What do AI answer accuracy checks answer for you?
They answer a question most businesses only ask after something goes wrong. Is what the AI engines say about us actually true? Wrong opening hours, a closed branch, an old price or a service you never offered can all turn up in AI answers.
The term answer accuracy refers to comparing each claim an AI engine makes about your business against a fact sheet you have approved.
Being mentioned is only half the job. If the details are wrong, a buyer may ring the wrong number or turn up at a shop that closed last year. Accuracy checks catch those errors so you can deal with them. Our guide on when AI gets your business wrong explains why this happens.
How do accuracy checks work?
They compare every answer with a fact sheet that a person has approved.
- Build the fact sheet. A draft is made from your website, your Google Business Profile and your brief.
- Approve it. A person reviews and approves the fact sheet, so the checks rest on facts you have signed off.
- Split answers into claims. Each AI answer is broken into single claims, such as an address, a price or a service.
- Check each claim. Every claim is marked correct, partly true, wrong, out of date or not covered.
- Review the flags. A second check reviews anything flagged, which keeps false alarms low.
- Group into issues. Similar problems are grouped, with the engines that said it, how many answers and a suggested fix.
Checks run on a regular schedule, every 14 days by default.
What do the verdicts mean?
Each claim gets one of five verdicts. The table shows what each one means.
| Verdict | What it means |
|---|---|
| Correct | The claim matches your fact sheet |
| Partly true | Some of it is right, but part is wrong or missing |
| Wrong | The claim goes against your fact sheet |
| Out of date | It was true once, but no longer is |
| Not covered | Your fact sheet doesn't say either way |
"Not covered" is useful in its own right. It often shows a gap in your fact sheet, or a detail you have never published clearly.
How are locations and offers handled?
Locations and offers each have their own list, with start and end dates. That lets the checks spot a branch that has closed or an offer that has ended.
Each list has one setting that matters a lot. If you mark a list as complete, anything not on it is flagged. If it is not complete, only details that can be proven wrong are flagged. This is helpful for multi-location brands, where the full branch list is known.
How do you find where a wrong detail came from?
For ChatGPT, Perplexity and Gemini, each claim links to the page the engine cited for it. So you can often see the exact source straight away.
Claude is asked without web search and names no source. Claude with search is also offered as a separate engine. For Claude, a source finder does the digging. It searches the cited pages, your own website, pages other engines cited for the same question, and Google. It then shows the pages that state the wrong detail, with the matching text. The page on Perplexity, Gemini and Claude tracking explains why the engines work differently.
What do you see on screen?
You see an accuracy score over time and per engine, with a list of open issues underneath. Each issue shows the claim, the engines that made it, how many answers it appeared in and a suggested fix.
Issues can be sent to Work as tasks, with the evidence attached. When later answers stop repeating the error, the issue is marked fixed. That gives you a record of what was wrong and when it cleared.
Why does accuracy matter for AI search?
Because people act on what AI engines tell them. A wrong detail costs you a call, a visit or a sale, and you may never know it happened. Every mention also counts for more now. According to Pew Research Center, people clicked a normal search result in just 8% of visits when a Google AI summary appeared, against 15% when none did. The scarce mentions you do get need to be right. AI engines can also produce a hallucination, a detail with no real source at all.
No tool can make an AI engine say the right thing. What you can do is fix the pages it relies on and make your facts clear everywhere. The entity audit helps with the second part.
Who uses it?
Multi-location brands use it to keep branch details right. Regulated firms use it to catch claims they could never make themselves. Agencies use it across clients, and accuracy stays hidden from client users until it is switched on for the workspace. It is on the Professional plan and above.
Where it fits
Accuracy checks read the answers collected by AI visibility tracking. Work turns each issue into a task and measures whether it cleared. The entity audit and Perplexity, Gemini and Claude tracking help you understand how each engine forms its view of you.