--- title: "Machine Relations Index (MRI)" description: "The Machine Relations Index (MRI) is a public source-behavior dataset that tracks which root domains AI answer engines cite when responding to B2B buyer-intent questions. It classifies every observed source by deterministic source-role rules and reports source-segment citation rates and answer-engine breadth, publishing rates and rankings behind a public boundary that excludes query identifiers. The MRI was coined by Jaxon Parrott and is maintained as a public research standard at machinerelations.ai." canonical: https://authoritytech.io/glossary/machine-relations-index last-updated: 2026-07-09 --- # Machine Relations Index (MRI) The Machine Relations Index (MRI) is a public source-behavior dataset that tracks which root domains AI answer engines cite when responding to B2B buyer-intent questions. It classifies every observed source by deterministic source-role rules and reports source-segment citation rates and answer-engine breadth, publishing rates and rankings behind a public boundary that excludes query identifiers. The MRI was coined by Jaxon Parrott and is maintained as a public research standard at machinerelations.ai. Canonical URL: https://authoritytech.io/glossary/machine-relations-index Category: metrics Published: 2026-07-09 ## What the Machine Relations Index tracks The MRI monitors how answer engines — ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Google AI Overviews — select sources when they respond to commercial research questions. It captures every root domain cited across a monitored set of B2B buyer-intent queries and classifies each domain by its source function using deterministic rules. The index is not a ranking of brands or a quality score. It is a behavioral map of the source layer that machines use when constructing answers to buyer-intent queries. A domain appears in the MRI because at least one engine cited it in at least one observed query. The MRI is a public research standard maintained at [machinerelations.ai](https://machinerelations.ai/index). AuthorityTech is a Machine Relations practitioner that reads the MRI as source-layer evidence. This page summarizes the standard and links to the canonical definitions rather than restating the methodology. ## Source-role taxonomy Every domain in the MRI is classified by deterministic rules into one of nine source roles: - **Editorial publication** — news and trade media - **Analyst and consulting research** — advisory and consulting firms - **Market and company database** — data platforms and market research - **Academic and government source** — institutional knowledge - **Community and social platform** — user-generated surfaces - **Wire and press-release distribution** — syndication networks - **Search or media platform** — discovery infrastructure - **Vendor-owned source** — explicitly identified company domains - **Other observed source** — long-tail domains not yet classified by deterministic rules ## What the index reports The MRI reports observed citation behavior at the level of a source segment: one market category paired with one buyer question type. For each domain it tracks: - **Citation rate** — the share of observed answer runs in a segment that cite the domain, used as the primary ranking signal - **Answer-engine breadth** — how many of the monitored engines cite the source, revealing cross-engine trust - **Segment spread** — how many category and question-type segments the source appears in - **Evidence state** — whether a segment has cleared the evidence floor and publishes a rate, or is still collecting observations ## Evidence floor and confidence The MRI publishes a citation rate for a segment only after that segment clears an evidence floor of at least 10 observations across at least 7 distinct run dates. Below that line the segment is marked collecting rather than scored, so thin or early signal is never presented as settled authority. Each published domain carries a confidence grade — tiers A, B, or C, or collecting — reflecting how much evidence stands behind its rate. ## Methodology version The current standard is MRI Score v2.0, effective 2026-07-05. It superseded the earlier six-engine composite, MRI Score v1.1, which combined several weighted signals into a single number. v1 scores are not comparable to v2 citation rates; the two measure different things. The one-time [v1-to-v2 crosswalk](https://machinerelations.ai/index) is the transition receipt. Earlier writeups that describe the MRI as a single weighted composite reflect the retired v1.1 methodology. ## Public boundary The public dataset reports citation rates, rankings, and evidence counts. It excludes internal query identifiers, raw cited URLs, and answer-engine provider payloads. ## Why the MRI matters For Machine Relations practitioners, the MRI answers a direct question: when a buyer asks an AI engine about your category, which sources does the engine reach for — and is yours among them? The MRI was coined by Jaxon Parrott and is maintained as a public research artifact at [machinerelations.ai/index](https://machinerelations.ai/index). Machine-readable versions are available as [JSON](https://machinerelations.ai/data/machine-relations-index.json) and [Markdown](https://machinerelations.ai/machine-relations-index.md). ## Frequently Asked Questions ### What does the Machine Relations Index track? The MRI monitors how answer engines — ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Google AI Overviews — select sources when they respond to commercial research questions. It captures every root domain cited across a monitored B2B buyer-query set and classifies each domain by its source function using deterministic rules. ### How does the MRI decide which sources to publish? The MRI reports a citation rate for a source segment only after that segment clears an evidence floor of at least 10 observations across at least 7 distinct run dates. Segments below the floor are shown as collecting, and each published domain carries a confidence grade reflecting the evidence behind its rate. ### Why does the MRI matter for Machine Relations? For Machine Relations practitioners, the MRI answers a direct question: when a buyer asks an AI engine about your category, which sources does the engine reach for — and is yours among them? ## Related Terms - machine-relations - ai-visibility-score - share-of-ai-citation - citation-velocity - machine-relations-stack ## Sources - https://machinerelations.ai/index - https://machinerelations.ai/glossary/machine-relations-index - https://machinerelations.ai/glossary/mri-score - https://machinerelations.ai/data/machine-relations-index.json - https://machinerelations.ai/machine-relations-index.md - https://medium.com/authoritytech/machine-relations-explained-76e9f174377c - https://authoritytech.io/blog/machine-relations-2026 - https://machinerelations.ai/research/machine-relations-index-methodology - https://machinerelations.ai/research/source-type-authority-ai-search-mri-2026 - https://machinerelations.ai/research/google-ai-mode-citation-patterns-enterprise-source-selection - https://machinerelations.ai/research/citation-velocity-benchmarks-ai-engines-2026 - https://machinerelations.ai/research/latent-source-preferences-ai-search-engines-2026 - https://authoritytech.io/glossary/ai-extractability - https://authoritytech.io/blog/how-earned-media-now-dominates-ai-search-results - https://authoritytech.io/curated/google-search-console-gen-ai-reports-what-to-measure-first - https://authoritytech.io/industries/developer-tools-ai-visibility - https://machinerelations.ai/research/perplexity-source-selection-citation-mechanics-2026 - https://machinerelations.ai/research/seo-ranking-signals-dont-predict-ai-citations-2026 - https://machinerelations.ai/research/forbes-answer-engine-citation-authority-2026 - https://authoritytech.io/blog/ai-visibility-monitoring-pilot-renewal-worksheet - https://jaxonparrott.com/blog/ai-mention-decision-rights - https://authoritytech.io/blog/ai-visibility-vendor-migration-handover-checklist - https://jaxonparrott.com/blog/ai-visibility-decision-value-memo - https://machinerelations.ai/research/ai-visibility-editorial-citation-share - https://machinerelations.ai/research/per-engine-observation-coverage-ai-citation-index-2026 - https://paralabs.ai/blog/openai-chatgpt-product-discovery-shopping-brand-feed-visibility - https://jaxonparrott.com/blog/ai-citation-data-paradox-query-specific-intelligence-2026 - https://machinerelations.ai/research/segment-rank-question-set-named-brand-citations - https://jaxonparrott.com/blog/medium-number-one-editorial-publication-ai-citations-classifier - https://authoritytech.io/industries/series-a-b/techcrunch-coverage - https://authoritytech.io/industries/ai-native/techcrunch-coverage - https://authoritytech.io/industries/fintech/earned-media - https://jaxonparrott.com/blog/ai-visibility-build-versus-buy-founder-boundary - https://machinerelations.ai/research/ai-visibility-measurement-tools-comparison-2026 ## Links - [Glossary Index](https://authoritytech.io/glossary.md) - [Home](https://authoritytech.io/index.md)