ChatGPT Describes Your Brand Wrong: How to Fix It

If an AI assistant quotes your old price, your old company name, or a positioning you dropped two pivots ago, the fix is almost never the model — it's the evidence the model retrieves. Retrieval usually wins: testing 1,200+ questions across six top models including GPT-4o, Stanford researchers found that large language models override their own correct prior knowledge more than 60% of the time when handed incorrect retrieved content. The mechanism that lets one stale directory listing overwrite a correct answer is the same mechanism you use to fix it.

The timing explains why "we updated the website" hasn't worked yet. GPT-5.6 launched on 9 July 2026, and OpenAI's model catalog lists all three variants — Sol, Terra and Luna — with a knowledge cutoff of "Feb 16, 2026". Reprice in March, and the newest model on the market has never natively heard of it. Anything after mid-February exists for ChatGPT only if it can be fetched.

Being mentioned and being described correctly are two different metrics

Most teams track AI visibility with one question — do the engines mention us? — and stop once the answer is yes. That's where the real problem starts, because a mention carries whatever description is attached to it, and buyers act on the description harder than most marketing teams expect. G2's March 2026 survey of 1,076 B2B software buyers across North America, EMEA and APAC found 51% now start research with AI chatbots more often than Google, up from 29% in April 2025, and 69% chose a different vendor than originally planned based on AI guidance. Read that second figure slowly: seven in ten shortlists are being rewritten mid-research by a system that may be working from your February facts. G2's Tim Sanders calls it a move "from reference to inference."

Semrush's survey of 622 US business professionals in March–April 2026 shows what happens next: 92% say AI has shaped their vendor shortlist, 75% trust AI vendor recommendations, and 71% then visit the recommended vendor's site. That verification step is where a stale description turns expensive. The buyer arrives with a number in their head, finds a different one on your pricing page, and quietly rounds the gap down to "this vendor isn't straight with people." Semrush found 27% already complain that AI recommendations don't reflect real pricing or contract structures — and that frustration lands on vendors, not on the assistant.

Nor is misdescription a quirk of your category. Journalists at 22 public service media organisations assessed 3,000+ assistant responses for the EBU and BBC in October 2025 and found 45% contained at least one significant issue — failings the EBU's Jean Philip De Tender called "systemic, cross-border, and multilingual." And none of it looks like an error to your buyer: the Tow Center found eight AI search tools answered incorrectly on more than 60% of 1,600 queries, yet ChatGPT signalled a lack of confidence just fifteen times out of two hundred responses. Nobody hedges on your behalf.

Three layers, three clocks

So why is the correction slow to land? Because one ChatGPT answer about your company is assembled from at least three systems on different refresh schedules: frozen parametric memory from training, a retrieval layer that fetches live pages at answer time, and — if you sell physical products — a merchant feed. OpenAI documents the first two as separate crawlers with separate jobs: GPTBot crawls content for training foundation models, while OAI-SearchBot powers ChatGPT's search features.

The training clock is slower than the release dates suggest. Anthropic is unusually candid about this, publishing two dates per model: a "reliable knowledge cutoff" — the date through which knowledge "is most extensive and reliable" — and a broader "training data cutoff." Claude Opus 5 lists a reliable cutoff of May 2026, Fable 5 and Sonnet 5 January 2026, and Haiku 4.5 February 2025 — Haiku's training data runs five months past its reliable date. The practical read: recent facts sit in the unreliable tail of training even when they technically fall inside the window. Your March repricing may be in there thinly, competing with years of confident repetition of the old number. Google, meanwhile, publishes no knowledge-cutoff dates at all on its Gemini model pages, so treat any Gemini cutoff you see quoted as unverified.

Which leaves retrieval as the only fast lane — and there the ClashEval result becomes strategy rather than trivia. The effect is asymmetric: "the more unrealistic the retrieved content is (i.e. more deviated from truth), the less likely the model is to adopt it." So the claims most likely to be swallowed whole are the ones that are stale but plausible — last year's price, the retired plan name, the old category. They trip no absurdity filter, because they were true. That's the threat and the opening at once: you can't edit the model, but you can usually edit what it retrieves.

Diagnose before you repair: a four-question triage

Rewriting your pricing page before you know where the wrong fact lives is how teams spend a quarter fixing the wrong layer. Four questions get you to the source.

1. Does the wrong claim come with a citation? Reproduce it in a fresh or logged-out session so personalisation and chat memory aren't shaping the answer, then ask the engine to source the claim. A citation attached to the wrong fact means retrieval — some live page still says it, and that's fixable in weeks. No citation, and the claim surviving with browsing off, points at parametric memory, which moves only on the model-release clock.

2. Is it one engine or all four? Disagreement is normal, not malfunction. A June 2026 analysis of 3,750 responses across 50 brands and 250 unbranded category queries on GPT-5.2, Gemini 3 Flash and Perplexity sonar-pro found cross-model agreement on the top recommended brand was just 41.6%. Use the split diagnostically: wrong on all four is almost certainly a source problem; wrong on one — especially an older or smaller model — points at that model's memory.

3. Whose page is it? Search the wrong fact verbatim on Google and Bing, and list every live page still asserting it: your own old blog post, a directory profile, a stale comparison listicle, a press release, a review profile. That list is your work queue.

4. How many runs before you believe it? Fewer than you think, spread wider than you think. A July 2026 variance study of 12,933 LLM responses covering 20 brands, 8 languages and 3 models found resampling accounted for 34.8% of variance and query language 26.5%, while brand identity alone accounted for 1.5% — and a repeat past the fifth reduces error by only 0.0003 per unit of budget. Asking ChatGPT the same question ten times is close to the least efficient use of a testing budget. Spend it on paraphrases, engines and, if you sell internationally, languages.

The output is a fact grid: your ten canonical facts (price, plan names, category, positioning line, HQ, leadership, integrations, ownership) down one axis, the four engines across the other, right/wrong/absent in every cell. It's a query you'll re-run rather than a one-off audit — Geoptimizer runs your buyer prompts live on ChatGPT, Gemini, Claude and Grok with web search enabled and returns a per-engine breakdown with citations in about 30 seconds, which is the shape this triage needs: which engine said what, and what it cited.

Repair case 1 — the stale price

Pricing is the most common wrong fact and the most damaging, because it's the one buyers verify. Fix it in four places, in this order.

Make the current price plainly crawlable. Vercel and MERJ's analysis of AI crawler traffic found none of the major AI crawlers render JavaScript — OAI-SearchBot, ChatGPT-User, GPTBot and ClaudeBot fetch JS files without executing them. A price that only exists after client-side hydration, or that lives in a PDF rate card, is a price they can't see. (That study was published in December 2024 on one CDN's traffic, so trust the direction more than the exact figures.)

Date the offer in markup — and check the date isn't in the past. Google's merchant listing docs require an active price and priceCurrency, and warn that "your listing may not display if the priceValidUntil property indicates a past date". A priceValidUntil left at 2024 is a machine-readable announcement that your own price has expired.

If you sell physical products, the feed is the price of record. Inside ChatGPT's shopping surfaces the number doesn't come from your website at all. The Agentic Commerce Protocol feed spec used by merchants approved for Instant Checkout states that "our system accepts updates every 15 minutes" and tells merchants to update whenever pricing or availability changes, because "OpenAI relies on merchant-provided feeds." A perfect pricing page won't correct a feed nobody refreshed.

Then fix the page actually being cited — which usually isn't yours. Lily Ray's analysis of 100 B2B "best [category] software" queries in Google AI Overviews, tested across April, May and June 2026, found self-promotional listicles cited 323 times, and in 224 of those cases — 69% — Google cited a brand's own page but recommended competitors named inside it. Her summary is worth pinning above the desk: "A citation is not a recommendation." Peec AI's analysis of roughly 200,000 AI responses across eight engines shows the flip side — rank-1 placement in a frequently cited listicle lifted mention probability by 16.5 percentage points in B2B SaaS. Third-party lists are amplifiers, and one carrying your 2024 pricing amplifies that. Our guide to getting into the "best tools" lists AI engines actually cite covers the outreach side.

Repair case 2 — the old name and the abandoned positioning

A rename is harder than a repricing, because the wrong fact isn't a number in one field — it's an identity spread across hundreds of pages you don't all own. Four moves do most of the work.

Contradict the old claim; don't just stop repeating it. Publish one canonical, dated, plain-HTML fact page covering current name, price, plan names, category, positioning and leadership — and have it state that the old thing was true and no longer is. "Formerly known as X; the Starter plan was retired in March 2026" beats silently deleting X, because a model can only adopt a correction some retrievable page actually states. Omission gives retrieval nothing to override the old claim with.

Use the identity fields that already exist. Google's Organization structured data supports name, alternateName ("another common name that your organization goes by"), legalName and multiple sameAs URLs, plus iso6523 and naics, which the docs describe as working "behind the scenes to disambiguate your organization from other organizations". There are no required properties, so this is a ten-minute change. For a rebranded company, alternateName set to the old name is the cheapest disambiguation signal available.

Don't 404 the old-name URLs. In that same Vercel/MERJ dataset, ChatGPT's crawler spent 34.82% of its fetches on 404 pages and Claude's 34.16% — against 8.22% for Googlebot. Roughly a third of the crawl attention you need for the correction is already burning on dead ends. Redirect the old URLs, or better, replace them with pages that carry the correction.

Then work the third parties in citation-share order, because citation supply is concentrated enough that one abandoned profile does outsized damage. Ahrefs' Brand Radar ranking of domains ChatGPT cited across US queries in July 2026 put Reddit at 16.7% mention share, Wikipedia at 8.9% and Forbes at 3.3%; an independent count using Similarweb data across ~600,000 US citation events put Wikipedia at 13.15% and Reddit at 11.97%. Exact percentages are tool-dependent — a methodology gap worth understanding before you trust any single visibility number — but both agree on the shape: those two domains together account for roughly a quarter of ChatGPT's US citations. As 5W's Ronn Torossian puts it, "AI engines don't rank authority — they assemble answers."

Which makes them high-leverage and slightly awkward. Wikipedia's conflict-of-interest guideline says representatives are "strongly discouraged from editing affected articles directly" and should propose changes on talk pages with the {{edit COI}} template. Wikidata is the easier door: an item qualifies if it refers to a "clearly identifiable conceptual or material entity that can be described using serious and publicly available references", so companies that fail Wikipedia's notability bar can still hold a correct, machine-readable item. And where a third-party page is genuinely gone or gutted, Google's Refresh Outdated Content tool covers pages you do not own.

Repair case 3 — the negative framing

Staleness and hostility are different problems, and separating them before you escalate saves a lot of grief.

Start with where negativity originates. Testing across ChatGPT, Gemini, Perplexity, Claude, Grok and Copilot, Jordan Brannon, president of Coalition Technologies, reports that negative commentary typically surfaces from Reddit, Yelp, Quora, the Better Business Bureau, Google Business Profile reviews and Clutch, and concerns operational things — turnaround time, availability, returns, shipping, stock. Positive framing tends to come from your own site: FAQs, about pages, testimonials. He also flags a false positive worth ruling out first: sentiment tooling can misread protective phrases like "no hidden fees" as negative, because the risk word does the work.

The engines also disagree about whom to criticise. BrightEdge data covering Google AI Overviews and ChatGPT found negative sentiment in about 2.3% of AI Overview brand mentions versus 1.6% in ChatGPT — 44% more likely to criticise — and that when both went negative on the same query, they flagged different brands 73% of the time. The distribution matters more than the level: 85% of Google's negative sentiment appeared at the informational stage, while 19.4% of ChatGPT's landed at consideration-to-purchase versus 1.5% for Google. If that holds in your category, negativity in ChatGPT arrives much closer to the decision. One caveat: this comes from a press release with no disclosed sample size or date range, so treat it as directional and verify it on your own brand.

Then draw the line between a bad review you should answer and a false claim you can escalate. A slow-shipping complaint on Reddit is an operations story, fixed with operations and better public documentation. A fabricated allegation is a legal question, and 2026 has produced precedent. Wolf River Electric, a Minnesota solar installer, alleges Google's AI Overview said it faced a state Attorney General lawsuit, while "none of the referenced materials in fact contained the information Google claimed they did". In Germany, the Munich Regional Court I ruled in May 2026 that an AI Overview "does something different: it synthesises, summarises, and structures", rejecting the aggregator defence. Stale pricing is not defamation, though — keep the tracks separate, and save escalation for claims that are genuinely false and damaging.

One surface brand owners forget: their own. If your rebrand never reached the document store behind your support bot, custom GPT or internal RAG assistant, that assistant is still telling customers the old thing — and here the liability is directly yours. The BC Civil Resolution Tribunal held Air Canada liable for negligent misrepresentation after its chatbot described a refund policy the airline didn't have, noting that "it makes no difference whether the information comes from a static page or a chatbot." Re-index your own systems first: fastest fix on the list.

What you can't fix, honestly

A repair playbook is only trustworthy if it also says what doesn't work.

You can't compel a correction. In the noyb complaint filed with the Austrian data protection authority in April 2024, OpenAI acknowledged it could filter or block data on certain prompts but not rectify inaccurate output. Note the scope: GDPR rectification covers personal data — a founder's bio, not a company's price list.

You can't file your way in. Google's guidance is explicit: "You don't need to create new machine readable files, AI text files, or markup to appear in these features." Read that as a statement about eligibility, not accuracy — nothing there stops a stale third-party page from being the one retrieved. Google does name one real gate: to appear as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown with a snippet. A stray nosnippet on your corrected pricing page removes it from the surface you're trying to fix.

llms.txt is not the repair. Adoption grew 8.8× in the year to May 2026, from 4,088 to 36,120 files across 3M+ sites tracked by Originality.ai — but Ahrefs' server logs across 137,000 domains found 97% of those files received zero requests in May 2026, with AI retrieval bots at 1.1% of requests. Crawler access is the part of that checklist that pays; our 20-minute technical setup for llms.txt and AI crawler access puts the two in evidence order.

And blocking a bot doesn't stop the description. OpenAI states that "sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers," while disallowing GPTBot only signals that content shouldn't be used for training. A company that blocked GPTBot after a rebrand hasn't stopped ChatGPT describing it — it has only stopped the model learning the new facts natively, while leaving every old third-party page in play.

One last thing worth saying out loud, since most posts on this topic quietly assert the opposite: nobody has published a credible, reproducible measurement of how long a corrected page takes to displace a stale claim in ChatGPT's answers. Confident timelines circulate — two to four weeks, six to eighteen months — with no traceable methodology behind them. The propagation time for your brand is measurable, though, and only by measuring it.

Make "described correctly" a metric, not a fire drill

The failure mode isn't that teams don't fix wrong descriptions. It's that they fix them once, in a panic, and never confirm the fix landed.

Instrument it instead. Re-run the fact grid per engine on a schedule and log the date each cell flips from wrong to right — that log is your propagation curve, and it beats any vendor's blanket timeline. Track sentiment in the same report rather than feeling it; in Geoptimizer's published AI Visibility Score it carries a 20% weight alongside mention rate at 35%, citation rate at 25% and prominence at 20%, which gives "how they describe us" a number to sit beside "whether they mention us." Because AI answers are nondeterministic, judge movement against a rolling window with a confidence band rather than a single chat. Microsoft now reports part of this directly: Bing Webmaster Tools launched AI Performance in public preview on 10 February 2026, covering total citations, cited pages, grounding queries and page-level citation activity for Copilot and Bing's AI summaries.

For a practical framework to measure both ChatGPT ad placements and organic AI visibility alongside those propagation curves, see measuring ChatGPT ads and organic AI visibility.

For the wider picture of which sources each engine leans on, our engine-by-engine review of the 2026 citation evidence is the companion piece.

FAQ

Why does ChatGPT give wrong information about my brand when our website is correct? Because a ChatGPT answer is assembled from at least three layers with different refresh clocks: frozen training data, live retrieval, and merchant feeds for shopping. GPT-5.6 shipped in July 2026 with a February 2026 knowledge cutoff, so recent changes exist for the model only if they can be retrieved. Retrieval can also work against you — models override their own correct knowledge more than 60% of the time when the fetched page is wrong, so one stale listing can outweigh your accurate pricing page.

How do I tell whether it's the model's memory or a stale page? Ask the engine to cite the claim. A citation attached to the wrong fact means retrieval, fixable in weeks by correcting or replacing the cited page. If the claim survives with browsing off and no citation, it's parametric memory, which changes only on the model-release clock. Wrong on all four engines usually means a source problem.

Can I ask OpenAI or Google to correct what their AI says about my company? Not as a routine service. OpenAI's position in the 2024 noyb case was that it can filter or block data on certain prompts but not rectify inaccurate output, and GDPR's rectification right covers personal data — a founder's biography, not a price list. Google's Refresh Outdated Content tool helps only when a third-party page has actually been removed or gutted.

Should I edit our Wikipedia article to fix the outdated description? Not directly. Wikipedia's conflict-of-interest guideline strongly discourages company representatives from editing affected articles themselves, and asks them to propose changes on the talk page using the {{edit COI}} template, with paid editors disclosing who is paying them. Wikidata has a lower notability bar and is directly editable, which makes it a practical place to keep correct, machine-readable company facts.

The short version

Being mentioned is table stakes. Being described accurately is what buyers act on, and it decays quietly every time you reprice, rename or reposition. Diagnose which layer is wrong before you rewrite anything, fix the retrievable evidence instead of arguing with the model, and treat the third-party pages the engines cite as part of your own surface area.

Then measure the repair. You can check what ChatGPT, Gemini, Claude and Grok currently say about your brand with a free scan — mention rate, citation rate, prominence and sentiment, per engine — and run it again a month after your fixes to find out how long the correction actually took to land.