Figr AI - Product-aware AI that thinks through UX
by•
Figr is an AI product agent for PMs. Parse your live app via Chrome extension, import from Figma, drop in docs and analytics. It maps flows, spots edge cases, runs UX reviews, builds A/B variations and prototypes that match your app's design language. Every recommendation backed by 200K+ UX patterns.
Replies
Super impressive! Looking forward to trying it out with one of the products I'm currently building.
What's the next milestone on your roadmap?
Figr AI
@tudor_moldovanu Thank you! Would love to hear how it works with your product once you try it.
On roadmap, we're going deep on two things. First is making the context graph even smarter, so Figr gets better the more your team uses it across projects. Second is tighter dev handoff, so what Figr generates is closer to production-ready code.
Triforce Todos
Love the ‘think before design’ approach 👏 Most AI tools skip the product thinking part — this feels genuinely useful for real teams.
Figr AI
@abod_rehman Thank you Abdul :)
@moksh_garg
Hey Moksh.
I loved the idea and will give it a try soon.
I just wonder do we need Claude Code or Codex or any kind of agentic coding tool in order to use Figr AI for actual development?
Thanks!
A really cool product for business analysts and designers. I’ve already shared it with my team to check out.
The "think before it designs" positioning is exactly what's missing from most AI design tools. I've used V0 and Bolt — they're great for speed but terrible at catching "what happens when the API fails?" edge cases. Does Figr flag these in the flow mapping stage or only during prototype review?
Figr AI
@dronidev Hello Andrey, Figr has both the the modes. You can identify the edge cases and complete the flow during initial mapping phase else if you already have a prototype Figr can can help you review it.
The product-aware framing is what catches my attention here — most AI design tools feel like glorified asset generators that have no idea what your product actually does or who uses it. The delta between generic AI output and context-aware suggestions is enormous in practice. Curious how Figr AI ingests and maintains that product context over time — especially as the product evolves and earlier assumptions become stale.
The "product-aware" framing is what catches my attention here — most AI design tools feel like glorified autocomplete, but grounding suggestions in actual product context could genuinely change how PMs and designers align early. We're building an invoice scanning product and the UX decisions around edge cases (damaged invoices, weird layouts) are where we constantly struggle to get AI and design thinking to meet. How does Figr actually ingest and maintain that product context — is it a one-time setup or does it learn continuously from how your team ships decisions over time?