The internal AI tool that's transforming how Stripe designs products | Owen Williams
Owen Williams, a design manager at Stripe with an engineering background, built an internal prototyping tool called 'Protodash' that lets designers and PMs build high-fidelity, on-brand Stripe prototypes without needing deep coding knowledge. The tool evolved from a set of Cursor rules and a React app into a full browser-based studio ('Protodash Studio') with AI-assisted iteration, design review modes, and dev box hosting. Unexpectedly, PMs became even heavier users than designers, transforming how product work is communicated and reviewed at Stripe.
Summary
Owen Williams, a design manager at Stripe who leads the developer experience space, built an internal AI-powered prototyping tool called 'Protodash' over approximately 18 months. The core problem he was solving was that designers using external tools like V0 kept producing prototypes with mismatched design systems — what he calls 'indigo blur slop' — because those tools didn't know Stripe's internal design system called 'Sail.' His engineering background gave him a pragmatic approach: bundle Stripe's design system components, cursor rules, and an MCP server integration into a starter project that designers could open in Cursor or Claude Code and immediately get 90% of the way to a realistic, on-brand prototype.
The first version was essentially a React router with Stripe's design system components, an MCP server for the Sail design system, and a set of opinionated Cursor rules that taught the LLM how to use the project correctly — including rules to check the design system before writing any code, and explicit prohibitions against hallucinating components or defaulting to Tailwind. Designers could run this locally or, more powerfully, spin up a dev box from a single URL in about two minutes, getting a shareable prototype link without ever running npm.
The second evolution, 'Protodash Studio,' brought the entire experience into the browser — inspired by the goal of building something like V0 but specifically for Stripe. It features an embedded LLM interface for building and iterating on prototypes without needing Cursor open, a visual annotation mode where users can click directly on prototype elements and leave AI-addressable comments, a structured design review mode that replaces Google Doc feedback tables with inline comments and AI-generated summaries, and state/variant controls for quickly switching between business models, data states, languages, or fidelity levels (including a grayscale mode and a low-fi monospace mode).
A surprising outcome was that PMs became the heaviest users of the tool, not designers. Owen initially felt nervous about this but came to see it as transformative: PMs can now explore ideas visually at high fidelity, test prototypes with users earlier, and have more productive conversations with designers about actual work rather than resource allocation. The tool also dramatically changed design-to-engineering handoff — one team working on the Radar fraud detection product built a fully interactive multi-step prototype that an engineer could use as a near-direct source of truth, with all the same components already in place.
Owen built nearly all of this by 'yelling at Claude Code for 18 months,' with his engineering background providing the architectural judgment needed to direct AI effectively. He emphasized that internal tooling has a significant advantage over production software: it doesn't need to be production-grade, which dramatically lowers the bar and accelerates iteration. He is now hiring a design engineer specifically to continue developing Protodash.
Key Insights
- Owen found that PMs became even heavier users of Protodash than designers, which initially made him nervous but ultimately proved transformative — it gave PMs the visual tools to manifest their ideas, made them better communicators with designers, and enabled much earlier user research on prototypes rather than static documents.
- Owen argues that one of the most critical components of making AI prototyping reliable is building extremely opinionated cursor rules — including explicit prohibitions like 'do not use Tailwind unless told to' — because LLMs will otherwise hallucinate entire design systems or default to generic styling without telling the user.
- Owen describes a design-to-engineering handoff transformation where a team on Stripe's Radar product delivered a prototype as a pull request that an engineer could use almost directly, because all components in the prototype were the same ones used in production — something Owen says has never happened in his career as a design manager.
- Owen's architecture insight for Protodash Studio was to wrap the existing React prototyping tool in a browser-based layer so designers never need to open a terminal, run npm, or configure anything — a dev box spins up in about a minute from a URL and the prototype is immediately shareable, eliminating the local machine barrier that previously excluded non-technical designers.
- Owen argues that prototyping complex data dashboards with all their states, filters, zero-data and full-data conditions, and internationalization was 'nearly impossible' in Figma, and that code-based prototyping now lets teams show how real users will actually experience a product — including switching business models, merchant names, or locale in seconds.
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