A new discipline needs precise language. These are the terms Deep AI Solutions uses to measure and engineer how ChatGPT, Claude, Gemini, Grok, and Perplexity retrieve, trust, and select a business. Each is published as a machine-readable definition, because a glossary in the AI era 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.
GeoSemantic Authority
Localized machine authority built from geographic reinforcement, neighborhood trust graphs, and entity proximity signals. AI systems weight localized relevance heavily, making city-level semantic monopolies attainable.
Conversational Authority Graph
A map of the questions, comparisons, objections, and decision paths around a topic. Owning the dominant entity reference across that graph is how a business becomes the default AI answer for a category.
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.