Fixing Wrong or Outdated Info AI Says About Your Brand

Ask ChatGPT, Gemini, or Grok about your own company and there is a real chance you will not recognize what it says back. Wrong pricing, a product you stopped selling two years ago, a founder who never worked there, or a flat "is this even a real company" tone. This is not a rare glitch. It is a normal, documented pattern across every major AI platform, and it is quietly shaping how buyers see your brand before they ever visit your website.

This guide explains why AI models get brand facts wrong, how to find every place it is happening to you, and the exact steps that actually fix it. We also cover what does not work, since a lot of advice online wastes a marketing team's time chasing the wrong lever. For the mechanics behind why models choose one brand over another in the first place, see our guide on how LLM models decide which brands to mention.

Facts, figures and platform guidance in this article were checked in September 2026. AI accuracy behavior changes often, so retest the specific claims that matter to your brand before you plan a large correction project around them.

It is tempting to treat a wrong AI answer as a one off mistake. The data says otherwise.

A March 2025 study from Columbia University's Tow Center for Digital Journalism tested eight major AI search tools across 1,600 queries and found that the tools gave an incorrect answer to more than 60 percent of them overall. The gap between the best and worst performer was enormous.

Source: Tow Center for Digital Journalism, Columbia Journalism Review, March 2025

Two details from that same study matter even more for brand owners than the raw error rate. First, the tools rarely admitted doubt. ChatGPT misidentified content in 134 out of 200 tests, yet used any hedging language in only 15 of those answers. It sounded confident whether it was right or wrong. Second, paid, premium tiers were not more accurate. They were, if anything, more confidently wrong than free versions, since a more fluent answer is not the same thing as a correct one.

Separate research points the same direction. According to PAN Communications' 2026 study of senior B2B buyers in the United States, 73 percent now use AI as their first stop when researching a vendor, and 31 percent of the ChatGPT citations returned for senior level B2B queries were misattributed or fully fabricated. That is not a small edge case. It is close to a third of the answers your most important buyers see.

This is also becoming a boardroom issue, not just a marketing one. Among large US companies surveyed in 2025, 38 percent named AI misrepresentation as their top business concern, ahead of cybersecurity at 20 percent.

To fix this, you first need to understand that an AI answer about your brand is rarely a single invented fact. Most wrong answers trace back to one of three layers.

Training data. This is what the model absorbed before its knowledge cutoff. You cannot edit this directly, and chasing it is largely a waste of time, since the next model version will retrain on a fresh snapshot of the web anyway.

Live retrieval. When a question needs current information, tools like ChatGPT Search, Gemini, Perplexity and Copilot search the live web and pull from what they find. This is the layer you can actually influence, and it is where most correction work should go.

Third party sources. Review sites, business directories, comparison articles, old press coverage and abandoned profiles all feed both training data and live retrieval. A stale Crunchbase entry or an outdated G2 listing can outlive your own corrected website by months.

One detail surprises most marketing teams: your own website is a small part of what a model actually reads about you. Research from McKinsey in 2025 estimated that a brand's own site typically makes up only 5 to 10 percent of the material a model draws on when it forms an opinion about that brand. The rest comes from everywhere else on the web, which is exactly why updating your homepage alone rarely fixes a wrong AI answer.

Wikipedia carries outsized weight in this mix. More than 80 percent of major AI models treat Wikipedia as a foundational reference source, and it makes up a meaningful share of the training data behind models like GPT. If your brand has a Wikipedia page, a small error there can cascade into dozens of AI answers. If you do not have one and a competitor does, that absence itself becomes a gap models fill with less reliable sources.

The instinct to email OpenAI or Google support and ask them to fix a fact is understandable, but it does not work the way a business listing correction does. There is no account manager who edits what a model believes about your company.

OpenAI's official position, laid out in its Help Center guidance on reporting content, is that reports are reviewed by OpenAI's model quality team, who may apply filters to reduce reliance on an unreliable source going forward. That is meaningfully different from a guaranteed, immediate correction of one specific fact. A thumbs down on a wrong answer, with the exact false claim written in the feedback box, is worth doing on every platform where the error shows up, but treat it as one input among several, not a fix by itself.

There is also real legal weight behind getting this right. In the widely cited Moffatt v. Air Canada decision, a Canadian tribunal held the airline responsible after its website chatbot invented a bereavement fare policy that did not exist. Air Canada argued the chatbot was a separate entity responsible for its own answers. The tribunal disagreed and held the company liable for what its AI told a customer. The broader lesson for any brand: once an AI system speaks in your name, you own what it says, whether that system is your own support bot or a third party model repeating something it read about you.

Since you cannot edit the model directly, the effective strategy is to control what it reads. This is slower than a support ticket, but it is the only approach that actually moves the needle, and it compounds over time.

Step 1: Reproduce and document the error. Ask the exact question a buyer would ask, across ChatGPT, Gemini, Claude, Perplexity, Grok and Copilot, in a fresh conversation with no prior context. Screenshot the answer, note the date, the exact prompt, and whether the answer includes a citation you can inspect. This becomes your before and after record, and if the error ever becomes a legal or PR matter, your evidence file.

Step 2: Trace the claim to its source. Where the platform shows citations, such as ChatGPT Search or Perplexity, open the cited page and check whether it actually supports the claim in the answer. Sometimes the citation is stale. Sometimes the model summarized the source incorrectly even though the source itself was accurate. These require different fixes.

Step 3: Fix your own pages first. Rewrite the specific page that should hold the correct fact so the accurate version sits in the first 100 words, clearly and without hedging. A page that says "Learn more" with the fact buried below the fold rarely gets extracted cleanly.

Step 4: Fix third party sources. For every outdated directory, review site, or old article repeating the wrong fact, request a correction from that publisher directly and offer the correct, canonical version to link to. This step is the one most teams skip, and it is usually the one that matters most, since retrieval systems weigh independent corroboration heavily.

Step 5: Strengthen your entity data. Keep your brand description, pricing, product names and leadership details identical across your website, Wikipedia if you have a page, Wikidata, LinkedIn, Crunchbase, G2 and any major directory in your category. Inconsistent descriptions across these properties is one of the most common reasons models struggle to place a brand confidently, a pattern we cover in more depth in why your brand doesn't appear correctly in AI search.

Step 6: Get the correction crawled. A fixed page that no crawler has seen yet fixes nothing. Submit the updated URL in Google Search Console and Bing Webmaster Tools, and use the IndexNow protocol so Bing, and by extension Copilot, picks up the change in hours instead of weeks. Confirm your robots.txt is not accidentally blocking OAI SearchBot, PerplexityBot, ClaudeBot or Google Extended.

Step 7: Report the specific output. On every platform where the wrong answer appeared, use the built in feedback option and state the exact false claim in plain language. This will not instantly rewrite the answer, but it feeds the signal that helps the platform's quality team deprioritize the unreliable source.

Step 8: Retest on a schedule, not once. Freshness decays fast. Research from AI visibility firm NeuroRank found that content cited 82 percent of the time at 30 days after publication can fall to as low as 37 percent by 180 days. Rerun your original prompt set monthly across all major platforms, in a fresh conversation each time, and track whether the correction has actually propagated.

Correcting a wrong fact rarely means fixing one thing. In audits we have run through RankinLLM's platform, it is common to flag well over a dozen inaccurate AI claims tied to a single brand within one review cycle, ranging from outdated pricing to features attributed to the wrong product tier. The pattern holds across categories: the brands with the most product lines, the most recent pricing changes, or a recent rebrand tend to carry the most accumulated errors, simply because there is more stale material about them scattered across the web.

The practical implication is that a correction project is rarely a single afternoon of edits. It is closer to a short audit, a prioritized list of sources to fix, and a monthly retest cadence until the numbers hold steady.

Track these on a simple dashboard rather than relying on memory of what "seems better."

A fact corrected on one platform can stay wrong on another for months, since each assistant updates on its own retrieval cycle. Track all of them, not just the one you check most often. Our guide on tracking your brand across ChatGPT, Perplexity and Gemini walks through setting this up in more detail.

Treating one correction as done forever. AI answers vary by prompt, by platform, and over time. A fact fixed today can drift back to being wrong after the next retrieval cycle if the underlying sources were not actually cleaned up.

Fixing only your own website. Since your own site is a small share of what models read, this alone rarely resolves the problem. Third party sources need the same attention.

Chasing training data. You cannot edit what a model has already absorbed, and the next model release will retrain on a new snapshot anyway. Spend your effort on retrieval and source quality instead.

Assuming a thumbs down fixes it. Feedback helps the platform's quality team over time, but it is not a substitute for correcting the actual source, and it will not instantly rewrite an answer.

Blocked crawlers. A surprisingly common, quiet cause. If your robots.txt or CDN settings block AI crawlers, even a perfectly corrected page will never be seen.

Can I contact OpenAI or Google directly to correct a specific fact about my business?

Not in the way you might contact a directory to fix a listing. You can report content through OpenAI's official reporting tools, but there is no correction hotline that instantly rewrites a fact. The reliable path is fixing the sources the model reads and retrieves from.

How long does a correction take to show up in AI answers?

For live search based answers, such as ChatGPT Search or Perplexity, corrections often show measurable movement within two to five weeks once the source is fixed and recrawled. Model only answers, without live retrieval, can take much longer, since they depend on the next training update.

Is my brand likely to have wrong information floating around even if I have never checked?

Very likely, based on the research above. Run the exact questions your buyers ask across ChatGPT, Gemini, Claude, Perplexity, Grok and Copilot today, in a fresh conversation, and see what comes back before assuming everything is fine.

Does fixing AI hallucinations also help my SEO?

Yes, generally. The same work, consistent entity data, fresh content, accurate third party listings and clean crawler access, supports both traditional SEO and AI accuracy, since both systems ultimately depend on trustworthy, well structured, well corroborated information about your brand.

If you want to see exactly what AI tools are currently saying about your brand, and how many of those claims are actually accurate, you can check it directly.