Definition
What is Entity SEO?
Entity SEO shifts the optimisation paradigm from keyword matching to identity building. In traditional SEO, brands compete for keywords. In Entity SEO — and especially in the AEO context — brands compete to be recognised as distinct, authoritative entities that AI engines can unambiguously identify, describe, and recommend.
An entity, in this context, is a thing with a distinct, well-defined identity: a brand, a product, a person, a concept. AI engines build internal representations of entities based on the signals they encounter across the web: Wikipedia entries, Knowledge Graph data, structured data markup, consistent cross-platform profiles, and authoritative content. The richer and more consistent these signals, the stronger the entity representation in AI models.
For AEO, Entity SEO is critical because AI engines synthesise answers by reasoning about entities and their relationships, not by matching keywords. When a user asks ChatGPT "what is the best CRM for small businesses," the model draws on its internal entity representations of CRM products, evaluating attributes like target market, features, pricing, and reputation. Brands with strong entity signals are more likely to be included in these evaluations.
Building entity strength involves several practices: ensuring consistent NAP (Name, Address, Phone) and brand information across all platforms, creating and maintaining a Knowledge Panel on Google, implementing Organization and Product schema markup, securing Wikipedia entries and Wikidata entries where eligible, building consistent author entities for thought leadership content, and using llms.txt and llm-profile.json to explicitly define your entity for AI systems.
Entity SEO also involves disambiguation — ensuring AI engines do not confuse your brand with similarly named entities. This is particularly important for brands with common names or those operating in sectors where terminology overlaps (for example, AEO in marketing versus AEO in customs and trade).
The relationship between Entity SEO and AEO is symbiotic. Strong entity signals improve how AI engines represent your brand, while AEO monitoring reveals whether your entity signals are being interpreted correctly. Together, they form a feedback loop that strengthens brand identity across the AI landscape.
Why it matters
AI engines reason about entities, not keywords. A brand with a clear, well-connected entity identity is more likely to be included in AI-generated recommendations and comparisons. Weak or ambiguous entity signals cause AI engines to overlook your brand or misrepresent it, directly harming AI visibility.
Real-world examples
- 1
A SaaS company strengthening its entity by aligning its Wikipedia entry, Crunchbase profile, G2 listing, and llm-profile.json with consistent descriptions and attributes
- 2
An author building entity authority by linking their personal site, LinkedIn, conference bios, and bylined articles with consistent Person schema markup
- 3
A brand disambiguating its name from a competing entity by implementing explicit sameAs links and a detailed llms.txt clarifying its market context
Frequently asked questions about Entity SEO
Explore related concepts
Knowledge Graph Optimization
technicalKnowledge Graph Optimization is the practice of ensuring your brand, products, and key people are accurately represented in knowledge graphs — the structured data layers that AI engines and search engines use to understand entity relationships, attributes, and authority.
Structured Data for AI
technicalStructured 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
technicalllm-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.
Schema Markup
technicalSchema Markup is a structured data vocabulary from Schema.org that provides machine-readable annotations about web content. In the AEO context, it helps AI engines understand the type, meaning, and relationships of your content, increasing the likelihood of accurate extraction and citation.
AI Visibility
strategyAI 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.
Start with the pages and proof that AI can actually use
Run the free audit to see what blocks AI from citing your site. Use the trial when you need ongoing monitoring, attribution, prompt discovery, and team workflows after the first fixes are live.