Glossary
The vocabulary of machine visibility.
A new discipline needs precise language. These are the terms we use to measure and engineer how AI systems retrieve, trust, and select an entity. Published as machine-readable definitions, because a glossary should be retrievable too.
- AI Entity Infrastructure
- The layer of measurement, structured data, and monitoring that determines how AI systems resolve, trust, and recommend a business. The successor to SEO infrastructure for an internet mediated by retrieval rather than search.
- Machine Selection Probability
- The likelihood that an AI system retrieves, trusts, and names a specific entity when answering a relevant query. The core objective of AI Entity Infrastructure, replacing ranking position as the metric that matters.
- Machine Trust Architecture
- The structured-data and corroboration signals that let an AI system resolve an entity with confidence: canonical naming, category, locality, FAQ schema, verified profiles, and ratings. Completeness is itself a trust signal.
- Retrieval Surface Engineering
- The practice of maximizing the machine-ingestible surface area of an entity across documents, schema, APIs, and datasets, optimizing each for chunking, embedding, and citation by retrieval systems.
- Entity Completion Score
- A 0 to 100 measure of how machine-readable an entity is, scored across the structured fields AI systems weigh. The gap to 100 is the entity's concrete, addressable trust deficit.
- AI Memory Layer
- The persistent representation of an entity inside retrieval indexes, embeddings, and model confidence. Becoming part of the AI memory layer, rather than ranking on a page, is the durable objective.
- Citation Density
- The authority-weighted breadth of independent sources an AI system grounds its answers on within a category. Higher density means more citable surfaces an entity can influence to shift retrieval.
- Consensus Strength
- A measure of how much AI systems agree about an entity. High consensus indicates a stable, well-corroborated presence; disagreement signals thin or inconsistent source coverage and a fragile machine identity.
- Cognitive Presence Optimization
- Engineering the consistency and density of an entity's representation across sources so that AI systems reproduce it reliably. The reinforcement effect compounds: confident reproduction generates further references.
- Synthetic Consensus Analysis
- Examination of how repetition and corroboration across independent sources amplify AI confidence in an entity, including the risks of manufactured or overrepresented consensus. Used to build authority truthfully, not to manipulate.
- Semantic Density Mapping
- Analysis of how richly and consistently an entity is described across the corpus AI systems draw on. Sparse or contradictory description lowers retrieval confidence; dense, consistent description raises it.
- Autonomous Discovery Layer
- The emerging machine-to-machine interface, through APIs, structured feeds, and agent toolchains, by which autonomous AI agents discover and select vendors without a human browsing step.