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DerivateX Glossary
Plain-English definitions for the AI search and SEO terms B2B SaaS teams need to know — citation engineering, GEO, AVS, entity optimization, and more.
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AEO (Answer Engine Optimization) is the practice of structuring content so that search engines and AI-powered answer features retrieve and surface it as a direct, accurate response to a specific user question.
An AI citation is an instance in which a large language model names, recommends, or references a specific brand, product, or entity within a generated response, as a result of the model determining that the brand is relevant and credible in the context of the user's query.
AI Exposure is top-of-funnel generative visibility: the degree to which AI-enhanced search and answer systems show, sample, or impress your content before a recommendation or a click occurs.
AI Overviews are AI-generated summaries that Google places at the very top of the search results page, pulling from multiple indexed sources to answer a user's query directly, before a single organic result appears.
AI Visibility Score (AVS) is a 0 to 100 scoring methodology that measures how frequently and prominently a brand is cited across AI-powered answer tools, including ChatGPT, Perplexity, Claude, and Gemini, when users ask questions relevant to its product or category.
Brand Radius is the distance your brand travels as a named recommendation across commercial categories inside AI-generated answers.
Citation Engineering is the practice of deliberately structuring content, entity data, and brand signals so that large language models reliably cite a brand when users ask questions relevant to its product or service.
Citation Radius is the distance your published content travels as cited evidence across topic categories inside AI-generated answers.
Decision coverage is how well your tracked prompts span real buying situations that change which vendor gets shortlisted or chosen, compared with raw prompt volume.
A default substitute is the brand a large language model names first when asked what to use instead of a given product.
A Discoverability System is an organic growth model that plans, builds, and measures brand presence across the surfaces buyers use to discover software, including Google search, AI answers, communities, review platforms, video, and other corroborating sources.
Entity optimization is the practice of structuring a brand's content, metadata, and third-party presence so that large language models and AI search tools can clearly identify the brand as a distinct entity, associate it with the correct category, and retrieve it in response to relevant queries.
Generative engine optimization (GEO) is the practice of structuring content so that AI-powered answer engines, including ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot, retrieve, cite, and accurately represent that content in generated responses.
The GEO Measurement Stack is a layered measurement model for generative engine optimization that follows Exposure → Pages → Queries → Prompts → Traffic → Revenue.
Grounding Material is the pages and passages that appear in Google AI answers as evidence supporting claims in the generated response.
An LLM Referral Channel is an analytics channel grouping that collects website visits referred from large language model products and AI assistants, including ChatGPT, Perplexity, Claude, Gemini, Copilot, and comparable tools.
LLM SEO is the practice of structuring, formatting, and distributing content so that large language models, including ChatGPT, Claude, Gemini, and Perplexity, retrieve and cite that content accurately when generating answers to user queries.
LLM visibility is the degree to which a brand is present in AI-generated answers across large language model platforms, including ChatGPT, Perplexity, Claude, and Gemini, when users ask questions relevant to its product, service, or category.
A money prompt, or money query, is a buyer question whose answer can materially change which vendor gets shortlisted or chosen.
An objection prompt is a prompt built to surface reasons a large language model would advise against choosing your brand, or against choosing a competitor.
A Presence Matrix is a planning grid that crosses high-value buyer questions with discovery surfaces and scores each intersection as strong, weak, or absent.
A prompt context stack is the layered set of who, why, constraints, and outcome details that turns a vague prompt into a realistic buying situation for AI testing.
Prompt Distance is the number of clarifying clues an LLM needs before it recommends your brand on a money prompt that never names you.
Prompt-based search is the behavior of entering a full natural-language question or instruction into an AI-powered tool, which then generates a synthesized prose answer rather than returning a ranked list of links.
A Query Neighborhood is the cluster of related queries and topics surrounding a cited page, assembled so teams can infer the kinds of AI prompts that page is likely serving when engines do not reveal the exact prompt.
Recommendation dependency is how much a brand's AI recommendation relies on its own website versus third-party evidence.
Recommendation Rate (by ICP) is the rate at which AI answers mention, shortlist, recommend, or recommend first your brand across buying situations defined for your ideal customer profile.
A retrieval stack is the layered set of sources a model leans on, in order, when building a recommendation.
A source deletion test is a diagnostic where you strip the cited sources from an AI answer and re-ask the question to reveal what still supports the recommendation.
A Substitution Graph is the map of which brands AI treats as default replacements for each other when a product is not available, not suitable, or explicitly ruled out.