Polymet is a design-to-code AI that turns sketches, screenshots, and prompts into production-ready React components.
Polymet is built for product teams that live in design systems. Describe an interface or drop in a screenshot, and it generates React and Tailwind code that follows your design tokens and component conventions. Designers iterate visually while engineers receive code they can actually merge. It handles multi-screen flows and states, and integrates design system files so generated UI stays on-brand.
Polymet’s design-system awareness saves the usual cleanup: generated components pick up your tokens, spacing, and typography instead of inventing new values. Multi-screen flows with states mean you can prototype complete interactions, not just static screens.
Design-to-code quality improves dramatically when the system understands your components, which is Polymet’s core thesis. Product teams report that generated screens arrive needing less cleanup than plugin-based exports, especially for design-system-heavy apps.
Polymet offers a free tier with limited generations. Paid plans raise output limits and add team and design-system features.
Product designers and frontend engineers collaborating on UI who want faster design-to-code turnaround.
Yes, with a free tier; production teams need a paid subscription.
Polymet integrates with design and development workflows; programmatic access is provided to team customers.
Where Figma plugins output static code, Polymet emphasizes design-system fidelity and conversational iteration.
React and Tailwind are the primary output targets, matching most modern product stacks.
Yes, the conversational interface is approachable for designers, while engineers control the final code output.
Yes, generated components include responsive behavior for common breakpoints.
State handling can include theme variants when your design system defines them.
Web components are the primary target; mobile output depends on your stack.
Yes, design system mapping lets output reuse your established component patterns.
Yes, token-aware generation is precisely aimed at teams maintaining consistent component libraries across products.
Yes, the conversational interface is approachable while still giving engineers control over final code output.
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