AI systems are forming opinions about your Web3 project right now. Most of them are wrong.
Lauren helps Web3 founders understand how AI systems currently see their project—and builds the clarity, structure, and authority needed to change that picture.
Recent projects
When someone asks ChatGPT about your project, what do they hear?
One of three things happens: they get an accurate description, a vague or distorted one, or silence. For most Web3 projects, it’s the second or third—and the cause is almost always the same.
The information available about your project is fragmented, inconsistent, or structured in a way AI systems can’t reliably interpret. They synthesise what they can find, and what they find doesn’t reflect what you actually built.
This matters more than it used to. AI systems are increasingly where people go to understand, compare, and decide. How they represent your project shapes whether it gets taken seriously—by developers, investors, and the people you’re trying to reach.
A system for becoming visible, credible,
and consistently referenced
LAYER 01
Explainability
Rewriting core pages, defining your product as an entity, and building the explanation systems that give AI something accurate and reliable to work from. The foundation everything else depends on.
LAYER 02
Authority & reputation
Building consistency across channels, developing your external presence in the places that shape AI systems, and establishing the entity recognition that makes AI reference you accurately over time.
LAYER 03
AI content architecture
Structuring content for AI retrieval and citation: definition pages, comparison content, FAQ structures, and internal linking that reinforces your key entities. Optimised for machine interpretation.
Why this work requires someone who actually understands Web3
AI visibility work for Web3 is harder than it sounds. The products are genuinely complex, the terminology is contested, and the information environment is fragmented across chains, forums, and documentation that most content strategists never touch.
Lauren has spent six years working in Web3 content—with Algorand Foundation, Decentraland, Concordium, and others—on the underlying problem that makes AI visibility difficult: turning technically complex products into explanations that are clear, structured, and credible.
The AI visibility system draws directly on that background. Understanding what AI systems need starts with understanding what makes Web3 projects genuinely hard to explain.
The work also draws on a background in copywriting, brand narrative, and content strategy. If you need that work done independently of the AI visibility system, get in touch.
