The bottleneck in AI frontend generation isn't the model. It's the quality of the intermediate representation you hand it.
Figma-to-code tools optimise for the wrong thing: CSS extraction, HTML generation, developer handoff. They emit noisy exports with broken hierarchy, absolute positioning everywhere, and metadata that has nothing to do with how a UI is actually built.
Feed that to an AI agent and you get hallucinated, inconsistent code. The real bottleneck in AI frontend generation isn't the model. It's the quality of the intermediate representation you hand it.
UI to JSON treats a design like source code for a compiler. It extracts the semantic structure a developer would infer by eye, then normalises it into a UI Abstract Syntax Tree built specifically for machine reasoning.
Every export tool I'd used optimised for completeness: capture every property, every pixel, every node. UI to JSON is the opposite bet. The value isn't in what it records, it's in what it confidently discards. Deciding which visual details carry meaning, and which are noise, was the whole design problem.
The insight that landed with people was practical: you don't need MCP servers or screenshots that burn tokens to get an AI from design to code. A small, clean, semantic representation does more than a large noisy one. Building it as a shipped plugin, not a demo, forced that to survive contact with messy real files made by people who never expected a machine to read them. That constraint is what makes the output trustworthy.
Layout, component, token, naming, and constraint extraction into AI-optimized JSON, with optional markdown context.
Code-generation adapters and multi-framework output so the AST flows straight into a coding agent.
Breakpoint inference, animation extraction, and accessibility recommendations layered onto the spec.
Semantic diffing, live code preview, and feedback loops between design systems and generated code.