How to Write About Pages for AI Citations

How to write about pages for AI citations: publish honest identity pages answer engines can extract for “what is [brand],” “who founded,” and “what does [company] do” questions — freeze entity prompts first, state what you are + for whom + key facts, keep names consistent, and re-probe the same wording. No invented history or fabricated claims.

About pages for AI citations are owned identity pages that answer “what is [brand],” “who is [company],” “what does [product] do,” and related entity questions in extractable form. Buyers and researchers ask AI those questions when shortlists form — often before comparison or pricing questions. Engines often ground identity answers in about pages, organization pages, Wikipedia-style summaries, and a few clear product pages. This guide is the content craft for that surface: which entity prompts to freeze, how to write what-you-are + for-whom + key-facts machines and humans can use, and what not to fabricate. It is not a promise that an about page guarantees a citation. Pair with brand entity consistency, answer-first content, when AI gets your brand wrong, and buyer prompt sets.

When an about page is the right hypothesis (and when it is not)

If free-check or paid probes never surface identity questions for your domain, do not invent a giant about-page program. Measure demand first. Some brands correctly keep founder bios short — ship honest extractable facts, not a novel.

Freeze the commercial prompts before you write

1. Collect real wording — “what is [brand],” “who founded [company],” “[brand] company,” support tickets, competitor about pages, and existing AI probe rows.
2. Group by job — definition (“what is”), category placement (“[brand] is a…”), founder/history, geography/HQ, and “who it’s for” as separate groups when they appear.
3. Freeze exact strings for baseline and re-probe. Do not rewrite the prompt after you publish to force a prettier sample.
4. Weight by commercial value — brand confusion that blocks pipeline and strategic product lines, not which story is easiest to screenshot (fix prioritization).

An about page without a frozen prompt set is a content bet with no measurement contract.

Page skeleton answer engines can parse

• Primary answer first — first screen states what the organization is, who it serves, and the category in plain language before a long brand story.
• What → for whom → proof anchors — three extractable blocks with checkable facts (category, customer type, founding year / HQ / product lines when true).
• Scope and limits next to claims — regions served, product lines, and what you are not — not only a hero slogan.
• Honest history only — dates, acquisitions, and founder claims marketing and legal will stand behind; no invented origin myths.
• Entity names consistent — your brand/product string matches sitewide naming (entity consistency); legal names labeled when different from consumer brand.
• FAQ residual only after identity — “where are you based,” “when founded,” “who owns” — with FAQ craft, not as a substitute for a missing lead definition (FAQ pages for AI).
• Schema only when true — Organization / AboutPage / FAQPage JSON-LD must match visible text; never markup fake awards or invented founding claims (schema for AI citations).

Honesty rules (hardcoded safety, not strategy judgment)

• No fabricated history or metrics — do not invent founding stories, customer counts, or rankings solely to win a prompt.
• No contradiction with product or legal pages — category, ownership, and claims must match what go-to-market will defend.
• Label brand vs legal entity — when the consumer brand differs from the registered company, say so clearly.
• One primary about URL when possible — multi-brand orgs need clear per-brand pages; do not mash unrelated products into one vague “we do everything” block without structure.
• Regulated industries — health, finance, education, and legal claims need the same review path as any public claim; about-page GEO does not bypass compliance.

Ship → re-probe loop (no invented lifts)

1. Baseline — freeze identity / “what is” prompts; log presence, position notes, and cited-instead domains on each engine you care about.
2. Publish one about-page hypothesis — one primary identity URL (or brand hub section) for the highest-weight commercial group.
3. Wait for crawl reality, then re-probe the same wording — label moved / unchanged / mixed / not yet. Never invent lifts (citation-lift standards).
4. If unchanged — inspect cited-instead: do engines still prefer Wikipedia, peers, or publishers? Improve extractable identity facts or corroboration — do not thrash about copy weekly for “GEO.”
5. Cadence — after major rebrand, acquisition, product-line, or HQ changes, re-check those identity prompts on purpose (re-probe cadence).

What about-page programs should not do

• Publish slogan-only fluff with no category, customer type, or fact anchors.
• Add Organization or award schema for claims that are not visible or not real.
• Rewrite free-check prompts until one ChatGPT sample recites your about blurb.
• Claim multi-engine wins from a single friendly chat screenshot.
• Treat schema or llms.txt alone as the identity strategy (llms.txt is mechanism, not a switch).

How jujuGEO supports about-page work

jujuGEO discovers buyer-style questions (including “what is [brand]” shapes when they appear for your domain), probes live engines, shows who is cited instead, drafts gap-specific answer-ready fixes, and re-probes after publish. Start with a free AI visibility check to see whether identity-shaped gaps exist, then freeze the real entity questions before rewriting about copy. Related: when AI gets your brand wrong, entity consistency, and what is AI visibility.