Machine Relations Glossary

Core Concepts

AI Citations

An AI citation is a reference that an answer engine — ChatGPT, Perplexity, Gemini, Google AI Mode, or Claude — attaches to a specific source when constructing a response. Machine Relations tracks citations separately from unlinked mentions. A citation identifies a source URL.

AI Search Engine

An AI search engine is a query interface that can retrieve web material and synthesize it into a conversational answer. The answer may include citations that identify the pages used during generation.

AI Visibility

AI Visibility describes a brand's presence and prominence in answers generated by ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. It can be tracked through cited, named, or recommended appearances across a fixed set of category-relevant queries.

Citation Architecture

Citation Architecture is the structural practice of organizing source-backed content into self-contained passages with clear meaning, context, and attribution so that AI answer engines and human readers can extract, verify, and reuse individual claims without losing accuracy.

Cross-Domain Citation Flywheel

A cross-domain citation flywheel is a Machine Relations model for recording how owned sources, external corroboration, and observed AI citations inform successive publication decisions. Premise: the model describes an analytical workflow; it does not prove that one surface causes another to be selected or that citation authority compounds.

Earned Authority

Machine Relations definition: Earned Authority is an MR label for independently published references to an entity.

Entity Chain

Machine Relations definition: An Entity Chain is an MR inventory of the public identity records, identifiers, independent references, and observed answer-engine citations connected to one entity.

Entity Clarity

Machine Relations definition: Entity Clarity is the degree to which a brand has a coherent, machine-readable identity across the surfaces that describe it — its structured data, third-party references, and knowledge graph presence.

Entity Graph

A structured representation of named entities and their typed relationships that search engines and AI answer systems use to resolve identity, classify categories, and select sources for retrieval.

Entity Optimization

Machine Relations definition: Entity optimization is the practice of making an entity's public identity records explicit, consistent, attributable, and testable across a declared set of surfaces.

Extractable Content

Extractable content is the Machine Relations term for content whose bounded passages preserve the claim, its subject, and its attribution when read apart from the rest of the page. Extractability is a passage-level observation, separate from retrieval and citation outcomes.

Machine Gatekeeper

Machine Relations definition: An AI-mediated system that selects, ranks, synthesizes, or cites information between a user query and the sources or entities shown in an answer.

Machine Relations

Machine Relations (MR) is the discipline coined by Jaxon Parrott, founder of AuthorityTech, in 2024 for managing how organizations are represented, sourced, and cited in AI-mediated discovery. MR defines a five-part operating model: earned authority, entity clarity, citation architecture, distribution, and measurement. In this model, GEO, AEO, AI SEO, and LLMO are distribution practices rather than the whole discipline.

Machine Resolution

Machine Resolution is a Machine Relations term for observing whether and how a named entity appears in an AI answer to a category or intent query. Coined by Jaxon Parrott, it is a measurement convention, not a claim of access to an engine's private retrieval or ranking process.

MR Stack

The five-layer Machine Relations operating framework: Earned Authority, Entity Optimization, Citation Architecture, GEO/AEO Distribution, and AI Visibility Measurement. Coined by Jaxon Parrott in 2024 and published by Machine Relations.

Performance PR

Machine Relations definition: Performance PR is the practice of evaluating public-relations coverage against declared, observable post-publication outcomes rather than output volume.

PR 2.0

Machine Relations definition: PR 2.0 is an MR operating concept for coordinating earned-media work with observation of how published sources appear and are cited across a declared set of AI answer surfaces. It retains public relations and records coverage and AI visibility as separate observations.

Zero-Click Answer

In Machine Relations, a Zero-Click Answer is an answer interaction that is completed without the user opening an external link. The label describes observed click behavior only. It does not establish whether the answer cited sources, whether those sources supported the answer, or whether the answer influenced a later action.

Tactics & Optimization

AEO (Answer Engine Optimization)

Answer Engine Optimization is the practice of structuring content so that AI-powered answer interfaces can extract, attribute, and present it as a direct response to a specific question.

Attribution Magnet

A content asset designed around material that another author or answer system can identify, quote, and attribute to a named source.

Earned Media Placements

Earned media placements are unpaid mentions, features, or citations in third-party publications — news outlets, trade journals, podcasts, or analyst reports — secured through editorial judgment rather than advertising spend.

GEO (Generative Engine Optimization)

Generative Engine Optimization (GEO) is the practice of adapting content for discovery, extraction, and citation in generated answers.

LLMO

LLMO (Large Language Model Optimization) is a Machine Relations term for publishing and measuring information intended to remain legible to future model-training or model-update processes. It separates observations made without visible retrieval from observations made with retrieval enabled. The distinction is a measurement convention, not proof that a response came from a specific training source or that a publishing tactic changed model weights.

Tier 1 Media Placement

Machine Relations definition: A Tier 1 media placement is unpaid, independently selected editorial coverage in a publication that meets a declared set of observable publication criteria.

Metrics & Measurement

AI Visibility Score

A quantitative measure of how often and how prominently a brand appears in AI-generated answers across a defined query set and engine mix.

Brand Web Mentions

Brand web mentions are linked or unlinked textual references to a company, product, person, or other named entity on web pages outside that entity's own properties.

Citation Decay

Citation Decay is a measurable decline in an entity's observed citation rate between comparable measurement windows. It records a downward change in AI citation presence — separate from organic search rankings, claim support, or business outcomes.

Citation Gap

The measurable divergence between a brand's traditional search ranking and its citation frequency inside AI-generated answers. A brand can have strong organic search presence while remaining absent from AI-generated answers for the same query.

Citation Rate

Citation Rate is the ratio of eligible AI answer runs that cite a specified domain or entity to all eligible runs under a declared query set, engine set, matching rule, and observation window.

Citation Velocity

Machine Relations uses Citation Velocity for the change in observed citation presence between comparable collection periods.

Entity Resolution Rate

Machine Relations definition: Entity Resolution Rate is the share of observed answer-engine responses that mention a target entity and match a declared set of identity attributes, under a disclosed query set, engine panel, collection window, and matching rule.

Machine Relations Index

The Machine Relations Index (MRI) is a public source-behavior dataset that reports source-segment citation rates: how often monitored AI answer engines cite each root domain within a subject-category and buyer-question segment. A segment publishes a rate only after it clears the artifact's evidence floor; thinner segments remain collecting. The public artifact also reports rankings, evidence counts, source roles, engine breadth, and confidence grades while excluding internal query identifiers, raw cited URLs, and provider payloads. The MRI was coined by Jaxon Parrott and is maintained as a public research artifact.

MRI Score

The Machine Relations Index v2 reports source-segment citation rates — how often AI answer engines cite each source domain — published only once a segment clears the evidence floor of at least 10 observations across at least 7 distinct run dates, with each domain graded into confidence tiers A, B, C or collecting by how much evidence stands behind it.

RAG Citation

RAG Citation is a Machine Relations measurement term for a source attribution presented in an AI answer generated with retrieval. The observation records the displayed citation separately from whether the source was retrieved for a particular claim and whether the source actually supports that claim.

Recommendation Rate

The share of observed decision-intent answers in which an AI system includes a brand as a recommended option under a declared measurement protocol.

Sentiment Delta

Sentiment Delta is a Machine Relations measurement convention for recording differences between a brand's declared positioning and descriptions returned by AI systems for a fixed query set. It describes observed differences; it does not establish why they occurred or whether they changed buyer behavior.

Strategy & Frameworks

Algorithm Credibility Moat

A Machine Relations strategy model for a brand position supported by recurring citation, clear entity identity, and durable source coverage.

GEO vs SEO

Generative Engine Optimization (GEO) and Search Engine Optimization (SEO) serve different discovery layers. SEO optimizes content for search engine ranking position through keywords, backlinks, and technical performance. GEO optimizes content for AI engine citation and extraction through quotable facts, comparison tables, structured data, and entity clarity. Machine Relations classifies both as Layer 4 distribution tactics.

MR vs PR

Public Relations (PR) focuses on relationships with journalists and editors and on earning editorial coverage. Machine Relations (MR) is the Machine Relations discipline for observing and managing how an organization is represented, sourced, and cited across named AI-mediated discovery surfaces. In the MR taxonomy, PR can contribute independent editorial references; MR measures those references separately from identity records, owned content, answer-surface presence, and citation outcomes.

Zero-Click PR

Premise: Machine Relations defines Zero-Click PR as an earned-media strategy that evaluates placements by how often AI answer engines cite, attribute, or mention the brand inside generated answers rather than by referral traffic alone.