How to Write an About Page That Builds Trust With AI
Key takeaway
An About page builds trust with AI answer engines when it replaces vague mission-speak with verifiable, specific facts: named people with real credentials, a founding story with dates and details, and claims that match what other sites already say about you. Generative engines don't "believe" a company because the copy sounds sincere — they cross-reference it. A page full of adjectives gives them nothing to check.
Key takeaways
- Replace abstract claims ("passionate about innovation") with checkable facts (founding year, team size, named roles, real numbers).
- Make sure your About page facts match your LinkedIn, Crunchbase, GitHub, and press mentions — inconsistency is treated as a red flag, not a rounding error.
- Add Organization and Person schema markup so the page's facts are machine-readable, not just human-readable.
Why AI engines treat About pages differently than you'd expect
Most founders write an About page for a human skimming it for ten seconds, so it ends up as a mood board: a mission statement, a stock photo, three bullet points about "values." That works fine for a human who's already decided to trust you because they found you through a friend or a Product Hunt post. It works poorly for an AI system trying to answer "who built this tool" or "is this a legitimate company," because how does ChatGPT decide what to cite comes down to whether the page contains extractable, corroborated facts — not tone.
Retrieval-augmented systems pull your page into context, then the model has to decide whether to repeat a claim as fact. A sentence like "founded in 2023 by two former Stripe engineers, now used by 400 SaaS teams" gives the model something concrete to quote. A sentence like "we believe in building products people love" gives it nothing — there's no entity, no number, no claim that could be verified or falsified, so it gets skipped or paraphrased into mush.
What AI models actually check before trusting a claim
Language models weight information partly by internal consistency: does this fact appear the same way in multiple places the model has seen during training or retrieval? This is the same mechanism behind how AI search works more broadly — corroboration beats persuasion. Three consistency checks matter most for About pages:
- Name matching. If your About page says "founded by Priya Nair" but your LinkedIn says "Priya N." or your GitHub org lists a different name entirely, that's a mismatch a crawler-based system can surface even if a human wouldn't notice.
- Date matching. A founding year on your About page that contradicts your domain registration date (easily checked via WHOIS-adjacent tools) or your first Product Hunt launch date creates friction.
- Claim matching. If you say "trusted by 10,000 developers" but no external source — a press mention, a review site, a case study — repeats that number anywhere, it reads as unverifiable marketing copy rather than fact.
None of this means you need external validation for every sentence. It means the specific, high-stakes claims (customer counts, funding, team size, year founded) need to be consistent everywhere they appear, because that's exactly what a retrieval system samples when deciding whether to trust the rest of the page.
The five elements a trust-building About page needs
1. Named humans with real, checkable roles. "Our team of experts" is functionally invisible to an AI system — there's no entity to attach authority to. "CTO Daniel Ruiz, previously at Segment, now leads the crawler infrastructure" gives a model a person, a credential, and a role it can cite.
2. A founding story with actual details, not just sentiment. Skip "we started this company because we saw a problem." Say what the problem was, when you started, and what the first version looked like. Specificity is what separates a citable origin story from filler — the same principle behind why technical documentation gets cited by AI models instead of ignored: concrete detail survives summarization, vague language gets compressed to nothing.
3. Verifiable operational facts. Integrations you support, platforms you're built on, where your data comes from. If you built an AI SEO agent that integrates with Notion and GitHub, say that plainly on the About page — it's a specific, checkable claim a prospect or a model can confirm just by looking at your docs or changelog.
4. External consistency signals (sameAs). Link out to your LinkedIn company page, your founders' personal LinkedIn or X profiles, your GitHub org, and any press coverage. These aren't just backlinks for humans to click — when paired with schema markup, they tell machine readers "this entity has an external footprint that agrees with what's written here."
5. Structured data markup.
Add Organization schema with founder, foundingDate, and sameAs properties, and Person schema for named team members with their jobTitle. Schema.org documents the full property list — most teams use maybe six of them and get most of the benefit. This turns your prose into machine-readable facts instead of asking a model to infer structure from paragraph text.
Common mistakes that make About pages invisible to AI
The most common failure isn't dishonesty — it's abstraction. Founders default to language that sounds safe and universal because it's easier to write when you don't have a content team: "customer-obsessed," "cutting-edge," "built different." None of that survives contact with a retrieval system looking for facts to extract.
A second failure is treating the About page as static. Teams update their pricing page and blog constantly but leave the About page untouched for two years, so it says "a small team of three" while the company page on LinkedIn shows fifteen employees. That mismatch doesn't just look sloppy to a human who checks both — it's the exact kind of inconsistency a model uses to discount the rest of the page's credibility.
A third: burying the one fact that actually matters. If your product handles sensitive data, say who's responsible for security and what standard you follow — a body like the National Institute of Standards and Technology publishes frameworks (NIST CSF) that companies reference when they want a checkable, third-party-anchored trust claim instead of "we take security seriously."
A simple template you can adapt
Rather than a single flowing narrative, structure the page as answerable chunks — it mirrors how pillar pages get structured for AI search engines: discrete, quotable sections beat one long paragraph.
- Who we are — company name, founding year, one-line description of what you build.
- Who's behind it — named founders/team with real prior roles, linked to LinkedIn or personal sites.
- What we've built — specific product facts: what it does, what it integrates with, what stage it's at (beta, GA, number of users if accurate).
- How to verify us — links to GitHub, press mentions, case studies, or a status page.
- How to reach a real person — a named contact or support channel, not just a form.
Five sections, each answerable in two or three sentences, each containing at least one fact a model could quote without needing to guess at meaning.
How to test whether your About page is working
Ask an AI answer engine directly: "Who founded [your company] and what do they build?" If the answer is vague, generic, or wrong, that's a direct signal your About page isn't giving retrieval systems enough to work with — or that your facts aren't consistent across the web. At Seolyn we treat this as a standing test whenever we audit a founder's site: the About page is often the single highest-leverage page for entity trust, yet it's usually the least maintained page on the domain.
A second test: search your own name plus your company name and see what surfaces. If the top results contradict your About page — different founding date, different team size — fix the discrepancy at the source rather than hoping the model picks the version you prefer.
Frequently Asked Questions
Q: Does an About page actually affect whether AI tools cite my company?
Yes, indirectly. AI answer engines weigh entity trust when deciding what to repeat as fact, and the About page is usually the densest source of named-entity information on a site, making it a primary reference point for who you are and whether your claims check out elsewhere.
Q: Should I add schema markup to my About page even as a small startup?
Yes — Organization and Person schema take under an hour to add and give retrieval systems structured facts (founder name, founding date, social profiles) instead of forcing them to infer that information from prose, which they do inconsistently.
Q: How specific should founding details be if my company is very new?
As specific as is true: exact month and year founded, what the first version of the product did, and who built it. A precise, small claim ("launched in beta in March") is more trustworthy to both humans and AI systems than a vague, larger-sounding one ("industry-leading platform").
Q: What's the single biggest mistake founders make on About pages?
Writing for tone instead of facts — filling the page with adjectives about culture and mission while omitting names, dates, and numbers that could actually be verified against LinkedIn, GitHub, or press coverage.
Q: Do customer testimonials belong on an About page?
They can help, but only if they're attributable to a real, checkable person or company — a named reviewer with a title and company is far more useful as a trust signal than an anonymous quote, which AI systems and skeptical readers alike tend to discount.
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