Entity SEO: Making Sure AI Knows What Your Brand Is

Entity SEO is the work of making your brand one clearly defined thing, with one name, one description and one category, that search engines and AI models can recognize wherever it appears. When your website, your profiles and the coverage about you agree, answer engines have what they need to describe you accurately and to connect you with the questions buyers ask about your category.

When those sources disagree, it shows in AI answers: an outdated product description, a founder who left years ago, a mix-up with a company that shares your name, or no mention at all in your own category. More keywords won't fix that. Consistent, verifiable facts will.

This guide covers what you control directly (your about page, Organization markup, your profiles), what's realistic with Wikidata and Wikipedia, and how to check what engines say about you today.

What an entity is, and why answer engines care

A keyword is a string of characters. An entity is the thing the string refers to. "Mercury" might be a planet, an element, a car brand or a bank, and a system answering a question has to decide which one the text means. Google has organized information this way for years through its Knowledge Graph, and its Organization markup documentation says some properties help tell one organization apart from others.

Answer engines face the problem twice. A model's built-in knowledge comes from training text, so how consistently your brand was described across the web shapes what it "knows" without searching. When an engine does search, it has to work out whether the "Acme" on one retrieved page is the same Acme as on another before combining them. Neither process is documented in detail, but both plausibly reward the same thing: many independent sources saying the same specific things about you.

Build an entity home on your own site

Your own site is the reference point everything else should agree with. SEOs often call it the entity home: the page that states, plainly, what the organization is. For most brands that's the about page, supported by the homepage and contact page.

A strong about page answers the questions assistants get asked about companies:

• What the company is, in one sentence that names the category: "Northwind is a payroll service for independent restaurants in the US Midwest."
• Who it's for and what it does differently
• When it was founded, by whom, and where it's based
• Former names, if you've rebranded, so older mentions can be connected to the current brand
• Links to the official profiles you list in your structured data

Write that first sentence as if it will be quoted, because it might be, and reuse it in your homepage meta description, social bios and press boilerplate. Keep the contact page factual too: a real address where one applies, a phone number or email, and the legal name if it differs from the brand name.

Organization structured data and sameAs

Organization structured data states the same facts in machine-readable JSON-LD. Google's Organization structured data documentation lists no required properties, recommends placing the markup on your homepage or on a single page that describes the organization, and suggests the most specific subtype that fits, such as OnlineStore for an ecommerce site or LocalBusiness for a physical location.

The sameAs property is where the entity work happens. It lists URLs on other websites that carry more information about your organization, such as social or review profiles, and tells any system reading the markup that those profiles describe the same company. A minimal example:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Northwind Payroll",
  "alternateName": "Northwind",
  "url": "https://www.example.com/",
  "logo": "https://www.example.com/logo.png",
  "description": "Payroll service for independent restaurants in the US Midwest.",
  "foundingDate": "2019",
  "sameAs": [
    "https://www.linkedin.com/company/example",
    "https://www.crunchbase.com/organization/example",
    "https://www.youtube.com/@example",
    "https://github.com/example"
  ]
}
</script>

A few rules keep it useful:

• List only profiles that are officially yours or specifically about your company.
• Keep the name and description in the markup identical to what visitors read.
• Add a Wikipedia or Wikidata URL only if an entry already exists.
• Update the list when you open, close or rename a profile.

Keep expectations realistic. Google says no special structured data is needed for AI Overviews or AI Mode, and other engines haven't documented whether they read it. Treat the markup as a cheap way to remove ambiguity, not a ranking lever. The free AEO audit checks whether a page carries Organization markup and how many sameAs profiles it links to.

Make every profile tell the same story

Engines also read the profiles and listings that describe you, and that's where contradictions creep in: a LinkedIn page with last year's tagline, a Crunchbase entry with the old headquarters, an app store listing for a product you've since repositioned.

Run a consistency pass at least once a year, and after any rebrand, move or pivot:

1. Write a canonical fact sheet: brand name with its exact capitalization, legal name, one-line description, category, founding year, founders, headquarters, website URL and current logo.
2. List every profile you control: LinkedIn, Crunchbase, X, YouTube, GitHub, app stores, review platforms, industry directories, and Google Business Profile if you have a location.
3. Compare each profile with the fact sheet and fix the differences, starting with the profiles that rank for your brand name.
4. Search your brand name alongside old names, old addresses and former executives to find third-party pages carrying outdated facts, and ask those publishers for corrections where the error matters.
5. Point every profile at the same website URL, and make sure your sameAs list includes each one.

Businesses with physical locations also need name, address and phone consistency, covered in how local businesses show up in AI answers.

Wikidata and Wikipedia: what's realistic

Wikipedia and Wikidata are widely reused reference sources, which is why brands want in. Both are run by volunteer communities with rules worth reading first.

Wikipedia's notability guideline for companies asks for significant coverage in multiple reliable secondary sources that are independent of the company. It explicitly says press releases, routine announcements, passing mentions, and paid or sponsored articles don't count. That last point matters if you buy placements: sponsored articles can help answer engines find and describe you, but they don't count toward a Wikipedia article.

Wikipedia's conflict of interest guideline strongly discourages people from editing articles about their own company directly. Paid editors must disclose who is paying them, and editors with a conflict are asked to propose changes on the article's talk page for review, or to submit new articles through Articles for Creation. Undisclosed paid editing tends to get spotted, and the likely result is a deleted article and a damaged reputation.

Wikidata sets a lower bar: its notability policy accepts items about a clearly identifiable entity that can be described using serious, publicly available references. Unreferenced or promotional items can still be nominated for deletion, so cite independent sources for each statement and keep the facts identical to your fact sheet.

The realistic order is to earn independent coverage first and let Wikipedia follow if the coverage supports it. A brand without a Wikipedia article can still be described accurately by answer engines when the rest of the web is consistent. Digital PR for AI citations covers how to earn that coverage.

Teach engines your category through third-party mentions

Your own site says what you are. Third-party pages are what put you in answers about a category. When a dozen independent publications each describe you as "a payroll service for independent restaurants", that pairing of brand and category appears in the kinds of pages engines retrieve for category questions, and, over longer cycles, in the text models learn from. That's reasoned inference rather than a documented rule, and the pillar guide on backlinks and AI citations explains the chain in full.

For entity purposes, what matters is that those mentions match your fact sheet. Give journalists, partners and publishers the same boilerplate, and make sure placement articles use your exact brand name and category phrase. Spread mentions across publications that cover your field, such as business and finance publications for a fintech brand, rather than one site. How the link and the mention each contribute is covered in backlinks vs. brand mentions.

Check how engines describe you now

Before changing anything, find out what answer engines currently say. Ask each major engine the same questions, with web search on:

1. "What is [Brand]?"
2. "What does [Brand] do, and who is it for?"
3. "Who founded [Brand], and where is it based?"
4. "What are the alternatives to [Brand]?"
5. "What are the best [category] options for [audience]?"

Record whether each answer gets your category, audience and key facts right, whether it confuses you with another organization, and which sources it cites. Wrong answers usually trace back to a specific page, such as an outdated profile, an old article or a namesake's website, and correcting that source tends to work better than publishing a new page that contradicts it. Where an engine lets you switch search off, compare answers: differences hint at what the model learned in training versus what it found today. Re-run the set monthly using the routine in how to measure AI citations.

If the gap is coverage rather than accuracy, independent publications in your category are how you close it. The catalog lists 10,397 placements, each scored for how likely answer engines are to retrieve and cite the publication.