What should you do when ChatGPT has wrong information about your company? Gemini may show an old address, Copilot may confuse the business with a similarly named brand, and Google AI Overviews may surface a phone number from an outdated directory.
Users may treat a concise AI answer as a conclusion, so factual errors are a reputation risk. Record the answer, find the source, correct the public information footprint, and test again. The workflow below is designed for SEO, PR, support, and business owners.
Why AI Can Be Wrong About a Brand
Models do not have one guide to the truth about companies. They collect the answer from different layers: training data, web search, search engine indexes, official websites, directories, reviews, media, and user discussions. OpenAI explicitly explains that ChatGPT can give inaccurate or misleading answers, including fictional facts, dates, quotes, or sources (OpenAI Help Center). This is a normal limitation of generative systems, but for businesses it has very practical consequences.
Most errors do not appear because a model "treats the brand badly." The usual cause is conflicting public information. A site may retain old prices or addresses; Google Business Profile, Bing Places, and local directories may disagree; an outdated press release may still rank; reviews may repeat a problem the business has already fixed; or key facts may exist only in PDFs and images. The first step is therefore to trace the likely source of the error rather than complain about the answer alone.
What are the most dangerous mistakes
Not all inaccuracies are equally critical. If an AI system calls a company an "internet marketing agency" instead of a "digital marketing agency," that is a wording issue. If it gives the wrong phone number or says the company has closed, the error can already cost leads. A risk-based framework helps prioritize these cases.
| Error Type | Example | First Action |
|---|---|---|
| Contact details | Old phone number, wrong address, or outdated opening hours | Check the website, Business Profile, directories, and maps |
| Commercial terms | Old prices, incorrect delivery terms, or a discontinued plan | Update service pages, FAQs, pricing, and structured data |
| Services and products | AI says the company does not provide a service that it now offers | Add or strengthen the service page |
| Legal facts | Incorrect license, owner, or country of registration | Publish an official source on the site and check external profiles |
| Reputation | The model repeats an old claim as relevant | Find the source, prepare a factual clarification |
| Brand confusion | Another company's product or controversy appears in the answer | Reinforce entity signals and publish a clear distinction where needed |
Critical errors should be taken to work on the same day. These include incorrect contacts, claims of business closures, false legal data, mentions of scams, dangerous advice on behalf of the brand, or situations where the user may lose money.
Where and what to check
One screenshot from ChatGPT does not show the full picture - different systems have different sources and modes, so the audit is done in batches: ChatGPT with and without web search, Google Search with AI Overviews, Gemini, Microsoft Copilot, Perplexity, Claude with web search, if your audience uses it. Check not only in Ukrainian: for some brands, the English-language information footprint is stronger. If the company operates in Poland, Germany, or the United States, add the market language; If the name is in Latin, test both Latin and Cyrillic variants.
Errors often appear not on the query "brand name", but on the periphery - in comparisons, supplier selection, reviews, prices, local scenarios. Therefore, the pool must be wider than the brand core.
| Query group | What we check | Examples |
|---|---|---|
| Basic facts | Does AI correctly describe a company | "what is [brand]", "what [brand] does" |
| Contacts | Address, phone, schedule, branches | "[brand] address", "how to contact [brand]" |
| Services | Does AI see the current offer | "does [brand] [service]", "[brand] price" |
| Reputation | What tone and claims are repeated | "[brand] reviews", "can [brand] be trusted" |
| Comparison | Do not confuse with competitors | "[brand] vs [competitor]", "alternatives [brand]" |
| Local queries | Is the geography correct | "[service] Kyiv [brand]", "[brand] nearby" |
For each request, record the date, platform, country or language, exact prompt, response, link, error type, and screenshot. Without this, in two weeks, the team will argue not about the decision, but about "what exactly was there." If you need to assemble a pool systematically, it is first useful to go through the material how to choose the right queries for monitoring AI visibility.
How to find the source of the error
The weakest point in working with AI errors is the temptation to correct the answer itself. But the answer is often just a symptom. Let's imagine a specific case: ChatGPT writes that the service "no longer works in Ukraine." The temptation is to complain about the answer. It is more useful to copy this phrase in quotes and search on Google and Bing. It turns out that a year ago, the company published news about a temporary pause, the news was never updated, and the outdated paragraph is still indexed and read by the model as relevant. It is not the "ChatGPT response" that is being fixed, but that old page.
The general route is as follows: save the answer and all the sources shown, look for the wrong phrase in quotes, check the pages of your site (home, "About us", contacts, services, FAQs, old landing pages), then Google Business Profile, Bing Places, Apple Business Connect and maps, then external profiles - Clutch, G2, Capterra, marketplaces, LinkedIn, social networks. If the source is not visible, repeat the query in web search mode or in a system that shows citations. And analyze not only the URL, but also the role of the source: the page could form the main conclusion or simply confirm a secondary fact - more about this in the article how to analyze sources based on AI.
What to fix on your own site
The official website should be a canonical source about the brand - not in the sense of "AI is obliged to believe it", but in the sense of "this is where the clearest, most relevant and machine-understandable description of the company is". Check the main page (who you are, what you do, for whom, where you work), the "About the company" page (legal and brand name, team, management, if it is publicly important), contacts (address, phone, email, schedule, map), service pages (current wording, prices or pricing principles, restrictions), FAQ for queries where AI is most often wrong, and trust pages - certificates, licenses, cases, reviews.
The main thing is to remove contradictions. If there is one address in Contacts, another in the footer, and a third in the old PDF, AI can pick up any. Old pages don't have to be deleted: it's often better to update them, put them canonical, make a 301 redirect, or add a "material updated" block - depending on whether the page has traffic, links, and historical value.
Separately, it helps to make the facts understandable to machines - not by "magic optimization", but by a normal information architecture: one stable brand description on the main page and "About us", a block "key facts about the company", visible update dates, HTML text instead of data in pictures, logical titles and FAQs, links to official profiles and structured data Organization, LocalBusiness, Product, Service, FAQPage where they correspond to visible content. Google explains that Organization markup helps you better understand your organization's administrative data and distinguish it in search (Google Search Central). In its guidelines, Bing explicitly links classic SEO practices to eligibility for AI-generated experiences and citations and adds an important caveat: structured data can support clearer grounding but does not guarantee visibility, and markup must accurately reflect visible content (Bing Webmaster Guidelines). The technical part of schema is analyzed separately in the article how structured data affects AI visibility.
Business profiles, maps, and external sources
For local businesses, errors often come not from the site, but from profiles and maps. Google says in its help that you can update your address, hours, contacts, photos, website, description, and services in verified Business Profile (Google Business Profile Help). Check at least Google Business Profile, Bing Places, Apple Business Connect, Facebook and LinkedIn, local directories, and relevant marketplaces. Particular attention is paid to NAP data (name, address, phone): if they are different on dozens of sites, it is more difficult for systems to understand which option is relevant. But don't reduce everything to the phone: for AI, the category of business, description of services, geography, photos, reviews, and Q&A also matter.
Third-party sources are often stronger than the site in reputation queries, and this is normal - the user wants more than just the official position of the brand. A simple rule by type works here. In media and press releases, look for outdated facts and ask the editors to update the story or add a note. In catalogs and ratings, update the description, category, cases, and links. On review platforms, respond factually without arguing emotionally. On forums, give an expert answer where community guidelines allow it. In social networks, remove outdated pinned posts and contacts. Avoid two extremes: ignore external sources because "everything is right on our site", and massively create artificial mentions - AI systems are getting better at seeing weak duplicate pages, and this is a risk for reputation.
When to send feedback on AI platforms
Feedback is useful but rarely replaces source corrections. It should be used when the response is clearly harmful, contains a fictional fact, refers to an unreliable source, or violates brand rights. OpenAI has instructions for reporting content in ChatGPT: you can complain about a specific response through the interface, and if GPT violates the trademark - through the trademark dispute form (OpenAI Help Center). Google AI Overviews also has a feedback mechanism for a specific review (Google Search Help). For Bing/Copilot, the logic is similar: feedback plus working with indexing - after updating the page, it should be reindexed, in particular through the URL Submission Tool in Bing Webmaster Tools (Bing Webmaster Tools). Rule of thumb: feedback is an escalation channel, not the main method of correction. The main work is still in sources, indexing, profiles, and public facts.
If the mistake is already hurting sales
When customers are already asking "why does AI say you're closed" or "why does ChatGPT say you don't have a license," act like in a reputation incident. Capture examples (screenshots, links, date, country, platform, prompt), identify risk (leads, legal implications, customer safety), give sales and support a short factual script of the response, update the official page that can be linked, find and correct sources, send feedback if necessary, and after 7-14 days, repeat the check with the same pool.
A public refutation is not always needed. If the error is narrow and comes from an old directory, it is enough to correct the data. If the AI repeats a statement that customers or partners are already seeing, it is better to create a calm page with facts - without aggression, without exaggerating the problem, with dates and official evidence. Aggressive denials are dangerous: they can reinforce the negative frame if the problem has not yet become public.
How to measure if fixes have worked
AI responses don't update instantly: different systems have different indexes, caches, and sources, so don't evaluate the next day's result with one prompt. Realistic dynamics - basic audit on day 0, fixing pages and profiles in the first days, re-checking critical requests in a week, the entire pool - in 2-4 weeks, and control in 6-8 weeks to see if the error did not return.
It is worth looking not at one figure, but at several shifts: what proportion of answers no longer contain false facts, whether models have begun to cite your site or profile, whether the recurrence of errors in different systems has disappeared, whether updated pages are cited instead of old ones, whether the tone and competitive context have changed. For this, it is more convenient not random manual checks, but constant monitoring. If you already have an AI visibility report, the material how to turn an AI visibility report into a plan for SEO, content, and PR will help you turn it into tasks.
Prevention and typical command errors
It is impossible to eliminate hallucinations completely, but you can reduce the chance of incorrect brand claims. Review priority prompts, frequently cited pages, external domains with inaccuracies, business profiles, indexing issues, and old pages that still receive traffic. Run an additional check after a rebrand, relocation, service launch, pricing change, or leadership change. Assign owners across SEO, PR, support, and legal so each type of problem has a clear response path.
Most often, companies lose time not because of the complexity of the topic, but because of the wrong point of effort: they try to fix the AI prompt (this helps in one chat, but does not change the answer for others), refresh only the main page, delete the page without a redirect, ignore English-language mentions, block crawlers without understanding the consequences, or do not recheck the result.
Frequently Asked Questions
Can you just ask ChatGPT to correct the answer? Within the same dialog, the model will take into account the refinement, but this will not correct the response for other users and will not change the sources. We need to work with a public information footprint.
How long does it take for the AI to update the information? There is no single deadline. If the answer is based on web search, changes may appear after reindexing; If the bug sits in the training layer, the horizon is longer. It is realistic to schedule inspections in 1, 2, 4 and 8 weeks.
Why does AI not cite our site if everything on it is correct? A third-party source may be better indexed, more authoritative, or more directly relevant to the query. Your site may also lack a page for a specific need such as reviews, comparisons, pricing, or local service.
What if AI confuses us with another company? Reinforce the distinction between entities: full name, country, sphere, domain, logo, sameAs, About Us page, structured data, directory profiles. If the confusion comes from a specific external source, start with it.
When a lawyer is needed? When the response contains defamation, trademark infringement, impersonation, false legal statements, or information that could directly harm clients. Then you need screenshots, URLs, dates, response wording, and official documents.
Key takeaways
Manual verification is useful at the start, but a spreadsheet becomes difficult to manage across dozens of prompts, languages, and competitors. VYDAI checks how AI systems answer your prompts, whether they mention the brand, which sources they cite, and which competitors appear beside it. It is not a "fix ChatGPT" button; it helps identify the problem earlier and turn it into specific tasks for SEO, content, PR, and support. Create an account or view the demo to inspect your priority prompts.
