Learn to verify service and brand claims in AI answers.

Start with a set small enough to inspect carefully, selected from the work that matters to the business. Include your main appliance services, an important brand distinction, a territory boundary and a service you exclude. For a small operation, a dozen well-chosen scenarios can be a practical starting worksheet, not a statistical sample of all customers. Expand when a finding raises a specific question, such as whether an error affects every branch or only one brand. Preserve the original group so you can make consistent comparisons. Testing hundreds of combinations is less useful if nobody opens the cited pages or turns incorrect descriptions into an assigned correction.

Save the exact answer, displayed business name and destination URL before changing anything. Check whether the mistake concerns a similar trading name, a former business, a branch or an unrelated provider. Review your own public name, website address and telephone details, then inspect any cited directory entry. Correct mismatches in sources you control and request edits from relevant listing owners. Use the platform's feedback route where available to report the specific wrong association. Keep this as an identity error in the audit rather than counting it as a successful mention. Test the branded question again later, while recognizing that you may not be able to identify every source used to produce the response.

No. An answer is evidence of what that platform returned in that context, not proof of a particular cause. Inspect the cited sources and compare them with your current pages. If your website clearly says something inaccurate, fix that factual problem. If the website is correct but a directory is outdated, address the directory. If all visible sources are accurate, retain the error and report it without inventing a hidden ranking explanation. Separate the observation, verified evidence and working hypothesis in your notes. That distinction prevents unnecessary rewrites and makes it easier to see whether later changes correspond with better descriptions across comparable scenarios.

It should contain the tested questions, business facts used for comparison, dated answers or captures, source URLs and a list of specific findings. Separate discovery from accuracy: missing from an unbranded shortlist differs from being incorrectly described as accepting warranty work. Each actionable item should identify the incorrect or missing fact, its business consequence, the source to investigate, the responsible person and the proposed next check. Include what remains unknown. A dashboard number by itself cannot tell your dispatcher what to fix. The report should finish with a manageable correction queue and a repeatable review method, while keeping booked-repair results separate from observed AI mentions.