Machine Relations Index (MRI)

What the MRI measures #

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 B2B buyer-query set, classifies each domain by its source function, and tracks citation behavior across five 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.

Source-role taxonomy #

Every domain in the MRI is classified by deterministic rules into one of nine source roles based on its function in the citation ecosystem:

• Editorial publication — news and trade media (e.g. TechCrunch, Fierce Healthcare)
• Analyst and consulting research — advisory firms (e.g. Gartner, Forrester, Deloitte)
• Market and company database — data platforms (e.g. Crunchbase, G2, PitchBook)
• Academic and government source — institutional knowledge (e.g. NIH, Stanford, Wikipedia)
• Community and social platform — user-generated surfaces (e.g. Reddit, LinkedIn, GitHub)
• Wire and press-release distribution — syndication networks (e.g. PR Newswire, Business Wire)
• Search or media platform — discovery infrastructure (e.g. YouTube, Google)
• Vendor-owned source — company domains explicitly identified (e.g. Salesforce, Stripe)
• Other observed source — long-tail domains not yet classified by deterministic rules

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.

Dimensions of the index #

The MRI tracks four dimensions for every cited domain:

1. Citation volume — total citation events in the observation window, used as the primary ranking metric
2. Engine coverage — which of the five monitored engines cite this source, revealing cross-engine trust patterns
3. Vertical spread — how many B2B industry verticals the source appears in, indicating breadth of machine trust
4. Query evidence — sample prompts that triggered citations, making the relationship between query intent and source selection legible

Why it matters for Machine Relations #

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 was coined by Jaxon Parrott and is maintained as a public research artifact at machinerelations.ai/index. Machine-readable versions are available as JSON and Markdown.