AI reputation management when assistants get your story wrong

When an AI answer misses the mark

AI reputation management is a practical way to address inaccurate, outdated or incomplete descriptions of your company in assistant answers. It starts with the answer a person can see, then follows the evidence behind its claims.

Suppose a buyer asks about your service and gets an old product description. We would record the question and identify the outdated detail. Then we would check which visible sources support it. That gives your team a specific correction to consider, rather than a vague request to improve its reputation.

The work can cover several kinds of issue. A feature may have changed, a company description may be unclear, or a source may give only part of the story. We look at the exact statement and its supporting material before recommending edits. For a wider view of how assistants present information, this work sits within AI search visibility. If you are still working out which answer patterns matter, a GEO audit can help establish that broader starting point.

From one question to a useful record

A useful review turns a concerning answer into a small set of clear actions. We keep the question, the response and its visible evidence together. Your team can then check the finding without guesswork.

1. Capture the moment. Save the exact prompt, assistant name, response and any citations shown. Add the date of the check. It helps distinguish one observation from another.
2. Check each claim. Compare the answer with company information and relevant outside material. Mark what is supported, what is stale, and what needs more evidence.
3. Assign the next action. Note whether the team should update a page. It might prepare a factual request to a publisher, or keep the issue under review.

Our claim-by-claim review uses a 3-part entry: statement, supporting source and next action. We also record an owner for each change. That way, someone knows what to review or approve. This makes it easier to bring legal, product and communications colleagues into the right part of the work. A shared AI visibility monitoring baseline can carry those agreed questions into later checks.

Which source should you address first?

Start with the source closest to the claim, then check whether the supporting context is easy to find. The right first move is usually the one that corrects the evidence itself, not simply the one that adds more brand copy.

Company information you can update

Review product pages, documentation, leadership details, company descriptions and public announcements. Check that important facts agree across the materials people may consult. If an accurate explanation exists but is hard to locate, make it easier to find. Connect it to relevant supporting information. Our content for AI answers work can help make those explanations clearer.

Information held by others

If a third-party page contains a factual error or lacks essential context, prepare a short request. Point to the exact wording, explain the issue plainly, and attach evidence that supports the correction. Keep the request focused on verifiable facts. A disagreement with an opinion needs a different response from a provable mistake.

For example, say an industry page lists a discontinued feature as current. We would identify the statement and check the company’s current documentation. Then we would help prepare a precise update request. The evidence record would show what was sent and what remains open.

A clearer account before the next conversation

Picture a research collective publishing weekly notes on the evening before an AMA. Someone spots an assistant answer that describes an older focus for the group. We would capture the answer, compare it with the latest notes, and help the team decide which source needs clarification.

That kind of moment calls for care, not a rushed rewrite across every page. We first check whether the current material already explains the change. Then we identify a useful place to make the updated facts easier for readers to verify. If outside coverage needs context, we prepare a factual note for the team to review before it goes anywhere.

A review can include:

• A record of agreed prompts, answers and visible citations.
• Notes connecting each concern to a supporting source.
• Recommended edits for materials your team maintains.
• Draft wording and evidence for approved correction requests.
• Follow-up records showing what changed and what is still open.

For questions about Perplexity, we inspect the citations visible beside an answer. We compare them with the claims being made. Our Perplexity optimization work can focus that review on questions your audience is likely to ask.

The work your team can carry forward

The most useful outcome is a clear record of what needs attention, who can act, and what evidence supports the next step. Your team can correct its own information, provide proof, and decide whether to request an update from an outside publisher.

We keep the work grounded in approved facts. Before a public edit or correction request, your reviewer checks the wording and supporting material. We then note the action taken and keep the original observation available for comparison. If source access or technical structure is part of the issue, technical AEO may be relevant alongside the factual review.

Perplexity chooses which sources to show and how to present an answer. Outside publishers decide whether to change their pages. We can commit to the agreed review, approved edits or requests, and documented follow-up. We cannot promise a particular citation or answer. This is why we separate completed work from changes that still need another party’s decision.

If an answer raises a serious factual concern, send us its wording and the documents that support your account. We’ll review the material with you, agree the first questions to check, and return a practical action plan.

How it works

1. Share the answerSend the prompt, assistant name, response and any visible citations. Add the facts or documents that show what seems wrong.
2. Agree the questionsWe confirm which audience, assistants and claims matter first, and who can approve factual changes.
3. Trace the evidenceWe record the answers and connect each concern to company material or a relevant outside source.
4. Review the next actionsYour team checks proposed edits and correction requests before anything is changed or sent.
5. Compare later checksWe document what changed against the agreed questions and identify any open items for your team.