What to Do When AI Gets Your Business Wrong

Classify the claim before trying to fix it

Not every uncomfortable answer contains the same kind of problem. A wrong address is a factual error. A claim that the business offers a discontinued service is an outdated fact. A description such as “expensive” may be an opinion or an unsupported inference. The response should match the type of claim.

Profound's “parrot problem” analysis separates brand presence from the accuracy and character of what an AI answer says. For a service business, that distinction is practical: a prominent mention with the wrong booking information can send a customer in the wrong direction.

Prioritize errors that affect a customer's action, such as contact details, locations, available services, and material commercial conditions. Avoid spending the first hour debating a vague adjective while a broken phone number remains published.

Save enough evidence to reproduce the issue

Capture the exact prompt, full answer, date, platform, visible mode, and linked sources. Note whether the conversation already contained information about the business. A follow-up answer in a long conversation is not equivalent to a new discovery question.

Do not assume a cited page caused every sentence in an answer. A citation is a lead to inspect, not a complete explanation of the model's reasoning. Record uncertainty when the source of a claim cannot be established.

Check your own public facts first

Read the relevant website page, FAQ, footer, downloadable documents, and structured data. Look for the outdated claim or wording that could reasonably cause confusion. Check whether the correct detail is prominent enough for a new customer to find.

Consider a fictional appointment-only studio whose registered office is listed in the footer without a label. An answer presents that address as a walk-in location. The business should clarify the address's purpose and publish the actual appointment process. It should not invent opening hours to make the answer look more complete.

If the website already states the correct information, inspect legitimate public profiles and older documents. Use the business information checklist to distinguish branding differences from genuine contradictions.

Make a targeted correction

Update the source you control with a clear affirmative statement of the correct fact. For a discontinued service, explain the current scope and any useful next step. Keep the correction proportionate to the customer need.

For an external listing, use the platform's normal correction process. Identify the exact field, the current fact, and an appropriate public reference. Keep a record of the request and check the published result. Do not create fake accounts, reviews, or articles to overwhelm an incorrect statement.

For a subjective description, inspect whether a missing fact is encouraging an unsupported inference. A pricing page that hides scope may leave a reader unable to judge value. Publishing a clear explanation can help customers evaluate the service, but it does not entitle the business to control independent opinions.

A dedicated clarification page can be useful for a widespread misunderstanding. It should answer a real question clearly and link to the relevant service. Repeating the same false claim across many pages can amplify confusion without adding evidence.

Retest without promising control

First verify the corrected source itself. Confirm that the live page contains the new fact and that old internal links do not lead to a contradictory version. Then repeat the saved prompt under comparable conditions and record the result, including when the error persists.

Use a few related questions to test whether the correction is robust, while keeping the original prompt available for comparison. Do not declare success from one favorable answer after ignoring several unfavorable ones. Different tools and runs can continue to use different sources.

When a platform provides a feedback channel, a concise factual report may be appropriate. Submission is not proof of acceptance or a promise of a timeline. Keep “website corrected,” “external source corrected,” and “answer observed as correct” as separate states.

Finally, prevent recurrence by assigning ownership to the underlying fact. A clear maintenance process is more useful than repeatedly correcting symptoms. Use a repeatable prompt test to observe representation over time without claiming complete control over an AI system.

A real error we recorded on FoundUp

On September 28, 2026, a DataForSEO-mediated model response incorrectly expanded FoundUp's GEO service as “Geographic” audits. GEO in this service means Generative Engine Optimization. A same-day repeat still contained the error even though the live homepage included the correct definition. This is an observed error, not a successful correction case.

The answer included a link to getfoundup.ai in its text but no structured source annotations, so it did not establish which page caused the mistake or when that page was retrieved. Our response was to make the service definition consistent across visible pages and metadata and preserve the original test conditions. Read the exact prompt set and collection method before interpreting the sample.

Write a correction status that does not hide uncertainty

This is a failed answer check, not proof that the website definition was missing. A model may still make an error when the public source is correct. The practical record is “source checked; answer still wrong under these conditions”, which is more precise than “GEO fixed”.

Check the current service definition and answers, preserve the original test, verify the live release when it changes and schedule a comparable observation. If the answer later becomes correct, record that result without attributing the change to one edit unless the evidence supports it.