ChatGPT Wrong About Your Company? How to Fix It in 6 Weeks

Why does ChatGPT get facts about my company wrong?

Three mechanisms produce false answers about companies, and each has its own fix.

Why it matters commercially: G2 research reports that roughly half of B2B software buyers start their research in AI chatbots and that 71% rely on them at some point (G2 via PRNewswire). For the deeper mechanics of what these systems draw on, see how AI decides which brands to cite.

Which wrong answers are worth a six-week project?

Only errors that change a buying decision or carry legal risk justify a project; most errors are harmless drift.

The dangerous middle tier is commercial damage: false enough to change a purchase, not false enough for a courtroom.

How do I document the error and trace its source?

Document first, because AI answers are not reproducible by default: the same question gives different answers across sessions.

Week 1: the ten-prompt evidence log.

• 4 brand basics: "What is [company]?", "Is [company] still in business?", "Who owns [company]?", "Where is [company] based?"
• 3 buying prompts: "[company] pricing", "[company] vs [competitor]", "best [category] providers"
• 3 adverse prompts: "[company] problems", "[company] lawsuit", "is [company] legitimate?"

Run all ten on each platform your buyers use (ChatGPT, Gemini, Perplexity, Copilot, Grok), in clean or logged-out sessions, with web search on and off where possible. Per answer, record the date, platform and model version, whether browsing was on, the full text, a screenshot and the false claim verbatim. Every escalation later (a Wikipedia talk page, a vendor form, a lawyer) will ask what was said, where and when.

Weeks 1-2: trace the source.

• If the answer cites sources, open every cited link. One usually contains your false claim or something close to it. That page, not the chatbot, is the fix target.
• A useful follow-up: "Which sources say [false claim] about [company]?" Treat the reply as leads, not truth, and verify each one.
• If there is no citation and the answer is wrong with browsing off, you are facing training-data memory. There may be nothing on the live web to correct. Publish dense, correct, retrievable material so search-grounded answers override memory now and a later model snapshot can learn the right facts.

End with one line per false claim: claim, mechanism (memory, retrieval or entity confusion), source URL if one exists.

What is the Correction Stack, and in what order do I work it?

The Correction Stack is five layers ordered by how much control you have, starting where you have full control and same-day speed. Most teams invert it: they press "report" and stop. Platform report tools are unreliable as a primary channel (Mention Network); there is no published SLA and no obligation to act.

• Layer 0: your own pages. An About page stating founding year, ownership, status and locations in plain sentences; a pricing page with real prices; an FAQ that answers the exact buyer questions; an llms.txt file for crawlers. Cost: hours.
• Layer 1: structured entity data. Wikidata statements with references, schema.org Organization markup (foundingDate, address, sameAs), and consistent facts across LinkedIn, business registries and profile sites. This layer untangles entity confusion; see our Wikidata and knowledge graph guide.
• Layer 2: Wikipedia accuracy fixes. The highest-weight source many assistants lean on, and the easiest to damage by editing it yourself. Edit requests only (next section).
• Layer 3: third-party sources. Ask the publisher of the stale article for an update or a correction note, and refresh major profiles. If the claim came from coverage of a namesake, one accurate new piece gives retrieval something better to quote.
• Layer 4: platform feedback. Thumbs-down, report forms, privacy and legal channels. Submit once with your evidence log and expect nothing on a schedule.

The permanent version of this architecture (owned pages, entity layer, Wikipedia, monitoring as standing infrastructure) is the AI Reputation Stack.

Can I ask Wikipedia to correct an error about my company?

Yes, through disclosed edit requests on the article's talk page, never by editing your own article. A conflict of interest (COI) means editing about an organization you are connected to or paid by; Wikipedia's conflict of interest guideline asks such editors to propose changes instead of making them, and conflicted edits get reverted, tagged and archived.

1. Register an account and disclose your affiliation on your user page. If you are paid, the Wikimedia Foundation terms of use require disclosure. Our paid-editing disclosure explainer covers the mechanics.
2. Post an edit request on the talk page: quote the incorrect sentence, state the correct fact and attach an independent reliable source, not your own website.
3. Keep it factual and minimal. Uncontroversial corrections (founding year, headquarters, current operating status, a leadership change) are routinely considered; requests that upgrade adjectives are not.
4. For false claims about living people, the biographies of living persons policy requires poorly sourced claims to be removed, which helps well-documented requests.

Talk-page requests can take days to weeks depending on page traffic, and the effect on AI answers follows the normal propagation window after that. Do not edit your own page to speed this up.

Wikidata is more flexible: affiliated editors may correct statements directly if each statement is referenced and neutral. Audit your item for a wrongly attached dissolution date (a classic root of "AI says the company closed"), merged or duplicate records confusing you with a namesake, and outdated officers or websites. Google's Knowledge Graph and many AI systems read Wikidata, which makes it a quiet but high-leverage fix.

What can each platform's feedback channel actually do?

None offers a form to rewrite what it says about your company; brand facts move mainly when the sources behind answers change.

These are operational observations, not vendor commitments. Plan the six weeks around layers 0-3 and treat the buttons as a one-time submission.

What should I not do?

• Do not spam the feedback buttons. Ten thumbs-downs from your team look like abuse, not a louder signal. Report once, with evidence.
• Do not publish a rebuttal that repeats the false claim. A post titled "No, [Company] has not shut down" gives retrieval systems the exact string you want forgotten. State the true fact in headlines and structured data.
• Do not go lawyer-first over ordinary errors. A wrong founding year has no editor to compel and no single document to retract. Legal review fits defamation-grade output (fabricated crimes, fraud, regulatory violations), especially if repeated across platforms; then your evidence log becomes the exhibit file.
• Do not declare victory after one clean answer. Sampling variance produces correct answers by accident.

How do I know the fix worked?

Re-run the same ten prompts on the same platforms, in clean sessions, at weeks 2, 4 and 6, and classify each false claim:

• Fixed: correct in two consecutive runs, with browsing on and off.
• Partial: correct when the model searches, still wrong from memory.
• Unstable: alternates between right and wrong.

A quiet week 2 is normal. Partial at week 4 usually means your strongest sources updated but the entity layer still disagrees, so re-audit layer 1. Unstable at week 6 means the trace missed a source; return to the citation step with fresh answers. After week six, drop to a monthly cadence with the same frozen prompts; when manual logging stops scaling, see our review of AI brand monitoring tools.

Some cases will not fully resolve:

• No-source hallucinations cite nothing and match no live document. The only mitigation is density: enough consistent, retrievable truth that search-grounded answers outweigh memory.
• Model-version lag. Your fix can land in search-grounded mode while older or offline modes stay wrong until the vendor ships an update.
• Heavyweight namesakes can bring the confusion back with each new model. Distinct entity data reduces the frequency; nothing eliminates it.

In those cases monitoring catches regressions early and builds a dated record of what persists.

How WikiBusines helps

We work on the part you can influence: the sources assistants read. Paid Wikipedia work is always disclosed on Wikipedia in WikiBusines' name, and we never guarantee publication or any AI output.

• AI visibility audit, €490: what ChatGPT, Gemini, Perplexity, Copilot and Grok say about you today, which sources they lean on, and which layer to fix first.
• Wikidata entity, from €680: a referenced, deduplicated entity record, which addresses wrong dates and namesake confusion.
• Wikipedia editing, €700-€1,900, for accuracy fixes on an existing article, and annual support from €720/year to keep it accurate. If you have no page yet, the notability audit from €490 (credited toward page creation if you order it within one month from €2,400) shows whether one is realistic.

To start, contact us with the claim, the platform and a screenshot.