Skip to main content
AEO

How to Build an Entity Profile for AI Search

An AI search entity profile is a consistent, evidence-backed description of who your company is, what it offers, who it serves and why each claim should be trusted.

By Rahul AUpdated September 22, 20268 min read

See which of these you are already failing.

On this page
  1. 1. What exactly should your entity represent?
  2. 2. Which facts belong in the canonical profile?
  3. 3. How do you prove the profile with first-party pages?
  4. 4. Which external evidence should you prioritise?
  5. 5. How should you test the profile in AI answers?
  6. 6. What should you change when the entity is confused?
  7. 7. How do you connect entity work to search evidence?
  8. 8. Which entity profile process should a small team choose?
  9. Related reading
  10. Sources consulted

1. What exactly should your entity represent?

Define the entity before changing pages or collecting mentions. For most companies, the entity is the organisation and its relationship to a specific category, audience, location, product set or founder, not every topic the website happens to discuss.

Write one plain-language sentence that answers, “What is this company, for whom, and in which category?” Add the names people use for the company, including its legal name, trading name, product names and common abbreviations. Record distinctions that prevent confusion with similarly named businesses.

Check whether the sentence remains accurate without marketing adjectives. A useful profile can separate the company from a product, parent organisation, former brand, partner or unrelated business. It should also identify the primary market and the problem the company solves.

The first failure mode is trying to make an entity stand for too many things. A broad profile creates conflicting signals across pages and third-party sources. Keep the core entity narrow, then model products, people, locations and parent companies as related entities rather than blending them into one description.

For more context, read AEO vs SEO vs GEO: What to Measure and Fix First.

2. Which facts belong in the canonical profile?

Create a canonical fact sheet containing only claims the company can support and keep current. Include the official name, category, founding or operating status where relevant, headquarters or service area, ownership relationship, products, audience, industries served, distinctive capabilities and links to authoritative pages.

Separate facts from positioning. “Provides payroll software for small employers” is a factual description when the site and product documentation support it. “The easiest payroll software” is a preference claim that needs stronger, independent evidence and may not belong in the core profile.

Check every fact for an owner, source URL and review date. Mark claims as current, changed, disputed or retired. A profile that contains an old market, discontinued product or former company name can cause an assistant to combine past and present identities.

Choose one preferred wording for important facts, but keep legitimate variants in a separate vocabulary field. Consistency helps, while forcing every page to use identical prose can make the site unnatural. The profile is a control document for writers, developers and public-facing teams, not copy to paste everywhere.

For more context, read AI Search Technical SEO Checklist: What to Fix First.

3. How do you prove the profile with first-party pages?

Build a small first-party evidence set that makes each important claim easy to verify. Start with an About page for identity, a contact or location page for operating details, product pages for what the company provides, and documentation or policy pages for precise capabilities and limitations.

Check whether each page answers one question completely instead of scattering the answer across navigation, images and scripts. A product page should identify the product, its intended user and its relationship to the company. An About page should not quietly use a different category or describe a different audience.

Use stable page titles, descriptive headings, visible text and clear internal links between the organisation, products, people and locations. Structured data can help machines interpret relationships, but it cannot rescue contradictory visible content. Keep schema values aligned with what a visitor can verify on the page.

The important decision is whether a claim deserves its own evidence page. Give durable, commercially important facts a stable destination. Leave temporary campaign language out of the canonical profile, because short-lived promotions are weak evidence of what the entity is.

4. Which external evidence should you prioritise?

Prioritise independent sources that describe the company accurately in the context buyers care about. Suitable evidence may include relevant industry directories, professional associations, partner pages, customer documentation, regulatory records, interviews and publications, provided the wording and relationship are genuine.

Check source quality before chasing volume. Ask whether the publisher has a reason to verify the company, whether the page names the same entity, whether the category is accurate, and whether the page is likely to remain available. A large collection of thin profiles can add noise rather than confidence.

Correct errors through the source owner instead of creating competing versions. Keep a record of the exact name, category, URL and relationship each source uses. Differences such as a missing legal suffix may be harmless, while a wrong location, merged product or outdated ownership claim can create a materially different entity.

Do not treat third-party mentions as a request to control the narrative. The useful trade-off is accuracy over coverage. A smaller set of trustworthy, context-rich references usually gives assistants clearer evidence than many generic listings with little information.

5. How should you test the profile in AI answers?

Test the entity profile with neutral questions that a buyer, researcher or recommender would actually ask. Use prompts such as “What does [company] do?”, “Who is [company] for?”, “What alternatives exist?”, and “Which companies provide [category] for [audience]?” Include spelling variants, product names and ambiguous terms.

Check the answer for four separate outcomes: correct identification, correct category, accurate relationships and useful supporting citations. An answer can name the company while describing the wrong audience, or give a plausible summary without citing the page that proves it. Those are different failures and need different fixes.

Run the same prompts across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode. Outputs vary by engine, query wording, retrieval and time, so one successful response is not proof of a stable profile. Cituna tracks whether all seven engines mention and cite a brand for buyer questions each day, and shows competitors and cited pages instead.

Keep a dated sample of prompts and answers for diagnosis, not vanity scoring. The goal is to identify which fact or relationship is being lost, then trace it back to evidence.

6. What should you change when the entity is confused?

Fix the earliest wrong association rather than rewriting every page. If an assistant identifies the wrong company, resolve names, domains, locations and ownership references first. If it identifies the company but assigns the wrong category, align the About page, product pages, titles, navigation and credible external descriptions around the accurate category.

Check whether the problem is absence or contradiction. Missing evidence calls for a clear first-party page or an accurate independent reference. Contradictory evidence calls for retiring outdated pages, correcting listings and clarifying relationships. A new paragraph cannot reliably cancel a still-indexed old description.

If the company is omitted from category answers, inspect whether its pages state the audience, use case and distinguishing capability in terms buyers use. Avoid forcing unrelated keywords into copy. The decision rule is simple: change the source that would most naturally answer the mistaken question, not the page with the highest traffic by default.

Record each change alongside the fact it is meant to support. Retest the original prompt set after recrawling or indexing time has passed, because answer engines do not update all sources at the same pace.

7. How do you connect entity work to search evidence?

Connect entity changes to search data so the team can distinguish a clearer profile from a temporary answer fluctuation. Google Search Console can show which pages and queries attract conventional search visibility, while AI answer checks show whether engines use or cite those pages in generated responses.

Check whether the pages that receive relevant search impressions also express the facts the entity profile depends on. A page may attract a query while failing to state the company’s relationship to the category. Conversely, a carefully written profile page may be authoritative but have little search demand. Both observations matter, but they call for different actions.

Use a page-to-claim map with columns for claim, supporting URL, search evidence, AI answer evidence and next action. Cituna joins answers from its seven tracked engines to Google Search Console data and provides SEO, AEO and GEO fixes. That combination helps connect a mistaken answer to a page or query without treating one score as the whole diagnosis.

Check the source of every recommendation before editing. Entity work should improve clarity for people first, while measurement helps decide which unclear or unsupported relationship deserves attention.

8. Which entity profile process should a small team choose?

Choose manual review for a small, stable profile, shared measurement for a growing company, and automated daily monitoring when engines, competitors or markets make manual checks unreliable. The right process depends on how quickly the profile changes and how many buyer questions need testing, not on the number of pages alone.

Check the workload at each stage: maintaining the fact sheet, reviewing first-party evidence, correcting external records, sampling prompts and assigning fixes. A spreadsheet can coordinate the first stages, but it will not by itself show daily changes across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode.

Cituna includes all seven engines on every plan, without per-engine add-on fees. Its entry plan includes Search Console and an MCP server, while the API is available on Max. Those capabilities suit teams comparing repeatable monitoring with ad hoc checks, but a manual process may be sufficient when the entity and query set rarely change.

Make the final decision by asking whether a missed or incorrect answer has enough commercial cost to justify ongoing monitoring. Document the chosen prompts, source owners and review cadence so the profile remains operational rather than becoming a one-time content project.

Sources consulted

Run a free AI visibility scan

Drafted with AI assistance from our own research and Search Console data, and reviewed by Rahul A before publishing. Rules and prices change; check the linked official source before you act.

Frequently asked questions

What is an entity profile in AI search?

An entity profile is the consistent, evidence-backed description of a company and its relationships. It covers the organisation’s name, category, audience, products, locations, ownership and distinguishing facts. AI engines use signals from first-party pages and external sources to form answers, so contradictions can produce an incomplete or incorrect profile.

How many AI search engines should an entity profile be tested against?

Test the engines your buyers use, including ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode. Each can retrieve different sources and phrase answers differently. Cituna tracks mention and citation results across all seven engines, rather than requiring a separate per-engine measurement setup.

Should structured data be the first entity profile fix?

Usually not. First align visible company facts, product relationships, names and supporting pages. Structured data can clarify those relationships for machines, but it should match information visitors can verify on the page. Schema cannot reliably correct contradictory copy, outdated external listings or an unclear category.

How can a company tell whether an entity profile is improving?

Compare the same neutral buyer prompts over time and inspect identification, category, relationships and citations separately. Join those observations with page and query data from Google Search Console. Improvement means the relevant engines increasingly describe the right entity with accurate evidence, not simply that the brand appears in one favourable answer.

Can a small company build an entity profile without buying a tool?

Yes. A fact sheet, source register, first-party page audit, external citation review and repeatable prompt set can support manual work. A monitoring tool becomes more useful when prompts, competitors and engines multiply or answers change often. Cituna is one option for tracking seven engines and connecting their answers with Search Console data.

See how AI engines see your brand

Start a free 3-day trial and see the exact buyer prompts you lose across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode, with a prioritized AEO, GEO and SEO action plan and the fixes to win them.

3-day free trial · Card required, cancel anytime · Works with ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode

Start free trial