Lexful is an AI-first documentation tool built for Managed Service Providers. I joined as founding design partner in September 2025, with a brief to take the product from zero to one. Five months later, we launched with eight design partners, over a hundred founding circle members, and another hundred in the pipeline. This is the story of how we got there.

The Challenge

The MSP documentation space is well-established but underserved by modern tooling. The opportunity was to build something genuinely AI-native. Not a legacy tool with AI bolted on, but a product designed from the ground up around how AI can make documentation faster, smarter, and more useful for the people doing the work.

The challenge from a design perspective was twofold: move fast enough to validate assumptions with real customers, and build a foundation robust enough to scale when those assumptions proved out.

My Role

As the founding design partner, I was responsible for the full scope of product design. That meant everything from sitting with customers and synthesizing what they told us, to defining the interaction model, to building the design system that the engineering team would build against. I was also involved in shaping the product strategy, identifying design partners, and contributing to product-led growth thinking.

There was no design team. It was a founding environment, which means every decision mattered and nothing could be precious.

The Process

Listening First

We started with customers before we built anything. I led a series of design partner interviews to understand how MSPs were currently handling documentation, where the pain was, and what they actually needed versus what they said they needed. Those conversations shaped everything that came after.

Rather than manually synthesizing hours of customer meetings, we used AI to identify patterns across conversations quickly. This let us move from customer insight to product decision in days rather than weeks, and meant we could run more conversations without losing fidelity.

An AI-First Way of Working

AI wasn't just in the product, it was in how we built the product. We used it to synthesize meeting notes, surface recurring themes across customer conversations, and rapidly prototype responses to what we were hearing. The goal was always the same: get something in front of customers as fast as possible, learn, and iterate. In a five-month runway, that pace is the only thing that works.

Building the Design System

One of my first substantive decisions was to build a proper design system before building the product. That's not the obvious call in a founding context, where the instinct is to move fast and clean up later. But I've seen what happens when you skip it, and in an environment where engineering velocity is everything, a solid foundation pays back immediately.

I built the design system from zero, matching every primitive and component to the ShadCN component library the engineering team was using, and structuring the supporting tokens and utilities to align with Tailwind CSS. The system was built with variables and semantic tokens throughout, with conditional logic around each component, so that it could support both rapid human design work and AI-assisted workflows as the team and tooling evolved.

The practical result was that when the engineering team needed a new component or pattern, the design decision was already made and documented. No back and forth, no inconsistency, no debt.

Outcomes

Those numbers represent real customers who believed in the product enough to commit before it was finished. That doesn't happen without a product that feels considered and trustworthy from the first interaction.

Reflection

What this project reinforced for me is that design's highest leverage in a founding context is not the screens. It's the decisions you make about how to listen to customers, how to translate what you hear into product direction, and how to build systems that let a small team move at a pace that would otherwise be impossible. The screens matter too, but they're downstream of everything else.

Building AI-first isn't about adding AI features. It's about designing a product and a process where AI is embedded in how value gets created: for the customer and for the team building it.