How to find and fix wrong or negative claims AI makes about your brand

Why claims go wrong

Most wrong claims come from one of four places. Outdated information: a price, a feature or a policy that changed, still repeated by old pages. Thin information: the engine has little to go on, so it generalises from the category. Third-party framing: a review or forum thread that is unfair or about a different product. Confusion: your name resembles another brand, product or person.

Knowing which of these you are facing decides the fix. Outdated and thin information are fixed by publishing clear, current facts. Third-party framing may need a correction or a better balance of sources. Confusion needs disambiguation on your own pages and profiles.

Step 1: collect the claims, not just the score

A sentiment percentage tells you something is off but not what to do. You need the actual sentences. Run branded prompts across engines, on a schedule, and extract each statement the answer makes about your brand along with its tone.

Group them into themes: pricing, support, reliability, accuracy, ease of use, integrations. Count how often each recurs and on which engines. A claim made once in one answer is noise. A claim repeated across engines and weeks is a real part of how you are described.

Related reading: Why AI names your competitors instead of you: a diagnostic checklist and Branded vs unbranded prompts: why you need to track both.

Step 2: separate three kinds of claim

Wrong: factually incorrect, such as a feature you do not offer or a price that is out of date. These are the highest priority and the most fixable.

Unfair or incomplete: technically defensible but missing context, such as a limitation stated without mentioning a workaround, or a comparison that ignores a segment where you are strong.

True and unflattering: an accurate description of a real weakness. These are not errors. They are feedback, and the right response is often to improve the product or the policy, then publish the change.

Step 3: trace the source

Engines that show citations make this easy: open the pages cited beside the claim and look for the statement. Where there are no visible citations, search for the phrase and for the underlying fact, since it usually exists somewhere.

Common sources are review sites, comparison articles written by competitors or affiliates, old press coverage, forum threads, outdated documentation and your own legacy pages. Keep a list with the claim, the source URL, how old it is and who controls it.

Step 4: correct what you can

Your own pages first. Update outdated facts, remove or redirect obsolete pages, and make the correct information easy to find and quote: a pricing page that states prices in text, a plain feature list, a clear explanation of limits.

Profiles and listings you control next: directories, marketplaces, social profiles, app store pages and company databases. Make descriptions consistent with your site.

Third-party sources: contact the publisher with the specific correction and evidence, politely and briefly. Many will update a factual error. Do not try to remove honest criticism, and do not pay for or fabricate reviews. It is a policy risk and engines are designed to discount it.

Step 5: publish a direct answer

For each recurring doubt, write a page or section that addresses it plainly. If engines say your support is slow, publish your actual support hours, response targets and escalation paths, with any recent improvements. If they say you lack an integration, state which integrations exist and what the roadmap is, with dates.

Write in the form of the question people ask, answer in the first sentence, and support with specifics. Pages like this give an engine something accurate and quotable that did not exist before.

Step 6: measure again, with the same prompts

Re-run the same branded prompts on the same schedule. Compare the share of positive claims and the frequency of the specific wrong claim. Because answers vary, judge by a trend over several runs, not one.

OClever extracts claims from every answer, groups them into themes with example sentences, shows which sources they relied on, and lets you mark a claim as incorrect so it is handled separately from true criticism. The branded section of the report lists what engines praise and what they doubt, with counts, so you can see whether a fix reduced the doubt.