My Design Process

My end-to-end, systems-driven approach to designing products and systems, built to bring clarity to ambiguity, align teams, and ship work that holds up in production.

Design Pillars

Principles I use to guide decisions at every stage.

How I Design

Six stages that balance structured thinking with fast, intentional iteration.
Stage 1

Frame the Problem

Align on the real problem, success criteria, and constraints before solutioning.

This stage involves
  • Clarify goals, success metrics, and non-negotiables
  • Map users, jobs to be done, and key pain points
  • Align with PM and engineering on scope and intent
  • Surface risks, dependencies, and unknowns early
AI supports with
Stage 2

Rapid Exploration

Explore directions quickly and validate assumptions before committing to a path.

This stage involves
  • Generate options with sketches, rough flows, and whiteboards
  • Pressure-test feasibility with engineering early
  • Align on the experience before visual polish
  • Use edge cases to break weak concepts fast
AI supports with
Stage 3

Information Architecture and Systems

Turn concepts into structure, flows, and system-level rules.

This stage involves
  • Define hierarchy, navigation, and core user paths
  • Map logic, state changes, and interactions
  • Identify component needs, variants, and reuse opportunities
  • Consider downstream impacts across the ecosystem
AI supports with
Stage 4

UX Scenarios and Mid-Fidelity Design

Translate structure into usable interactions and validate comprehension before high fidelity.

This stage involves
  • Design mid-fi screens for core scenarios
  • Run narrative walkthroughs to spot friction
  • Address edge cases before polish
  • Gather cross-functional feedback and refine direction
AI supports with
Stage 5

High-Fidelity Design and Collaboration

Move to production-ready clarity with accessibility and implementation in mind.

This stage involves
  • Finalize UI, interaction patterns, and component usage
  • Prototype key journeys end-to-end
  • Partner with engineering to confirm feasibility
  • Document states, logic, and edge cases clearly
  • Support QA with specs, notes, and references
AI supports with
Stage 6

Delivery and Iteration

Ship, validate outcomes, and iterate based on real usage.

This stage involves
  • Stay close to implementation through regular check-ins
  • Validate the build against design intent
  • Observe usage and identify friction points
  • Iterate using qualitative and quantitative signals
AI supports with

Why This Process Works

  • Creates shared clarity early so teams move in one direction
  • Reduces engineering churn by de-risking decisions upfront
  • Scales from zero-to-one work to complex system redesigns
  • Maintains quality through structure, reuse, and clear specs
  • Uses AI for speed and scale while keeping judgment human-led

Let’s build something great.

Always open to connecting about roles, systems work, and solving messy problems.

Contact me Connect on LinkedIn