How to Find, Fix and Stop AI Hallucinations About Your Brand
Key takeaways
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Ask ChatGPT about your company and read the answer slowly. Not the headline, the details. The founding year. The pricing model. The list of integrations. The competitors it puts you next to. The one-line description of who you're for.
For a lot of B2B brands, something in there is wrong. Maybe it's a product you retired two years ago. Maybe it's a feature you never had, or a competitor's pricing attributed to you, or a description that fit the company you were three pivots ago. And it's stated with exactly the same confidence as everything that's right, which is what makes it dangerous.
That's a brand hallucination. You'll never see most of them, because they happen in private conversations between buyers and their assistants. But they're shaping first impressions right now, and unlike a bad review, nobody is going to send you a link so you can respond.
What is an AI hallucination about your brand?
An AI hallucination about your brand is a false or misleading statement that an AI assistant such as ChatGPT, Claude, Gemini or Perplexity makes about your company, products, pricing, people or history. Hallucinations happen when a model fills gaps in its knowledge, relies on outdated or conflicting sources, or confuses your brand with another one. |
Why AI gets brands wrong
Language models are built to produce fluent, plausible text. When the information they have is strong and consistent, fluent and accurate usually line up. When information is thin, old or contradictory, the model still produces something fluent. It just isn't always true. That's not a bug a vendor will patch next quarter. It's a property of how these systems work.
Three conditions make brand errors more likely. There isn't much written about you, what does exist is out of date, or it contradicts itself across sources. Mid-market B2B companies tend to hit all three at once. They change products and pricing often, they get less coverage than big brands and their facts end up scattered across old press releases, partner pages, review profiles and directory listings nobody has touched in years.
Retrieval helps, but it doesn't solve the problem. Retrieval-augmented generation gives a model real sources to work from, but a grounded answer is only as accurate as what it finds. In May 2024, Google's AI Overviews famously suggested adding glue to pizza sauce, apparently drawing on an old joke from a Reddit thread. The system did exactly what it was designed to do. The source was the problem, and that's usually true for brand errors too.
Four kinds of error
When I audit what AI says about a company, almost every error falls into one of four types, and sorting them matters because each has a different cause and a different fix.
The most common is the stale error, like old pricing, retired products, former executives. These come from outdated pages that are still out there and still trusted. Close behind is the skewed error, which is technically true but misleading, like "mostly for small teams" or "hard to implement." Skewed framing usually traces back to an old cluster of reviews, a dated comparison article or one loud forum thread.
Then there's the swapped error, where your brand gets credited with a competitor's feature or confused with a company that has a similar name. That's almost always a sign of weak or inconsistent entity signals. And finally the invented error, meaning features, integrations, customers or awards that never existed. Invented errors are the rarest in my experience, but they're the most dangerous, because buyers act on them and then feel misled when reality doesn't match.
Why this is a business problem, not a curiosity
It's tempting to treat a wrong AI answer as a funny screenshot to share in Slack. I'd resist that. Buyers increasingly do their early research in AI assistants, and a wrong fact at that stage can knock you off a shortlist before anyone ever talks to your sales team. You won't lose the deal. You'll just never know it existed.
There's a legal edge too, at least for your own tools. In February 2024, a Canadian tribunal held Air Canada responsible for incorrect refund information that its own website chatbot gave a customer, rejecting the argument that the chatbot was somehow responsible for its own words. Third-party assistants are a different situation, but the underlying lesson travels well. What an AI says about you becomes part of how people experience your brand, whether you wrote it or not.
Finding the errors
The process is straightforward, but it rewards patience. Start with direct questions to each major assistant. What does this company do, how is it priced, who are its customers, what does it integrate with, who leads it and who are its competitors? Run each question several times, because answers vary from run to run and a single answer can miss errors or exaggerate them, the issue explained in AI non-determinism.
Then break the responses into individual claims rather than judging each answer as a whole. Mark every claim correct, wrong or misleading, note any sources the assistant cites and tag each error with its type. The cited sources are your fix list. Finally, add a few category questions that don't name you at all, like "best tools for…," which is where skewed framing tends to show up. Those results also feed directly into your share of model and AI sentiment analysis.
Fixing what you find
You can't log into ChatGPT and edit what it says about you. What you can do is change what it reads, and that turns out to be more powerful than most people expect.
Start with a single source of truth, one clear, current page that states your key facts in plain language. What you do, who you serve, how you're priced, what you integrate with, when you were founded and who leads the company. Keep it in plain HTML, structure it clearly, back it with Organization schema and update it every time something changes. Schema markup examples shows how.
Then clean up what's outdated. Old press releases, retired product pages, stale partner listings and abandoned directory profiles are all votes for the wrong answer. Update them, redirect them or ask whoever owns them to fix them. For swapped errors, make your entity unmistakable, with the same name, description and category everywhere, with sameAs links connecting your site, LinkedIn, review profiles and other official pages. Entity authority for B2B SaaS and what is a knowledge graph explain why this matters so much.
Skewed errors take a different kind of work. Trace the framing back to its sources, respond publicly where you can, request corrections where you can't and earn fresh coverage that reflects who you are today. Online reputation management covers the honest version of that playbook. And use the feedback buttons inside the assistants themselves. They won't fix things on their own, but they're free and they create a record.
How fast fixes work
It depends where the error lives. If an assistant is answering from live search, the process covered in search grounding, corrected sources can change the answer within days or weeks of being indexed. If the error is baked into the model's training data, it can persist until a new model version ships, which may take many months.
That's why I'd always start with the sources assistants are actively retrieving, then work on broad, long-term presence. You'll see the first wins quickly, and the slower fix keeps working in the background. Then make prevention a habit. Update your fact page on every change, retire old pages properly, announce changes in places models can actually read and re-run your fact-check questions every quarter. Treat it like a security scan for your reputation.
Brand hallucinations in the Engine Optimization Matrix™
Hallucinations live in the LLMO row of the Engine Optimization Matrix™. The Schema cell, organization and author entity links with sameAs profiles, prevents swapped errors. The Authority cell, mention density across high-trust domains, crowds out stale and skewed ones. And the Citation cell is where you check whether ChatGPT, Claude or Perplexity actually describe you correctly when someone asks.
For related reading, see LLM SEO, brand mentions in AI answers, anatomy of an AI response and what is llms.txt.
A model can't recommend a brand it doesn't know, and it can't describe one it knows wrong. The Demand Doctrine™ for LLMO covers how to get your brand into what the models remember, accurately. Out November 18. Want all four engines? Mastering AI Visibility.
Frequently asked questions
What is an AI hallucination about a brand?
It's a false or misleading statement an AI assistant makes about a company, such as wrong pricing, invented features, outdated leadership or confusion with another brand.
Why does AI get facts about my company wrong?
Usually because information about you is thin, outdated or inconsistent across sources, so the model fills gaps or repeats old facts.
What are the types of brand hallucinations?
Most fall into four types called Stale (outdated facts), Swapped (confused with another brand), Invented (made-up facts) and Skewed (true facts with misleading framing).
Can I ask ChatGPT to correct what it says about my company?
You can flag answers with feedback tools, but the reliable fix is correcting the sources models learn from and retrieve.
How long does it take to fix an AI hallucination?
Answers based on live search can change within days or weeks of corrected sources being indexed. Errors in training data can persist until a new model version ships.
How do I check what AI says about my brand?
Ask several assistants direct questions about your company, run each prompt more than once, log every claim and note the cited sources.
Does schema markup help prevent hallucinations?
It helps, especially for confusion with similar brands. Organization schema with sameAs links makes your identity clearer to search engines and AI systems.
Are companies responsible for what AI says about them?
For third-party assistants, generally not. For your own chatbot, a Canadian tribunal held Air Canada responsible for incorrect information its chatbot gave in 2024.
About the author. Harold Bell is the founder of MQL Magnet and has spent about 16 years in enterprise technology marketing, working with brands like Cisco, Rubrik and Nutanix in different capacities. He's a member of the Forbes Communications Council, the creator of the Engine Optimization Matrix™ and the author of Partner Over Product. |
