# AI Brand Fact Check: How to Verify What AI Says About You

> AI brand fact check in five steps: list every claim, match it to your facts, trace the cited pages and read the sentiment words. Free checker.

- Author: Simran Rawat, Growth Executive, Nurdd
- Published: 2026-11-07
- Publisher: Noma by Nurdd (https://nomahq.app)
- Original page: https://nomahq.app/blog/ai-brand-fact-check

An **AI brand fact check** means taking an AI answer about your company, splitting it into single claims and checking each one against your own approved facts. Do that before you score tone. A warm answer with last year's price is still wrong, and only a claim-by-claim check shows it.

## Key takeaways

- Check claims, not vibes. A sentiment score can read positive while the answer states a wrong price, plan or date.
- Keep one dated facts sheet (pricing, product, integrations, dates, policies) as the source of truth before you read any answer.
- Split every answer into one-fact claims and give each a verdict: correct, wrong, outdated, unsupported or missing.
- Trace each wrong claim to a cited page. Fixing the page the engine read works better than reporting the answer.
- Read the words attached to your brand name separately. They show positioning problems that no single fact check catches.
- Repeat on a fixed prompt set and on more than one engine, because one answer is a sample, not a verdict.

## Why check AI answers claim by claim?

AI engines write fluent text whether or not they know the fact. [OpenAI, September 2025](https://openai.com/index/why-language-models-hallucinate/), explains one reason in its research on hallucination: training and evaluation reward a model for guessing rather than for admitting it is unsure. A model that guesses will state a price or a feature with the same confidence as a fact it holds.

The scale of the problem is documented outside marketing. A study coordinated by the European Broadcasting Union and led by the BBC involved 22 public service media organisations in 18 countries working in 14 languages. The EBU's headline finding was that AI assistants misrepresent news content ["45% of the time" (EBU, October 2025)](https://www.ebu.ch/news/2025/10/ai-s-systemic-distortion-of-news-is-consistent-across-languages-and-territories-international-study-by-public-service-broadcaste). [Search Engine Journal's summary of the study](https://www.searchenginejournal.com/ai-assistants-show-significant-issues-in-45-of-news-answers/558991/) adds that 45% of responses contained at least one significant issue and that sourcing was the most common problem, affecting 31% of responses at a significant level. That study tested news questions, not brand questions, so do not read it as a brand error rate. It does show that an answer can be mostly right and still carry a wrong detail, and that the cited source is often the weak point.

That is why a single sentiment score fails as an audit. Sentiment tells you how the answer feels. It does not tell you that the engine quotes a retired plan, lists an integration you dropped or dates your founding wrongly. Those are the errors a buyer acts on.

## What is an AI brand fact check?

An AI brand fact check is a repeatable audit with four inputs: the answer, your facts, the sources the engine cited and the wording used about your brand. It produces a list of claims with a verdict each and a named page responsible for each error.

It differs from three things people confuse it with:

- **Mention tracking** asks whether you are named. A fact check asks whether what is said is true.
- **Sentiment analysis** asks how positive the language is. A fact check asks whether each statement is correct.
- **A one-off prompt test** samples one answer. A fact check runs a fixed prompt set so a change in the result is a change in the world. See [AI citation stability](https://nomahq.app/blog/ai-citation-stability) for how much answers move between runs.

## How do I fact check what ChatGPT says about my brand?

Use five steps. They work the same for ChatGPT, Gemini, Perplexity and Claude, and each step produces something you can hand to a named owner.

### Step 1: Build a dated facts sheet

Write down what is true today, with the page that proves it. Keep it to facts that can be right or wrong:

- Pricing: plan names, prices, billing terms, trial length.
- Product: what it does, what it does not do, integrations, limits.
- Dates: launch date, founding year, release dates, discontinued items.
- Policies: returns, guarantees, security certifications, support hours.
- Identity: legal name, spellings, the name you are confused with.

Leave out anything you cannot prove on a public page. If a fact is not written down somewhere an engine can read, you cannot expect the engine to state it. Mark each row with a last-checked date.

### Step 2: Collect the answers

Choose 10 to 20 prompts a buyer would ask, a mix of branded ("What does [brand] cost?") and unbranded ("best [category] tools for [use case]"). Run each on the same engines, from a fresh session, and save the full text, the engine and the date. Noma by Nurdd's [guide to choosing prompts](https://nomahq.app/aeo-guide/choosing-prompts) covers the five prompt types and how to keep a set comparable.

### Step 3: Split each answer into claims

One sentence often holds three facts. Split it. "Acme starts at $49 a month, integrates with Slack and was founded in 2019" is three claims. Splitting keeps a wrong price from hiding inside a paragraph that is otherwise right, and it gives the fixer one thing to correct.

Give every claim one verdict:

| Verdict | Meaning | Typical cause |
| --- | --- | --- |
| Correct | Matches your facts sheet | Clear, consistent sources |
| Outdated | Was true, no longer is | Old page, review or listing still cited |
| Wrong | Contradicts your facts | Mixed-up entity, bad third-party page, guess, framing that conflicts with your positioning |
| Unsupported | Not on your facts sheet and not provable | Model filling a gap |
| Missing | True and important, but absent | Fact not stated on a crawlable page |

### Step 4: Trace each error to a cited page

Open the sources the engine cited for that answer. Match the wrong claim to the page that states it. Three outcomes are common: your own page is stale, a third-party page is stale or wrong, or no cited page says it at all, which points to a guess.

In Noma, [the cited sources view](https://nomahq.app/help/article/what-are-sources-and-why-do-they-matter) lists the pages each tracked answer draws on, and [the actual answers can be read](https://nomahq.app/help/article/can-i-see-the-actual-answers) next to them. Doing this by hand is possible: expand the citations in ChatGPT, Perplexity and Gemini and copy each URL into your log.

### Step 5: Read the words attached to your name

Now read sentiment, but as vocabulary. Underline every adjective, comparison and caveat that sits next to your brand: "affordable", "limited", "best for small teams", "older". These phrases are your positioning as the engine states it. A fact can be correct and the framing still work against you, for example when every answer calls you "basic". [What brand sentiment in AI answers means](https://nomahq.app/blog/what-is-brand-sentiment-in-ai-answers) explains how to read those words, and [the sentiment fix guide](https://nomahq.app/blog/fix-negative-ai-brand-sentiment) covers what to do about negative ones.

## A worked example with a fictional brand

The example below is illustrative. "ExampleCo" and its numbers are invented to show the method, not drawn from any real audit.

| Claim in the answer | Facts sheet says | Verdict | Cited page responsible | Owner |
| --- | --- | --- | --- | --- |
| "Plans start at $29 a month" | Starts at $39 since this year | Outdated | A 2024 review on a third-party site | PR: request correction |
| "Integrates with Salesforce" | Integration exists | Correct | Own integrations page | None |
| "Offers a free plan" | No free plan, 3-day trial | Wrong | No cited page states it | Web: add a plain pricing FAQ |
| "Supports SOC 2 reporting" | Not on facts sheet | Unsupported | None | Security: decide and publish |
| Described as "a budget option" | Positioned as mid-market | Wrong | Comparison article | Content: publish positioning page |

Four of five rows end with a named owner. The correct row needs none. That is the test of a good fact check: each finding is something a person can fix this week.

## How do I find out which sources AI engines use to describe my brand?

Look at the citations behind each answer, not at the answer text. Perplexity and ChatGPT search show linked sources; Gemini and Google AI features show source links beside the response. Record the URL, the domain and whether it is yours.

Then sort by frequency. If one review site is cited in eight of twelve answers about your pricing, that page is the highest-value correction you have. Noma's [earned media guide](https://nomahq.app/resources/earned-media-for-ai) shows how to rank outlets this way. For ChatGPT, [how ChatGPT search picks and shows sources](https://nomahq.app/blog/chatgpt-search) explains what you are reading.

One caution: some answers carry no sources, and cited pages change between runs. A source seen once is a lead, not a cause. Repeat the prompt before you act.

## How is this different from the existing guides on AI getting a brand wrong?

Noma already publishes two related guides. They answer different questions, and the fact check sits between them.

| Guide | Starts from | Main output | Use it when |
| --- | --- | --- | --- |
| This fact check | A facts sheet and an answer | A claim-by-claim verdict list with a cited page per error | You need to find what is wrong |
| [When AI gets your brand wrong](https://nomahq.app/blog/when-ai-gets-your-brand-wrong) | A known error | A correction route for that error | You already know what is wrong |
| [Fixing negative sentiment](https://nomahq.app/blog/fix-negative-ai-brand-sentiment) | Negative wording | Source fixes and a re-test on a fixed prompt set | The tone is the problem, not a fact |

In practice: run the fact check first, send confirmed errors to the correction runbook ([When AI Gets You Wrong](https://nomahq.app/resources/ai-correction-runbook)), and treat the framing findings from step 5 with the sentiment guide.

## What should I do when Perplexity or Gemini gets facts about my business wrong?

1. Record the prompt, engine, date and the full answer.
2. Rerun it twice. If the error does not repeat, log it and move on.
3. Find the cited page that carries the error, if there is one.
4. Fix your own page first: state the fact in a plain, dated sentence near the top, and update structured data to match.
5. Ask third-party publishers to correct theirs, quoting the exact line.
6. Use the engine's feedback route as a last step, not the first.
7. Recheck on the same prompts after a week and note whether the claim changed.

Check that crawlers can read your fixes. The free [AI Crawler Checker](https://nomahq.app/tools/ai-crawler-checker) shows whether GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot and Google AI can fetch your site. A corrected page that is blocked changes nothing.

## Which tools have shipped fact-check features?

Several AI visibility vendors have publicly announced fact-check features. The table lists only what each vendor says on its own pages, as checked on 2026-11-06. It makes no claim about how well any of them work.

| Vendor | What its public page describes | Source |
| --- | --- | --- |
| Writesonic | Fact Check flags claims AI gets wrong about a brand and traces each to the page AI cited. Announced 5 November 2026. | [Writesonic press release, Yahoo Finance](https://finance.yahoo.com/technology/ai/articles/writesonic-traces-ai-says-brand-154200795.html) |
| Peec AI | Fact-checking compares what AI said with facts you provide, at the level of a single claim. | [Peec AI documentation](https://docs.peec.ai/brand-perception-fact-checking) |
| Promptwatch | Fact Check takes a claim you mark accurate or inaccurate and reads pages crawled from your site; listed on Business, Agency Scale and Enterprise, using agent credits. | [Promptwatch changelog](https://promptwatch.com/changelog) |
| Profound | FactCheck, announced 14 July 2026, to measure the accuracy of AI answers about a brand. | [Profound blog](https://www.tryprofound.com/blog/introducing-factcheck) |

Peec AI's documentation page for its fact-checking feature. Source: Peec AI.

The pattern across all four is the one in this guide: your facts on one side, claims split out from answers on the other, and a trace to the page behind each. You can run the same method by hand with a spreadsheet, which is the right way to learn what your own facts sheet is missing before you buy anything.

## Where Noma fits

Noma by Nurdd asks 13 AI engines, including ChatGPT, Gemini, Perplexity and Claude, the questions your buyers ask, every day. For a fact check it shows the answer text, the sources each answer cites, where competitors are named instead, and the words that come with your brand name as sentiment. You match the claims to your facts sheet; Noma supplies the answers and the pages behind them.

See it on your own brand: [book a demo](https://nomahq.app/demo) or [start the 3-day free trial](https://platform.nomahq.app/signup). A card is taken at signup so billing can start on day 3; cancel before then and nothing is charged.

## Is there a free tool to audit what AI says about my brand?

Yes. Noma's free [AI Visibility Checker](https://nomahq.app/tools/ai-visibility-check) shows whether ChatGPT, Gemini, Claude and Perplexity recommend your brand, who they name instead and what to fix. Use it as step 2 of this method: it gives you answers to read against your facts sheet, with no account needed.

Two more free checks help with the fixes. The [Content AI-Readiness Grader](https://nomahq.app/tools/content-grader) shows which passages on a page an AI is likely to quote, which tells you whether your corrected fact is stated clearly enough. The [free brand audit](https://nomahq.app/brand-audit) checks how AI engines describe a brand.

## Common mistakes in an AI brand fact check

- **Checking one answer once.** Answers vary between runs, so one sample proves little. Repeat each prompt.
- **Having no facts sheet.** Without a written source of truth, every disagreement becomes an argument about memory.
- **Grading whole answers.** A paragraph marked "mostly right" hides the one wrong price.
- **Fixing the answer instead of the source.** Reporting an answer does not change the page it came from.
- **Ignoring engine differences.** Engines draw on different sources, so a fact right in one can be wrong in another. [Why engines disagree](https://nomahq.app/blog/why-engines-disagree) explains the reasons.
- **Skipping the recheck.** A fix is only confirmed when the same prompt returns the corrected claim.

## How we researched this

We reviewed primary and vendor sources: OpenAI's published research on hallucination, the EBU and BBC news integrity study and a trade-press summary of it, and the public documentation, changelogs and press releases of Peec AI, Promptwatch, Writesonic and Profound, plus Noma's own help articles. We ran no in-house study for this post and report no Noma results. All facts, features and dates were checked on 2026-11-06. Vendor features may change after that date.

## What to do next

Write the facts sheet this week, run the 10 to 20 prompts and score every claim. Then send each error to its owner with the page that caused it. Start with the free [AI Visibility Checker](https://nomahq.app/tools/ai-visibility-check). When you want the same check run daily across 13 AI engines, [book a demo](https://nomahq.app/demo) or [start the 3-day free trial](https://platform.nomahq.app/signup).