My AI-Powered Design Process

Not a shortcut. A force multiplier. AI runs through every phase, from discovery to engineering handoff, cutting cycle time without cutting corners.

Tools in my workflow

Figma
Claude
Cursor
Perplexity
Gemini

Start in an LLM

Before touching Figma, I use Claude to clarify intent, draft short PRDs, and surface risks, edge cases, and initial approaches. Faster alignment, fewer wasted cycles.

  • Session recordings, NPS verbatims, and support tickets fed directly into context
  • Patterns surfaced in hours, not weeks
  • Every design decision traces back to real customer behavior

01

Validate with Self-Serve Research

Talk directly with customers, run quick tests, and use what you learn to adjust direction. Research is a velocity tool, not a gate.

  • AI synthesizes interview notes and identifies recurring themes
  • Assumptions stress-tested before a single component is built
  • Direction adjusts early, not after eng has already built it

02

Prototype Using AI Tools

No wireframes. Day one is a working prototype. Use Cursor and Claude Code to build and iterate on flows and simple interfaces. AI generates code while I guide structure, behavior, and UX quality.

  • Clickable, interactive simulations from voiced prompts
  • Stakeholders feel the product, not review a static mockup
  • Partner with engineers to decide what moves into the product

03

Bring Work into Figma

Validated concepts move into Figma for full state coverage, system alignment, and production readiness. This is where craft happens.

  • Accurate tokens, correct states, proper spacing, motion-ready
  • Every component maps back to the design system
  • Multiple form behaviors and flows tested simultaneously

04

Async-First Stakeholder Alignment

No scheduled design crits. No screen shares. Prototypes go out async. Stakeholders review on their time, leave timestamped reactions.

  • Syncs happen with real context, not cold first impressions
  • Review cycles shrink, feedback gets broader
  • Momentum stays

05

Edge Cases & Scale Simulation

Enterprise products fail at the edges. I design for them on purpose.

  • Empty states, permission boundaries, degraded network: all prototyped
  • AI generates realistic data sets and simulates load behaviors
  • Problems caught in prototype, not in production

06

Developer Handoff

Figma is the source of truth. Not “ready for handoff” in theory. Actually ready.

  • Specs, tokens, states, and annotations structured for direct implementation
  • In-browser reviews during build, flagging drift early
  • Engineers implement intent, they don’t interpret it

07

Every phase here has been pressure-tested on real products, from 0-1 launches to platform overhauls and complex feature work. The case studies are where it shows.