How to fix wrong information AI says about your brand

The four kinds of wrong claims

Wrong claims come in four shapes: outdated facts, details nobody ever published, credit handed to a competitor, and products or features you have that go missing. Semrush grouped AI brand misinformation the same way, and each shape points at a different source to fix.

Outdated facts are the most common and the most fixable: an old price, a retired plan, a founder who left three years ago, a feature you shipped in 2024 that answers still call new. The source is usually your own site, or a third-party page that copied your site when it was true. Semrush's write-up of the four types makes the same observation: most errors trace back to specific sources the engine has weighted heavily.

Fabricated details are numbers nobody stated anywhere: a support response time, a funding round, an integration count, a customer number. They are harder to trace because no page says them, so you look for the two or three pages the answer mixed together.

Competitive misattribution pins a competitor's limitation or feature on you, and it clusters in “X vs Y” answers where both brands appear in one passage. Missing products are the inverse: you have a product line the answer does not know about, usually because the only page describing it is thin, unlinked, or behind a form.

There is no correction hotline at the engines

None of the engines runs a queue where a brand submits a fact and an editor swaps it into future answers. You can report content, but reporting is aimed at material that breaks the platform's terms or the law, not at factual disputes about your company.

OpenAI's own help page on reporting content in ChatGPT describes the scope: reported domains and content may be reviewed, and filters or other mitigations may be applied to reduce the system relying on unreliable sources. That is a reliability review of sources, not a brand-edit desk. In-product feedback such as a thumbs-down is a signal at best, and no engine promises a date by which anything changes.

This is not obstruction so much as architecture: there is no editor sitting on your category, and answers are reassembled from the web each time they are asked. Which is good news for you, because the web is editable by you. The correction path runs through the source page.

The fix sequence: find, trace, correct, re-run

Find the exact wording of the claim, trace which page fed it, correct that page, then re-run the prompts and watch for the claim to stop appearing. Skipping the second step is why most correction attempts fail.

1. Find the claim. Run the prompts that would surface it: your category questions, “is X worth it”, “X vs Y problems”, and any prompt where a salesperson last heard the wrong thing. Note the engine, the exact wording, and how many answers carry it. Save the wording verbatim, because you need an exact string to search for later.
2. Trace the source. Open the URL the answer cited. If there is no citation, list the domains that usually feed that prompt and read them for the claim. Include your own pages: an old pricing post or a 2022 blog article repeating a retired fact is the most common source you control.
3. Correct at the source. Update the page so the current fact sits where the wrong claim lived, in the first lines of that section. Publish the canonical fact somewhere crawlable: pricing page, FAQ, docs. Use structured data where the fact is a defined attribute, such as product or FAQ markup. If the source is a third-party page you can edit (your profile, a directory listing), edit it. If you cannot edit it, contact the owner and publish a clearer canonical page that the engine can prefer.
4. Re-run and watch. Trigger fresh runs of the prompts that carried the claim, then check those prompts over the following weeks rather than the following hours. Keep the prompts in the daily set so a reversion gets caught instead of surfacing in a sales call six months later.

The sequencing matters more than the polish. A beautifully rewritten page that nobody re-fetches, and a corrected page next to four other sites still repeating the old fact, both leave the answer unchanged.

How long before the answer changes

Expect weeks to months, and treat the range as wide. A claim disappears fastest when one page fed it and that page gets fetched often; it lingers when several sources repeat it and you only fixed one.

Recrawl speed is the part you can nudge: keep the corrected URL in your sitemap, submit it through the search console tools you already use, and make sure the search bots are allowed (see how engines choose sources). The part you cannot hurry is how many independent sources still say the old thing.

What advisible keeps while you wait

The loop needs three things in one place: the exact claim, the domain that feeds it, and a running record of whether it still appears.

The answer archive holds every original answer, so you can search for the exact wording and see which engines and prompts carry it. The Sources view with CSV export shows the domains feeding those answers, which is how you find the page to correct instead of guessing. And brand facts turn the fix into something measurable: register the true statement once, and under Brand Perception in the Fact-checking tab each answer gets a verdict of Contradicted, Supported, Inconclusive or Not covered. When the claim stops appearing, the verdicts move with it.

One caution on reading those verdicts: small samples swing. A week where two answers carried the claim and three did not is not a trend. Wait until the prompt has enough answers behind it, and every number in the product says how many it is based on. The wider fact-checking workflow is in fact-checking AI claims against your brand facts.