I find problems (opportunities) no one put on the brief.

The highest-leverage design work happens before anyone opens Figma, Cursor, or Claude Code.

I align on the real problem, map the system, and identify what's missing.

I use AI to move through execution faster than most teams expect.

.ai-native practice

I'm fluent in AI the way some designers are fluent in Figma.

Most designers using AI use it like a tool. Most designers designing AI products treat AI as the subject matter. I do both, and the work is faster and more accurate because they reinforce each other.

01 Research synthesis in hours, not days

I surface patterns across qualitative and quantitative studies fast. A week of affinity mapping compresses to a morning, freeing the rest of my time for insight and delivery decisions that actually shape the work.

02 Domain ramp-up on complex technical spaces

Whether it's AI/ML, fintech, genomics, or audio engineering, I get credible fast. I can walk into a stakeholder meeting in a new domain and ask the right questions within days of starting.

03 10x exploration

I can generate, stress-test, and kill weak ideas before investing in execution. Branding directions, interaction paradigms, moodboards, copy patterns: I run several comparative systems in parallel and arrive at a strong direction in the time it used to take to explore one. More directions tested means stronger work survives to delivery.

The AI fluency above doesn't replace the process below. It compresses the slow parts so I can spend more time on the parts that need judgment.

how i work

I'm fluent in AI the way some designers are fluent in Figma.

I design AI products and use AI tools and frameworks to design them. Whether working on the dev tool database layer or a frontend consumer app, that delightful loop of double fluency means I move faster, go deeper, and produce better-informed work than designers who aren't familiar with how AI systems work.

I know which tools to reach for at each stage, how to get outputs that are actually useful, and how to build on them quickly rather than starting from scratch. It's just how I work now.

I use AI to compress the slow parts of the process (like research synthesis, domain ramp-up, content generation, early exploration) so I can spend more time on the alignment and decisions that require a designer's eye and judgment.

Find the real problem. Then design the right system.

Most design problems arrive pre-framed and wrong. My process starts by questioning the brief, aligning on what actually needs solving, and mapping the system before I touch a frame. Everything after that moves faster because of it.

Interrogate the brief

The problem as presented is rarely the problem worth solving. I dig into what leadership actually needs vs. what they asked for — that conversation is where the most leverage lives.

Discovery

Research that informs, not decorates

Competitive analysis, user interviews, surveys — focused on market gaps and target behavior, not checkbox activities. ICP research that actually changes decisions.

Research

Frame the opportunity

Define the dream product vs. the minimum lovable version. Shape the scope before anyone writes a line of code or pulls a Figma frame.

Strategy

Move from lo-fi to hi-fi fast

Quick wireframes to test ideas, copy, and flow. Review, cut unnecessary complexity, then upgrade to a modular system that creates consistency and speed across the whole product.

Design

Document, ship, measure

Structured specs, tracked feedback, outcomes measured against intent. Not shipped-and-forgotten — shipped-and-learned-from.

Delivery

Iterate on what actually matters

Not everything needs another pass. I use real data and stakeholder signal to identify which problems are worth solving next — and which ones were never the real problem to begin with.

Growth

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