When AI Gets Your Brand Wrong: How to Audit and Correct Inaccurate AI Answers

You cannot directly edit what ChatGPT, Perplexity, or Google AI Overviews says about your brand. You can establish authoritative facts, identify where answers diverge from them, correct misleading sources, and retest whether those corrections appear in subsequent answers.

The useful unit of work is not a brand mention. It is a specific claim: a price, capability, eligibility rule, integration, geographic restriction, or product-category description. A brand can appear frequently in AI answers and still be represented inaccurately enough to lose a sale.

An effective accuracy audit connects each questionable claim to an approved fact, a likely source, a correction owner, and a repeatable test. Here is how to build that process.

What can go wrong when AI describes your brand?

Most commercially important errors fall into five categories:

• Outdated facts: discontinued plans, old prices, previous product names, or superseded policies.
• Incorrect capabilities: an assistant says a product supports a feature it does not, or omits a feature it now supports.
• Mistaken positioning: a specialist product is described as a broader platform, or a current product inherits the category of an earlier offering.
• Missing conditions: a feature is available only on certain plans, in specific regions, or through an add-on, but the answer presents it as universal.
• Entity confusion: facts about a similarly named company, parent business, or former product are attributed to your brand.

Two documented incidents illustrate why this matters.

In Moffatt v. Air Canada, a 2024 British Columbia Civil Resolution Tribunal decision concerned incorrect bereavement-fare guidance from Air Canada's website chatbot. The tribunal held the airline responsible for the inaccurate information. The decision can be found through the tribunal's decisions site. This was a company-operated chatbot, not an independent search assistant, but it demonstrates the commercial consequences of incorrect policy guidance.

Google's May 2024 explanation of problems with AI Overviews discussed examples involving satirical content and sarcastic or troll content from discussion forums. The lesson is different: an answer can draw on material that exists online yet still be inappropriate or factually wrong.

Together, these examples support two separate checks: Is the underlying information correct, and has the assistant represented it correctly?

How do you build a verified brand fact sheet?

Create a controlled reference document before testing answers. Otherwise, reviewers may disagree about whether an answer is wrong, outdated, or merely phrased differently.

The fact sheet should be an internal register, not another promotional overview. Each row should describe one verifiable claim.

For an illustrative software company, a row might read: “Single sign-on is included in the Enterprise plan; it is not included in the Standard plan.” That is more auditable than “Enterprise-grade security.”

Start with facts that affect purchase decisions:

1. Current product names and category descriptions.
2. Pricing structure, billing terms, and what requires a quote.
3. Plan-specific capabilities and limitations.
4. Supported integrations and whether they are native or partner-built.
5. Geographic availability, eligibility, and support policies.
6. Certifications and compliance statements, with their exact scope.

Require approval for sensitive claims. An assistant calling a product “compliant” may be materially different from saying the vendor holds a particular certification.

The internal sheet establishes truth for the audit. Public pages make that truth discoverable. Do not publish confidential information simply to make the sheet comprehensive.

Which buyer questions should you test?

Build a small, stable question set around decisions buyers actually make. Use sales objections, support tickets, site-search queries, and customer interviews rather than generic requests to “tell me about the brand.”

Useful prompt patterns include:

• “How much does [product] cost, and what is included?”
• “Does [product] support [capability] on the entry-level plan?”
• “Can a company in [country] buy [product]?”
• “Is [brand] a [category A] product or a [category B] product?”
• “Compare [brand] and [competitor] for [specific requirement].”
• “What limitations should I know before choosing [product]?”

Include both branded questions and category questions that might recommend your brand. Incorrect positioning can emerge only when the assistant compares alternatives.

For a manageable initial audit, use 15–25 questions across your priority assistants and repeat each question in fresh conversations. This is a practical starting scope, not a statistically representative sample of all buyer experiences.

Record the exact prompt, full answer, date, interface, visible model label, language, relevant location settings, and citations. Keep prior conversation context out of baseline tests.

Test the surfaces buyers use. An API response is not necessarily equivalent to the consumer application. For Google, record whether an AI Overview appeared; no overview is not an accuracy failure.

How do you distinguish source errors from unsupported claims?

Break each answer into factual claims, then compare them with the fact sheet. Evaluate recommendations and subjective descriptions separately: “a good fit” is not the same kind of claim as “includes unlimited seats.”

For every inaccurate claim, inspect the linked pages and classify the evidence path:

A citation is not proof that a claim is supported. Open the page, locate the relevant passage, and check its date and scope.

Equally, no visible citation does not prove the model invented the claim. It may reflect prior training, undisclosed retrieval, conversation context, or synthesis. Label it “no visible support,” not a proven hallucination.

Treat citations as investigation leads, not a complete causal explanation of how an answer was produced.

Which corrections should you prioritise?

Prioritise by buyer harm, recurrence, and your ability to address the underlying information.

A false claim about eligibility, security, pricing, or a critical capability usually deserves attention before an imprecise category description. An error recurring across several tested answers deserves more investigation than an isolated wording issue.

Maintain an issue log with:

• The incorrect claim and approved replacement.
• Affected questions and assistants.
• Observed frequency within your test set.
• Supporting or conflicting URLs.
• Business impact and severity.
• Correction owner, action date, and retest status.

Then work through three correction layers.

1. Correct owned sources first

Update pricing pages, documentation, FAQs, help centres, product pages, and downloadable assets. Look for contradictions across them.

Put important qualifications beside the claim they modify. If an integration requires an add-on, say so in the integration description—not only in a separate pricing footnote.

Use explicit product names, descriptive headings, and clear effective dates where dates matter. Provide accessible HTML explanations rather than relying solely on screenshots or PDFs.

If an obsolete page must remain available, label it as historical and point to the current version. Use redirects when appropriate for users and the site's information architecture, not as an assumed AI correction mechanism.

2. Address influential third-party sources

Check review profiles, partner listings, marketplace entries, comparison articles, and other pages appearing in erroneous answers.

Send correction requests that identify the exact statement, explain what changed, and link to authoritative evidence. Ask for factual accuracy, not favourable coverage.

You can usually edit a vendor-managed listing more directly than an independent review. Keep those workflows separate, and recognise that publishers control their own editorial decisions.

3. Submit assistant feedback where available

Use feedback or reporting mechanisms for demonstrably inaccurate answers, particularly consequential policy or safety claims. Include the prompt, incorrect statement, and authoritative correction.

Feedback is an escalation route, not a guaranteed update channel. Publishing more pages that repeat the preferred claim is also not a substitute for resolving contradictory information.

How should you retest and measure improvement?

Rerun the original questions after changes are published and discoverable. Record what changed and when, then use a consistent retest cadence appropriate to the risk—for example, weekly during an active correction effort and monthly afterward.

Retain the original wording for comparability. Add a separate set of paraphrased questions to see whether improvement extends beyond one formulation.

Measure:

• Material error rate: answers containing at least one predefined material error, divided by relevant answers reviewed.
• Claim recurrence: how often a particular incorrect claim reappears.
• Source persistence: whether obsolete or corrected pages continue to be cited.
• Correction durability: whether accurate answers persist across later test rounds.

Keep denominators and relevance rules consistent. Report missing answers and absent AI Overviews separately rather than counting them as accurate responses.

Preserve answer snapshots. A useful audit trail shows the original error, the intervention, and subsequent observations—not just a dashboard score.

What can your team control—and what cannot it guarantee?

Your team controls the accuracy, consistency, accessibility, and maintenance of owned information. It can request third-party corrections, submit feedback, and run disciplined tests.

It cannot guarantee that an assistant retrieves a particular page, accepts a correction, stops citing an old source, or gives identical answers to every buyer. Index updates, retrieval choices, model changes, and personalisation can all affect results.

Improvement after a page update is encouraging, but it does not by itself establish causation. Avoid presenting a single successful retest as a permanent fix.

The operational goal is to reduce recurring, consequential errors and make accurate information easier to find—not to promise complete control over AI answers.

FAQ

Should we publish an “AI fact sheet” page?

A concise public facts page can help if it answers real questions and remains maintained. It should complement authoritative pricing, product, and policy pages, not introduce another conflicting version of them.

Can structured data fix incorrect answers?

Appropriate structured data can make supported facts more machine-readable. It does not compel an assistant to use those facts or guarantee accurate interpretation. Keep it consistent with visible page content.

What if an assistant repeats an error after the source is corrected?

Check for duplicate pages, cached or historical versions, and third-party repetition. Continue controlled retesting and use available feedback channels. A corrected source and a corrected answer are separate milestones.