AEO Glossary

Share of Model (SoM) measures how frequently a brand is mentioned or recommended by AI engines in response to relevant queries. It is the AI-era equivalent of Share of Voice, quantifying your brand's presence across ChatGPT, Perplexity, Gemini, Claude, and other answer engines.

Citation Rate measures the frequency at which an AI engine references a specific source domain when generating responses. Unlike Share of Model, which tracks brand mentions, Citation Rate specifically tracks when your website URL or domain is cited as a source.

Competitor Visibility in AEO measures how often and how favourably your competitors appear in AI engine responses compared to your brand. It provides the competitive context necessary to understand whether your AI visibility position is strong, weak, or at risk.

llms.txt is a plain-text file placed at a website's root that provides structured, machine-readable information about a brand, product, or organisation specifically for consumption by large language models. It functions as a "robots.txt for AI" — telling AI crawlers what your brand is and how it should be described.

Technical AEO encompasses the infrastructure and technical configurations that help AI engines discover, crawl, parse, and cite your content. It includes AI-specific crawl policies, structured data implementation, llms.txt files, site architecture optimisation, and content formatting for AI consumption.

AI Crawlers are automated bots operated by AI companies that scan websites to collect content for training data and real-time retrieval. Major AI crawlers include GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot (Perplexity), Google-Extended (Google), and Bingbot (Microsoft).

Structured Data for AI refers to the use of schema markup (JSON-LD, microdata) and AI-specific files (llms.txt, llm-profile.json) to provide machine-readable context about your content, products, and brand to both search engines and AI engines.

llm-profile.json is a JSON-LD structured data file placed at .well-known/llm-profile.json that provides machine-readable brand identity, offerings, expertise, and preferred citation formats to AI crawlers and language models.

AEO — AI Engine Optimisation — is the practice of optimising a brand's digital presence to maximise visibility, accuracy, and favourability in AI-generated responses. It is the AI-era evolution of SEO, focused on how brands appear in ChatGPT, Perplexity, Gemini, Claude, and other answer engines.

An Answer Engine is any AI-powered system that generates direct answers to user queries rather than returning a list of links. ChatGPT, Perplexity, Gemini, Claude, AI Overviews, and Microsoft Copilot are all answer engines that synthesise responses from multiple sources.

AI Visibility refers to the extent to which a brand is present, accurately represented, and favourably positioned across AI engine responses. It is the aggregate measure of how discoverable your brand is when users ask AI engines questions relevant to your products or services.

A Citation Network is the web of authoritative sources that AI engines draw from when generating responses about a topic. Building your brand into this network means ensuring your content is referenced by other sources that AI engines trust and cite.

AI Overviews Optimization is the practice of structuring content and building authority signals specifically to appear in Google's AI Overview panels — the AI-generated summaries shown at the top of search results pages for qualifying queries.

Content for AI refers to the practice of creating and structuring website content specifically to be effectively consumed, understood, and cited by AI engines. It involves answer-first formatting, clear factual claims, structured data, and comprehensive coverage of topics.

AI Search Optimization is the broad practice of optimising digital content and brand presence to perform well across all AI-powered search interfaces, including conversational AI (ChatGPT, Claude), AI-native search (Perplexity), and AI-enhanced traditional search (AI Overviews, AI Mode).

A Query Bank is a curated collection of search queries used to systematically measure AI engine visibility. It represents the questions your target audience asks AI engines about your product category, used as the basis for calculating Share of Model and other AEO metrics.

Brand Mention Tracking in AEO is the process of systematically monitoring when and how AI engines mention your brand in their responses. It goes beyond simple name detection to analyse context, sentiment, accuracy, and competitive positioning of each mention.

Detection, Diagnosis, Resolution (DDR) is the three-phase operational framework used in AEO to systematically identify AI visibility issues, analyse their root causes, and implement targeted fixes. It transforms AEO from reactive guesswork into a structured improvement process.