Machine Relations Index
A public research index of the sources answer engines cite when they respond to B2B buyer-intent questions. It tracks root domains, source roles, engine coverage, vertical coverage, and the prompts used to observe citation behavior.
Machine Relations is the discipline of becoming legible, credible, and citeable to machine-mediated discovery systems. The index measures the public source layer those systems use when answering commercial research questions.
The useful split is not owned versus earned. It is source function: editorial publications, analyst research, market databases, academic and government sources, community platforms, wire distribution, search/media platforms, and vendor-owned sources.
| Source class | Domains | Citations | Citation share |
|---|---|---|---|
| Editorial publication | 195 | 1,154 | 7% |
| Analyst and consulting research | 14 | 424 | 2.6% |
| Market and company database | 21 | 1,035 | 6.3% |
| Academic and government source | 57 | 352 | 2.1% |
| Community and social platform | 9 | 740 | 4.5% |
| Wire and press-release distribution | 6 | 146 | 0.9% |
| Search or media platform | 2 | 633 | 3.8% |
| Vendor-owned source | 3,878 | 11,998 | 72.8% |
Loading Machine Relations Index data...
Answer-engine citation monitoring across Perplexity, ChatGPT Browse, Gemini grounding, Claude Web, and Google AI Mode. The Machine Relations Index includes every cited root domain from the observed B2B buyer-query set, classifies each source by role, and excludes raw cited URLs, evidence paths, provider payloads, placement availability, account metadata, and internal opportunity metadata from the public artifact.
Generated 2026-05-21 from rows observed since 2026-05-10. Machine-readable versions are available as markdown and JSON.