Only 39% of Google's AI Overviews are fully accurate, meaning correct and fully backed by their own citations, according to an April 2026 study Oumi ran at the request of the New York Times. ChatGPT, Gemini, Perplexity, and Claude make the same kind of mistakes about specific companies: a discontinued product, a former executive still listed as CEO, a competitor's price quoted as yours. All four platforms have a way to report an answer like that. None of them promise it goes away. Here's what actually happens when you press report on each one, and the fix that works when the button doesn't.
Why an AI hallucination about your brand doesn't have a single fix
A generative engine's wrong answer about your brand, what the industry calls a hallucination, rarely traces back to one editable page. It can come from stale training data, a bad source retrieved live, or an error introduced while the system stitches several sources into one answer, so getting a wrong page taken down often changes nothing about what the model says next.
The scale is bigger than most brand teams assume. Only 39% of Google's AI Overviews qualify as fully trustworthy, and roughly a third of the individual claims inside them aren't supported by whatever source the citation points to, according to Oumi's analysis of more than 4,000 factual queries. We've covered separately how often AI engines get brand facts wrong, and who's actually liable when they do, and the short version is that this isn't a rare glitch. It's a structural property of how these systems write answers.
OpenAI's reporting button teaches the model, it doesn't erase your answer
ChatGPT's thumbs-down button, followed by selecting Safety or Legal concern, is the fastest way to flag a wrong answer about your company, and OpenAI's content reporting webform is the fuller version of the same process. Neither one comes with a promise that the specific answer you saw gets corrected or removed.
OpenAI's own help center is direct about what happens next: reported content "may be reviewed by OpenAI's Model Quality team, which may apply filters or other mitigations to help prevent ChatGPT from relying on unreliable sources in future responses." Read that carefully. It describes a future prevention step, adjusting which sources the model trusts, not a guarantee that the exact wrong statement about your brand gets deleted from ChatGPT's behavior today. A customer who saw the wrong answer yesterday isn't getting a correction; you're reducing the odds the same error resurfaces for the next person who asks.
A platform's feedback button teaches the model what to avoid next time. It rarely erases the specific wrong answer a customer already read.
Google splits the job between AI Overviews feedback and your Knowledge Panel
Google runs two separate correction systems that brands routinely confuse. Thumbs-down feedback and "Report a problem" on an AI Overview feed Google's ranking and safety systems generally. Claiming your Knowledge Panel and flagging a specific fact inside it corrects the entity data that AI Overviews, and increasingly Gemini, actually draw from.
The first path is fast and vague: rate the overview, optionally click "Share more feedback", and Google says the input will help the experience improve, without specifying whether or when. The second path is slower and specific. To flag a fact inside your Knowledge Panel, you claim it through Search, Search Console, YouTube, X, or Facebook, then click Feedback below the panel, select the flag next to the wrong fact, and describe exactly what it should say instead, ideally with a verification link. Google confirms the submission by email and gives claimed, verified panels priority review.
That second path matters more than it looks. A knowledge graph is the structured record of facts an entity is built from, and it's the same kind of record both Google's AI Overviews and Gemini increasingly check before stating something about a company. We've written about why AI engines often trust your Wikipedia and Wikidata entries more than your own website for the same reason: it's the entity record, not the page copy, that gets checked first.
Gemini's feedback flag is per response, not per fact
Gemini's correction tool works response by response. Click the thumbs-down icon below any answer, select "Bad response," choose a reason, and add detail; for anything broader, go to Settings & Help and select Send feedback instead.
Both routes require you to be signed in, and Google's own documentation notes that the conversation and any attached files get collected along with the report. What the process doesn't offer is a way to flag one specific fact the way Knowledge Panel feedback does. If Gemini gets your headquarters city wrong in three different conversations, you're filing three separate reports rather than correcting one record. That's the practical argument for treating Gemini's button as a signal you send on the answers that matter most, a comparison query, a pricing claim, rather than a queue you work through systematically. The entity fix from the previous section does more of the real work here too, since Gemini pulls from the same knowledge graph Google's search products share.
Perplexity and Claude give brands the least to work with
Perplexity and Claude are the two engines with the least documented process. Perplexity's help center routes an incorrect citation into a general bug report: message Contact Support with your device, network, and steps to reproduce. Claude offers a thumbs-down button and an email address, with no public detail on what happens to either.
Neither company publishes anything close to OpenAI's Model Quality language or Google's claimed-panel priority review. If your brand gets misrepresented in Perplexity or Claude, reporting it is closer to sending a message into a support queue than triggering a defined correction workflow. Claude doesn't retrieve from Google's index at all; it pulls live sources through Brave Search, a separate index with its own ranking signals. That's an argument for treating the source page Brave Search would surface, not Anthropic's inbox, as the lever that actually moves what Claude says. The same logic applies to Perplexity, which builds its own citation index rather than inheriting one from a search partner: fixing the page most likely to get crawled and grounded does more than any bug report will.
The fix that actually lasts: correct the source, then track what changes
None of the four platforms guarantee a specific wrong answer gets retroactively corrected. The move that holds up across all of them is fixing whatever source document the system is actually grounding on: your own structured data, your Knowledge Panel, and the third-party entity records like Wikipedia and Wikidata that AI engines check for entity-level verification.
That's a slower fix than clicking a thumbs-down icon, but it's also the one that survives a model update, a new training run, or a switch in which index an engine happens to be grounding on that week. Prioritize by damage, not volume: a wrong answer to a comparison query or a pricing question costs more than a wrong founding year. That prioritization matters because outright factual errors are usually the smaller problem. Seer Interactive's analysis of 28,123 AI responses about its own brand found only 2.8% contained inaccurate information, but AI platforms declined to answer branded questions at all 31% of the time on average, and refused comparison prompts specifically about 80% of the time. A hallucination is rare. A gap where your brand should be but isn't, especially on the question a buyer is actually asking, is common.
We've covered the mechanics of tracking brand mentions across ChatGPT, Gemini, and Perplexity if you don't already have a repeatable way to catch a new hallucination before a customer does, and AI sentiment tracking covers the other half of this work: a corrected fact and a corrected tone are different problems, and the second one usually surfaces first.
Frequently Asked Questions
Can you actually get ChatGPT to remove a wrong answer about your brand?
Not directly. OpenAI's own help center says reports go to its Model Quality team to adjust which sources ChatGPT trusts in future responses, not to delete a specific past answer. Reporting through the thumbs-down flow or the content webform is still worth doing, but pair it with fixing the source page the model is likely pulling from.
How is Google's AI Overviews feedback different from Knowledge Panel feedback?
AI Overviews feedback, thumbs up or down and Report a problem, trains Google's ranking and safety systems generally, with no fact-specific fix. Knowledge Panel feedback lets you claim your entity and flag one specific fact for correction, with priority review once the panel is verified. The Knowledge Panel path is slower but changes the record AI Overviews and Gemini actually draw from.
Does Claude use the same sources as ChatGPT or Google?
No. Claude retrieves live web sources through Brave Search rather than Google's or Bing's index, so a page's ranking on Google doesn't determine whether Claude cites it. Fixing how a source page performs in Brave's own index does more to correct Claude's answers than reporting to Anthropic directly.
How often do AI engines actually get brand facts wrong?
It depends on what's being asked. Oumi's 2026 analysis found only 39% of Google's AI Overviews were fully accurate and source-backed across general factual queries, while Seer Interactive's brand-specific study found just 2.8% of responses about its own company were inaccurate. The bigger issue in that study was that AI platforms declined to answer at all 31% of the time.
What's the most durable way to fix wrong AI information about a brand?
Correct the underlying source data the AI systems are grounding on: your website's structured data, your Google Knowledge Panel, and your Wikipedia and Wikidata entries. Reporting buttons can reduce a specific error's odds of recurring, but source-level fixes survive model updates and outlast any single report.