A prospect gets on a sales call already convinced of something false wrong pricing, a discontinued feature, a category you left years ago because ChatGPT told them so with total confidence. You spend the first five minutes of the call correcting a hallucination instead of selling. That scenario is now common enough that it’s stopped being a funny anecdote and started being a real revenue problem.

AI search systems summarize your brand for buyers before they ever land on your website. When that summary is wrong, it shapes demand quietly and invisibly, long before you find out. Here’s how to actually find it, trace it, and fix it.

Why AI gets your brand wrong in the first place

It’s rarely random. A few consistent causes show up across nearly every case:

Stale or conflicting sources. If one directory lists your old address and your website shows the new one, the model may repeat the outdated version, or blend both into something that’s accurate nowhere.
Knowledge cutoffs and access limits. OpenAI itself acknowledges ChatGPT can get facts wrong due to training cutoffs, inability to access certain live pages, paywalls, or robots.txt restrictions meaning a page that’s technically accurate might simply be invisible to the model.
Weak or thin source material. When there isn’t enough reliable, specific information available, the model can generate a plausible-sounding answer rather than admit uncertainty.
Bad co-citation. If your brand keeps appearing in listicles for a market you’ve exited, on legacy partner pages, or in outdated software directories, AI systems can over-associate you with the wrong category simply because that’s the company you keep across the web.

Step 1: Audit what AI is actually saying

You can’t fix what you haven’t measured. Build a structured audit before you touch anything:

Test 20–50 prompts across four categories: branded (your company name directly), comparison (you vs. a named competitor), category (best tools/providers for your market), and problem-based (the actual pain points your buyers search for)
Run the same prompts across ChatGPT, Perplexity, Gemini, Claude, and Microsoft Copilot accuracy varies meaningfully by platform, so a clean answer on one doesn’t mean you’re clear everywhere
Log everything in a simple tracker: platform, exact prompt, AI’s claim, accuracy rating (correct / incorrect / partially correct), and a timestamp
Categorize what you find factual errors (founding date, HQ, employee count), descriptive errors (features, positioning), and reputational errors (misattributed quotes, wrong reviews) since each type needs a different fix

Step 2: Trace the error back to its source

This is the step most brands skip, and it’s the one that actually matters. Correcting the AI’s output without fixing the underlying source just means the same wrong answer resurfaces next month.

Check the platform’s citations first where available Perplexity and Google AI Overviews show sources directly, which makes tracing straightforward
For ChatGPT, which doesn’t always show inline citations in standard chat, search for the exact wrong phrase or claim to find where it likely originated
The source is often not the page the AI directly cites it can be a search snippet, an old directory listing, a stale comparison article, or an index entry that shares the same outdated fact
Look specifically for conflicting information across your own controlled properties (website, LinkedIn, G2, Crunchbase) the model has no way to know which version is current if you’re contradicting yourself publicly

Step 3: Fix what you control, and pursue what you don’t

Once you know the source, the fix splits into two lanes:

Sources you control:

Update every public profile with the correct, current information website, social profiles, directories, review platforms
Add or refresh structured data Organization, Article, and FAQ schema on your key brand pages, so AI systems can parse facts with less ambiguity
Consider publishing an llms.txt file at your site root: a structured summary of your authoritative brand facts, aimed specifically at AI systems rather than human visitors
Check your robots.txt for accidental blocks. A meaningful share of sites unintentionally block GPTBot (used for training) while trying to opt out of AI training, without realizing they can allow OAI-SearchBot (used for ChatGPT Search) separately blocking both means you’re invisible to ChatGPT’s search results entirely, not just its training data

Sources you don’t control:

Reach out to publishers of outdated comparison posts, directories, or review sites with the corrected information a polite, specific correction request works more often than you’d expect
Prioritize by influence: a correction on a frequently-cited industry directory matters more than one on a low-traffic blog nobody links to
Build stronger, more current evidence elsewhere to outweigh weak external sources over time, since AI systems weigh a preponderance of consistent evidence rather than a single fix

Step 4: Use platform reporting tools as a supplement, not the fix

Every major AI platform has a native feedback mechanism:

ChatGPT: thumbs-down icon on the response, then flag it as incorrect
Google AI Overviews: thumbs-down icon, then “Report a problem“
Perplexity: thumbs-down or the “···” menu, then “Report“

Use these every time you spot an error. But treat them as a supplement, not a primary strategy there’s no guaranteed turnaround and no confirmation a correction actually gets made. Fixing the underlying sources the model draws from is what reliably and durably changes what it says.

Step 5: Monitor continuously, not just after a crisis

AI answers shift over time as models retrain, re-crawl, and re-weigh sources a fix today doesn’t guarantee accuracy in three months. Brands that stay ahead of this treat it as ongoing monitoring, not a one-time cleanup:

Re-run your prompt audit on a regular cadence (monthly is a reasonable starting point for anything customer-facing)
Track not just your brand name but the comparison and category prompts too, since that’s often where the more damaging errors hide
Watch for new co-citation patterns being newly associated with an old category or a legacy partner can reintroduce an error you already fixed once

The bottom line

An AI system giving wrong information about your brand isn’t a glitch you wait out it’s a signal that your public information is stale, conflicting, or thin somewhere the model can see. Audit what’s being said, trace it to its actual source, fix what you control, push on what you don’t, and keep checking. The brands handling this well aren’t reacting to a bad answer a prospect forwarded them they’re catching it before it ever reaches a sales call.

Kashish Arora

SEO & GEO specialist.

How to fix incorrect information about your brand in ChatGPT search