ChatGPT hallucinating about your company? Fix the cited source, force a recrawl with schema and llms.txt, then verify the correction holds across engines.
ChatGPT hallucinating about your company is rarely the model inventing facts out of nothing. MaxAEO, a vendor that sells AI-visibility monitoring software, tracked 412 confirmed brand-fact errors across 63 B2B companies between September 2025 and May 2026 and found the majority were stale, wrong-page, or mismatched-entity errors, not fabrication. MaxAEO doesn't publish its error-confirmation methodology, and the finding validates the exact problem its product monitors for, so treat the split below as a useful, directional read from a close observer rather than an independently audited figure. Directional or not, it changes the fix. This isn't a PR crisis you manage with a statement. It's a mechanical problem: capture the error, correct the source, force a recrawl, and verify the correction actually stuck.
That last part is the one most teams skip. They fix the web page, feel better, and never check whether ChatGPT noticed. Given that 94% of B2B buyers used an LLM somewhere in their purchase journey over the past year, per 6sense's 2025 Buyer Experience Report, a wrong fact sitting uncorrected in ChatGPT reaches a prospect before your sales team gets a chance to. Getting cited correctly is the flip side of the same problem we cover in how to get cited by ChatGPT, Perplexity, and Claude; this post is what to do when the citation is already wrong.
Most of the time, it isn't hallucinating in the strict sense at all. It's citing something real that happens to be wrong, or repeating something that used to be true. The 412-error analysis above breaks the causes into three buckets, and each one needs a different fix.
This is the largest bucket by far. ChatGPT ran a live search, found a page that answers the question, and quoted it faithfully. The problem is the page itself: a stale review, an old pricing article nobody updated after a plan change, an abandoned directory listing from a company that pivoted three years ago. The model did its job correctly. The source it trusted didn't deserve the trust.
This is also the good news. A retrieval error has a direct fix, because the wrong fact lives on a page you or a partner can edit, and correcting that page is a normal content refresh, not a model problem.
About a quarter of errors trace back to the model answering from parametric memory, the facts it absorbed during training, before it ever runs a live search. If your pricing, your leadership, or your product name changed after the training cutoff, the model can state the old version confidently and cite nothing, because it isn't retrieving anything. It's recalling.
This bucket showed no improvement without an underlying model update, per the same analysis. Fixing your website doesn't touch a memory the model formed before your website changed. It's the hardest category to move, and it needs its own playbook, covered below.
The smallest bucket, and the one that looks most like classic hallucination. The model blends two companies with overlapping names, or guesses to fill a gap in weak or thin source material about you specifically. If your company shares a name with a larger, older, or more heavily indexed brand, this is the error you're most likely to see.
The fix here overlaps with retrieval errors: give the model more, clearer, disambiguating material about your specific entity so there's less gap to guess into. Organization schema that names you unambiguously helps, though as the fix sequence below shows, it's one input among several, not a switch you flip.
Here's why this isn't just an SEO inconvenience. A Canadian small-claims tribunal ordered Air Canada to honor a bereavement-fare policy its own chatbot invented, after the airline argued it couldn't be held responsible for its chatbot's statements, effectively treating the bot as a separate legal entity. Tribunal Member Christopher C. Rivers called that "a remarkable submission," writing that "it should be obvious to Air Canada that it is responsible for all the information on its website," chatbot included. That case was about a company's own chatbot rather than a third-party model citing a stale page about it, but the direction is the same: wrong information stated confidently by an AI, about your company, is increasingly treated as your statement, not the model's problem.
The recommended remediation runs in a fixed order: capture, correct the source, force a recrawl, verify. Skipping a step is usually why a "fixed" answer reappears a month later. Do these five, in this order, for every confirmed error.
Here's what that looks like end to end. It's a composite built from the exact retrieval-error pattern described above (a stale pricing or feature page outliving a plan change), not a single named company, but the shape is one we see constantly: a prospect asks ChatGPT "does [product] support SSO on the free plan" and gets a confident yes, when SSO moved to a paid tier over a year ago. Walking that one error through all five steps is the fastest way to see why the order matters.
Screenshot the full response, save the exact prompt, and run it two or three more times to confirm the error repeats rather than being a one-off. You'll need this record twice: once to diagnose which of the three causes above you're dealing with, and once later to prove the fix actually held. For the SSO example, that's a screenshot of the answer, the literal question typed in, and three re-runs across the same session and a fresh one, all returning the same wrong claim.
Correct the fact on the page ChatGPT actually cited, not just on your homepage. Then check what else is ranking for the same query. This is where retrieval errors often get half-fixed: a team corrects its own site and stops, while a G2 listing, a Capterra profile, or a Crunchbase entry with the old fact still outranks the company's own page for that exact question. G2 and Capterra listings already beat most company blogs for software recommendation queries, which means they can just as easily out-cite a corrected fact if you don't align them too. Fixing the source page is, in effect, a targeted content refresh: the same discipline of re-verifying facts and re-dating the page, aimed at one specific claim instead of a whole post. In the SSO case, the actual culprit is rarely the company's own pricing page, which usually got updated when the plan changed. It's more often an old comparison post or a review site still quoting the free-tier feature list from before the change.
Correcting a page doesn't automatically tell any crawler to look at it again. Three things make a recrawl more likely and faster. Add or refresh Organization and Product schema so the entity is unambiguous once a crawler reaches the page. Keep an llms.txt file current so AI crawlers have a clear map to your most important, most current pages. And confirm your robots.txt allows the search-citation crawlers, like OAI-SearchBot and Claude-SearchBot, even if you've blocked the separate training crawlers, GPTBot and ClaudeBot, for opt-out reasons. Blocking the wrong bot by accident is a common way a fix never gets seen at all. For the SSO example, that means refreshing Product schema with the current plan-to-feature mapping, so "SSO" is tied to the right tier in structured data, not just in prose.
Schema alone won't carry this step. One AEO agency's proprietary weighting model, built from correlation work across 50+ benchmark sites, puts schema at roughly 15% of citation signal on branded queries, with the rest split across on-domain coverage, third-party corroboration, and documented results. Treat that specific number as one interested party's estimate, not an audited figure; it's also not the same finding as our own schema markup for AI Overviews research. The one controlled test on this, Ahrefs tracking 1,885 pages that added JSON-LD schema against 4,000 matched control pages between August 2025 and March 2026, found AI Mode citations up 2.4% and ChatGPT up 2.2%, both statistically indistinguishable from noise, and Google AI Overviews down 4.6%, a small but real decline the study doesn't attribute to schema. The two sources don't agree on a number, but they agree on the direction: schema is hygiene that helps other signals land, not the fix itself.
For the "recrawl" piece specifically, don't just wait. Google's own documentation on asking Google to recrawl a page recommends the URL Inspection tool in Search Console for a small number of pages, and notes crawling can still take anywhere from a few days to a few weeks even after you request it. Submitting the request is still faster than waiting for the next scheduled crawl to happen on its own.
Use ChatGPT's thumbs-down and feedback option directly on the wrong response. This step is easy to skip because it feels like shouting into a well, and it won't force an instant correction on its own. Do it anyway, alongside the source-side fix, not instead of it. It's a real signal channel and it costs thirty seconds.
This is the step most teams drop once the source page looks right. Run the exact same prompt daily. A reasonable bar to call the fix complete is the corrected fact appearing in at least 80% of ten daily runs for seven consecutive days, since a model can produce one clean answer and then serve a stale one on the next request from a cached or delayed retrieval. Re-running "does [product] support SSO on the free plan" daily is exactly the kind of narrow, repeatable prompt this step is built for. If you want proof the recrawl actually happened rather than guessing from the prompt alone, reading GPTBot and ClaudeBot hits in your server logs shows whether the citer bots even revisited the corrected page before you re-test the prompt.
Correction speed varies a lot by engine, and ChatGPT is not the fastest. Median time from fixing the source to the corrected fact showing up: Perplexity in 9 days, Microsoft Copilot in 19, ChatGPT (with search on) in 24, Claude in 26, Google AI Overviews in 31, and Gemini slowest at 33, according to the same tracked analysis of confirmed brand-fact errors.
The gap comes down to retrieval frequency. Perplexity runs a live, multi-source search on every query, so a fresh page can surface within days. ChatGPT leans more on a cached and periodically refreshed index, so a correction has to wait for the next meaningful recrawl before it's even eligible to be cited, on top of however long that recrawl takes to reach and reprocess your page.
While you wait on ChatGPT specifically, don't sit still. Push the same corrected fact through every channel Perplexity already favors, since a live-search engine will often reflect your fix well before ChatGPT does, and a correction that's visible on Perplexity is still a correction a prospect can see. Complete every step in the fix sequence above regardless of which engine you're chasing first: the recrawl signals that speed up ChatGPT are the same ones that keep Perplexity's live search finding the right version too.
Everything in the fix sequence assumes there's a live source to correct. Training-data errors break that assumption, since the wrong fact isn't coming from a page at all: it's coming from what the model absorbed before its cutoff, and a tracked sample of these showed no movement without an underlying model update.
That doesn't mean you're stuck, just that the fix works differently. You can't force a recrawl of a memory. What you can do is make the correct fact so consistently present, on your own domain and across every third-party source that mentions you, that it's the only version left standing by the time the model retrains or falls back to a live search rather than pure recall. In practice that means republishing the correct fact on your pricing page, your G2 and Crunchbase profiles, your changelog, and anywhere else a future training crawl or a live-search fallback might find it, then treating the wait itself as expected rather than a sign the fix failed. This is also where fact-checking your own content before it ships pays off twice: it stops you from ever becoming a training-data error for someone else's product comparison, the same way a competitor's outdated page can become one for you.
Getting a fact corrected once is a one-time fix. Not publishing wrong facts in the first place, and catching a competitor's stale claim about you before it compounds, is the ongoing discipline. That's the same verification loop Lyra runs on every post she writes: every statistic checked against a current source, every link fetched and confirmed, before anything reaches a pull request you review and merge.
Fixing a hallucinated fact about your company is a five-step mechanical process, not a crisis to manage. Lyra applies the same fact-checking discipline to every post she writes, so your own site never becomes someone else's stale source.
Step by step
Capture the exact wrong claim, the prompt, and the date
Screenshot the full answer, save the exact prompt that produced it, and run it two or three more times to confirm it repeats. You need this record to prove the fix worked later, and to show a support team what they're looking at.
Fix the source page, then the third-party listings above it
Correct the fact on the page ChatGPT actually cited. Then check whether a G2, Capterra, Crunchbase, or directory listing outranks your own site for the same query, and correct those too, since a citer often prefers the third-party page.
Force the recrawl
Add or refresh Organization and Product schema, keep llms.txt current, and confirm robots.txt allows the search crawlers behind AI citations (not just the training crawlers). Then request indexing through Search Console's URL Inspection tool rather than waiting.
Submit in-product feedback on the wrong answer
Use ChatGPT's thumbs-down and feedback flow on the specific wrong response. It doesn't force an instant fix, but it's a signal channel worth using alongside the source-side fix, not instead of it.
Re-test on a schedule until the correction holds
Run the same prompt daily for at least a week. A recommended bar is the corrected fact appearing in at least 80% of ten daily runs for seven consecutive days before you consider it fixed, since a single clean answer can still be followed by a stale one.
FAQ
Usually not fabrication. A tracked analysis of 412 confirmed brand-fact errors across 63 B2B companies found 61% were retrieval errors (ChatGPT cited a real, live page that's just wrong), 24% were training-data errors (the model's frozen pre-cutoff memory), and only 15% were entity conflation, where it blended you with a similarly named company. Identify which bucket your error falls into before you try to fix it, since the fix is different for each.
Capture the exact wrong claim with a screenshot, the prompt, and the date. Fix the source page it's citing, and check the third-party listings (G2, Capterra, Crunchbase, Wikidata) ranking above your own site for the same query. Force a recrawl with Organization and Product schema, a current llms.txt, and robots.txt access for the citer bots. Submit in-product feedback on the wrong answer. Then re-test on a schedule, since this rarely fixes itself in one pass.
Courts are already saying yes for company-run chatbots. A Canadian small-claims tribunal ordered Air Canada to honor a bereavement-fare policy its own chatbot invented, rejecting the airline's argument that the bot was a separate legal entity. That ruling was about Air Canada's own chatbot, not a third-party model like ChatGPT citing a stale page about you, but it sets the direction: regulators and courts increasingly treat AI-stated information about a company as the company's own statement.
Not by itself, and the exact contribution is disputed. One AEO agency's proprietary model estimates schema at roughly 15% of citation signal on branded queries, but the only controlled test on the question, Ahrefs tracking 1,885 pages that added JSON-LD, found close to zero measurable effect on AI citations. Either way, Organization and Product schema make your entity easier to parse once a crawler reaches your page, but they won't outrank a wrong G2 listing or overwrite a stale training memory on their own.
Then fixing your site won't touch it. Training-data errors come from the model's frozen memory before its cutoff, and a tracked sample of these showed no change without an underlying model update. The workaround is indirect: publish the correct fact prominently and repeatedly across your own domain and third-party sources, so that whenever the model does retrain or fall back to live retrieval, the correct version is what it finds everywhere.
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