Entity SEO for AI: Make Your Brand Easy to Recognise and Describe

What does "entity" actually mean for a brand?

An entity is the real-world thing itself — your company, as a distinct, identifiable subject — as opposed to any one page's description of it. Google's own language for this, from its generative-AI-features guidance, frames it as part of how its systems "understand the content of the page, as well as to gather information about the web and the world in general, such as information about the people, books, or companies" being discussed. The practical version: your company exists as a concept that many different sources describe, and the question is whether those descriptions agree with each other and with the facts.

This matters more for AI answers than it did for classic search, because a generative answer is actively synthesizing a description of you from multiple sources in the moment someone asks — not just ranking and linking to one page. A contradiction a reader would never notice sitting in two separate search results can get merged into a single, confidently wrong sentence in an AI answer.

Which profiles and pages actually anchor it?

A small, specific set of sources carries outsized weight because they're widely treated as reference points by both people and systems:

• Your own About page — the one description you fully control, and the one most AI systems will treat as authoritative for basic facts (what you do, who you serve) if it's clear and consistent.
• Wikipedia — governed by notability and verifiability policy: a company article requires "significant coverage in reliable sources that are independent of the subject." Wikipedia's own policy is explicit that "advertising, press releases, autobiographies, and the subject's website are not considered independent," and that self-promotion, product placement and press releases "are not valid routes to an encyclopedia article." You can't simply write your own Wikipedia page into existence; it has to be earned through independent coverage.
• Wikidata — a structured, machine-readable companion to Wikipedia. Because Wikipedia's notability bar acts as a filter, Wikidata effectively inherits that same credibility signal: an entity with a Wikipedia article is treated as a verified, real-world thing other systems can reference with more confidence than an unverified claim.
• Crunchbase and LinkedIn — business-facing profiles that typically carry founding date, headquarters, funding and employee-count facts, often cited alongside or instead of Wikipedia for company data.
• Your own schema markup (Organization type, with sameAs links) — the direct, structured statement of who you are and which other profiles belong to you, in a format designed to be machine-read rather than interpreted from prose.

How do contradictions across these sources confuse an engine?

Each source above can state a slightly different fact — a different founding year, a different headquarters city, a different one-line description of what you do — without any single one being obviously wrong on its own. The problem shows up when a system has to pick one, or blend them, to answer a question. A generative answer doesn't footnote each fact to its source the way a results page implicitly does by showing you which link said what; it just states something, and whichever source it leaned on (or how it averaged conflicting ones) isn't visible to the reader who then has to sort out whether it's accurate.

A real audit: five sources, one brand

Run live for this post, on a real, named public company — Freshworks, an Indian-founded SaaS company — to show what an actual entity audit turns up, not a hypothetical one.

Evidence: Live source check, Freshworks (real, named public company — NASDAQ: FRSH), 10 October 2026, logged out. Own About page and Wikipedia were directly readable and broadly consistent on the core facts (SaaS company, India-founded, customer/employee service software), described in different vocabulary ("AI-first service software" vs. "cloud-based software-as-a-service... CRM and ITSM tools") rather than contradicting each other outright. Crunchbase and LinkedIn — two of the five sources most guides (including this one) recommend checking — were not retrievable logged out: Crunchbase returned a 403, and LinkedIn's company page required a login. Outputs vary by access method and account state; a signed-in check of either platform may surface different, fuller text than a logged-out one.

The finding here is as much about the audit itself as about Freshworks: two of the five standard entity-anchor sources are effectively closed to a quick, logged-out check, which is itself worth knowing before you promise a client or your own team a fast five-source audit. A real audit of your own brand needs a signed-in pass on Crunchbase and LinkedIn, not just the two freely-fetchable sources.

What does sameAs actually do?

sameAs is a schema.org property that links your Organization markup to your other verified profiles — your real Wikipedia page, your Crunchbase profile, your official LinkedIn and social accounts. In plain terms, it's a direct, structured statement: "the company described on this page is the same entity as the one described at these other URLs." For a brand whose name is shared by other unrelated companies, or whose name alone is ambiguous, this is one of the few concrete, low-cost things you control that helps a machine reader disambiguate which real-world entity a page is actually about. It's a clarity signal, not a ranking lever — see our guide to structured data for AI search for what schema is and isn't evidenced to do.

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Company Name",
  "url": "https://yourcompany.com",
  "sameAs": [
    "https://en.wikipedia.org/wiki/Your_Company",
    "https://www.crunchbase.com/organization/your-company",
    "https://www.linkedin.com/company/your-company"
  ]
}

How do you check how an engine actually describes you?

1. Ask a neutral, buyer-style question about your category — not "tell me about [your brand]," which can pull from a narrower or more generous context than an organic mention would.
2. Read the answer against your own facts: is your category, your founding details (if mentioned), and your one-line description accurate?
3. Repeat in a fresh chat, since the same question doesn't reliably return the same wording twice — see how many runs you actually need before treating one answer as settled.
4. If something's wrong, trace it to the source likely feeding it (your own site's wording, a stale Wikipedia edit, an outdated Crunchbase entry) and fix it there, not just in the next prompt you try.
5. For a fast, single check rather than a manual log, the AI Citation Snapshot runs one real question through one engine and shows the live answer plus its cited sources. Entity confusion is only one source of wrong or missing answers — see your website isn't the source AI uses most for the broader ecosystem of sources that shape what gets said about you.

Checklist

• Your own About page states what you do in one clear, consistent sentence — the same wording used elsewhere.
• Wikipedia (if you have an article) and Wikidata facts match your own stated founding year, headquarters and core description.
• Crunchbase and LinkedIn checked directly, signed in if needed, not assumed accurate from memory.
• Organization schema present with a complete, correct sameAs list pointing to your real profiles.
• A neutral, no-brand-name buyer question asked on at least one engine, in a fresh chat, to see how you're currently described.

Tools to use

• Google's Rich Results Test, to confirm your Organization/sameAs schema validates.
• A manual, signed-in check of your own Crunchbase and LinkedIn listings (automated, logged-out tools can't reliably reach either, per the audit above).
• The AI Citation Snapshot for a quick, single-question read of how an engine currently describes you.

Common mistakes

• Treating entity consistency as a one-time setup task rather than something that drifts as pages, profiles and schema get updated independently over time.
• Writing a new, more polished one-line description for a rebrand or new homepage without updating the same sentence on Wikipedia, Crunchbase or LinkedIn — leaving the old wording live elsewhere.
• Assuming a lack of a Wikipedia article is fixable by writing one yourself — Wikipedia's notability policy explicitly excludes self-published and promotional sourcing; an article has to be built on independent coverage.
• Skipping sameAs schema entirely because "nobody confuses us with another company" — the risk isn't your own confidence, it's whether a machine reader parsing a page with no explicit entity link has to guess.

FAQ

What's the difference between entity SEO and schema markup? Schema is one tool for entity SEO — the structured, machine-readable way to state facts and link profiles. Entity SEO is the broader practice of keeping your identity consistent everywhere, including in plain prose on pages with no markup at all.

Do I need a Wikipedia page to have a clear entity? No, but it helps if you qualify — Wikipedia's independent-sourcing bar makes an article a strong, third-party-verified anchor. Many smaller or newer companies won't meet that bar yet, and that's fine; focus on your own site, schema and the profiles you do control.

Is fixing entity consistency a proven way to improve AI citations? Treat it as clarity, not a proven ranking or citation lever — removing a source of confusion for any system trying to describe you, rather than a guaranteed visibility boost.

How do I check if Crunchbase or LinkedIn describe us correctly? Check both directly, signed in — this post's own live audit found both blocked a logged-out, automated check, so a manual pass is required to actually see the page content.

What to do next

1. Write (or tighten) one clear sentence describing what you do, and use the identical wording on your About page and in your Organization schema.
2. Manually check your Wikipedia (if any), Crunchbase and LinkedIn listings, signed in, for outdated or conflicting facts.
3. Ask one neutral, no-brand-name buyer question on an AI engine and compare the answer to your own facts.

ContextOwl sells one-time AI visibility reports. The AI Citation Snapshot is a free, single-question check of what one engine currently says and cites about your category.