AI systems are forming opinions about your project right now, based on whatever information they can find. The audit tells you exactly what that picture looks like and what needs to change.
When someone asks ChatGPT, Perplexity, or another AI system about your project, one of three things happens: it describes you accurately, it describes you vaguely or incorrectly, or it doesn’t mention you at all.
The issue tends to be that the information available about your project is fragmented, inconsistent, or structured in a way AI systems can’t reliably interpret. The result is either silence or distortion, and both have consequences for how your project is understood and recommended.
What the audit covers
The audit looks at four things.
How AI systems currently describe you
I run your project through the major AI platforms and document exactly how they represent you: what they say, what they get wrong, and what they omit. This is your baseline.
Consistency across sources
AI systems synthesise information from multiple places. If your project is described differently across your website, docs, social channels, and third-party mentions, that inconsistency directly affects how AI systems interpret and reference you. I map where the gaps are.
Clarity of your core product explanation
Most Web3 projects struggle to explain what they do in plain, structured language, and AI systems reflect that back. I assess whether your product explanation is clear, complete, and structured in the way it needs to be for reliable machine interpretation.
Content and entity structure
I look at how your website and content are structured for AI retrieval, whether key entities are defined, whether your content architecture supports citation, and where the structural weaknesses are.
What you receive
At the end of the audit you receive a written report covering your current AI visibility picture, a clear breakdown of where the problems are, and a prioritised roadmap of what to address first. The report is honest and direct, it gives you the full picture without softening it.
The roadmap sets the direction for everything that follows. For most clients, the audit is the starting point for moving into the full AI visibility system, where the findings get implemented.
How it works
The audit takes up to one week from start to delivery. Before I begin, we’ll have a short call to understand your project, your current content, and any specific concerns you have about how AI systems are representing you. The rest is handled on my end.
Pricing is based on project complexity and the depth of your current digital footprint. Get in touch and I’ll confirm the scope and cost before anything starts.
Start with the audit
If you’re not sure how AI systems are describing your project, or you suspect the picture isn’t good, the audit is the right place to start. Get in touch and we’ll take it from there.
FAQs
What is an AI visibility audit?
An AI visibility audit documents how AI systems like ChatGPT, Perplexity, and Claude currently describe your project: what they get right, what they get wrong, and what they omit entirely. It gives you a factual baseline of your AI presence and a prioritised roadmap for fixing it.
Why do Web3 projects have AI visibility problems?
Because the information available about most Web3 projects is fragmented, inconsistent, or structured in ways AI systems can’t reliably interpret. Documentation lives across websites, GitHub repos, Discord channels, and old blog posts that contradict each other. AI systems synthesise what they can find, and what they find doesn’t reflect what was actually built.
How is an AI visibility audit different from an SEO audit?
An SEO audit focuses on how search engines rank your pages. An AI visibility audit focuses on how AI systems describe you when someone asks about your project in conversation. The ranking signals are different: AI systems care about entity clarity, consistency across sources, and structured content that can be extracted and cited, not keyword density or backlink profiles.
What do I receive at the end?
A written report covering four things: how AI systems currently describe you, where your information is inconsistent across sources, whether your core product explanation is clear enough for machine interpretation, and where your content and entity structure has gaps. The report includes a prioritised roadmap of what to address first.
How long does it take?
Up to one week from start to delivery. Before the audit begins, there’s a short call to understand your project, your current content, and any specific concerns. The rest is handled on my end.
Is this just for Web3 projects?
The audit is designed for technically complex products where the information environment is fragmented. Web3 is the primary focus because that’s where the problem is most acute and where the background is deepest. But the same methodology applies to any project where AI systems are forming an inaccurate picture: AI infrastructure, dev tools, compliance tech, fintech.