What to Do When AI Misrepresents Your Brand

If AI is misrepresenting your brand, the first step is to turn the faulty sentence into a verifiable record. Which question, on which date, and on which platform produced that answer? Does the answer cite a source? Is the statement genuinely false, or is it a negative judgment about the customer's choice?

Separating these questions matters. "This product may not suit small teams" can be a judgment. "This product is no longer sold" is a claim you can compare against a current product page. The two situations do not call for the same correction process. The method below helps you treat false information separately while you measure brand perception.

Identify the type of error

Save the full answer and flag the problematic sentence. Then place it into a category. To start, you can use the representative classification below.

Situation in the answer	Class	First check
Another company's services attributed to you	Identity confusion	Name, domain and industry
An old price presented as current	Freshness error	Official pricing source and date
A feature you don't offer is claimed	Product-info error	Product documentation and actual scope
You're deemed unsuitable for a certain team	Judgment	Rationale and usage context
An unqualified comparison is made	Ambiguous comment	The full statement and its sources

Use separate fields for negative sentiment and false information. An answer that describes an accurate product limitation may look negative; a positive answer may praise a feature you don't actually offer. A single sentiment score does not show this difference.

Build a source for the correct information

Add verified information, a source URL, a check date, and a responsible person to every correction record. For pricing, the pricing page; for a technical capability, current product documentation; for company identity, the official about page can all be suitable starting points.

If your own pages contradict each other, resolve that conflict first. For example, if an old blog post describes a feature as if it were in every plan while the pricing page shows a different scope, publishing a new explanation alone will not erase the old statement. Find and correct the affected pages one by one.

For source credibility, it also matters that the author, the method used to produce the information, and the relevant expertise are clear. Google's content guidance recommends making these elements assessable by the reader. Use this approach not as a guaranteed formula for fixing an AI answer, but to raise the quality of your own information. Google's helpful content guidance.

Interpret the answer's sources correctly

If the assistant shows a link, open the page and check whether the false claim is actually there. The presence of a source link in an answer does not prove that every sentence is backed by that source. The source may be current while the answer has misinterpreted it.

If the claim appears on a third-party page, make sure your correction request includes the problematic statement, the correct information, and the official source. Present a checkable difference rather than an accusatory text. Record the date the request was sent and its outcome; don't consider the job done until you see the page change.

If no source is shown, don't guess which page the answer came from and speak with certainty. Write "no source stated" in the record. Where available, you can use the platform's feedback mechanism, and at the same time improve the consistency of your own public information.

Prioritize by impact on the customer

Not all errors carry the same urgency. Wrong product availability, wrong pricing, or confusion with another company can directly affect a purchase decision. A minor difference in wording may not require the same level of priority.

When you prioritize, weigh the error's recurrence, which customer questions it appears in, and the decision it affects together. Don't count the same record reappearing on different screens as a new incident. If the problem recurs across many independent answers, widen the scope of your review.

When you review Maya's brand perception analysis, examine actual answer examples alongside the positive and negative labels. Competitive benchmarking is a workspace that helps you see whether the same evaluation criterion is applied to other brands as well.

Track the correction with fresh answers

After the correction date, collect fresh answers with the same questions. Don't count a replayed old answer as a new measurement. Keeping the platform and date range fixed, check whether the error recurs.

A representative follow-up record might read: "Product documentation updated; the next check group showed no pricing error, identity confusion persisted in two answers." That phrasing is more measured than "AI now knows our brand correctly." It shows the scope of the correction and leaves the next task open.

The goal is not to make every answer positive. It is to let the customer decide based on current, accurate information about your brand that fits its context.