AI Visibility Glossary

How often and how prominently a brand appears in the answers generated by AI assistants like ChatGPT, Gemini, Claude, and Perplexity.

Search experiences where the result is a synthesized answer generated by a large language model, often with inline citations, instead of a list of links.

The process by which AI models find, evaluate, and decide which brands to surface in their answers.

A system that responds to a user query with a synthesized natural-language answer rather than a list of documents. ChatGPT, Gemini, Claude, and Perplexity are all answer engines.

How large language models describe, categorize, and contextualize a brand when asked about it, effectively the brand image AI models hold.

Multi-turn, dialogue-based search where users refine queries through follow-up questions and the AI maintains context across the conversation.

A search that ends without the user clicking through to any source because the answer is delivered directly in the result.

The full set of online signals, owned, earned, and third-party, that AI models can use to learn about a brand.

An AI system that suggests products, tools, or brands to a user in response to a question, effectively acting as a personalized buyer's guide.

An AI interaction that spans multiple back-and-forth messages, with the model preserving context across the entire conversation.

A composite metric that summarizes how strongly a brand appears across AI answers, combining mention rate, position, and citation share into a single 0–100 number.

The percentage of AI answers in a defined category in which a brand is mentioned, relative to its competitors.

Where in an AI answer a brand typically appears: first paragraph, mid-answer, or near the end. Lower numbers mean higher prominence.

When an AI answer explicitly links to the brand's own domain as a source. The most valuable form of AI mention because it can drive direct traffic.

The percentage of tracked AI prompts in which a brand is named at least once.

A composite indicator of how authoritative AI models consider a brand to be in its category, driven by citations, sentiment, and consistency across sources.

The discipline of optimizing content, entities, and citations so generative AI engines name and recommend a brand inside their answers.

A program of work focused specifically on improving how AI models describe, rank, and recommend a brand.

How much weight AI models give a brand's content when generating answers, a function of expertise signals, citations from trusted sources, and content freshness.

Improving how brands appear in structured knowledge graphs, Google's, Wikidata, and the implicit graphs inside large language models.

How recently a piece of content has been updated, a signal that AI retrieval systems use to choose between competing sources.

A broader SEO practice that combines classic search optimization with optimization for AI answer engines.

The signals AI engines weigh when deciding which brands and sources to include in an answer, including authority, freshness, structure, and entity clarity.

Optimization techniques specifically aimed at being included in the direct answers that AI assistants produce.

The effect that content included in a model's training corpus has on what the model later says about a brand.

A technique where an AI model fetches external documents at query time and grounds its answer in them, the foundation of most modern AI search.

Whether AI agents and retrieval crawlers, GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and others, can access and parse a site's content.

Search that matches on meaning rather than exact keywords, powered by embedding models that represent text as vectors.

The ability of AI systems to identify a brand, product, person, or place as a distinct entity and link it to a stable identifier.

The AI capability that interprets the meaning, intent, and context of a user's natural-language query.

The discipline of measuring how AI models describe, categorize, and contextualize a brand, and which sources shape those descriptions. One layer beneath visibility tracking.

Whether AI-generated answers describe a brand in positive, neutral, or negative terms.

Tracking which competitors AI models recommend in your category and how their visibility evolves over time.

Designing and refining the set of prompts used to test AI visibility, so that the prompts reflect real buyer questions and produce stable, comparable results.

Measuring how a brand performs in ChatGPT vs. Gemini vs. Claude vs. Perplexity side by side, so you can spot the exact models where you're invisible.

Measuring and improving brand visibility across multiple AI models at once, instead of optimizing for just one.

The classification of an AI prompt into intent categories, informational, navigational, commercial, or transactional, so brands can prioritize the prompts that drive revenue.

Monitoring which of a brand's pages AI engines cite as sources, how often, and which prompts trigger each citation.

The continuous practice of measuring brand presence, sentiment, and citations across AI assistants, the AI equivalent of traditional brand monitoring.

The week-over-week change in how AI models describe a brand, driven by shifts in the model's source mix.

Tracing each AI description of a brand back to the specific URLs, press articles, reviews, and structured sources that taught the model.

How AI models rank and frame a brand against a defined competitor set on identical prompts, order, leader vs alternative labels, and recommendation strength.

Strajist's signature layer: strategic deep questions asked to 8 AI models to surface the model's underlying belief about a brand, beyond surface mentions.