When someone asks ChatGPT, Perplexity, or Gemini what your company does, the engine builds its summary from a handful of predictable sources: your homepage entity block, your title tag and meta description, your About page, and third-party corroboration on Wikipedia, LinkedIn, Crunchbase, and G2. It does not read your whole site, and it does not take your word alone. OtterlyAI’s 2026 analysis of more than one million AI citations found brand-owned domains earn only 47.5 percent of citations, with community and third-party sources taking 52.5 percent, which means in 2026 roughly half of your AI summary comes from pages you do not control.
That summary is now a sales asset or a sales liability. Buyers ask AI engines “what does X do” and “is X legit” before every demo, consult, and discovery call, and the engines answer in three confident sentences. Profound’s analysis of 100,000 prompts found only 11 percent of cited domains appear on both ChatGPT and Perplexity, so the summary a buyer sees depends heavily on which engine they asked. You cannot dictate the answer, but you can shape every input it is built from.
What do AI engines actually read when they summarize your website?
They read a shallow, prioritized slice: the homepage above the fold, the title tag and meta description, the About page, and whatever structured data and third-party sources confirm or contradict those pages. ChatGPT with browsing typically fetches a few pages and a few external results; Perplexity retrieves against its own index of hundreds of billions of URLs and averages around 22 citations per answer, versus roughly 8 for ChatGPT, per a Qwairy study of more than 118,000 answers.
The mechanics matter. GPTBot and ClaudeBot crawl and cache your pages, retrieval systems chunk them into passages, and the model assembles a summary from the passages that most directly answer “what does this company do.” Pages that state the answer in plain declarative sentences get pulled; pages that open with slogans give the engine nothing to quote. If your homepage headline is “Welcome to a better tomorrow,” the engine skips it and summarizes you from LinkedIn instead.
Blocked crawlers make it worse, not better. Sites that block GPTBot entirely still get summarized, just from third-party data only. We cover that trap in can ChatGPT see my website.
Why do ChatGPT, Perplexity, and Gemini describe the same company differently?
Because they retrieve from different indexes, weight sources differently, and refresh on different schedules. Perplexity searches the live web on every query. Gemini leans on Google’s index and the Knowledge Graph. ChatGPT blends training data with selective browsing, so it sometimes describes the company you were two years ago. The Profound finding that 89 percent of cited domains are exclusive to one platform explains most of the disagreement between engines.
Freshness rules differ too. OtterlyAI’s data shows engines prefer sources updated within roughly 6 to 18 months and look for confirmation across two to four independent domains before stating something as fact. A company that rebranded, repositioned, or changed pricing recently will get an accurate summary from Perplexity and a stale one from ChatGPT until the cached picture catches up.
The practical consequence: you do not have one AI summary, you have at least four (ChatGPT, Perplexity, Gemini, Copilot), and they drift apart unless every major source tells the same story. Consistency across your site, LinkedIn, Crunchbase, and G2 is what pulls the four versions back together.
Curious what each engine says about your company right now? Run the free AI visibility audit and get the actual summaries ChatGPT, Perplexity, and Google AI Mode serve to your buyers.
Which pages and sources shape your AI summary the most?
Five inputs do almost all the work. Fix them in this order and the summary follows, because each one feeds the passages engines quote and the corroboration they require.
1. The homepage entity block
The first 100 words of your homepage should state name, category, what you do, who you serve, and one proof point in plain language. This is the single most quoted passage in company summaries. Subscribe PR’s own block reads as one declarative sentence about being a PR and AEO company, and that phrasing shows up nearly verbatim in engine answers. The full pattern is in how to optimize your homepage for AI search.
2. Title tag and meta description
Engines treat the title tag as the entity’s label and the meta description as its one-line definition. “Acme | Home” wastes the label. “Acme: inventory management software for restaurant groups” writes the first line of your own summary. Keep both literal, current, and consistent with the homepage block.
3. The About page
When an engine wants founding date, founders, headquarters, size, and mission, it goes to the About page. Vague storytelling pages force engines to guess or to pull those facts from Crunchbase instead. State the facts in scannable prose, keep them current, and make sure they match your LinkedIn and Crunchbase profiles to the letter.
4. Third-party profiles: LinkedIn, Crunchbase, G2, and Wikipedia
This is the corroboration layer, and it settles disputes. If your site says one thing and LinkedIn says another, engines either hedge or side with the third party. Claim and update the LinkedIn company page, Crunchbase profile, G2 or Clutch listing, and any Wikipedia or Wikidata entry. With brand domains earning under half of all citations per OtterlyAI, these profiles are not optional extras; they are half the summary.
5. Press, reviews, and Reddit
Independent coverage supplies the adjectives. A TechCrunch mention, an industry award, a strong G2 review average, or a Reddit thread praising (or trashing) your support all leak into how engines characterize you. You cannot script this layer, but PR and review generation weight it in your favor, which is exactly why PR and AEO work as one discipline.
How do you test what AI says about your company?
Run a fixed prompt battery across four engines, log the answers, and diff them against your positioning. This takes 30 minutes a month and most companies have never done it once. Here is the workflow.
Step one: write six prompts. “What does [company] do?” “Is [company] legitimate?” “[Company] pricing.” “[Company] vs [top competitor].” “Best [your category] for [your target customer].” “Who founded [company] and where are they based?”
Step two: run all six in ChatGPT, Perplexity, Gemini, and Copilot. Use fresh sessions with no history, because memory contaminates results. Log every answer and every citation in a spreadsheet.
Step three: score each answer on three checks. Accuracy: are the facts right? Positioning: does it describe you the way you describe yourself, in your category, for your audience? Sources: which domains did it cite, and are those pages current?
Step four: flag the gaps. A wrong founding date traces to a stale Crunchbase entry. A mushy category description traces to a vague homepage headline. A competitor named as the better option traces to review-site gaps. Every bad sentence in an AI summary has a findable source, and the citations tell you where to look.
Repeat monthly. Summaries drift as indexes refresh, and the log turns anecdotes into a trendline you can act on.
How do you change a wrong or weak AI summary?
Fix the source the engine cited, strengthen your canonical pages, and let the refresh cycle work. Engines do not take correction requests; they take better evidence. The sequence that works: correct the third-party record (LinkedIn, Crunchbase, G2, a stale news page), rewrite your homepage entity block and About page so they state the correct facts in quotable sentences, and add Schema.org Organization markup with sameAs links tying your profiles together so engines resolve everything to one entity.
Timelines vary by engine. Perplexity and Gemini usually reflect fixes within days to a few weeks because they retrieve live. ChatGPT can lag for months on cached facts, though browsing-enabled answers improve as soon as the sources do. For persistent errors, the escalation path (including feedback channels and building overwhelming corroboration) is laid out in how to fix wrong AI information.
One warning: do not respond to a bad summary by blocking crawlers. Removing your own site from the evidence pool hands the entire summary to third parties, and OtterlyAI’s numbers say those third parties already control more than half of it.
If an AI engine is misdescribing your company to prospects, every day it stands costs you deals you never see. Grab the free AI visibility audit and find out exactly which sources are feeding the summary.
FAQ: how AI summarizes websites
Does AI read my entire website before summarizing it?
No. ChatGPT, Perplexity, and Gemini pull a small set of high-priority pages: the homepage, About page, title and meta data, plus external corroboration from sources like LinkedIn and Crunchbase. Deep service pages and blog posts rarely feed the company-level summary. That is why the first 100 words of your homepage carry more summary weight than the other 10,000 words on your site.
Why does ChatGPT describe my company incorrectly?
Usually because it is drawing on stale cached data or an outdated third-party source. ChatGPT blends training data with selective browsing, so rebrands, pivots, and pricing changes lag there longer than on Perplexity, which retrieves live. Trace the wrong fact to its source, fix that source, and update your homepage and About page so every current signal agrees.
Can I control what AI says about my business?
You can shape it, not dictate it. You fully control your homepage entity block, meta description, About page, and Organization schema, and you can claim and correct LinkedIn, Crunchbase, and G2. OtterlyAI’s citation data shows third-party sources supply about half the evidence, so PR, reviews, and profile hygiene are the other half of the job.
How often do AI summaries update?
Perplexity refreshes in near real time and Gemini follows Google’s crawl, so both can reflect changes within days. ChatGPT updates unevenly: browsing-backed answers improve quickly, while cached facts can persist for months. Treat it as a rolling window and recheck your prompt battery monthly rather than assuming one fix propagates everywhere at once.
Does schema markup change how AI summarizes my site?
It helps disambiguation more than citation. Organization schema with sameAs links tells engines which LinkedIn, Crunchbase, and Wikipedia records belong to you, which prevents entity mix-ups with similarly named companies. Ahrefs research found schema alone does not lift citation rates, so treat it as plumbing that makes your other signals legible, not as a growth tactic by itself.
Which third-party profiles matter most for B2B companies?
LinkedIn and Crunchbase anchor the factual record (size, funding, leadership, category), while G2 and Clutch supply the evaluative layer engines quote when buyers ask “is X any good.” Wikipedia and Wikidata matter enormously when you qualify, since engines treat them as high-trust arbiters. Keep all of them consistent with your homepage positioning.
Your AI summary is now the first impression most buyers get, delivered by a voice they trust more than your ads. The companies winning in 2026 treat that summary like a landing page: tested monthly, sourced back to specific inputs, and rewritten at the source when it drifts. Run the six-prompt battery this week. Whatever the engines are saying about you, it is better to be the first to know.
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