The 30-Minute AI Audit: Check What AI Says About You

The 30-Minute AI Audit

You’ve heard that B2B buyers are using AI to research vendors. You’ve read the Gartner research showing that buyers complete roughly 60% of their journey before contacting a supplier. You suspect AI is shaping how those buyers see your company.

But you haven’t checked. Not because you don’t care. Because the path from “I should look into this” to “I actually know what ChatGPT says about us” keeps getting pushed down the priority list.

Here is a process you can run in 30 minutes, using four free tools, that will tell you exactly where you stand. No subscription, no consultant, no spreadsheet required. You, four tabs, and six tests.

Open four tabs: ChatGPT, Claude, Gemini, and Perplexity. Run each test in all four. The point of checking all four is that these systems draw from different sources, weight different signals, and produce different answers. A single AI model’s response is a data point. Four models’ responses give you a pattern.

If you’ve already done this kind of checking one platform at a time, the five AI prompts we published give you a narrower version. This audit is broader: six tests, scored, with a rubric for prioritizing what to fix.

Test 1: The Company Name Test (5 Minutes)

Type your company name into each of the four tools. Use the plain version, the one a buyer would type if they’d heard your name at a trade show or seen it on a spec sheet.

Watch for three things:

Accuracy. Does the AI describe what you actually do? If you make industrial filtration systems and the AI calls you a “water treatment company,” that’s a Clarity problem. The description is close enough to feel right but wrong enough to send buyers to the wrong conclusion.

Completeness. Does the AI mention your main products, services, or differentiators? A generic two-sentence summary that describes any company in your category is a signal that your content lacks the specificity AI systems need to cite you confidently.

Consistency across tools. Does ChatGPT describe you differently than Claude? If one says you’re a “manufacturer of precision machined components” and another says you’re a “metal fabrication shop,” that inconsistency is a signal. AI systems run your data through three gates: Extraction, Correlation, and Synthesis. When different systems extract different facts about you, it usually means your data is inconsistent across the sources they crawl.

Write down what each tool says. Note where they agree and where they diverge. The disagreements are more important than the agreements.

We’ve written about what happens when you type your company name into ChatGPT before. The short version: what comes back is what your buyers are working with. If it’s wrong, every conversation that follows starts from a flawed premise.

Test 2: The Category Test (5 Minutes)

Ask each tool: “Who are the best companies for [your category]?”

Use the category label your buyers use. If you make injection-molded packaging, ask “Who are the best injection molding companies for packaging?” If you build custom automation systems, ask “Who are the best industrial automation companies?”

Three outcomes to watch for:

You appear. Good. Note where you rank (first mention, third, seventh) and how you’re described.

You don’t appear, but competitors do. This is a Coverage problem. Your content doesn’t give AI systems enough substance to recommend you for this category. The data trust problem underneath your traffic numbers is usually the root cause.

The category itself is misunderstood. Sometimes the AI reframes your category entirely. You ask about “industrial RFID tags” and it lists consumer tracking companies. That tells you the category language on your site doesn’t match how the broader market describes what you do.

Write down who appears. These are the companies AI is steering buyers toward instead of you.

Test 3: The Product Specificity Test (5 Minutes)

Ask each tool about a specific product or service you offer. Use the product name, not a generic description.

For example: “What is [Product Name] from [Your Company]?” Then ask: “What are the key features of [Product Name]?”

This test surfaces two problems:

Fabrication. The AI invents features, specs, or capabilities you don’t offer. This is a Hallucination Risk issue, and it’s more common than companies think. AI systems fill gaps in their knowledge with plausible-sounding details. If your product pages are thin, the model guesses.

Confusion with competitors. The AI describes your product using a competitor’s specs, or attributes your product to a different company. This happens when your structured data and product descriptions aren’t specific enough to distinguish you from similar offerings.

The eight factors that determine whether AI trusts your data include Specificity as a major weight. Vague product descriptions score low. AI systems won’t cite content they can’t verify with confidence.

Write down every inaccuracy. These are the things your buyers are being told with authority.

Test 4: The Comparison Test (5 Minutes)

Ask each tool: “Compare [Your Company] vs [Competitor Name].”

Pick a real competitor, one you actually lose deals to.

Three things to evaluate:

What evidence does the AI cite? Does it reference your website content, your case studies, your spec sheets? Or does it pull from directory listings, review sites, and third-party profiles? If the AI is building its comparison from sources you don’t control, that’s where your Trust Debt lives.

How does it frame the comparison? Does it present you as equivalent, inferior, or does it hedge? The framing matters because buyers use these comparisons to build shortlists.

What does it get wrong? Inaccurate revenue figures, wrong headquarters location, outdated product lines, misattributed capabilities. Each error is a data point that needs correcting somewhere in your external footprint.

Write down the framing and the errors.

Test 5: The Consistency Check (5 Minutes)

This is the test that surfaces the most uncomfortable findings.

Open your own website in another tab. Pull up your homepage, your About page, and your top product page. Now compare what those pages say against what the AI tools said in Tests 1 through 4.

Look for three patterns:

Your site says X, AI says Y. This is the most damaging pattern. Your website says you were founded in 1998. AI says 2005. Your site lists 12 product lines. AI mentions 4. Your site emphasizes your ISO certification. AI doesn’t mention it. These gaps mean AI systems aren’t extracting your data correctly, usually because of weak structure, missing schema, or poor knowledge graph connections.

AI is more specific than your site. Sometimes AI provides details your own site doesn’t clearly state. That means the AI is pulling from third-party sources and treating them as authoritative over your own content. Your own site should be the most specific, most structured, most trustworthy source of information about your company. When it isn’t, you’ve handed control of your narrative to directories and review platforms.

Your site has data AI missed entirely. You have a case study with quantified results, a proprietary process, a certification none of your competitors hold. AI doesn’t mention any of it. This means your content exists but isn’t extractable. The information is there, but AI systems can’t parse it confidently enough to cite.

Write down every mismatch. This list is your prioritized fix queue.

Test 6: Scoring Your Results (5 Minutes)

Score each test on a simple pass/fail basis across the four tools.

Company Name Test.

• Pass: All four tools accurately describe what you do.
• Partial: Most tools get it right, but one or two are inaccurate.
• Fail: Multiple tools misdescribe your core business.

Category Test.

• Pass: You appear in the top three mentions across at least three tools.
• Partial: You appear, but inconsistently, or lower than third.
• Fail: You don’t appear at all.

Product Specificity Test.

• Pass: AI describes your products accurately with no fabrications.
• Partial: Minor inaccuracies, but the core description is right.
• Fail: AI fabricates features or confuses you with competitors.

Comparison Test.

• Pass: AI cites your own content and frames you competitively.
• Partial: AI cites third-party sources, framing is neutral.
• Fail: AI frames you unfavorably, cites weak sources, or gets facts wrong.

Consistency Check.

• Pass: Your site and AI descriptions align closely.
• Partial: Minor mismatches, nothing damaging.
• Fail: Significant contradictions between your site and AI.

Count your results. A rough guide:

0 to 1 passes: You have significant Trust Debt. AI is actively misrepresenting your business to buyers, and you’re losing deals you don’t know about. Fix the foundation: structured data, consistent NAP (name, address, phone) across all platforms, specific product descriptions, and a knowledge graph connection.

2 to 3 passes: You’re in the middle. Some things work, some don’t. The gaps are costing you specific deals in specific categories. Prioritize the tests you failed and fix those first.

4 to 5 passes: You’re ahead of most companies in your category. Your data is consistent, AI describes you accurately, and you appear in category queries. The work from here is depth: building out Coverage for the long tail of buyer questions.

What to Do With What You Found

If your audit surfaced problems, you have two paths.

Fix the foundation yourself. Start with the data AI can’t trust. Make your product descriptions specific enough to cite. Make sure your company name, location, and category are consistent across your website, your Google Business Profile, and every directory that lists you. Add structured data so AI systems can parse your pages with confidence. Write content that answers the buyer questions your audit revealed are going unanswered. This is foundational work. It takes time, but it compounds.

Get a diagnostic that measures all eight Clarity factors, benchmarks you against competitors, and hands you a prioritized roadmap. The Clarity Diagnostic runs the full analysis. You get a scored report, a competitor comparison, and a fix list ordered by impact. Thirty minutes tells you something is wrong. A diagnostic tells you exactly what, why, and what to do about it.

The gap between “something is wrong” and “here’s the fix” is where most companies stall. They run a version of this audit, see the results, get concerned, and then don’t know what to do next. That’s the gap a diagnostic closes.

The 30-minute version is enough to start. It’s enough to know whether you have a problem worth solving. And if the audit told you what you suspect it told you, the next step is getting the full picture.