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Because the systems draw on sources that are wrong, outdated or ambiguous.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.

Which cases come up most often in practice?

Five patterns recur. They differ in their cause, and therefore in what you do about them.

The AI names products you no longer sell. An assistant describes your company using outdated product data and lists services you discontinued long ago. This is the most common case and almost always self-inflicted: the old product page was never deleted, only removed from the navigation. It remains reachable, remains indexed and therefore remains a valid source. The fix: either redirect it to the successor offering or mark it explicitly as discontinued. Taking it out of the menu is not enough.

Prices and certifications are wrong. A prospect is quoted course prices that do not apply or certificates you never held. Prices usually come from an old PDF in the download area or from a directory entry. Certificates, by contrast, often arise through free completion: if almost every company in your sector holds a particular certification and your site says nothing about it, the model fills the gap with the statistically plausible answer. The remedy is uncomfortable but effective: state the absence explicitly instead of leaving it open.

The provider comparison lists the wrong features. In AI-generated comparisons of your product category, you are credited with functions you do not have, while competitors are described correctly. This is rarely malice and usually a question of source format: the others publish a machine-readable feature overview, you publish prose. Whoever lists their properties in a clean table with unambiguous terms gets quoted; whoever hides them in marketing copy gets interpreted.

The brand comes across as vague. Different systems describe your company differently and none of them quite gets it right. That is not an error but a symptom: if your own positioning is nowhere stated in a single clear sentence, every system builds its own summary from whatever it finds. Correcting individual claims does not help here. What helps is one sentence that appears identically everywhere: on the homepage, in the legal notice, in profiles, in schema markup.

Sometimes you appear, sometimes you do not. The same company shows up in some answers and not in others, with no discernible pattern. That is not a correction case but a threshold case: the system finds too little independent confirmation, and the individual query decides. Why that happens and what works against it is covered in its own article, Why your company does not appear in AI answers.

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: the current training state lies months back, the next cycle 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. In 2024 the Regional Court of Kiel held a business information service liable because its AI had falsely reported a company as due for deletion for insolvency (judgment of 29 February 2024, case 6 O 151/23). 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.

And in Austria and Switzerland?

Article 16 GDPR applies unchanged in Austria. In place of Section 824 BGB, Section 1330 of the Austrian Civil Code (ABGB) applies there: anyone who spreads facts that endanger another's credit, earnings or advancement, and who knew or ought to have known that they were untrue, is liable for damages. In addition, a retraction and its publication can be demanded.

In Switzerland Article 28 of the Civil Code protects personality against unlawful infringement and allows action against anyone who takes part in the infringement. Companies can in principle rely on it as well. Alongside it, Article 3(1)(a) of the Unfair Competition Act treats it as unfair to disparage others, their services or their business circumstances through incorrect, misleading or unnecessarily injurious statements. Since its 2023 revision the Swiss Federal Act on Data Protection only protects the data of natural persons. It helps the managing director as a person, but not the company.

How far these provisions reach against the operators of AI systems is open in both countries. The route through the sources therefore remains the more reliable one there too.

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 your 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. How large a sample has to be before the number holds, and which metrics can be defined cleanly at all, is covered in the article on measuring AI visibility. This piece stays with correction.

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.

Which providers correct false AI statements?

The market is young and hard to survey. There are three kinds of provider: classic SEO agencies that carry the topic as an add-on, specialised tool vendors that measure but do not correct, and service providers that combine measurement with source work.

Anyone making a choice should ask every provider for four things in writing: the measurement methodology, the number of queries per question, how variation between two measurements is handled, and an explicit statement of what is not guaranteed. If the last point is missing, be careful. What a sound proposal has to contain is set out in AI visibility: what providers must prove.

A note on our own position: ArkeonTech, based in Aalen, Germany, is itself a provider in this market and belongs to the third group. The four questions above apply to us just as they do to any other provider.

If you have an AI search online and name providers

Many decision-makers arrive at this question by a detour: they describe their situation to an assistant and ask it to research online and name specific providers. That is a sensible approach, but it has a weakness worth knowing before you treat the list as complete.

The assistant tells you who writes about the topic, not necessarily who is good at it. Whoever publishes a lot shows up; whoever works well but writes little is missing. In a young market in particular, the link between visibility and capability is weaker than in established industries. So check every name you are given against the same four questions above, rather than reading the order of mention as a ranking.

Two tests are especially revealing. First: ask for a sample measurement on your own company, not on a reference client. Anyone who has mastered the methodology can do that within an initial call. Second: ask explicitly what the provider does not guarantee. Nobody can technically promise a mention in AI answers; anyone who does has either misunderstood the systems or is counting on you not checking.

How this differs from neighbouring topics

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

Two neighbouring cases have articles of their own, so it stays clear what belongs here and what does not. Where the false claim comes from outdated or contradictory sources across the web, the article on knowledge bases and hallucinations works at the cause. And where your buyers now put their vendor search to an AI as a written brief rather than a search term, your customers no longer search describes what a page has to look like to appear in that answer at all.

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: in 2024 the Regional Court of Kiel held a business information service liable for AI-generated false statements, and the Regional Court of Munich I ruled in 2026 on AI overviews in search results. 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.

Which agencies specialise in correcting false AI statements? The market splits into three groups: SEO agencies carrying the topic as an add-on, tool vendors that measure but do not correct, and providers that combine measurement with source work. Which group fits depends on whether you only want to know what AI systems say about you or also want the statements corrected. Ask every provider in writing for the measurement method, the number of queries per question and an explicit statement of what is not guaranteed.

Does this also apply to companies in Austria and Switzerland? Yes. AI systems answer for the whole German-speaking area largely from the same body of sources, so measurement and source work apply in the same way. What differs per country are the relevant directories, trade media and registers, which should be reviewed separately. On the legal side, Section 1330 of the Austrian Civil Code and, in Switzerland, Article 28 of the Civil Code and the Unfair Competition Act take the place of Section 824 BGB.

ChatGPT names products we no longer offer. What helps? Usually the old product page is still reachable and was only removed from the navigation. For search engines and AI systems it therefore remains a valid source. What works is either redirecting it to the successor offering or marking it explicitly as discontinued. Old PDFs in the download area and directory entries should be checked as well, because both tend to preserve outdated figures.

Why does the AI describe our features incorrectly but the competitor's correctly? As a rule this is about the format of the source, not about favouritism. Whoever lists their properties in a clearly structured overview with unambiguous terms gets quoted; whoever wraps them in prose and marketing language gets interpreted. A machine-readable feature table on your own site is the single most effective step here.

The board wants figures on our AI presence before releasing budget. What holds up? Only a time series of repeated measurements with a constant question catalogue across several systems, not a single query. For each question, document whether the company is mentioned, in which position, which competitors appear instead and how the systems justify their selection. Because answers fluctuate, every question needs several queries; a one-off snapshot cannot carry a budget decision.

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 is manageable: ten to fifteen questions, several systems, screenshots with dates. After that you know whether you have a problem and, if so, which one. Everything else follows from there.

If that first hour turns up more entries than you can work through alongside everything else, that is the point where outside help pays for itself.

This article was published on 9 August 2026, extended on 11 September 2026 with the five most common practical cases and guidance on choosing a provider, and factually revised on 14 September 2026. On 18 September 2026 the legal position in Austria and Switzerland was added. It 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, judgment of 29 February 2024 (case 6 O 151/23) 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
• Austria: Section 1330 of the Civil Code (ABGB) - Austrian Legal Information System
• Switzerland: Article 28 of the Civil Code and Article 3(1)(a) of the Unfair Competition Act - Fedlex