AI Visibility Glossary

Answer Engine Optimization is the practice of structuring your brand and content so AI systems understand, trust, cite, and recommend you across answer engines like ChatGPT, Claude, Gemini, and Perplexity.

AI Search is how modern answer engines retrieve, synthesize, and surface information — replacing ranked lists of links with composed answers drawn from trusted sources.

AI Visibility is the larger objective behind every modern optimization discipline — whether AI answer engines know your brand exists, understand what you do, and recommend you to users.

Authority Signals are the corroborated, cross-source trust signals — mentions, citations, consensus, and expertise — that make AI systems confident enough to cite and recommend a brand.

Brand Representation is how accurately AI systems understand who you are, what you do, and who you serve — the clarity of your brand as an entity an engine can confidently name.

A citation is when an AI system references your brand or content as a source within a generated answer — the clearest signal that an engine trusts you enough to attribute information to you.

Content Atomization is the practice of turning one core idea into many discoverable, citable assets across formats and platforms, multiplying the surfaces where AI can find and reference you.

An entity is a distinct, identifiable thing — a brand, person, product, place, or concept — that AI systems can recognize, disambiguate, and connect to other entities in a knowledge graph.

Generative search is a search experience where an AI composes an answer from multiple sources rather than returning a list of links — the surface that Generative Engine Optimization (GEO) targets.

Generative Engine Optimization focuses on increasing visibility inside AI-generated search experiences such as Google AI Overviews and Perplexity — a complement to both SEO and AEO.

Indexability is how easily AI systems can retrieve and parse your content — driven by structured data, clean markup, accessible pages, and a clear entity home.

A knowledge graph is a structured network of entities and the relationships between them, which AI systems reason over to understand context, corroborate facts, and connect a brand to its world.

Network Effects describe how a dense, consistent presence — co-mentions, references, and a wide entity footprint — compounds to reinforce a brand across the web and the AI systems that read it.

Retrieval-Augmented Generation (RAG) is the technique where an AI model retrieves relevant external sources at answer time and uses them to ground its response — which is why being retrievable and citable matters.

Schema markup is a standardized vocabulary (schema.org) for adding structured data to pages, helping AI and search systems understand entities, articles, FAQs, and the relationships between them.

Structured data is machine-readable information — often expressed as schema markup — that tells AI systems explicit facts about your brand, content, and relationships, improving indexability and understanding.