AI Search Brand Monitoring: Finding Incorrect Descriptions of Your Company in AI Answers

How Do You Find Wrong Descriptions of Your Company in AI Answers?

Ask each major AI assistant a fixed set of questions about your company, run each several times, save the full answers and compare every claim against a one-page fact sheet of what is true. Then trace each error to its likely source and fix what you control.

The work has a natural rhythm. Audit, classify, trace, correct and re-check. It is closer to reputation management than to technical SEO, and it should sit alongside your visibility tracking and citation monitoring rather than replace them. Visibility tells you whether you are mentioned. This tells you whether what is said is right.

Five Kinds of Wrong Description

Different causes sit behind these. Assistants that retrieve live web results can repeat something that was true on an old page. Models answering from training data may know a version of your company that stopped existing. And when information is sparse, models sometimes fill in details that sound plausible. Our explainers on why AI search results get facts wrong and why AI hallucinates go deeper into the mechanics.

Build a Brand Fact Sheet and a Question Set

You cannot judge accuracy without a source of truth. Start with a single page listing the facts that matter, with dates.

Then write 15 to 30 brand questions. Include the blunt ones ("What is [brand]?"), buying questions ("How much does [brand] cost?"), comparison questions ("[Brand] vs [competitor]"), trust questions ("Is [brand] legitimate?") and questions about specific products or leadership. Include how a customer with a typo or an abbreviation would ask.

Sampling, Recording and Scoring

Run each question on the assistants your customers use, several times each, because answers vary from run to run. Save the complete text and, where shown, the cited sources. Then score every claim.

1. Accurate. Matches the fact sheet.
2. Partly accurate. Right in substance, wrong in a detail.
3. Inaccurate. Contradicts the fact sheet.
4. Unverifiable. Cannot be checked against your sheet, so investigate.

Add a severity rating. A wrong founding year is cosmetic. A wrong price causes support tickets. A false claim about safety, security, legal problems or pricing that induces purchases is serious and should be escalated the same day. Tracking the rate of inaccurate claims over time gives you the headline metric for this work, but read the full answers as well, since the worst errors hide in fluent sentences.

Where Did the Wrong Claim Come From?

If the assistant shows citations, start there. Open each one and look for the claim. Common culprits include:

• Your own outdated pages. An old pricing page, blog post or press release that was never updated or redirected.
• Third-party directories and review sites. Listings you did not write and have not touched in years.
• News and press coverage. Articles from a past product era, still highly ranked.
• Reference pages. Wikipedia, Wikidata and similar sources that other systems reuse.
• Competitor comparison pages. Pages that describe you unfavorably or outdated.
• Forum threads. Old discussions that describe issues you have since fixed.

If there is no citation, the answer probably comes from training data or from a product that did not retrieve anything. You cannot inspect that directly, but you can test: ask the same question with web search on and off, and compare. A claim that disappears with retrieval on and returns without it suggests a training-data issue.

What You Can Do, in Order of Reliability

1. Correct your own sources. Update or redirect old pages, add dates to time-sensitive facts and state key facts in plain text near the top of the page. An "About" or "Facts" page that lists name, products, pricing model and location is a clear reference for any system.

2. Add structured data. Organization markup with your legal name, logo, official URLs and sameAs links to your verified profiles helps systems connect the right entities and separate you from namesakes. Keep it accurate and matching the visible page.

3. Fix third-party sources. Contact directories and publishers to update stale listings. Ask for corrections in writing and keep records.

4. Handle reference sites properly. If a wiki-style page is wrong, follow the site's rules. Wikipedia discourages editing pages about your own organization, so use the talk page to propose changes with reliable sources, and never pay for edits in ways that break its policies.

5. Publish clarifying content. A short page addressing a common confusion, for instance "We are not affiliated with X," can help both people and retrieval systems.

6. Use each product's feedback tools. Most assistants provide a way to flag a wrong answer. It is not a guaranteed fix, but it costs little and can be documented.

What Not to Expect

There is no reliable button that removes a wrong statement from a model. Corrections to sources can change retrieval-based answers within days or weeks, though timing varies and nothing is guaranteed. Answers based on training data may not change until a new model is released. Meanwhile, the same question can produce different answers on different runs, as we explain in why AI search answers can change even when your website has not, so a fix can look like it worked and then reappear.

Avoid shortcuts. Do not stuff pages with hidden text or invented statements aimed at models. It violates platform rules, misleads readers and can backfire. And do not bury the problem: when a serious error is causing harm, tell affected customers plainly through your own channels while the correction works its way through.

Tracing One Wrong Price

Suppose a software company runs its monthly audit and finds that three of ten runs on one assistant say the product costs a price that was retired two years ago. The example is hypothetical, but the steps are the ones to follow.

First, the team checks whether the answers carry citations. Two of the three cite the same third-party comparison article. Opening it shows a table with the old price and a date from two years back. That is the source. A third answer has no citation, which suggests it repeated the same fact from training data or from a summary of that article.

Second, the team checks its own site. The current pricing page is right, but an old blog post announcing the previous plan still ranks well and states the retired price in its first paragraph. That is a second, controllable source.

Third, the fixes. The team adds a note and a link to the current plans at the top of the old blog post, updates its structured data, and emails the comparison site with a link to the live pricing page and a request to update the table. It then records the incident with dates and reruns the question set two weeks later. The wrong price shows up less often, though not never, and the team keeps monitoring rather than declaring victory.

The lesson is that a single wrong answer usually has a paper trail. Following it turns an unfixable-sounding problem into a short list of pages to edit or requests to send.

A Monthly Brand Accuracy Routine

• Rerun the brand question set on each assistant and record scores.
• Update the fact sheet whenever prices, products or policies change.
• Review the top cited sources for brand questions and fix any that are stale.
• Log each incident with date, platform, exact answer and action taken.
• Escalate serious errors the same day, and involve legal counsel where appropriate. This article is general information, not legal advice.

Frequently Asked Questions