Short answer
Entity SEO makes search engines and AI models recognise your brand as a distinct thing, describe it correctly and connect it with the topics you want to be cited for. An entity gap is where that breaks: the engines describe you wrongly, confuse you with another brand, or leave you out of topics your competitors own. You find gaps by asking the engines and comparing entities, and close them with consistent facts, fuller content and mentions.
Key takeaways
- Google began building search around entities in 2012, “things, not strings”, and AI answers are built from entities: brands, products, people and places.
- Entity gaps come in three kinds: identity (who you are), association (which topics you belong to) and coverage (which entities your content explains).
- Ask the assistants what your brand is and who leads your category, then compare their answers with the facts and with your competitors.
- Consistent names and descriptions, Organization markup with sameAs links, and pages that explain a topic’s entities close the gaps on your own site.
- Mentions on other sites carry the most weight: in Ahrefs’ study of 75,000 brands, branded web mentions correlated with AI visibility at about 0.66 to 0.71.
What an entity is, and why AI search runs on entities
An entity is a thing a search engine can tell apart from every other thing: a company, a person, a product, a place or a concept. Google began building search around entities in 2012, when it introduced the Knowledge Graph as a model of real-world entities and their relationships, “things, not strings”. At launch it held more than 500 million objects and more than 3.5 billion facts about them [1].
AI assistants answer in entities too. Ask for the best tools in a category and the answer is a list of brands; ask who to hire and it is a list of people and firms. Behind each name sits what the engines have learned about it: what it is, what it does and which topics it belongs to. Entity SEO is the work of making that picture accurate and complete for your brand, and it sits at the core of answer engine optimization (AEO), as I explain in what is answer engine optimization (AEO).
What an entity gap is
An entity gap is a place where the engines’ picture of your brand falls short. I look for three kinds:
- Identity gaps: the engines describe the brand wrongly, use an old name or product, or confuse it with another company of the same name.
- Association gaps: the engines know the brand but leave it out of the topics and categories it wants to be cited for, and name competitors there instead.
- Coverage gaps: your pages leave out entities that define the topic, the products, standards, people and concepts that the pages the engines cite all explain.
Identity and association gaps cost mentions, and coverage gaps cost citations, because a page that leaves out a topic’s entities matches fewer of the searches an assistant runs.
Association shows up in the searches themselves. In a September 2026 study of GPT and Gemini, when a brand’s own site was among the retrieved sources, the brand was mentioned in 49.0% and 58.4% of answers; when the assistant also searched for the brand by name, that rose to 91.4% and 100% [8]. The study is observational, and its direction matches the work: a brand the engines connect with a category gets searched for by name.
How to find entity gaps
Five checks find the three kinds of gap:
- Ask each assistant what your brand is, what it sells and who it is for, in a signed-out session, and compare the answers with the facts. Wrong or outdated descriptions are identity gaps.
- Ask the category questions your buyers ask, such as the best options for a need or the alternatives to a competitor. Brands named where you are missing mark association gaps.
- Look the brand up with Google’s Knowledge Graph Search API, which finds entities in Google’s Knowledge Graph [5]. A match with the right type and description means Google holds the brand as an entity; no match, or the wrong one, is a gap to close.
- Run the pages the engines cite for your priority questions, and your own pages, through an entity extraction tool such as Google’s Natural Language API, which finds the known entities in a text and scores how central each one is [6]. Entities that recur on the cited pages and are missing from yours are coverage gaps.
- List every place the brand is described (your site, profiles, directories, review sites and press) and note each name, description and category. Each variation is an identity gap in the making.
I run these checks with tools I build that extract and compare entities across a brand’s pages and its competitors’, and map the gaps against the questions buyers ask.
How to close entity gaps
Each kind of gap has its own fix:
- Identity: one name, one description and one category, used the same way on your site, your profiles and every directory. Consistency is the whole point, because a description that varies across sources gives answer engines several weak signals instead of one strong one. State the facts plainly on your home page and mark it up with Organization structured data, which Google says helps it understand your organization and disambiguate it from others, with sameAs links to your profiles [2].
- Association: publish pages that tie the brand to its topics in plain words, then earn mentions on the sites that already rank and get cited for those topics. In Ahrefs’ study of 75,000 brands across ChatGPT, AI Mode and AI Overviews, branded web mentions correlated with AI visibility at about 0.66 to 0.71 and YouTube mentions at about 0.74, a correlation Ahrefs is careful to separate from cause [7].
- Coverage: write each page so it explains the entities its topic depends on, and organise the pages as hubs and spokes so the site covers the whole subject. That is how entity work becomes topical authority, and it is the backbone of my content strategy for AI search.
On one program for a leading news portal, more than 120 queries moved into cited positions through entity coverage. Entity work runs through every program in my AEO consulting.
What structured data does, and where it stops
Structured data states facts in a form machines read without guessing, and it helps most with identity: Organization markup on the home page, with sameAs links to the brand’s profiles, ties the brand to the places that describe it [2].
Two limits keep it in its place. Google’s guidelines require markup to describe content that is visible on the page [3]. And for AI Overviews and AI Mode, Google says there is no special schema.org structured data to add [4]. Markup confirms what the page already says; the page, and the mentions around it, do the work.
How to tell the gaps are closing
Run the same checks every month on the same questions, and track three things by engine: whether each assistant describes the brand correctly, how often it names the brand on the category questions, and whether its searches start to include the brand by name, the pattern the September study tied to the highest mention rates [8]. I fold these into the monthly report described in how to measure AI search visibility.
A worked example: one brand, three entity gaps
The company and its findings here are illustrative. A payroll software company asks five assistants what it is. Two describe it by a product line it retired two years ago, one confuses it with a recruitment agency of a similar name, and none names it for “payroll software for restaurants”, the segment that brings most of its revenue. The pages the assistants cite for that question all explain tip pooling, scheduling integrations and state wage rules; the company’s restaurant page mentions none of them.
The fixes follow the gaps. For identity: one description, updated on the site, LinkedIn, two software directories and its review profiles, and Organization markup with sameAs links on the home page. For coverage: a restaurant payroll hub with pages on tip pooling, scheduling integrations and wage rules. For association: outreach to the three restaurant industry sites the assistants cite most. The same five assistants are asked the same questions every month, and the report tracks the descriptions and the restaurant answers engine by engine.
Frequently asked questions
What is entity SEO?
Entity SEO is the work of shaping how search engines and AI models recognise a brand, person or product as a distinct entity, and which topics they connect it with. It covers the facts about the entity, the content around it and the mentions of it on other sites.
Is entity SEO the same as schema markup?
Schema markup is one part of it. Markup states facts about an entity on your own pages; entity SEO also covers how the brand is described across the web, which topics it is connected with, and which entities your content explains.
Do I need a knowledge panel to be cited by AI?
A knowledge panel shows that Google holds the brand as an entity, which helps. Assistants also name brands they have learned about from pages and mentions across the web, so the work is the same either way: consistent facts, fuller content and mentions.
How do I find which entities a topic needs?
Collect the pages the engines cite for the topic’s main questions and run them through an entity extraction tool. The entities that recur across those pages, and the relationships between them, are the ones your pages need to explain.
How long does it take to close entity gaps?
Fixes on your own site and profiles can show within weeks, as the engines recrawl them. Association through mentions on other sites takes months, because it depends on other publishers.
Sources
- Google, Amit Singhal, “Introducing the Knowledge Graph: things, not strings”, 16 May 2012. blog.google
- Google Search Central, “Organization (Organization) structured data”, last updated 8 September 2026. developers.google.com
- Google Search Central, “General structured data guidelines”, last updated 10 July 2026. developers.google.com
- Google Search Central, “AI features and your website”, last updated 10 December 2025. developers.google.com
- Google for Developers, “Knowledge Graph Search API”, last updated 26 April 2024. developers.google.com
- Google Cloud, “Analyzing entities”, Natural Language API, last updated 26 October 2026. cloud.google.com
- Ahrefs, Louise Linehan, “Top Brand Visibility Factors in ChatGPT, AI Mode, and AI Overviews (75k Brands Studied)”, 12 December 2025. ahrefs.com
- Benjamin Tannenbaum, “From Prompt to Recommendation: A Fitted Stage Model of Brand Visibility in AI Search”, arXiv, 19 September 2026. arxiv.org