You search your own business name in ChatGPT. The model says you offer a service you discontinued two years ago. Or it quotes a price that is 40 percent below what you actually charge. Or it describes your company in a category you have never operated in, sourced from a profile someone else created on a directory you have never touched.
The instinct is to look for a corrections form. An email address. A way to flag the error to OpenAI.
There is no corrections form. There is no corrections inbox. OpenAI is not going to manually update what the model knows about your business.
That is not how any of this works.
The good news: you are not powerless. But the fix is upstream — not inside ChatGPT. Here is the process.
Why ChatGPT has wrong information about your business
ChatGPT does not browse the web in real time when you ask it about your company. Its knowledge comes from training data: a large-scale snapshot of the web assembled before a cutoff date, which includes your website, press coverage, business directories, review platforms, LinkedIn, social profiles, and anything else publicly crawlable.
If any of those sources contained outdated, inaccurate, or conflicting information at the time of training, that is what the model learned. It is not a bug in the traditional sense. The model is doing exactly what it was designed to do — synthesizing what it found. The problem is what it found.
This distinction matters because it tells you where to intervene. The web is the database. ChatGPT is running queries against that database. You cannot patch the query engine. You can only fix the database.
Three source types feed that database for most businesses:
- Owned sources: your website, your Google Business Profile, your LinkedIn company page, your social profiles
- Earned sources: press coverage, industry publications, citations, guest posts, backlinks
- Third-party sources: directories (Crunchbase, G2, Clutch, Yelp, Apple Maps), review platforms, data aggregators
All three contribute to what the model believes about you. Most businesses have only cleaned up the first one.
Three types of wrong — and why it matters which one you have
Not all AI errors are the same. The type of error determines the fix, the timeline, and how much effort to expect.
| Error type | What it looks like | Root cause | Fix timeline |
|---|---|---|---|
| Outdated info | Old pricing, discontinued service, former team members, old address | Your own site or a third-party source was accurate once but wasn’t updated | Days to fix the source, weeks to months to propagate |
| Incorrect sourcing | Wrong claim the model pulled from a directory, old press release, or misquoted article | A specific upstream source contains the error | Fix the source, then corroborate with correct info elsewhere |
| Hallucinated claim | Something that has no traceable source anywhere on the web | The model inferred or filled a gap when no clear source existed | Hardest — requires publishing authoritative content to displace the inference |
The first two are correctable through fairly direct means. The third requires you to give the model something real to work with where previously there was nothing.
How to fix what ChatGPT says about your business
The process runs in order of leverage — start where the signal is strongest.
1. Run a source audit
Before you fix anything, find out what the web actually says. Search your business name in Google and review the first two pages of results. Check your Crunchbase, G2, Clutch, Yelp, Apple Maps, and Bing Places listings directly. Search for your business name in quotes alongside common error terms: "[your business]" pricing, "[your business]" services, "[your business]" location. You are looking for the origin of the wrong claim — the specific URL that introduced it.
2. Update your owned sources first
Your website is the highest-authority signal you control, and it is the fastest to update. If ChatGPT has wrong information about a service, your pricing, or your positioning, publish a clear, direct page that answers the question correctly. Not buried in a paragraph — a self-contained section under a descriptive heading, written so a language model can extract it without ambiguity.
While you are there, update your Google Business Profile, LinkedIn company page, and any social profiles you maintain. Consistency across owned sources is a corroboration signal.
3. Correct the third-party sources
Claim your listings on the directories that showed up in your audit. Update the information. Where a source is outdated and you cannot edit it directly (an old press release, a journalist’s article with wrong details), consider reaching out to the publisher. For data aggregators, check services like Data Axle and Localeze that feed downstream directories — correcting an aggregator can update dozens of listings at once.
4. Publish authoritative content that answers the question directly
This is especially important for hallucinated claims. If the model invented something because no good source existed, the fix is to create the source. Write a dedicated page or section that directly addresses the question it got wrong: what your service actually includes, what your pricing structure actually is, what markets you actually serve. Structure it clearly with an H2 that matches the question, a direct answer in the first sentence, and supporting detail below. This is standard GEO optimization practice — content written to be extracted and cited, not just read.
5. Add structured data
Schema markup does not directly control what an AI says about you, but it gives crawlers an unambiguous, machine-readable version of your business facts: name, address, phone, services, price range. When that structured data is consistent with your page content and your third-party listings, you create a corroboration stack that is much harder for a model to misread. For service businesses, LocalBusiness and ProfessionalService schema are the relevant types. For SaaS or products, Organization and Product schema apply.
Does fixing one AI engine fix them all?
Mostly, yes — on different timelines.
ChatGPT, Gemini, Perplexity, Claude, and Copilot all pull from overlapping pools of web data. They have different training cutoffs, different retrieval mechanisms, and different weights for different source types, but they are all ultimately reading the same web. Fix the web, and you improve your signal across all of them.
The one exception worth noting: Perplexity and ChatGPT’s search mode can surface more recent web content than a model’s base training data. This means a correction published today may appear in Perplexity search results quickly but take much longer to reach ChatGPT’s baseline knowledge. AI agents operating on behalf of users are increasingly using real-time retrieval, which accelerates the feedback loop further. The answer is the same either way — fix the source — but manage your expectations on timing per platform.
There are no platform-specific workarounds worth pursuing. No browser extension, no API call, no prompt trick will override what a model believes about your business at inference time. The leverage is entirely on the input side.
How to monitor what AI says about your business
Fixing a known error is reactive. What you want is a system that catches drift before a potential customer finds it first.
Set up a monthly prompt audit. It takes about 20 minutes and requires no special tools — just three browser tabs.
The query set:
- Your brand name alone:
"[Business Name]" - Your brand name plus your core service:
"[Business Name] [primary service]" - Your category plus your location or market:
"[service type] in [city/industry]" - A direct competitor comparison:
"[Business Name] vs [Competitor]" - A question your buyers actually ask:
"who is the best [service type] for [use case]"
The platforms: Run all five queries in ChatGPT, Gemini, and Perplexity. Log the answers in a simple spreadsheet — date, platform, query, summary of what it said, any errors flagged.
What to look for: New wrong information that wasn’t there last month. Competitor mentions that displaced yours. Missing information where you should be present. Any hallucinated claims that appeared without a traceable source.
This prompt audit is the starting point for a broader GEO and AI search strategy — understanding what the model believes about you is the prerequisite for influencing what it says.
You cannot correct the model. You can correct the record.
The frustrating part of AI misinformation about your business is that there is no appeals process. No form, no ticket, no direct line to the team training the model.
But the underlying dynamic works in your favor once you understand it. These models are not inventing things arbitrarily. They are reflecting what they found on the web. The web is editable. Your owned sources are editable. Many of your third-party sources are editable. The corroboration signals that determine what a model trusts most — consistency, authority, extractability — are all things you can build.
Fix the database, and the model follows. It just takes longer than anyone wants it to.
If you want help auditing what AI engines currently say about your business and building a correction plan, that is work I do with clients.
