How to Fix Incorrect Information About Your Business in ChatGPT & AI Search

You ask ChatGPT, “What does [your company] do?” and it recognizes you — which is the good news. Then it gets the details wrong: it names a CEO who left two years ago, lists an office you closed, mentions a product you discontinued, puts you in the wrong industry, or blends you with a similarly named company. The instinct is immediate: “How do I tell ChatGPT this is wrong?”

That may not be the best first question. The better one is: where did the incorrect information come from, and why isn’t the correct information strong enough to replace it? AI business-information problems almost always originate upstream — in an inconsistent, outdated, or ambiguous information ecosystem — and the durable fix is to correct that ecosystem, not to argue with a single answer.

Why does ChatGPT have wrong information about my business?

ChatGPT may show incorrect or outdated business information when the public information surrounding a company is inconsistent, stale, ambiguous, or hard to verify. Depending on the specific ChatGPT experience, an answer may reflect current web retrieval, older model knowledge, the context of the conversation, or a combination — so a wrong answer isn’t necessarily proof that any single source is wrong; it can mean the web offers competing versions and the system picked one.

Where do AI systems get information about businesses?

The information ecosystem around a company can include its website, search indexes, news, directories, professional and social profiles, review platforms, marketplaces, customer and partner sites, associations, public documentation, structured data, and historical web content. Not every AI platform reads every source, and they don’t weight them the same way.

The strategic issue isn’t whether every AI system reads every website. It’s whether the web contains a clear, consistent, authoritative representation of the business — the premise behind how third-party sources influence AI recommendations.

10 common reasons AI has your business wrong

1. Your own website still contains outdated information

Sometimes the source of the “AI error” is sitting on the company’s own site — an old homepage or About line, a stale footer, team and product pages, old press releases, PDFs and media kits, or archived landing pages. Audit your own domain before blaming the model.

2. Structured data contradicts the visible website

The visible page says “Jane Smith, CEO” while the schema still names the previous CEO — or carries an old address, business name, or sameAs. Look for duplicate Organization or Person nodes and stale plugin/theme schema. Structured data can be technically valid and still be factually wrong (more on schema).

3. Third-party directories carry stale information

Google Business Profile, industry and professional directories, marketplaces, review platforms, and association listings may still show old facts. Prioritize the ones that matter rather than chasing every listing on the internet.

4. The business rebranded

A rebrand changes the company’s identity faster than it changes the web. The old name, domain, logos, social profiles, directory entries, press coverage, and customer mentions can linger for years. Where appropriate, state continuity explicitly (“formerly [Old Brand]”) — while planning to phase legacy wording out over time, not leave it forever.

5. The company merged or was acquired

Ownership, brand, products, executives, locations, and divisions all become ambiguous after a deal. Acquisitions create entity relationships that should be stated clearly instead of left for search systems to infer.

6. Another company has a similar name

Shared acronyms, product names, or founder names invite confusion. The less unique the name, the more explicit the disambiguation should be — full name, domain, geography, industry, leadership, and consistent descriptions across authoritative profiles.

7. Old products or services are still documented

A discontinued product, service, market, or division leaves references behind. Update the page, clearly label what’s discontinued, point to the replacement, or redirect when appropriate — without deleting genuinely useful history for no reason.

8. Leadership information is inconsistent

A former CEO, an old founder role, or a departed employee lingers across leadership pages, author bios, schema, professional profiles, press releases, and directories. The fix is to make the Person → Role → Organization relationship current and consistent everywhere it appears.

9. Geographic information is unclear

Headquarters, a physical office, a service area, and markets served are not interchangeable — but they’re often treated that way. An office that moved, a closed location, an expanded service area, or an outdated Google Business Profile can all produce a wrong “where.”

10. The AI may be relying on older knowledge

Here’s the nuance most guides skip: even if your current website is perfect, an answer may still reflect older information depending on the experience. Sometimes the web is correct and the AI answer is still stale. The response is the same — strengthen current authoritative information and use official feedback mechanisms where available — but don’t expect “update the site and ChatGPT changes tomorrow.”

The five layers of accuracy

The same framework behind AI Search Visibility applies directly to accuracy — accuracy improves when the entire digital information system increasingly agrees about what is true now.

The correction workflow: fix the source, not the screenshot

Before trying to correct the AI answer, find where the incorrect fact still exists on the web — then correct it at the source and make the current truth unmistakable.

Entity SEO is partly the practice of reducing unnecessary ambiguity about who or what something is (entity SEO, and how AI systems understand your business). Make sure the current information is retrievable in the first place, too — crawlable and technically sound.

Can you directly edit what ChatGPT says about your company?

Not through a universal dashboard — there is no profile where a business edits its facts across AI platforms. OpenAI has publicly indicated it can’t simply edit a specific “fact” inside the model itself; it can filter or adjust outputs, not rewrite stored knowledge. ChatGPT does offer an in-product feedback control (the thumbs-down on a response) to flag and explain an error, which OpenAI reviews — but flagging doesn’t rewrite the answer on the spot or guarantee a change, and it doesn’t replace fixing the sources. So the reliable path is upstream: correct the information ChatGPT can retrieve and cite, strengthen the current authoritative version, and use official feedback where available. (These controls evolve — verify current OpenAI documentation.)

Does correcting ChatGPT in a conversation update it globally?

No. Correcting an answer inside a conversation may change the context of that conversation, but it doesn’t update the model’s knowledge, search results, or other users’ conversations. Correcting a conversation is not the same thing as correcting the information ecosystem.

What if ChatGPT cites the wrong source — or none at all?

Where a citation is visible, inspect it. If the cited source is wrong and you own it, correct it; if it’s a third party, request an appropriate factual correction where possible — then strengthen the current authoritative information so the better version is easier to find. Don’t expect instant re-citation or removal. Where there’s no citation, source-tracing is harder: compare the same question across multiple prompts, traditional Google search, Google’s AI experiences, and Perplexity, and look for the same wrong fact repeating. Repeated errors across multiple systems can be evidence that the ambiguity exists beyond one answer — a reasonable inference, not a certainty.

Fixing wrong information in Google AI and Perplexity

The principle is identical — correct the underlying web — but the mechanics differ, and each platform needs its own attention. For Google, that means fixing the underlying pages, ensuring they’re crawlable and indexable, using Search Console’s URL Inspection to request (re)indexing of updated pages, keeping structured data and Google Business Profile current, and using available feedback controls — there is no “edit the AI Overview” button. If the problem is absence rather than inaccuracy, see why your business isn’t showing up in Google AI Overviews. Google’s Search Console generative-AI performance reports (launched mid-2026) show impressions in AI Overviews and AI Mode but not clicks or prompts, so they help you monitor visibility, not edit answers. For Perplexity, where answers cite sources, inspect the citations, correct the ones you own, and pursue third-party updates where appropriate (how citations work in Perplexity). A fact corrected in one system can remain wrong in another for a while.

How long does it take — and why does wrong information come back?

There’s no universal timeframe. It depends on crawling and indexing, how many conflicting sources exist, whether the answer draws on live retrieval or older model knowledge, and how quickly third parties refresh. The website can change today; the entire information ecosystem may not synchronize today. And when errors return, it’s usually because old or conflicting sources remain, the entity is still ambiguous, or only one source was corrected — if the web still contains competing versions of the business, one fix may not resolve the underlying disagreement.

How to monitor whether AI has the correct information

Brand accuracy should be monitored systematically, not only when someone notices a bad screenshot. Test consistent prompt families — identity (“what is [company]?”), leadership (“who is CEO of [company]?”), products/services, geography (“where is [company] headquartered?”), brand relationships, and reputation — and record, for each, whether the answer is correct, partially correct, incorrect, outdated, or unsupported, along with the platform, prompt, date, and any cited source.

A simple matrix makes disagreements obvious. (Illustrative values below — not real data.)

The mechanics of repeatable measurement are in how to measure AI Search Visibility and AI rank tracking vs. Google rank tracking.

When the AI isn’t actually wrong

Sometimes an answer only looks incomplete. A company complains that AI associates it only with Service A — but its website is 90% Service A and barely mentions Service B. Or it says the company is “local” when the site documents only one market. Sometimes an AI answer looks incomplete because the business’s digital footprint is incomplete. That’s not a correction problem; it’s a visibility problem — and the fix is to build out the real, current representation of what the business does and where, the broader work of becoming recommendable.

Prioritizing corrections

Not every wording difference is a misinformation crisis. Fix the facts that materially affect how customers and search systems understand the business.

What not to do

What an AI information-accuracy audit includes

Pulling it together, a focused accuracy audit reviews four source groups and turns the findings into a correction roadmap.

This is one focused slice of a broader AEO audit. And it’s how BuckStone approaches it: we don’t begin by trying to “correct ChatGPT” — we begin by identifying the source of the disagreement, then make the correct version of the business the easiest version to discover and verify. AI accuracy is an output; information consistency is the work underneath it. If you’ve already found one incorrect AI answer, the larger question is what else search and AI systems think they know about your business — which is exactly what the Digital Visibility Brief is built to surface.

Frequently asked questions