The Machine Relations Index (MRI) is a public source-behavior dataset that tracks which root domains answer engines — ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Google AI Overviews — cite when responding to B2B buyer-intent questions. It classifies every observed source by deterministic source-role rules, measures engine coverage and vertical spread, and publishes the full cited-domain set with query evidence. The MRI was coined by Jaxon Parrott and is maintained as a public research artifact.
The Machine Relations Index monitors how answer engines select sources when they respond to commercial research questions. It captures every root domain cited across a monitored set of B2B buyer-intent queries, classifies each domain by its source function, and tracks citation behavior across six answer engines over a rolling observation window.
The MRI is not a ranking of brands or a quality score. It is a behavioral map of the source layer that machines use when they construct answers to buyer-intent queries. A domain appears in the MRI because at least one engine cited it in at least one observed query — nothing more.
The scale is substantial. The current index tracks over 6,800 root domains across more than 30,000 citation events, observed over 247 active B2B buyer queries spanning 10 industry verticals (MRI dataset). The engines monitored are Perplexity, ChatGPT Browse, Gemini grounding, Claude Web, Google AI Mode, and Google AI Overviews.
Every domain in the MRI is classified by deterministic rules into one of nine source roles based on its function in the citation ecosystem:
The "Other observed source" bucket is deliberately large. The MRI does not overclaim classification: domains remain uncategorized until deterministic evidence supports a role assignment. This preserves research integrity over cosmetic completeness.
Current citation share by source role shows vendor-owned sources at approximately 31%, editorial publications at 18%, and market/company databases at 11% — demonstrating that AI engines draw from a diverse source mix rather than a single dominant category (MRI dataset).
The MRI tracks four dimensions for every cited domain:
Citation volume — total citation events in the observation window, used as the primary ranking metric. High-volume domains like Crunchbase, G2, and Deloitte consistently appear because multiple engines trust them across multiple verticals.
Engine coverage — which of the six monitored engines cite this source. A domain cited by five engines demonstrates broader machine trust than one cited by a single engine. Cross-engine coverage is the strongest signal of durable source authority. Independent research analyzing 30 million AI-generated citations across ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews confirms that citation patterns vary significantly by engine, with some sources dominating on a single platform while others hold cross-engine presence (Peec AI, 2026).
Vertical spread — how many B2B industry verticals the source appears in, indicating breadth of machine trust. A domain cited in cybersecurity, fintech, and healthtech simultaneously holds cross-vertical authority that single-vertical sources do not.
Query evidence — sample prompts that triggered citations, making the relationship between query intent and source selection legible. This dimension makes the MRI actionable: practitioners can see which types of buyer questions lead to which types of source citations.
The MRI serves three primary use cases for Machine Relations practitioners:
Source-layer diagnosis. Before investing in earned media, a brand can check which sources AI engines actually cite in their category. If Gartner, Forrester, and G2 dominate the citation layer for enterprise software queries, the path to AI visibility runs through those platforms — not through blog volume or press release distribution. BrightEdge's GEO Benchmark Report found that original research and proprietary data achieve citation rates of 38-65%, compared to 6-15% for standard blog posts — a 3-10x difference that makes source-layer intelligence strategically decisive (Averi, 2026). The MRI makes the source map visible before the investment.
Competitive citation intelligence. The MRI reveals which competitor domains appear in AI-generated answers for relevant buyer queries. If a competitor holds cross-engine citations while your brand does not, the gap is structural — not a content optimization problem. The fix is earned authority in the source types that engines trust, not more on-site content (how to get cited).
Category-level pattern recognition. By examining which source roles dominate citation behavior across verticals, practitioners can identify structural patterns. In some verticals, editorial publications drive the citation layer. In others, market databases or analyst research dominate. The MRI makes these patterns empirically visible instead of assumed.
The dataset is available in both JSON and Markdown formats under a Creative Commons BY 4.0 license, making it machine-readable for automated analysis and integration.
The MRI is not a brand ranking. It does not score or rank companies by citation quality, market share, or brand equity. It observes citation behavior at the domain level and reports what engines do — not what they should do.
It is not a substitute for AI Visibility Score monitoring. The MRI maps the entire source layer; AI Visibility Score tracks a specific brand's presence across AI-generated answers. They are complementary: the MRI shows the competitive landscape, while AI Visibility Score measures a brand's position within it.
The MRI is also not a prediction model. Citation behavior shifts as engines update their retrieval and ranking systems. The index reports observed behavior within its measurement window. Historical patterns inform strategy but do not guarantee future citation placement.
Machine Relations is the discipline of becoming legible, credible, and citable to machine-mediated discovery systems. The MRI is the empirical foundation: it shows what the source layer actually looks like, which sources engines trust across contexts, and where citation authority concentrates or disperses.
For 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 sits at Layer 5 (Measurement) of the Machine Relations Stack. It provides the feedback loop that makes the other four layers — Earned Authority, Entity Optimization, Citation Architecture, and Distribution — measurable and accountable. Without measurement data showing which sources engines actually cite, Machine Relations strategy operates on assumption rather than evidence.
The index was coined by Jaxon Parrott and is maintained as a public research artifact at machinerelations.ai/index.
Traditional media monitoring tracks where a brand is mentioned by journalists and publishers. The MRI tracks which sources AI engines choose to cite when constructing answers to buyer questions. The difference is the consumer: media monitoring measures human editorial decisions, while the MRI measures machine citation behavior. A brand can have strong media coverage and zero presence in the MRI if AI engines do not retrieve or cite those placements.
No. The MRI records citation events at the domain level, not brand endorsements. A domain appearing in the MRI means at least one answer engine cited content from that domain when responding to at least one monitored query. The citation may be neutral, comparative, or contextual — not necessarily a recommendation.
The MRI operates on a rolling observation window. The current dataset covers multiple weeks of continuous monitoring across six engines. As new queries are added or engines update their citation behavior, the index reflects those changes in subsequent observation periods.
Not directly. The MRI reports observed behavior — brands cannot submit domains or request inclusion. However, the Machine Relations framework provides the strategic path: build earned authority through Tier 1 placements, optimize entity clarity, engineer citation-ready content, and distribute through channels that AI engines trust. Brands that execute these layers consistently tend to appear in the MRI because they become the sources engines reach for.
Brand-prompted queries (e.g., "tell me about [company name]") would inflate vendor-owned domain citations and skew the dataset toward self-referential results. The MRI monitors buyer-intent queries — the questions buyers actually ask when researching solutions — because those reveal which sources engines trust for category-level answers, not just brand lookups.
AI Share of Voice is the proportion of AI-generated responses where a brand is mentioned, cited, or recommended relative to competitors for a defined set of category queries across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Distinct from traditional share of voice (media mentions) and search share of voice (ranking visibility), AI Share of Voice measures competitive position in the AI discovery layer.
A brand's measurable presence across AI platforms (ChatGPT, Perplexity, Gemini, AI Overviews). Replaces impressions as the key MR metric.
Citation Decay is the rate at which AI engine citations of a brand decrease over time without sustained earned media activity. AI engines continuously re-evaluate source freshness and authority, and brands that stop generating new high-quality signals see their citation presence erode as competitors produce newer, more relevant content.
The measurable divergence between a brand's traditional search ranking and its citation frequency inside AI-generated answers. A brand can rank #1 on Google and appear in 0% of ChatGPT, Perplexity, or Gemini responses for the same query.