Figr AI is a product design agent specifically built for product teams who already have an existing application and design system. Unlike traditional AI design tools that start from a blank canvas, Figr learns your product's existing patterns, decisions, and design language to help create production-ready UX that matches what you've already built.

Key features include capturing your live product in one click using a Chrome extension that reads your HTML, importing your design system from Figma with tokens intact, surfacing edge cases and UX reviews grounded in 200,000+ screens, remembering your product across sessions to avoid re-explaining, mapping user flows and journeys, spotting edge cases before development, running UX reviews, building A/B variations, creating prototypes that match your app's design language, reading analytics data, drafting PRDs and specs, and enforcing design system tokens.

The unique approach involves thinking through UX decisions first rather than just building what you ask. Figr analyzes your existing product context, searches through multiple data sources including web content and your documentation, questions and clarifies requirements, and provides reasoning based on proven patterns from 200,000+ UX patterns. It processes inputs like screenshots, Figma files, screen recordings, web captures, MCPs, product docs, and analytics CSVs.

Benefits include getting prototypes that look exactly like your product, finding edge cases before development, testing scenarios before handoff, accessing feedback grounded in real UX patterns, exploring A/B variations with rationale, one-click Figma export, built-in accessibility checks for designs, and UX review capabilities before leadership sees it. This helps teams ship without rework and reduces missed requirements during reviews.

The product targets product managers and product designers working on existing applications. It integrates with various inputs including Chrome extension for web capture, Figma files, screen recordings, analytics CSVs, product documentation, and MCPs. The system remembers product context that compounds over time and provides recommendations backed by analysis of 200,000+ UX patterns from real applications.