AI reputation management for wrong brand claims

Which AI reputation job fits your problem?

1 disputed claim calls for correction work. A wider discoverability gap calls for GEO. Start by separating a factual error from a visibility goal. That keeps work focused on what the answer gets wrong.

A correction review suits SaaS, finance, and crypto teams with verifiable facts. It does not replace a broader AI search visibility (GEO) plan when discovery is the main goal. If examples span several surfaces, begin with a GEO audit. Keep the specific reputation issue visible in that plan.

Before work starts, pick 1 to 3 representative prompts. Include the answer that concerns you. Add a neutral question about the same product. That comparison helps us separate a missing fact from a factual error.

3 review moves from answer to source map

3 review moves turn a captured answer into an action list: verify the claim, locate its evidence, then rank corrections. We keep the exact prompt beside each finding. Your team can review the reasoning.

1. Check the claim: We separate statements about the company, product, people, and policies. Your team confirms which facts are current and approved.
2. Trace the evidence: We review visible citations when available. Then we compare relevant public pages and profiles. We record conflicts instead of choosing unsupported wording.
3. Rank the fix: We identify the relevant correction, page or profile, and approver. Your team reviews sensitive claims before anything goes live.

The result is a source map, not a generic request to publish more. For a platform-specific issue, Perplexity optimization can sit beside the reputation review. This keeps a Perplexity answer problem distinct from broader AI visibility work.

A useful review file includes the original prompt, captured answer, assistant name, capture date, and source URLs. We return the claim, evidence, proposed action, and approval status together. Product, legal, and communications reviewers can use one record to resolve open points.

4 inputs for an AI answer review

4 input groups let us check an answer without guessing at the correct facts. Send the material you already trust. We flag gaps before recommending public changes.

• Answer record: Exact prompt, full answer capture, assistant, and date checked.
• Claim notes: The sentence or detail that is wrong, incomplete, or out of date. Add why it matters to buyers.
• Approved facts: Current product descriptions, policies, service limits, and company naming. Mark facts needing legal or compliance review.
• Source pack: Relevant product pages, company profiles, documentation, and dated announcements. Include URLs and supporting files.
• Priority: The audience and question that matter most. Tell us whether this is one answer or a repeated pattern.

We use your source pack to identify pages needing an update. We also flag claims with no public evidence yet. For missing buyer explanations, content for AI answers can support clearer, evidence-led copy. Technical access or page structure belongs in a separate technical AEO workstream.

Keep sensitive information out of public copy until your team approves it. A short note naming the factual owner saves review rounds. If you have only one answer capture, send it anyway. We can identify what evidence is still needed.

How are the review windows set?

Each window follows the work being checked: intake, source review, or follow-up. We agree the sequence after seeing how many claims and public pages need attention.

A focused review moves from intake to a prioritized correction list. Approved page or profile changes follow that review. We log each completed update. Timing for third-party publication stays separate from work on pages your team controls.

When the concern spans repeated questions, AI visibility monitoring can track a prompt set over time. For facts scattered across public references, entity and knowledge graph building may address broader source consistency. We keep either addition distinct from the original correction list.

Keep the correction record precise

The record should show what changed, where it changed, and what remains open. Perplexity and other assistants choose which public sources to retrieve. They also choose how to summarize them.

Publishers control their own pages. We can’t edit an assistant’s response directly. We also can’t promise that a correction will appear in its next answer.

• Evidence check: We link each recommendation to a public source or mark the evidence gap.
• Approval check: Your team confirms regulated, sensitive, or product-specific facts before publication.
• Completion check: We log the approved update, its location, and the prompts used for follow-up.

DigitalMedia1 gives you a prompt-by-prompt record, not a vague claim that an answer has been fixed. Send 1 to 3 exact prompts, answer captures, and the source material your team trusts. We’ll review the evidence and return a written quote within 2 working days.

How it works

1. 1. Send the answer capturesShare exact prompts, full answers, assistant names, and capture dates. Add the claims that need checking.
2. 2. Confirm the factsSend current product information and approved language. Your team identifies claims needing legal or compliance approval.
3. 3. Review the source mapWe connect disputed wording to public evidence. Then we return ranked corrections for your review.
4. 4. Approve the updatesYou approve sensitive facts and proposed copy. We support agreed page or profile changes and log completed work.
5. 5. Check the same promptsWe revisit the original prompt set. The report records observable differences beside completed work.