When AI Repeats a Wrong Fact About Your Brand: A Guide to Fixing It

For three months straight, whenever someone asked about the SEO agency Seer Interactive, ChatGPT, Perplexity, and Google AI Overviews gave the same answer: high turnover among account managers. The actual numbers told a different story. Seer's employee retention rate stood at 79%. The wrong data traced back to a single five-year-old client review, published across a handful of websites. In other words, AI was misleading users based on one questionable post.

Seer isn’t the only company to experience this. Columbia University's Tow Center tested 1,600 queries across eight AI search engines and got a wrong answer more than 60% of the time. The cost of such a mistake can be high. For example, in February 2023, Google Bard’s demo version got the telescope that captured the first image of an exoplanet wrong, causing Alphabet’s shares to lose 7.7% of their value in a single day — that’s about $100 billion in market capitalization.

One inaccurate answer from an AI assistant, and a company can lose a client without ever knowing why.

Why AI gets your business wrong

AI doesn't fact-check. It predicts the most likely answer based on the sources it read during training and the web pages it retrieves at query time. If the only source available happens to be wrong, the AI assistant repeats that error confidently and without qualification, because it can't tell a reliable source from a random mention when both sound equally credible.

Typing the correct information into a chat and trying to "convince" the AI doesn't work. That conversation only fixes the answer within your own session. The source the assistant pulls from for every other user stays exactly the same.

It’s not the answer you should fix but the source it is based on.

What to do when AI gets a fact about your company wrong

"Wrong" is a broad category. Sometimes it's an outright error, like Seer's turnover claim. Other times it's an outdated price, an achievement nobody updated, or one negative review that outweighed everything else. Whichever kind you're dealing with, you should follow these steps:

1. Find the source of the error

Before fixing anything, figure out exactly which text the AI is drawing from. Ask ChatGPT, Perplexity, and Gemini the same set of questions about your brand: "What do you know about Company X," "What's its reputation," "Is it worth working with." Then check which fact keeps coming up and where it comes from.

For Seer, running through these questions showed the exact same wording about turnover across all three services. Not just a similar theme, but the identical phrase. Three independent services couldn't have landed on that same wording by coincidence if each were pulling from different sources. That match was the signal that the information came from a single shared source.

2. Fix the content the company owns

The easiest case is when the error is in your own content: on the website, in the social accounts, in an own blog hosted on a platform like Medium, or in a Wikidata entry. You update the information and that’s it. No need to negotiate with a third-party platform to make a change. We’d recommend paying special attention to directories like Wikidata specifically, since AI considers these entries to be up-to-date even when that information is actually outdated.

3. Outweigh the source with a more authoritative article

Sometimes you can’t edit a source. The error might be in someone else's article, an old press release, or a review on an independent platform the company doesn't control. In that case, the source needs to be outweighed by a more authoritative one. Publish a new piece with the correct fact in a media outlet the AI will trust more than the old source. This works because earned media accounts for 84% of AI citations.

Basically, you have two options. Pitching journalists for free coverage is preferable, but that often means weeks of back-and-forth. What’s even riskier is that the journalist will rewrite the piece to fit their own editorial style, so you may not get the exact wording you need.

The second option is to secure a paid placement through a vetted media placement platform like PRNEWS. It has a catalog of 107,000+ media outlets, in which you can place your articles with corrected facts. You can filter the catalog by country, topic, audience, or metrics to find outlets authoritative enough to outweigh the original source. From there the fact is published in the exact wording it needs: the numbers, dates, and source links stay intact, with no pitching cycle and no editorial rewrite to dilute it.

Securing a paid placement this way speeds up the process from weeks to days and lets the correct information get into AI-generated answers much faster than traditional pitching.

4. Word the fact so it gets cited

It doesn't matter where the corrected fact runs, whether on your own site or in a new piece at a third-party outlet. AI relays what it can verify. Adding specific numbers, quotes, and source links is the strongest citation factor, boosting citation rates by up to 40% compared with plain descriptive text.

A line like "we're market leaders" is a claim with nothing to back it up. Therefore, AI can't cite such a claim as fact. A line like "in 2025, we served 400 clients, and 92% renewed their contracts" gives a specific number that can be tied back to the company as its source. That's why a correction has to change not just the fact itself but its form, replacing subjective language and adding numbers, dates, and a data source.

5. Maintain consistency

A one-time fix doesn't guarantee a lasting result. For Seer, publishing an article with correct information worked at first: Perplexity started citing the correct retention figure. But a couple of months later, the old turnover claim crept back into the answers, meaning the fix hadn't held. So the company published a second article. This time with a full year of updated data and a direct breakdown of how the error had spread in the first place. After that, the AI answers stayed correct. Without fresh mentions, it seems that AI can drift back to the old source.

At the same time, AI doesn't weigh sources the way a human researcher would: it doesn’t cross-check a company's website against external sources and figure out which ones to trust. AI systems are bad both at spotting a contradiction between sources and at weighing it when they answer. They tend to lean on whichever source simply appears more often, more recently, or is closer to what the model already "learned" during training.

A company can't count on AI to reconcile conflicting mentions on its own and pick the more reliable one. The model will go with whatever shows up more often or more prominently in the sources it reads.

The correction sticks in AI answers, but it needs to be kept up-to-date

To sum it up, the workflow comes down to five steps:

1. Locate the source with the mistake that AI is quoting,
2. If it’s your own source, fix the content,
3. If it’s a third-party source, outweigh it with articles on more authoritative media outlets,
4. Word the fact with numbers and sources so it gets cited,
5. Maintain a consistent publishing flow so the correction holds.

How fast new information appears in the answers depends on how the specific AI system works. Services that search for an answer in real time can pick up a new source quickly: Perplexity typically updates within 1 to 7 days, and Google AI Overviews within 1 to 3 weeks. Models that answer purely based on the training data can take months, until the next version ships.

In other words, a company can do all five steps correctly and still not see an instant result. However, the faster you publish new information, the faster it will get into AI-driven answers.

All five steps rest on one principle: AI doesn't invent facts about a company; it relays what's already out there on the web. Seer fixed the situation in two days only because it managed to identify the problem just in time and knew how to fix it. For most brands, AI is already shaping their reputation faster than the PR team realized. Companies that don't check what ChatGPT says about them end up learning the truth from lost clients instead of from analytics.