How can I fact-check what AI is saying about my brand?
A step-by-step method to fact-check what ChatGPT, Gemini and Perplexity say about your brand, trace wrong claims to their sources, and the tools that help.
Sooner or later someone on your team pastes a ChatGPT answer into Slack with the words "that's not right." Maybe it quotes an old price. Maybe it credits a competitor's feature to you, or names the wrong founder, or says you do not integrate with something you have supported for two years. One screenshot is an anecdote. Fact-checking what AI says about your brand properly means turning that anecdote into a repeatable process, and then finding where the wrong claim came from.
Why AI gets brand facts wrong
Engines answer from a mix of model memory and pages fetched at question time. Errors tend to come from four places:
- Stale pages. An old pricing page, an archived press release or a review written two years ago that still ranks.
- Third-party summaries. Roundups and comparison posts that paraphrase you wrongly, then get cited by the engine.
- Gaps. If your own site never states a fact clearly, the engine fills it in from somewhere else.
- Confusion between brands. Similar names, shared categories and competitors that get blended into one answer.
Notice that three of the four are fixable on the web, not inside the model. That is why tracing the source matters more than arguing with the chatbot.
A fact-checking method that holds up
1. Write the source of truth first
Before you test anything, write down what is true: current plans and prices, feature list, integrations, founding facts, locations, what you do not offer. Keep it as a short document with the page on your site that backs each point. You cannot check an answer against a memory.
2. Build a prompt set around brand questions
Include what buyers ask: "what does [brand] cost", "is [brand] good for [use case]", "[brand] vs [competitor]", "who owns [brand]", "does [brand] integrate with [tool]". Add category questions where you should appear. Run the same set regularly.
3. Capture full responses, across engines
One engine's answer says little. Different engines draw on different sources, so a claim wrong in one may be right in another. Save the full text of each response, not a summary, so you can quote it back.
4. Extract claims and mark them
Go through each response and flag every checkable statement: price, feature, date, location, comparison. Mark each as correct, outdated, wrong or unverifiable.
5. Trace each wrong claim to a source
Look at the cited sources for that response. Is the wrong number on a third-party page? Your own old page? A forum thread? The citation list usually points straight at it. Where the engine searched also matters: query fan-outs show the sub-queries it ran, and those tell you which angle produced the bad source.
6. Fix the source, then check crawl access
Update your own page, ask the third party for a correction, and publish a clear page that states the fact plainly. Then check that AI crawlers can fetch the new version. If a bot is blocked or hitting errors, the engine keeps reading the old one.
7. Re-test on a schedule
Run the same prompts after the fix and again monthly. Track whether the claim changes and how long it takes.
Where the workflow maps to Promptwatch
Each step has a counterpart in Promptwatch, which is why it is the practical choice when this becomes a regular job rather than a one-off audit.
- Source of truth: the Brand Book holds your analysis instructions and decides what counts as a mention, and brand aliases catch name variants.
- Prompt set and engines: monitors run prompt panels daily per model, country and language, with Prompt Explorer and personas for building realistic questions.
- Full responses: every response is stored, with a per-response sentiment score from 1 to 100 and a weekly AI summary. Sentiment is not accuracy, so you still read the text.
- Tracing: citation analytics shows page and domain level sources, and the Page Tracker watches a specific URL, such as the outdated page you suspect. Query fan-outs per model show what the engine searched.
- Fixing: Content Refresh proposes diff-style updates to existing pages, and the Content Agent (Business and above) can publish corrections to WordPress, Webflow or Framer.
- Crawl check: Agent Analytics shows IP-verified AI crawler visits, failures and explanations per page.
- Tracking the fix: A/B period comparison and the Action Board.
One honest limit: Promptwatch shows you what the engines said and where it came from. It does not independently decide whether a claim is true. That judgment stays with a person who has the source of truth.
Pricing: free Explore plan (10 prompts, ChatGPT only), Essential $95 a month, Professional $245, Business $579. Crawler logs start on Professional.
Other tools with a fact-checking angle
A few vendors lean into accuracy. Qwairy lists a Fact Check feature, still in beta, on its €199 Growth plan. seoClarity lists hallucination monitoring inside Clarity ArcAI, with packages from $2,500 a month. Azoma describes Answer Integrity, which detects inaccurate product claims in AI answers, aimed at consumer brands selling through marketplaces, with sales-quoted pricing. AthenaHQ and Scrunch AI store and analyze responses and are worth a look if you want a task-oriented workflow. None of these removes the need for a source of truth you maintain.
What you cannot do
You cannot edit a model. You cannot force an engine to repeat a correction on every run. Answers vary, and a fix takes time to be picked up. What you can do is make the correct fact easy to find, keep wrong versions out of circulation, and measure whether the answers improve.