, so early or thin signal is never shown as settled. Every domain an engine cites is measured on equal terms — membership in any external catalog is recorded only as a label and is never used to decide what gets scored. The public Index reports rates and rankings; it excludes raw cited URLs, internal query identifiers, and provider payloads.", "methodology_version": "mri_score_v2.0", "canonical_copy": { "version": "2026-09-04", "mri_score_definition": "The Machine Relations Index v2 reports source-segment citation rates — how often AI answer engines cite each source domain — published only once a segment clears the evidence floor of at least 10 observations across at least 7 distinct run dates, with each domain graded into confidence tiers A, B, C or collecting by how much evidence stands behind it.", "mri_score_model": "Every domain an answer engine cites is measured on equal terms. For each subject category paired with a buyer question type, the model counts how many observed answer runs cited a domain and divides by the total observed runs for that segment to produce a citation rate. Rates are published only for segments that have accumulated enough observations across enough separate days to be stable; thinner segments are shown as still collecting rather than scored. Domains are ranked within their source type and across the full measured universe, and each domain carries a confidence grade reflecting the volume of evidence behind its rate.", "mri_score_supersession": { "previous_version": "mri_score_v1.1_6_engine", "superseded_on": "2026-07-05", "note": "The v1 composite score — engine breadth, query diversity, vertical spread, position quality, and temporal consistency combined into a single weighted number — is retired. v1 scores are not comparable to v2 citation rates; the two measure different things. The published v1-to-v2 crosswalk is the one-time transition receipt." }, "public_boundary_summary": "The public dataset reports citation rates, rankings, and evidence counts. It excludes internal query identifiers, raw cited URLs, and answer-engine provider payloads.", "update_cadence": "daily" }, "taxonomy": { "version": "mri_taxonomy_v2.0", "nodes": [ { "id": "ai-infrastructure", "label": "AI Infrastructure", "basket_version": "mri_taxonomy_v2.0" }, { "id": "ai-security-privacy", "label": "AI Security & Privacy", "basket_version": "mri_taxonomy_v2.0" }, { "id": "ai-visibility-geo", "label": "AI Visibility & GEO", "basket_version": "mri_taxonomy_v2.0" }, { "id": "consumer-finance", "label": "Consumer Finance", "basket_version": "mri_taxonomy_v2.0" }, { "id": "consumer-health", "label": "Consumer Health", "basket_version": "mri_taxonomy_v2.0" }, { "id": "consumer-products", "label": "Consumer Products", "basket_version": "mri_taxonomy_v2.0" }, { "id": "cybersecurity", "label": "Cybersecurity", "basket_version": "mri_taxonomy_v2.0" }, { "id": "deep-tech-hardware", "label": "Deep Tech & Hardware", "basket_version": "mri_taxonomy_v2.0" }, { "id": "education-training", "label": "Education & Training", "basket_version": "mri_taxonomy_v2.0" }, { "id": "emergent-prosumer", "label": "Emergent Prosumer", "basket_version": "mri_taxonomy_v2.0" }, { "id": "enterprise-software", "label": "Enterprise Software", "basket_version": "mri_taxonomy_v2.0" }, { "id": "family-software", "label": "Family Software", "basket_version": "mri_taxonomy_v2.0" }, { "id": "fintech", "label": "Fintech", "basket_version": "mri_taxonomy_v2.0" }, { "id": "healthcare-services", "label": "Healthcare Services", "basket_version": "mri_taxonomy_v2.0" }, { "id": "hr-talent", "label": "HR & Talent", "basket_version": "mri_taxonomy_v2.0" }, { "id": "igaming-betting", "label": "iGaming & Betting", "basket_version": "mri_taxonomy_v2.0" }, { "id": "industrial", "label": "Industrial", "basket_version": "mri_taxonomy_v2.0" }, { "id": "legacy-unmapped", "label": "Legacy News Topics", "basket_version": "mri_taxonomy_v2.0" }, { "id": "local-services", "label": "Local Services", "basket_version": "mri_taxonomy_v2.0" }, { "id": "logistics-freight", "label": "Logistics & Freight", "basket_version": "mri_taxonomy_v2.0" }, { "id": "martech-advertising", "label": "Martech & Advertising", "basket_version": "mri_taxonomy_v2.0" }, { "id": "physical-consumer", "label": "Physical Consumer Products", "basket_version": "mri_taxonomy_v2.0" }, { "id": "professional-services", "label": "Professional Services", "basket_version": "mri_taxonomy_v2.0" }, { "id": "sales-gtm-crm", "label": "Sales, GTM & CRM", "basket_version": "mri_taxonomy_v2.0" }, { "id": "vc-private-equity", "label": "VC & Private Equity", "basket_version": "mri_taxonomy_v2.0" } ] }, "window": { "start": "2026-05-10", "data_through": "2026-09-04", "days_observed": 111 }, "evidence_floor": { "min_observations": 10, "min_run_dates": 7 }, "source_ledger": { "event_count": 114340, "run_count": 14268, "eligible_query_count": 821, "engines": [ "chatgpt", "claude", "gemini", "google_ai_mode", "google_ai_overviews", "perplexity" ], "panels_observed": [ "legacy", "mri_v2" ] }, "total_domains": 20658, "source_event_count": 114340, "stratum_summary": { "total": 157, "collectable": 151, "not_collectable": 6, "published": 71, "collecting": 80, "empty": 62, "runs_observed": 14268, "cited_domain_observations": 30793 }, "strata": [ { "category": "ai-infrastructure", "question_shape": "best_x", "status": "published", "collecting_since": null, "runs_observed": 77, "runs_cited_domains": 204, "run_dates": 7, "not_collectable": false }, { "category": "ai-infrastructure", "question_shape": "how_choose", "status": "published", "collecting_since": null, "runs_observed": 106, "runs_cited_domains": 258, "run_dates": 7, "not_collectable": false }, { "category": "ai-infrastructure", "question_shape": "is_x_worth", "status": "published", "collecting_since": null, "runs_observed": 106, "runs_cited_domains": 265, "run_dates": 7, "not_collectable": false }, { "category": "ai-infrastructure", 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