How to audit your AI visibility: what the models are actually saying about your company

Most companies have no idea what large language models say about them. They have invested in SEO, in content, in brand positioning — and then never checked whether any of it translates into the AI layer that is increasingly mediating their buyers' research.

This is a straightforward problem to investigate. It requires no special tools. You just have to know what questions to ask.

The three things that determine AI visibility

Whether an AI model represents your company accurately comes down to three factors. Entity clarity: does the model have a coherent understanding of what your company is and does? Offer clarity: can the model accurately describe your products or services in response to relevant buyer queries? Competitive positioning: when buyers compare options in your category, does your company appear and appear correctly?

These are not search ranking questions. They are knowledge representation questions. The model has synthesised information from across the web into an internal representation of your company. That representation may or may not be accurate. It may be incomplete, outdated, or confused with a competitor.

How to test it yourself

The manual approach is to go into ChatGPT, Perplexity, and Gemini and ask them the questions your buyers ask. Not general questions about your industry — specific questions about the problem your buyers have before they find you.

"What companies offer [your service] in [your market]?" "Compare [your category] options for [your buyer profile]." "What should I look for when choosing a [your product type]?"

See what comes back. Does your company appear? Is the description accurate? Are you appearing in the right competitive context or the wrong one?

Most companies that do this exercise for the first time are surprised by what they find — either their absence, inaccuracy, or appearance in contexts that suggest the model misunderstands what they do.

The structured approach

Manual testing gives you a directional signal but not a systematic picture. A structured AI visibility audit tests a larger and more representative sample of buyer queries, scores visibility across multiple dimensions, and identifies the specific content gaps and structural issues that are causing problems.

This is what Visible en IA does. The audit covers 20 simulated buyer prompts across the main AI models, scores across 10 visibility dimensions, and delivers prioritised recommendations — including JSON-LD structured data templates ready to implement. The 90-day re-audit tracks whether the changes made a difference.

For B2B companies whose buyers research through AI before engaging sales, AI visibility is not optional. It is the new first impression. You can start the audit at visibleenia.com.

The web and content infrastructure that supports AI visibility — structured data, clear entity signals, consistent cross-domain information architecture — is part of the web and systems work we build at FJOM Studio.