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AI visibility audit for B2B suppliers
What an AI visibility audit is, what it measures across five AI engines, and how to run one for your company in a few minutes.
What is an AI visibility audit?
An AI visibility audit measures what large language models say about your company when a buyer asks. It is the AI-era equivalent of checking your search rankings, except the surface is now ChatGPT, Perplexity, Gemini, Claude and DeepSeek rather than a page of blue links. The audit asks each model the questions a real sourcing team would ask, then scores three things: whether the model recognizes your company at all, whether it surfaces you when a buyer asks for suppliers in your category, and whether it describes you accurately and favorably.
The distinction that matters is between being indexed and being legible. A supplier can have a website, a catalog and years of trade history and still be effectively invisible to an AI assistant, because the model never encoded reliable facts about it. An AI visibility audit exposes that gap in concrete terms: it shows you the exact answers the models give, so you can see where you are missing, misdescribed, or confused with another company.
Why it matters in 2026 sourcing
Procurement has a new first step. Before a category manager sends an RFQ, they paste the supplier name into an AI assistant and read whatever comes back. If the model returns a clear, confident description with the right products, certifications and country, the supplier makes the shortlist. If the model says it has no reliable information, or worse, invents details, the buyer moves on. That triage happens before any human at the supplier ever hears about the opportunity, which means AI visibility now decides which deals you are even considered for.
This is why a one-time website refresh is not enough. The models are the gatekeepers, and they form their view from the public web, third-party references and structured signals, not from a brochure. An audit tells you what that view currently is.
What an AI visibility audit measures
A rigorous audit runs the same prompts across several engines, because the models disagree and buyers use different ones. Reevol Signal queries five: OpenAI, Anthropic (Claude), Perplexity, Google Gemini and DeepSeek. It then rolls the answers into three pillars:
- AI knowledge: do multiple models recognize your company and agree on the basic facts, or do they draw a blank or contradict each other?
- AI recommendation: when a buyer asks for suppliers in your category and region, do the models surface you unprompted?
- AI verdict: when the models do describe you, is the read accurate and positive, or vague and hedged?
Those pillars combine into a single score out of 100, with a plain-language band so you know whether you are strongly present, moderately present, weak, or dark to AI buyers. For the full weighting, see understanding your Signal score.
How to run one
You can run a rough version by hand: open each AI assistant, ask "what do you know about [your company]?" and "who are the leading [your category] suppliers in [your country]?", and note where you appear and how you are described. That is useful, but it is slow, it is not repeatable, and a single model answer is noisy.
Reevol Signal runs the structured version free. It queries all five engines with a fixed prompt matrix, averages multiple runs to dampen model jitter, and returns your score with the specific answers behind it plus a short list of the highest-impact fixes. There is nothing to install and no sales call. Related reading: AI presence for suppliers and how procurement AI evaluates suppliers.
Run your free AI visibility audit
Enter your company on the Reevol Signal homepage to see how five AI engines know and rate you, and what to fix first. It is free, and it takes a couple of minutes.