AI Visibility Glossary
A practical glossary of the terms marketers need to understand as buyers move from search engines to AI-led discovery.
AI visibility is still a fast-moving category. Some terms come from SEO, some from AI search, and some are emerging because buyer behaviour is changing. This glossary explains the language behind AI visibility, answer engine optimisation, generative engine optimisation and product-level discovery in plain English.
A
8 terms- AI Visibility
- AI visibility is how often, how accurately and how favourably a brand, product or service appears in AI-generated answers. It looks beyond traditional search rankings to understand whether AI systems mention, recommend or ignore a business during buyer research.
- AI Visibility Audit
- An AI visibility audit tests how a brand, product or service appears across AI engines such as ChatGPT, Perplexity, Gemini and Claude. It usually analyses prompts, answers, citations, competitors, sentiment, evidence and recommended actions.
- AI Search
- AI search refers to discovery journeys where users ask questions to AI systems rather than typing short keyword phrases into traditional search engines. These journeys often involve longer questions, follow-up prompts and comparison-led research.
- AI Search Tracker
- An AI Search Tracker monitors how visibility changes over time across AI-generated answers. It helps marketers see whether their brand, products and competitors are becoming more or less visible in AI-led buyer journeys.
- AI Citation
- An AI citation is a source that an AI engine uses or references when generating an answer. Citations can include websites, directories, review platforms, media articles, forums, analyst reports, Wikipedia, Reddit and other third-party sources.
- AEO
- AEO stands for answer engine optimisation. It is the practice of improving how a brand, product or service appears in answer-based search environments, including AI-generated answers, featured snippets and conversational search tools.
- Answer Engine Optimisation
- Answer engine optimisation is the process of making content, proof and third-party signals easier for answer engines to understand and use. In AI search, this usually means improving clarity, authority, citations, product information and buyer-relevant evidence.
- Answer Library
- An answer library is a stored collection of AI-generated answers from previous audit runs. It helps marketers review how answers change over time and understand which prompts, products or buyer stages are improving or declining.
B
3 terms- Brand Visibility
- Brand visibility measures whether a company is recognised or mentioned by AI systems or search engines. It is useful, but it can hide product-level gaps if a brand is known but its specific products or services are not being recommended. See product AI visibility for the more commercially useful view.
- Buyer Journey Visibility
- Buyer journey visibility measures how a brand or product appears at different stages of buyer research, from early problem discovery through to comparison, evaluation and decision-making.
- Buyer Profile
- A buyer profile describes the type of person or organisation asking questions during an AI visibility audit. It may include role, industry, buying needs, pains, decision criteria and the language they are likely to use.
C
3 terms- Citation Source
- A citation source is any page, platform or publication that an AI engine uses to support an answer. For marketers, citation sources are important because they reveal where AI systems are getting their understanding of a market, product or competitor.
- Competitive Visibility
- Competitive visibility compares how often and how favourably a brand or product appears against competitors in AI-generated answers. It helps marketers understand who AI systems are recommending, ignoring or positioning as stronger. See how Odyssiant approaches this versus other tools in our comparison hub.
- Content Gap
- A content gap is a missing or weak piece of content that prevents a brand or product from being properly understood or cited. In AI visibility, content gaps often appear around product comparisons, use cases, proof points, pricing, case studies and buyer-specific questions.
E
2 terms- Entity
- An entity is a clearly identifiable person, company, product, place or concept that search engines and AI systems can recognise. Strong entity clarity helps AI systems understand what a business does and how it relates to a category.
- Evaluation Stage
- The evaluation stage is the part of the buyer journey where a buyer compares options in more detail. In AI visibility, this is where questions often focus on suitability, strengths, weaknesses, pricing, risks, reviews and alternatives.
G
4 terms- GPT
- GPT stands for Generative Pre-trained Transformer. It is the family of large language models developed by OpenAI that powers ChatGPT and many AI features now used in buyer research. GPT-based systems generate answers by predicting language from training data, retrieval sources and the user's prompt.
- GEO
- GEO stands for generative engine optimisation. It is the practice of improving how a brand, product or service is represented in generative AI answers.
- Generative Engine Optimisation
- Generative engine optimisation focuses on how generative AI systems discover, interpret and summarise information. It includes content quality, third-party proof, citations, brand clarity, product information and authority signals.
- Google AI Overview
- Google AI Overview is a Google search feature that uses generative AI to summarise answers at the top of some search results. It changes how users interact with search because they may receive an answer before clicking through to a website.
L
2 terms- LLM
- LLM stands for large language model. It is the type of AI model behind many conversational AI systems. LLMs generate answers by predicting and assembling language based on patterns in training data, retrieval sources and user prompts.
- LLM Visibility
- LLM visibility describes whether a brand, product or service appears in answers generated by large language models. It is closely related to AI visibility, but often focuses specifically on model-generated responses rather than wider search experiences.
M
1 term- Machine-Readable Content
- Machine-readable content is content that AI systems and search engines can easily access, understand and interpret. Clear HTML pages, structured headings, descriptive copy and accessible sitemaps are usually easier to process than hidden, gated or PDF-only content.
N
1 term- Not Mentioned
- Not Mentioned is a visibility outcome where an AI-generated answer does not include the brand, product or service being audited. This can be a major issue when competitors are mentioned instead.
P
4 terms- Product AI Visibility
- Product AI visibility measures how specific products, services, practice areas or solutions appear in AI-generated answers. It is often more commercially useful than brand visibility because buyers usually research problems, options and solutions rather than brand names alone.
- Prompt
- A prompt is the question or instruction given to an AI system. In AI visibility, prompts are used to test how buyers might ask about a problem, product, category, comparison or decision.
- Prompt Library
- A prompt library is a structured set of questions used to test AI visibility. It is usually organised by buyer profile, product, theme and buyer journey stage.
- Proof Point
- A proof point is evidence that supports a claim. In AI visibility, strong proof points may include case studies, customer examples, reviews, awards, analyst mentions, third-party coverage, data, certifications and measurable outcomes.
R
1 term- Retrieval
- Retrieval is the process of finding relevant external information to support an AI-generated answer. Some AI systems retrieve live web sources, while others rely more heavily on stored model knowledge or selected indexes.
S
3 terms- Search Visibility
- Search visibility usually refers to how visible a website is in traditional search engine results. It remains important, but it does not fully explain whether a brand or product is appearing in AI-generated answers.
- Source Gap
- A source gap appears when the information AI systems need exists weakly, inconsistently or only in places they do not cite. For example, a business may have strong claims on its website but little third-party validation elsewhere.
- Structured Content
- Structured content is information organised in a way that is easy to scan and interpret. Clear headings, comparison tables, FAQs, product pages, schema markup and concise summaries can help both users and AI systems understand a page.
T
1 term- Third-Party Proof
- Third-party proof is evidence about a brand or product that appears outside the company's own website. It may include reviews, directories, media coverage, analyst reports, customer stories, podcasts, forums and industry publications.
V
2 terms- Value Proposition Alignment
- Value proposition alignment measures whether AI-generated answers reflect the value a company wants to be known for. If AI systems describe the product in a weak, incomplete or inaccurate way, the value proposition may not be carrying through.
- Visibility Score
- A visibility score is a metric used to summarise how well a brand, product or service appears in AI-generated answers. It may consider presence, relevance, favourability, evidence, competitor position and friction.
Z
1 term- Zero-Click Search
- Zero-click search happens when users get the information they need without clicking through to a website. AI-generated answers can increase zero-click behaviour by resolving more of the research journey inside the answer itself.