How to check whether AI describes your brand correctly — a practical audit

When a customer asks an AI assistant “is this brand any good?” or “what does this company actually do?”, a model answers in seconds, with confidence, and usually without a single click to your website. That answer is now a primary touchpoint. Yet most brands have never read what the machines say about them, and have no process for checking whether it is accurate. This is a practical guide to running that check yourself across the major AI surfaces, scoring what you find, and deciding what to fix.

The reason this matters is volume, not novelty. ChatGPT alone reached roughly 900 million weekly active users by early 2026, and Google’s AI Overviews passed two billion monthly users in mid-2025. These systems are also imperfect describers: a Tow Center for Digital Journalism study found that AI search tools answered queries incorrectly in more than 60% of tests, often with high confidence. If a model can misattribute a news article, it can certainly misstate your founding year, your category, or a claim you would never make.

Why an AI brand audit is now table stakes

The old assumption was that people would search, see a list of links, and visit your site to learn the truth from the source. That assumption is weakening. Pew Research Center found that when an AI summary appears on a Google results page, users clicked a link only 8% of the time, versus 15% without one, and clicked a link inside the AI summary itself just 1% of the time.

In other words, the AI’s description of your brand is increasingly the destination, not a signpost to it. Consumers are leaning on these tools for commercial decisions too: one 2025 survey found 56% of U.S. consumers planned to use AI chatbots to compare prices and find deals. Whatever the model says about you is shaping consideration before a human at your company is ever involved.

Step 1: Build a fixed prompt set

An audit is only useful if it is repeatable, so start by freezing a list of prompts you will reuse every time. Mix the angles a real person would take:

• Identity: “What is [brand]?” and “Who founded [brand] and when?”
• Category: “What does [brand] do?” and “Is [brand] a [your category] company?”
• Comparison: “[Brand] vs [competitor]” and “Best [category] for [use case].”
• Reputation and claims: “Is [brand] trustworthy?” and “What are [brand]’s main products and prices?”
• Risk probes: “What are common complaints about [brand]?” and “Is [brand] safe to use?”

Keep the wording identical across runs. The point is to measure the brand, not your phrasing.

Step 2: Run the same prompts across every surface

Different engines draw on different data and behave differently, so test each one rather than assuming they agree. Cover the five that matter most today:

1. ChatGPT — run prompts both with and without its web-browsing mode, since the answers can diverge sharply.
2. Google Gemini — and separately, Google’s AI Overviews and AI Mode, which sit inside ordinary search.
3. Claude — useful for spotting where a model declines to answer or hedges.
4. Perplexity — citation-led, so it exposes which sources are shaping your profile.
5. Google AI Overviews — search your prompts in a logged-out, incognito window to approximate a neutral user.

Control for personalisation

Use a clean browser session, sign out where you can, and avoid leading the model. If you have a presence in multiple markets, repeat key prompts with a location cue (“in the UK”, “in the UAE”), because answers and the underlying sources shift by region and language.

Step 3: Score each answer against the truth

For every response, judge it on four dimensions and keep a simple spreadsheet:

• Accuracy — are the facts correct: founding details, ownership, locations, product names, pricing?
• Category fit — is the brand placed in the right market, or confused with an adjacent one?
• Claim safety — does the model attribute claims you cannot legally or factually substantiate (superlatives, guarantees, regulated assertions)?
• Citation quality — which sources is it leaning on, and are they ones you control or trust?

Mark each as correct, incomplete, or wrong. Note the exact wording of any error verbatim, because that phrasing often traces back to a specific source you can address. Given the citation accuracy problems the Tow Center documented, pay particular attention to confident answers with no source or the wrong source.

Step 4: Trace errors to their root

Most AI misdescriptions are not random. They are downstream of a real-world gap. When you find an error, ask where the model could have learned the correct version and could not:

• Thin or missing entity records — no clear Wikipedia, Wikidata, or authoritative directory entry leaves the model to guess.
• Inconsistent self-description — if your own site, social profiles, and press kit describe the company three different ways, the model averages them into something none of you would endorse.
• Stale third-party sources — an old article or a wrong listing can outweigh your current site.
• Competitor or namesake bleed — a similarly named entity contaminates the profile.

The fix is rarely “trick the model.” It is to make the correct, consistent version of your brand more available and more authoritative than the wrong one, then re-run the audit to confirm the change propagated.

The deeper problem: what is the source of truth?

Running an audit exposes an uncomfortable question. To grade an answer as right or wrong, you need a single, agreed reference for what is true about your brand: the canonical facts, the approved claims, the boundaries of what you will and will not say. Most organisations do not have one. The facts live scattered across a brand book, a legal folder, a few people’s heads, and a website that is months out of date.

This is the category emerging around brand knowledge graphs — a structured, maintained record of a brand’s verified facts and approved claims that acts as the reference an audit compares against and that downstream content can be checked for before it goes out. kbie.ai, built by Kapis AI Tech Private Limited, is one example of this approach: a brand’s knowledge graph as the source of truth, so that what AI says and what you publish can both be measured against the same canonical version and kept safe to publish. The broader point holds regardless of tool: an audit without a reference is just a list of opinions.

Conclusion

Checking how AI describes your brand is no longer an experiment for the curious; it is basic hygiene for any brand whose customers ask machines questions. Build a fixed prompt set, run it across ChatGPT, Gemini, Claude, Perplexity, and AI Overviews, score each answer on accuracy, category, claims, and sources, and trace every error to the gap that caused it. Do it on a schedule, because the models and their sources change. The brands that will be described correctly are the ones that decided, deliberately, what the correct description is.

FAQ

How often should we run an AI brand audit?

Quarterly is a sensible baseline for most brands, with an extra check after any major launch, rebrand, or news event. The underlying systems update frequently and reach is still growing — AI Overviews grew from 1.5 to 2 billion monthly users in roughly two months in 2025 — so a once-a-year check will miss meaningful drift.

The AI got a fact wrong. Can we just tell it to fix it?

Correcting a model in a single chat does not persist for other users. Durable fixes come from improving the sources the model relies on: your own site, authoritative third-party records, and consistent self-description everywhere your brand appears. Then re-run the audit to verify the corrected version is now what the model returns.

Why do different AI tools describe us differently?

Each engine is trained on different data, retrieves from different live sources, and applies different caution. The Tow Center study found failure rates ranging widely across eight tools, with one answering incorrectly 37% of the time and another 94% of the time. Auditing only one tool gives you a partial and possibly misleading picture.

Is this just SEO under a new name?

It overlaps but is not the same. Traditional SEO optimises for ranking and clicks; an AI brand audit measures whether the answer a model gives is factually correct and on-brand, even when no click ever happens. With Pew finding that only 1% of users click a link inside an AI summary, accuracy of the answer itself becomes the thing worth governing.