About Page AEO: How to Structure the Page AI Engines Verify You With

About page AEO is the practice of structuring a company's About page so it functions as a factual record an AI engine can extract and trust, rather than as brand copy. When ChatGPT, Perplexity, or Google AI Overviews answer a question like 'who founded this company' or 'when was this business started', the About page is the single most likely source they check first, because it is the one page on a site whose entire purpose is to state exactly that. Most About pages fail this job anyway, because they are written to persuade a visitor, not to answer a query.

The gap matters more for AEO than for traditional SEO. A search engine ranking algorithm can reward an About page for backlinks or domain authority even if the page itself is vague. An AI engine assembling a direct answer has no such workaround: if the founding year, founder name, or headquarters location is not stated as a plain fact somewhere on the page, or contradicted by Organization schema, the model either skips your site as a source or, worse, fills the gap with an outdated or hallucinated detail from an old press mention.

Why AI engines treat the About page differently

Every other page on a company site is arguing for something: a pricing page argues you should buy, a blog post argues a point of view, a product page argues a feature is useful. The About page is unusual because its implicit claim is narrower and more checkable: this is who we are, this is when we started, this is who runs it. That narrower claim is exactly the shape of fact an AI engine's retrieval layer is built to extract and quote with confidence, which is why About pages punch above their traffic weight as citation sources for identity queries even though almost nobody visits one on purpose.

It also means an About page written entirely in adjectives, 'passionate', 'innovative', 'customer-obsessed', gives a retrieval system nothing to extract. The model still has to answer the question if a user asks it, so it falls back to whatever secondary source does contain a fact: a Crunchbase profile, an old TechCrunch write-up, a LinkedIn company page. If any of those are stale, that stale fact becomes the answer, and your own site never gets the citation for a question about your own company.

The facts an AI engine actually extracts

A citable About page states each of the following as a plain, self-contained sentence, not implied by tone or buried in a paragraph about company culture:

• Founding year and, where relevant, the specific founding date
• Founder name or names, stated by full name rather than just a first-name signature on a letter
• Headquarters location, as a city and country at minimum
• What the company actually does, in one plain sentence with no jargon
• Approximate company size or employee count, if the company is comfortable disclosing it
• Notable milestones: funding rounds, acquisitions, major product launches, with the year attached to each
• Current leadership team, by name and title, not just a generic "our team" photo grid

None of this needs to read like a Wikipedia infobox. A single well-written paragraph such as 'CiteRank was founded in 2025 by [name] in [city] to help publishers measure and improve how often AI answer engines cite their content' gives a model everything it needs from one sentence, and it reads perfectly naturally to a human visitor too.

Organization schema: the machine-readable layer underneath

The visible paragraph is what a human reads and what most AI crawlers extract directly, but Organization schema on the same page gives a retrieval system a structured, unambiguous version of the same facts to fall back on when the prose is harder to parse. The two should always agree; schema that contradicts the visible text is one of the more common ways sites accidentally undermine their own citation odds.

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "CiteRank",
  "url": "https://citerank.dev",
  "foundingDate": "2025",
  "founder": {
    "@type": "Person",
    "name": "Founder Name"
  },
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "City",
    "addressCountry": "Country"
  },
  "description": "AEO audit tool that scores public URLs for AI citation readiness.",
  "sameAs": [
    "https://www.linkedin.com/company/citerank",
    "https://twitter.com/citerank"
  ]
}

The sameAs array deserves particular attention. It is the single field most directly responsible for entity resolution, the process by which an AI model decides that the company on your website, the company on LinkedIn, and the company on Crunchbase are the same entity rather than three unrelated mentions. Every profile listed in sameAs should use the identical company name and, where the platform supports it, the identical founding date and leadership names as the About page itself.

The mistakes that quietly break extraction

1. All narrative, no facts: paragraphs about mission and values with no founding year, no founder name, and no headquarters stated anywhere on the page.
2. Facts that contradict third-party profiles: an About page says 2024, LinkedIn says 2023, Crunchbase says 2025. An AI model has no way to know which is correct and may pick the wrong one, or hedge in a way that reads as untrustworthy.
3. No Organization schema at all, leaving the visible prose as the only extractable source and forcing the model to parse marketing sentences for facts they were not written to contain.
4. A leadership section with headshots and no names, or names with no titles, which removes the exact detail most 'who runs this company' queries are asking for.
5. Stale milestones: a funding round or headcount figure that was accurate two years ago and has not been updated since the page was first written.

Founder and leadership bios as their own citation surface

A named founder or executive is also a citable entity in their own right, and can carry Person schema of their own on the About page: name, jobTitle, and a sameAs link to their LinkedIn profile. This matters for a specific and increasingly common query shape, 'who is the CEO of [company]' or 'who founded [company]', where the model is not looking for a description of the company at all, just a name and a role. A named, schema-marked byline on blog content builds the same kind of individual authority signal that a Person entity on the About page reinforces at the organisational level.

Keeping it current

An About page is exactly the kind of content that gets written once at launch and never revisited, which makes it a natural decay risk. Content freshness applies here just as it does to blog posts: a headcount, funding stage, or leadership list that has not been checked in a year is a real liability for a page whose entire job is stating current facts. A quarterly check, matched against whatever the company already updates on LinkedIn, is enough to keep it aligned.

A working checklist

• State founding year, founder name, and headquarters as plain sentences, not just implied by tone
• Add Organization schema that matches the visible text exactly, field for field
• List current leadership by name and title, not a photo grid alone
• Add a sameAs array linking to LinkedIn, Crunchbase, and any other authoritative profile
• Add Person schema for the founder or CEO with a matching sameAs to their own LinkedIn profile
• Review the page quarterly for stale headcount, funding, or leadership facts
• Cross-check every fact against Wikipedia, Wikidata, and LinkedIn for consistency