Reality check

What do I do when AI states something false about my brand?

Last updated: September 14, 2026

First, the thing nobody tells you: there is no takedown form. You can't report a false claim to a model the way you'd report a bad Google result or a defamatory tweet. The model isn't storing a "fact about you" in a row you can edit. So you stop appealing to the model and start changing what it reads. Two moves. Correct the fact at the source, on the third-party pages the model actually cites and on your own structured data and About page, and then get the true fact to co-occur more often than the wrong one across independent sites. Modern assistants ground their answers by retrieving live pages when you ask, so once the retrieved pages say the right thing, the answer catches up. This is co-occurrence run backwards: the same repetition that gets you named for a topic is how you overwrite a wrong fact with a right one. Slow, unglamorous, and the only thing that works. One screenshot won't move it. A pile of correct, independent mentions will.

Why "just tell it it's wrong" does nothing

People's first instinct is to argue with the chatbot. You correct it in the thread, it apologizes, it agrees, you feel better. Then a stranger opens a fresh session and gets the same wrong answer. That correction lived and died inside your one conversation. The model didn't learn anything; it was polite. This is the trap: the interface feels like talking to a mind that can be updated, and it isn't. Where the wrong fact actually comes from is the text the model was trained on and the pages it retrieves at question time. That's the only place a fix can land.

Where the false fact actually lives

A false claim about your brand has a source, even when the model states it flatly with no citation. It came from one of two places. Either it's baked into training data, some old page, a merged-up confusion with a similarly named company, a stale fact that was true three years ago, or it's being pulled live from a page the model retrieved this second. The second kind is more fixable and more common now, because the assistants that cite sources are grounding answers in retrieval. And retrieval is the lever, because it means the answer is only as right as the pages it grabbed. Change the pages, change the answer.

Why this works, mechanically

Grounding a model's answer in retrieved documents, instead of letting it generate from memory alone, is a documented way to cut fabrication. Shuster et al. (2021) showed that retrieval augmentation reduces the well-known problem of knowledge hallucination in state-of-the-art chatbots. The practical read for you: a retrieval-grounded assistant is quoting the pages it found, so the fastest path to a corrected answer is a corrected set of pages for it to find. You are not editing the model. You are editing its reading list.

The correction playbook

In order, because order matters. Start where the model looks, not where it's easiest for you.

Find the actual source of the wrong fact. Ask the assistant the question that produces the error and, if it cites, note the pages. If it doesn't cite, ask "what sources say this?" and search the exact false phrasing yourself. You're looking for the page or two the wrong fact traces back to. Often it's one stale directory listing or an old article everyone else copied.
Fix your own structured data and About page first. This is the one surface you fully control. Your Organization schema, your About page, your footer, your profiles: make the correct fact explicit, unambiguous, and machine-readable. If the model has nothing authoritative from you, it fills the gap with whatever it found.
Correct the third-party pages the model cites. The directory listing, the wiki, the old press mention, the aggregator. Contact them, request the fix, submit the update. These are the pages doing the damage because they're what gets retrieved, and they're not yours, which is exactly why they're persuasive to a model.
Then flood the zone with the correct fact. This is the co-occurrence move. Get the true version stated across many independent, topically relevant pages: a fresh press mention, a corrected profile, a quote in an article, an updated roundup. You are not trying to delete the wrong fact. You are making the right one more frequent, so that when the model retrieves and weighs sources, the correct fact is the one that shows up everywhere.
Recheck across engines, on a delay. Corrections propagate at the speed of recrawl and reindex, not instantly. Check ChatGPT, Gemini and Perplexity separately, because they retrieve differently and one will update before the others. Give it weeks, not hours.

The part people underestimate

You can't win this with one perfect page. A single authoritative correction sitting on your own site loses to five stale third-party pages all repeating the wrong thing, because the model weighs what it finds across sources, and five beats one. The wrong fact got established the same way any fact gets established in a model: repetition across independent pages. So does the fix. This is the uncomfortable truth most reputation-management pitches skip, because "we'll get it corrected" sells better than "we'll out-repeat the error over the next two months." The mechanism doesn't care what sells. It counts.

And the reason this maps so cleanly onto everything else here: getting AI to say the right thing about you and getting AI to say anything about you are the same job. Both are won by co-occurrence, by being described accurately, next to your name, across many sources. A false fact is just co-occurrence you didn't want. You beat it with more of the co-occurrence you do.

Not sure what the engines are actually saying about you right now? Pressfront's free AI Visibility Report checks the live answers and the sites AI reads, so you can see the wrong fact before your customers do.

Related entries

Same mechanism What is co-occurrence, and why do mentions beat backlinks for AI?
Prerequisite Retrieval vs training: which one decides whether AI mentions you?
Related How consistent are AI recommendations, really?
Related Why is my brand invisible to AI even though I rank on Google?
Related How do I get mentioned on the third-party sites AI reads, without a backlink?

Changelog

September 14, 2026: First published. A reality-check playbook, grounded in the retrieval mechanism (Shuster et al., 2021) and tied to the co-occurrence entry as the same mechanism reversed.

Sources (primary): Shuster, Poff, Chen, Kiela and Weston, "Retrieval Augmentation Reduces Hallucination in Conversation", arXiv:2104.07567, 2021 (retrieval grounding reduces knowledge hallucination in chatbots); OpenAI, crawler docs (OAI-SearchBot and ChatGPT-User retrieve live pages to answer). Retrieval and correction behavior evolve across engines, so this entry is dated and revisited.