How common is it for AI to get facts about a business wrong?

It happens often enough to check. Errors usually come from outdated pages, confused names or thin information, so any business can be affected.

Which facts should I check first?

Start with the ones that cost you if they are wrong: pricing, what the product does, who it is for, location or availability, and who runs the company.

How do I tell where a wrong claim came from?

If the answer cites a page, open it. Many errors trace back to an old page, a third-party listing or a similar company with a near-identical name.

Do I need to check every assistant?

Yes. Each one searches and reads differently, so a claim can be right in one and wrong in another.

How to spot when AI says something wrong about your business: Citewake

To spot when AI says something wrong about your business, ask each assistant factual questions about you, check every claim against your own records, and log what is wrong along with the page it came from, if one is cited. Do it signed out, more than once, and across all four assistants.

An error that sits in an AI answer is read by people who never visit your site. That is reason enough to look.

Why does AI get facts about a business wrong?

Mostly for ordinary reasons, not mysterious ones.

Outdated sources. A pricing page, a directory listing or a review from a few years ago says something that is no longer true, and the assistant reads it as current.
Confused entities. Two companies share a similar name, or a company and its parent are blended. The assistant attributes one's facts to the other.
Thin information. If little is published about you, the assistant may fill gaps with something plausible. A fluent sentence is not a checked fact.
Second-hand claims. A roundup states something about you and the assistant repeats it.

You do not need to know exactly how a given assistant works inside to act on this. You need to see what it says and trace it.

What questions should you ask?

Use plain questions a customer would ask, and keep two groups apart.

Questions that name you:

"What does [your company] do?"
"How much does [your company] cost?"
"Who is [your company] for?"
"Where is [your company] based?"
"Does [your company] integrate with [common tool]?"

Questions that do not name you, where you might be described alongside others:

"What are the main tools for [your category]?"
"Which [kind of provider] suits [your typical customer]?"

The first group tests what is said about you directly. The second shows how you are placed against others. Keep them separate in your notes, because a wrong claim in a branded question and a wrong claim in a discovery answer call for different fixes.

How do you check the claims?

Take each answer and break it into individual claims. Then check each against a source of truth that you control, such as your pricing page, your product documentation or your company records.

Mark each claim:

Correct.
Wrong. Contradicted by your records.
Outdated. Was true once.
Unverifiable. Vague or unsupported, such as "widely praised".
Wrong entity. True of a different company.

Be strict about the difference between wrong and unflattering. A fair limitation described accurately is not an error, even if you would prefer it were not mentioned. Fixing a perception is a content job. Fixing a fact is a correction job.

Where did the claim come from?

If the answer cites sources, open every one. You are looking for the sentence that matches the claim.

If it is on your own site: the error is yours to fix, and it may be a stale page that you forgot.
If it is on a third-party page: note the page, because that is where a correction request goes.
If nothing cited supports it: the claim may be the assistant's own composition. Record it anyway, because it is still what a reader saw.

Citations show what the answer attributes to a page, not proof that the assistant used the passage you find. Treat them as strong leads. For more on reading the evidence, see how to read an AI answer like an analyst.

How do you make the check reliable?

Three habits make the difference between noticing an error and proving it.

Ask more than once. Answers vary between runs. An error that appears in three of five runs is a pattern. One that appears once may be a fluke. See why AI answers change every time you ask.
Check all four assistants. They search differently, so one can be right where another is wrong. Comparing answers across assistants covers the setup.
Save the evidence. Keep the full answer, the question, the assistant and the date. If you later fix the source and the error disappears, you want proof of what it said before.

What should the error log contain?

A short table is enough:

Field	Purpose
Date and assistant	When and where it appeared
Question asked	So you can reproduce it
The wrong claim, quoted	Exactly what was said
The truth	What your records show
Cited source, if any	Where to fix it
Runs affected	For example 3 of 5

Review the log after a round of checks and sort by cost. A wrong price or a claim that you offer something you do not is worth fixing first. A slightly dated description of your team can wait.

What happens after you find an error?

Fixing the cause is the next step, and it depends on where the error came from. Correcting a stale page, updating a listing and clarifying your own homepage all help. The practical steps are in how to fix outdated information in AI answers.

Be realistic about timing. An assistant does not change its answer the moment a page changes, and some wrong claims fade slowly. Re-run your check on the same questions after a while rather than expecting an instant result.

To see a first picture of how assistants treat your site, the Citewake checker is a quick starting point. For how Citewake handles disputed readings of its own data, including corrections that are added rather than written over, see the methodology.