When AI gets your company wrong: a source-led fix

What AI reputation work covers

Quick test: Ask 3 AI tools the same question about your company. Quote any false line exactly and save the date.

AI reputation management addresses how assistants describe a brand when their answers contain factual errors, stale details or an unbalanced account. The work is not simply adding keywords to a site; it is identifying the claim, checking its source and improving the evidence available to readers and systems.

This service is a fit when prospective customers ask assistants about your company, product, leadership, safety or reputation and receive an answer that is materially inaccurate or missing important context. It can also help a SaaS or crypto business whose products, naming or policies have changed and whose public information no longer tells a consistent story.

Start with examples, not a general feeling that visibility is poor. Save the exact prompt, answer, date checked and any cited links. Then mark each issue as factual error, outdated information, confusing wording or an opinion you disagree with. That distinction matters: factual errors may have a verifiable correction, while an unfavorable interpretation calls for better context rather than a demand to erase it.

For the wider discovery and visibility picture, see our AI search visibility overview and GEO audit.

How do we review wrong AI answers?

We review the answer as a reader would: capture the wording, inspect the citations shown and compare each claim with current, verifiable information. A source-by-source review keeps the proposed response tied to evidence instead of speculation about how an assistant produced its answer.

For each example, we record:

• The prompt and answer, including the date and the assistant checked.
• The specific statement that is wrong, incomplete or outdated.
• Any cited source and whether it supports the statement.
• The best available first-party evidence, such as a current product or policy page.
• The practical correction route: update an owned page, clarify a public profile, or request a review from a publisher.

This creates a decision list your team can act on. Address factual claims with direct evidence and consistent wording; do not publish a broad defensive statement when a precise product page or policy update would resolve the confusion. If the answer contains a citation, the citation is a useful place to investigate, but it does not establish that the source alone caused the response.

Our AI visibility monitoring work can extend this review into repeat checks. For answers tied to a specific assistant, compare the findings with our Perplexity optimization service.

What a brand can change to correct the record

A brand can improve the information it controls and make its public claims easier to verify. We turn the review into concrete edits and outreach recommendations, ordered by how directly each action addresses the recorded error.

Typical work may include:

• Updating product, company, pricing or policy pages so the facts are current and consistent.
• Adding clear definitions, dates and ownership details where readers could otherwise confuse products or entities.
• Reworking explanations so a specific claim is stated plainly and supported on the same page.
• Preparing a factual correction request for a publisher when its accessible page contains an error.
• Reviewing relevant structured information and technical access with the appropriate technical team.

We share proposed edits for your approval before publication or outreach. Your team supplies authoritative facts, confirms sensitive wording and retains control of account credentials and final statements. Where third-party information is involved, we prepare a concise request with the exact passage, supporting evidence and proposed correction; the publisher decides what to change.

When the issue is primarily a missing or unclear explanation, content for AI answers may be the next useful workstream. If the problem concerns how the company or product is identified across sources, consider entity and knowledge graph building.

Where Perplexity checks fit in

Rule of thumb: Fix the source before you complain about the answer. The answer follows the source.

Perplexity checks help establish whether a particular answer and its visible citations reflect your current information. They are one part of a wider review, not a substitute for checking the sources that a customer can open and assess.

To improve brand mentions in Perplexity, give us the question a customer would actually ask, the answer you received and any citations displayed with it. We compare those materials with your approved facts and identify whether a correction belongs on your site, in a source you can contact or in a clearer explanation for customers. This is a practical application of Perplexity citation best practices: make claims precise, support them with accessible evidence and keep important pages current.

We can also check agreed prompts across other assistants, keeping each observation separate rather than treating one answer as representative of every system. A useful record captures the date, prompt, answer, cited material and the action taken. That makes a later comparison meaningful even when the wording changes.

For ongoing measurement, AI visibility monitoring can keep observations organized. If your main goal is to appear more clearly in answers rather than address a specific inaccurate claim, start with the broader AI search visibility overview.

Your 6-point correction checklist

Follow these steps in order. Most wrong answers trace back to a small number of pages. If a line is hard to tick, that is useful news: it shows what to settle before any money or time is spent.

1. Save the exact wrong wording with date and tool.
2. Write the correct fact in one sentence.
3. Find the page the answer leans on.
4. Fix or clarify that page, or ask its owner to.
5. Publish the correct fact on your own site with a date.
6. Re-ask the same question after 4 weeks.

Terms in one line

Source trace: finding the page behind a claim an AI made.

Correction (in plain words: a fix to a page so the right fact is easy to find).

What to expect from the work

AI reputation work can improve the accuracy and clarity of information your organization publishes and can support evidence-based requests to outside publishers. It is most useful when the team can identify a specific answer, confirm the correct facts and make or request a relevant source update.

The review also helps distinguish a correctable factual mistake from an answer that reflects a source’s opinion, a disputed claim or missing context. That distinction prevents wasted effort: prioritize verifiable errors, then decide whether other concerns call for clearer explanations, a direct response or no action.

AI assistants control their own answer presentation, citation choices, refresh timing and response to changing sources. Third-party publishers control their pages and review requests, so we cannot promise that a particular answer, citation or wording will change; we commit to the agreed research, edits, outreach and documented checks.

Before engaging, gather approved product facts, current policy pages, examples of problematic answers and any relevant correction history. If a claim has legal or regulatory implications, have the appropriate adviser approve the wording before it is published or sent to a publisher.

How the engagement runs, step by step

An engagement moves from documented examples to approved corrections, then to a clear record of what was done. MarketingExtra uses a source-by-source review so your team can see the evidence behind each recommendation and decide which actions to approve.

The work begins with a kickoff checklist: target brand and product names, priority audiences, representative prompts, approved facts, current pages and any known disputes. We then return a prioritized action plan, separating edits your team controls from requests that require a publisher’s response. After approvals, we help prepare or implement agreed updates and log outreach and publication evidence.

Reporting is designed for decisions, not just a list of observations. Each update records the item reviewed, its source, the action taken, its status and the next check. You can use that record to assign internal owners and avoid repeating outreach without new evidence.

Send 3 things: 3 wrong AI answers you saw, the correct facts, and the links to your best current sources.

How it works

1. Share examples and approved factsSend representative prompts and answers, links to current sources, and the facts your team has approved. Flag sensitive claims or active disputes.
2. Review the answer and its sourcesWe document the wording, visible citations and evidence gaps, then separate factual errors from opinions or unclear context.
3. Agree on priority correctionsYou review a sequenced action plan covering owned content, technical checks where relevant and any publisher correction requests.
4. Make approved updatesYour team or our agreed delivery team completes the authorized edits and outreach. We record what changed and retain supporting evidence.
5. Check and reportWe revisit the agreed prompts and sources, then share a concise status record with completed actions and remaining open items.