AI Misinformation About Your Brand: How to Find It and Fix It
There is no complaints department. We went looking for a documented way to correct a factual error an AI assistant makes about a company, and no major provider publishes one. What exists instead are indirect controls: the sources these systems read at answer time. You cannot edit the model, so the work is to make the correct facts easy to retrieve, consistent everywhere, and hard to contradict — then check the answers regularly, because nobody will tell you when they change.
This post covers where AI answers about companies actually come from, which correction routes are documented by the provider and which are folklore, and what the legal position looks like as of 2026-09-06.
Key Takeaways
- No provider we checked documents a process for correcting a factual claim a model makes about a third-party company. Anthropic's support documentation offers a feedback button and general support contact; OpenAI's equivalent page could not be retrieved. This absence is the central finding (retrieved 2026-09-06, SOURCED where fetched).
- Retrieval is the lever you have. Google's own documentation describes grounding as a way to "reduce model hallucinations by basing responses on real-world information" — which means the pages it retrieves are the intervention point.
- Structured data is a hint, not a control. Google states plainly that it "does not guarantee that features that consume structured data will show up in search results."
- Knowledge panels can be claimed and edited by the entity's representative, with no published timeline or approval guarantee.
- Search is not run on every query. Google's Gemini API documentation bills per search "the model decides to execute" — so some answers about you come from training, with no live page to correct.
Two paths, and you can only reach one of them
Facts about your company reach an AI answer by one of two routes.
Live retrieval. The system runs a search at answer time and reads pages. Google's Gemini API documentation describes this directly: with the search tool enabled "the model handles the entire workflow of searching, processing, and citing information automatically," and the stated purpose is to "reduce model hallucinations by basing responses on real-world information" (Gemini API grounding documentation, retrieved 2026-09-06, SOURCED). The same page bills per search query "that the model decides to execute" — confirming that retrieval is conditional, not automatic.
Training data. The facts are already in the model's weights from whatever it read during training. No provider publishes the specific sources behind any individual company's facts, and we found no documentation from any of the major labs explaining how one company's information entered a training set (ANALYSIS, searched 2026-09-06).
That split determines everything about what you can do. Retrieved facts are correctable, because you can change what gets retrieved. Trained facts are not, because there is no page to edit and no documented mechanism to request a change.
On ChatGPT specifically. OpenAI's help documentation on search behaviour returned HTTP 403 to our automated request, so we could not verify it at source and do not quote it here. Nothing in this post is sourced to OpenAI material.
The correction routes that are actually documented
Three of them, all from Google, all about search surfaces rather than the models themselves.
Google Business Profile
Google requires businesses to keep the profile accurate and states that non-compliance can result in "removal of your business information from Google" (Google Business Profile guidelines, retrieved 2026-09-06, SOURCED). Read that as an obligation with teeth rather than a fast-fix channel — but for hours, location, contact details and category, it is the record Google treats as authoritative for your business.
Claiming or editing a knowledge panel
Google documents that "if you are the subject of or official representative of an entity depicted in a knowledge panel, you can claim this panel and suggest changes," while anyone else gets the feedback link to "suggest edits for review" (knowledge panel help, retrieved 2026-09-06, SOURCED).
What the page does not state anywhere: a timeline, an approval rate, or any guarantee that a suggested edit is accepted. Claim the panel, submit the correction, and treat the outcome as uncertain.
Organization structured data
Marking up your organisation gives search engines "explicit clues about the meaning of a page" (structured data introduction, retrieved 2026-09-06, SOURCED). Do it — the Organization type, with name, url, logo, description and sameAs pointing at your verified profiles elsewhere.
Then read Google's own hedge on the same documentation set: it "does not guarantee that features that consume structured data will show up in search results" (Organization schema reference, retrieved 2026-09-06, SOURCED). Structured data is a strong hint. It is not a control surface, and no documentation connects it to knowledge panel contents. If you want the mechanics, we covered them in making schema markup less complicated.
What is not documented, stated plainly
No lab publishes a company-fact correction process. Anthropic's support documentation on inaccurate outputs offers a thumbs-down control and a general support address, describes no formal process for correcting facts about specific companies, and advises users not to rely on the assistant "as a singular source of truth" (Anthropic support documentation, retrieved 2026-09-06, SOURCED). OpenAI's equivalent page returned HTTP 403 and we make no claim about its contents.
If you have been looking for the form to fill in, that is why you have not found it.
llms.txt is not an adopted retrieval standard, and Google says so directly. The specification site confirms several AI companies publish an llms.txt for their own developer documentation (llmstxt.org, retrieved 2026-09-06, SOURCED) — which is not the same as any provider committing to read yours.
Google settled the question in its own changelog on 15 June 2026: such files "aren't needed for Google Search (and won't negatively or positively impact your visibility or rankings)", though "it's fine if you want to maintain these files for other services or systems that use them" (Google Search Central documentation updates, retrieved 2026-09-06, SOURCED). Google's separate guidance on its AI features says the same thing more broadly: "You don't need to create new machine readable files, AI text files, or markup to appear in these features. There's also no special schema.org structured data that you need to add" (AI features and your website, retrieved 2026-09-06, SOURCED).
It costs almost nothing to publish. Expect nothing from it for Google Search, and note that no provider documents reading a third-party file either.
Wikipedia and Wikidata are widely assumed inputs. We found no provider documentation naming them as inputs to retrieval or to knowledge panels, so we are not going to assert it. Keeping a factually accurate Wikipedia entry is good practice for reasons that predate AI; treat any claim about its effect on model output as unverified.
The legal position, honestly
The case everyone cites is Moffatt v. Air Canada (2024 BCCRT 149), in which a tribunal held an airline liable for negligent misrepresentation by its own chatbot on its own website. Note what that is: a first-party agent the company deployed and controlled. We could not retrieve the primary decision — CanLII returned HTTP 403 to our request — so this description rests on secondary legal commentary and should be treated accordingly.
We found nothing addressing the different question: liability when a third-party model misdescribes an unrelated company. That does not mean nobody has a theory. It means we found no decided case, and you should not plan around one existing.
A routine that actually finds the problem
Nobody sends you an alert when an assistant starts quoting your 2023 pricing. The monitoring has to be deliberate.
- Write down your canonical facts. Founding date, headquarters, current pricing, what the product does and does not do, leadership names, category. One page on your own domain, dated. This is what everything else gets checked against.
- Ask the assistants directly, on a schedule. Monthly, ask each system the questions a prospect would ask: what does this company do, what does it cost, who is it for, what are the limits. Record the answers with the date and the model version.
- Note whether the answer cited anything. A cited answer is a retrieval problem — go fix the cited page or get it outranked. An uncited answer is likely trained and outside your reach; your route is to make the correct version widely retrievable so future retrieval finds it.
- Fix the upstream source, not the symptom. If an outdated third-party roundup is the citation, getting that page corrected or superseded does more than any structured-data change.
- Keep your own pages internally consistent. Contradicting yourself across your site, your profiles and your documentation is an invitation to guess. This is the same discipline as keeping AI content errors out of published pages, applied to facts about you.
- Re-check after any material change. New pricing, new positioning, a rebrand. The old facts persist in these systems long after your site changes.
None of this is fast. It is the set of levers that actually exists.
How This Guide Was Sourced
Written and maintained by the LogicBalls editorial team (logicballs.com).
Disclosure: LogicBalls builds AI writing tools. We benefit if people take AI accuracy seriously, which is why the section above says plainly that structured data guarantees nothing and that llms.txt is not a documented retrieval standard, rather than selling either as a fix.
Sources. Retrieval behaviour: Google's Gemini API grounding documentation. Correction routes: Google Business Profile guidelines, Google knowledge panel help, and Google Search Central's structured data introduction and Organization reference. Machine-readable files and AI features: Google Search Central's documentation updates log and its AI features guide. Provider feedback mechanisms: Anthropic's own support documentation. The llms.txt specification site. All retrieved 2026-09-06 and linked inline.
What could not be retrieved. OpenAI's help documentation returned HTTP 403, so no claim here is sourced to OpenAI material. A Microsoft support page describing Copilot's search behaviour returned HTTP 404 and is not cited. The primary text of Moffatt v. Air Canada returned HTTP 403 from CanLII; the case description above rests on secondary commentary and says so.
What we could not find, stated as a finding. No documentation from any major lab describing a process to correct a factual claim about a third-party company. No provider documentation naming Wikipedia or Wikidata as a retrieval input. No decided case on third-party model liability for misdescribing a company. Absence of documentation is not proof of absence, and it is the honest description of what a determined search returns.
No LogicBalls telemetry is used in this guide.
Frequently Asked Questions
Can I get an AI company to remove a wrong fact about my business?
There is no documented process for it. Providers offer general feedback controls and support contacts; none of the documentation we could retrieve describes a route for correcting entity facts. Personal data is a separate matter with its own legal rights, and that is not the same thing as a company fact.
Does structured data fix what AI says about my company?
It helps search engines interpret your pages, and Google states explicitly that it guarantees nothing about what appears in results. Add it because it is cheap and correct, not because it is a control.
Should I publish an llms.txt file?
It costs nothing, so there is no strong reason not to. There is also no provider documentation committing to read third-party files, so do not count on it doing anything.
How often should I check what AI says about us?
Monthly is a reasonable baseline, and immediately after any pricing, positioning or leadership change. Record the date and the model version with each answer, because both change underneath you.
The answer cites a page that is wrong about us. What now?
That is the good case — it is a retrieval problem with a fixable cause. Contact the publisher for a correction, and publish a clearly better, current page on your own domain so the correct version is the easier one to retrieve.
