An AI-assisted approach built on vector search, matching design intent to production code so teams spend less time translating a screen and more time deciding if it's right.
The design system already had the answer for most screens. The gap wasn't decisions, it was distance, between a finished design and the code that matched it.
A near-identical pattern usually already existed somewhere in Floret's 65+ components
Finding it meant searching documentation manually, by memory or by keyword
Once found, translating it into working code was still manual, screen by screen
System knowledge lived in people's heads, not somewhere machine-searchable
Not a different design process, the same judgment, applied to something machine-searchable instead of memory-dependent.
The goal was never "generate UI from scratch." Floret already contained the right answer for most screens, the task was finding and adapting it, consistent with the system's core philosophy of reuse over reinvention.
Represented Floret's patterns and components so they could be matched by intent, not just exact keywords, an AI-assisted approach built on vector search.
Developers describe what they need in plain language; Codegen retrieves the most relevant design pattern along with its implementation code, ready to adapt.
Codegen surfaces a strong starting point, not a final answer, the developer still decides whether the match is right for the context, same as before.
The bigger lesson wasn't about AI. It was about reducing the distance between intent and implementation. AI wasn't replacing expertise, it was making existing knowledge easier to discover, that's where the real value came from.