# About Machine Relations Machine Relations is the discipline of how organizations earn and hold citations from AI answer engines. machinerelations.ai is the public research and standards initiative that publishes the discipline's glossary, research, evidence, and measurements — and the sole publisher of this site. This page documents the site's provenance, its relationship to AuthorityTech, and the editorial standards every page is held to. Canonical URL: https://machinerelations.ai/about ## What Machine Relations is Machine Relations is the discipline of how organizations earn and hold citations from AI answer engines. machinerelations.ai is the public research and standards initiative that publishes its glossary, research, evidence, and measurements. The two are distinct: the discipline is the category; the initiative documents and measures it. Practitioners apply the standards; the initiative defines and publishes them. The initiative is the sole publisher of machinerelations.ai: the [glossary](/glossary), the [research library](/research), the [evidence base](/evidence), and the [Machine Relations Index (MRI)](/index). Research on this site is authored by Machine Relations Research, the initiative's research program. ## Origins Three related parts of the research program have distinct origins: - **The category.** [Jaxon Parrott](https://jaxonparrott.com), founder of [AuthorityTech](https://authoritytech.io), coined the term Machine Relations in 2024. - **The Machine Relations Index (MRI).** The citation-rate measurement methodology behind this site's recurring index. It is defined and published here, on machinerelations.ai. - **Answer-Source Fidelity.** The existence-versus-support methodology originated inside AuthorityTech's internal knowledge systems, where the distinction first mattered in practice: a citation being present was never proof the citation held. That internal test was generalized into the measurement method published here. ## Relationship to AuthorityTech AuthorityTech is the founding commercial practitioner of Machine Relations, applying the standards commercially for clients. machinerelations.ai and its publishing infrastructure are owned and operated by AuthorityTech; the Machine Relations discipline is not an AuthorityTech product. The relationship, stated plainly: - machinerelations.ai (this site) is the initiative that defines and publishes the discipline's standards and measurements, and it is the sole publisher of everything on this domain. - AuthorityTech is where the Answer-Source Fidelity method was first built and used internally, and it remains a commercial application of the standards. It owns and operates the site and its publishing infrastructure; it receives no author or publisher credit on this site's research. - The editorial publishing boundary is enforced mechanically, not by promise: the build fails if AuthorityTech holds an author, publisher, or creator role on any research or reference surface, if the structured data disagrees with the visible attribution, or if an AuthorityTech link appears outside its one permitted role. AuthorityTech links inside research and reference bodies are allowed only as named inline evidence citations to its published articles, data, or analyses; entity and provenance links — the AuthorityTech homepage and company profile — live only on this About page and the origin surfaces that document the term's coinage; commercial destinations (the lead-generation application, client-results, industries, pricing, contact, and booking surfaces, and audit-invitation language) never appear in research or reference output, and an unclassified AuthorityTech destination is treated as commercial until it is deliberately added to the evidence allowlist. Index findings are receipt-bound to the published MRI dataset and methodology version. That is the full extent of what the build enforces; editorial judgment is governed by the standards below, not by a guard. - Jaxon Parrott coined the term and founded AuthorityTech. This page is the canonical disclosure of that relationship. Research and reference pages on this site carry no AuthorityTech author, publisher, creator, or reviewer attribution; where AuthorityTech data appears inside a report, it is named inline as a source like any other. ## Editorial standards ### Content classification Every research page is classified as exactly one of five types, displayed on the page, in the research hub, and in the machine-readable layer: - **Study** — original empirical measurement run by Machine Relations Research. - **Index Analysis** — interpretation of Machine Relations Index (MRI) observations. - **Research Synthesis** — analysis built on external evidence and third-party studies. - **Reference** — definitions, frameworks, and category documentation. - **Practitioner Analysis** — operational implications for teams applying the standards. ### Evidence and receipts Claims carry named sources. Within the governed measurement program — the Machine Relations Index, the fidelity measurement surfaces, and reports typed Study or Index Analysis going forward — numbers link to the study or dataset that produced them, and index findings are receipt-bound: they trace to the published MRI dataset and the methodology version that produced them. Legacy pages published before this program are handled by classification, not retrofit: each carries its content type, and pages that predate a methodology change carry a dated supersession note rather than a silent rewrite. ### Corrections Material errors are corrected in place with a dated note. Superseded methodology is marked as superseded and kept legible; it is never silently rewritten. Every page has a machine-readable sibling at the same URL plus `.md` for verification against the rendered copy.