Correcting wrong information about your company in ChatGPT and AI answers
ChatGPT, Perplexity or Google's AI Overviews state something wrong about your company. I trace the claim to the page, profile, directory listing or Wikidata property producing it, correct or outweigh that source, submit it for recrawl, and tell you plainly which claims cannot be reached at all.
100
+Completed projects
Websites, landing pages and SEO projects for businesses in different niches and markets.
15
Years of experience
Hands-on experience in SEO, website development and digital strategy.
17
+Industries covered
From local services and startups to real estate, e-commerce and B2B projects.
3
Working languages
English, Polish and Russian — with content and structure adapted to each market.
What correcting wrong information in ChatGPT and AI answers includes
Evidence captured before anything changes
Each wrong answer screenshotted with the prompt, the platform and the date. Answers change; without the record there is nothing to compare against later.
Source tracing: the page, profile or directory listing behind the claim
The source producing the claim, found rather than guessed. Frequently it isn't your site: an outdated directory entry, a stale profile, a Wikidata property nobody has touched in years.
Classification: correctable at the source, or training data
A claim retrieved from a live page can be corrected. A claim from the model's training data can't, and you are told which is which before any work starts.
Correction of your own pages and structured data
Pages fixed, dated and made consistent, with schema.org markup stating the facts explicitly — because one corrected page against six stale ones loses.
Third-party sources, directories and Wikidata handled
Correction requests where platforms accept them, and outweighing where they don't. Registries, industry directories and the Wikidata item are treated as part of the record, not as someone else's problem.
Recrawl, re-test and a dated before-and-after
Corrected URLs submitted for recrawl, including to Bing — which matters more than its traffic share suggests, because it feeds Copilot. Then the original prompts re-run and compared.
Why wrong information in ChatGPT is fixed at the source, not with the platform
The instinct on finding that ChatGPT, Perplexity or Google's AI Overviews describe your company incorrectly is to contact someone about it. That instinct is why most of this effort goes nowhere. No channel exists through which a business corrects its own record with an AI assistant, and the feedback buttons that do exist are a signal rather than an edit.
What changes an answer is changing what the assistant reads. When it searches the web, it retrieves pages and summarises them — so the wrong claim exists somewhere public, and fixing that source changes the claim on the next crawl. Not because a request was processed, but because the input changed.
Wikipedia, Wikidata and the knowledge graph: where entity facts actually live
Assistants do not assemble a company from scratch on every question. They lean on structured entity sources: Wikipedia and Wikidata, business registries, industry directories and the profiles that feed a search engine's knowledge graph. When those are stale, empty or contradictory, the answer inherits the problem — and no amount of editing your own website reaches it.
So the work covers the entity as well as the site: the Wikidata item and whether it exists at all, the directory listings carrying your details, the profiles that rank for your own name, and the structured data on your pages that tells a machine what kind of organisation this is and what it sells. Where several people or companies share your name, disambiguation is the whole job — an assistant that cannot tell which one you are will name someone it can.
The step that makes an AI correction hold
One corrected page against six stale ones loses. That's why corrections revert: the source gets fixed, the answer improves for a fortnight, and then the older material reasserts itself because it's still there and still says otherwise.
Making the correct fact the consistent version everywhere — your site, your profiles, your listings, your Wikidata item, every directory carrying your details — is the step that makes a correction hold. It's also the least interesting part of the job, which is presumably why it gets skipped.
The honest limit: what can't be corrected in ChatGPT or any AI answer
Some claims can't be reached. If the wrong information comes from the model's training data rather than a retrieved page, source corrections don't touch it, and it persists until that model is retrained — on the platform's schedule, not yours, and possibly months away.
These are identifiable: ask the same question with search enabled and disabled. If the answer is right when the assistant searches and wrong when it doesn't, the error is in the model.
The workaround isn't a fix but it works: make the correct version dominant enough in retrievable sources that search-grounded answers stay right regardless of what the model remembers. Since a growing share of answers involve search, that covers most of the practical exposure — and I'd rather explain that than sell you a campaign against something no campaign reaches.
What this AI correction service doesn't cover
Not the assistant's opinion of you, only its facts. If an answer says something accurate that you'd rather it didn't, that's a different problem and mostly not a technical one. The method here corrects wrong claims; it doesn't suppress true ones, and I don't take that work.
This is one of four services under AI-Ready SEO & GEO, the broader work of making a business legible and accurate to AI search.
How the correction runs, step by step
Baseline: what ChatGPT and the other assistants say today
The wrong answers captured with prompts, platforms, dates and screenshots. If an audit was done, this is already in hand.
Tracing each claim to its source and classifying it
Every claim followed to the page, profile, directory or Wikidata property producing it, and marked correctable or training data. You get this before the work is priced.
Correcting the sources and consolidating the entity
Your own pages fixed and dated, structured data added, third-party sources corrected where possible and outweighed where not, so one version of the facts is consistent everywhere.
Recrawl, then re-running the same prompts weeks later
Corrected URLs submitted for recrawl, then the original prompts re-run after several weeks and compared against the baseline, including what did not move.
