False Claims About Your Company in ChatGPT: What You Can Do

A prospect asks ChatGPT about your prices and gets a figure that never existed. Or a certification you do not hold. Or an address that was valid three years ago. You usually find out by chance, because a customer mentions it in passing. The first question is then: who do you call to have it corrected? The short answer is unsatisfying: nobody.

Key takeaway: There is no direct correction route. You cannot send OpenAI, Google or Perplexity a report and request a correction the way you could with a business directory. What works is the detour via the sources: consistent facts on your own website, clean schema markup, and correct representation on the third-party sites these systems draw from. How quickly this takes effect depends on whether the system searches live or answers from its training state. For defamatory false claims, legal remedies come into play as well.

How do I find out what AI systems say about my company?

By asking, systematically rather than by chance. Most companies discover errors through a random hit and cannot tell whether it was an isolated case.

A sound assessment needs three things:

1. A fixed question catalogue. Ten to fifteen questions a prospect would actually ask: what does the company do? What does the service cost? Where is it based? What references exist? Is it reputable? What alternatives are there?
2. Several systems. ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot answer differently because they work differently. An error in one system does not mean all of them have it.
3. Documentation with a date. Screenshot or text copy with a timestamp. Without that basis you cannot later judge whether anything changed, and in a dispute you have nothing to show.

It matters to ask without being logged in and in a private session. Otherwise your own usage history shapes the answer and you do not see what a stranger sees.

Why do false claims arise in the first place?

Because the systems draw on sources that are wrong, outdated or ambiguous. In practice there are five causes:

The last two are the most uncomfortable because they cannot be traced to a faulty source. They arise precisely where your own presentation is imprecise.

Is there a direct route to correction?

No, and it is worth knowing that before spending time looking for one.

There is no form through which a company can request a correction of how it is represented in ChatGPT. These systems are not databases with entries that can be edited but models that formulate from probabilities. What OpenAI, Google and Anthropic offer are reporting channels for problematic outputs; no entitlement to a particular answer follows from that.

For personal data the situation differs, more on that below.

How do you actually correct the representation?

Through the sources. That is slower than a correction form, but it is the only route that works.

First: an unambiguous facts page on your own website. A subpage stating the core details in clear, machine-readable form: legal name, year founded, location, services, contacts, the essentials on pricing. Marked up with Schema.org so the details are not merely readable but structurally processable. This page is your reference point.

Second: eliminate contradictions in your own estate. Old subpages with superseded details, a PDF from 2023 in the download area, differing details in the legal notice. As long as two versions remain findable, the model decides which one to take.

Third: clean up third-party sources. Directories, review portals, Wikipedia entries, press articles, platform profiles. These often carry more weight than your own site, because models value independent confirmation over self-description.

Fourth: place the correct version where citation happens. Trade media, association sites, industry portals. When the right information appears at several independent places, it prevails.

How long until a correction takes effect?

That depends on how the given system works, and the difference is considerable.

Systems with live web search such as Perplexity or Google AI Overviews access the current index. If the corrected page is indexed and easy to find, the answer can change within days to weeks.

Answers from the training state only change with the next training cycle, which lies months back or in the future. When ChatGPT answers without web search, no amount of source correction helps in the short term.

In practice: do not expect a quick effect, and check at intervals rather than looking every day.

When is the legal route an option?

When the false claim is not merely inaccurate but damaging to business. Several approaches then apply and should be kept apart.

For personal data, Article 16 GDPR grants a right to rectification of inaccurate data. That covers information about natural persons, such as the managing director, not the company description as such.

For defamatory factual claims about the company, Section 824 of the German Civil Code comes into play, protecting against untrue credit-damaging assertions. A false association with insolvency, fraud or dubious practices falls into this area.

Case law is developing right now. The Regional Court of Kiel addressed liability for AI-generated false information about companies in 2026, and the Regional Court of Munich I ruled in May 2026 on AI overviews in search results. Both decisions show that providers are not categorically off the hook, particularly where they make content their own through their own processing.

For practice this means: document the finding with date and screenshot before doing anything else. Without that evidence every later step is difficult. And have the individual case reviewed by a lawyer before considering proceedings; this article does not constitute legal advice.

How do I measure my presence against competitors?

With the same question catalogue, evaluated differently. Instead of only checking whether the details about you are correct, additionally note:

• Are you mentioned at all? In which of the fifteen questions does your company appear, and in which not?
• In what position? First recommendation, side mention, or only in a list?
• Who is named instead? Record the competitors by name; that is the actual benchmark.
• How are the others justified? Systems often state why they recommend a provider. Those justifications reveal which signals work.

Across several measurement points this produces a time series you can put in front of a board. Single measurements do not serve that purpose because answers fluctuate.

A note on expectations: a mention in AI answers cannot be bought and cannot be directly controlled. What can be steered are the signals from which the systems make their selection. Anyone promising you a guaranteed placement is selling something they cannot deliver.

How often should you check?

Quarterly is enough for most companies, monthly in fast-moving markets. More important than frequency is that it happens regularly at all and that results stay comparable: the same question catalogue, the same systems, the same documentation format.

Additionally check when something fundamental changes: new services, a name change, a relocation, a piece in a major outlet.

How ArkeonTech approaches this

We run the assessment with a fixed question catalogue across the relevant systems and document every answer with a date. The result is a report separating three things: what is wrong, what is missing, and where competitors are named instead of you.

For correction we work on the sources: a facts page with schema markup, cleanup of contradictory details in your own estate, review of the most important third-party sources. What we do not offer is a guaranteed placement in AI answers, because no such thing exists.

How to become visible in AI systems in the first place is covered in our article GEO beyond SEO. This piece deals with the case where something is already stated about you and it is wrong.

Frequently asked questions about false AI claims

Can I force OpenAI or Google to delete a false statement? There is no general entitlement to a particular answer. For personal data, Article 16 GDPR grants a right to rectification; for defamatory factual claims about the company, Section 824 of the German Civil Code comes into play. Case law on this is emerging; the Regional Courts of Kiel and Munich I addressed liability questions in 2026. The individual case should be assessed by a lawyer.

Why does ChatGPT not know my company at all? Because too few independent sources report on you. Models reflect what exists online and is confirmed repeatedly. A well-made website alone often does not suffice if no directories, trade media or platforms pick up the details. For young or very local businesses that is the norm, not a fault.

Does it help to write the correction into the chat? No. What you state in a conversation applies to that conversation only and influences neither the model nor the answer another user receives. The widespread advice to correct the model in chat rests on a misunderstanding of how these systems work.

How do I tell whether an answer comes from training or from a web search? Usually from the citations. If the system names links, it searched live, and a correction at the source can take effect comparatively quickly. If it answers without sources, the statement comes from the training state and changes at the earliest with the next training cycle.

What about false claims about me personally as managing director? Here the legal position is clearer than for company data. This concerns personal data, for which Article 16 GDPR provides a right to rectification, and the general right of personality may additionally be affected, protecting professional reputation and social standing.

Is a monitoring tool worth it? For regular checks across several systems it can save effort. To get started, a question catalogue and a spreadsheet suffice. What matters is not the tool but that the questions stay constant and every measurement is documented with a date, otherwise no development can be read from it.

Conclusion

False claims in AI answers are annoying, but they are not an attack; they are a side effect of how these systems work. Understanding that means you stop looking for the correction form that does not exist and start cleaning up the sources the answers are built from.

The first step costs an hour: fifteen questions, three systems, screenshots with dates. After that you know whether you have a problem and, if so, which one. Everything else follows from there.

This article reflects the state of information as of 9 August 2026 and does not constitute legal advice.

Sources

• Regulation (EU) 2016/679 (GDPR), Article 16 (right to rectification)
• Section 824 German Civil Code (credit endangerment)
• Regional Court of Kiel (2026) on liability for AI-generated false information about companies
• Regional Court of Munich I, judgment of 28 May 2026 (case 26 O 869/26) on AI overviews in search results