I turn human needs and pain points into shipped AI products — from discovery to deployment. 22+ years spanning enterprise UX, GenAI strategy, agentic systems, and full-stack product development. Not just advising on AI — building with it.
There's a third lane emerging in AI organizations — and I've been building it for two years.
I've designed enterprise UX for Fortune 500 clients for 22 years. But in the last two years I went further — I built with AI, failed with it, learned from those failures, and shipped 40+ real products across 6 industries. The design fluency didn't go away. It got sharper.
"I can tell the difference between AI noise and AI signal — because I've seen both in production."
I didn't just pick up Cursor and start shipping prototypes. I built a repeatable human-centered methodology across real build cycles — understanding when to trust the model, when to override it, and how to design systems where humans stay in meaningful control.
"The methodology came from failure, not theory."
I understand the entire AI product lifecycle: human problem framing → concept validation → AI-assisted build → agentic pipeline design → responsible deployment → org capability building. I can direct teams at every layer and create the AI readiness clarity organizations are struggling to find.
Every AI product I've built is designed around one question: where does the human belong, and what does that moment feel like? Not as a safety checkbox — as the core design decision. That conviction comes from building real products where I got it wrong first.
"AI fills. Humans verify. That's not a limitation — it's the design."
Principles formed through real builds, not theory. Here's how I think about designing AI products.
Every AI product starts with a non-AI question: what is the actual human cost of the current experience? Pain points, friction, and failed workarounds — before touching any AI framing.
Which moments in the journey are repetitive, predictable, or data-rich? Map the AI opportunity space — where it adds signal vs. where it adds noise.
The interaction contract: what does AI decide autonomously, what does it suggest, what does the human always control? This is the UX architecture beneath the surface.
Use Claude, Cursor, Lovable and Gemini to build the actual product alongside the design. Feedback is about the real thing, not a simulation.
AI products drift. I build observability into AI features from day one — so products can learn without losing user trust.
The hardest thing to get right in AI product design isn't the AI. It's knowing exactly where the human belongs — and designing that moment to feel natural, not like an interruption.
AI fills, humans verify. Default outputs are AI-generated. The human's job is review, not creation.
Confidence is surfaced, not hidden. When AI is uncertain, the product says so. Users trust what they understand.
Override is always one tap. Human control is never more than one interaction away. AI helps, never gatekeeps.
Learning is bidirectional. When humans correct AI, the product gets better. Corrections are data, not failures.
Primary AI pair programmer. Built Ventura (77d, 137 commits) entirely using Claude via Cursor.
Integrated as AI insights engine in OEM Portal. Powers content generation in Creator Studio.
AI-native development environment. The canvas where design intent becomes real, deployable code.
Agentic pipeline design. Orchestrated 20-agent research system synthesizing 40+ live data sources.
Measured across 77 days of AI-assisted development vs. manual baseline
Creator Studio: monolith rebuilt into multi-tenant AI content platform
AI-fill model: one sentence → eight complete content fields
20-agent system delivering what 3–5 analysts need 2–3 hours to produce
Every project here went from idea to deployed product. Real users, real systems, real outcomes.
Solo-built a full investment coordination SaaS in 77 days using Claude as pair programmer. 137 commits, 15,000+ lines, 50+ features. One person with AI outpacing a traditional 3-person team.
Rebuilt a 3,144-line monolith into a production-grade, multi-tenant AI content platform in 12 days. Core design insight: one natural language sentence replaces 8 form fields — AI fills everything, humans tweak.
Unified 5 fragmented partner systems into a single AI-powered self-serve workspace. Onboarding dropped from 12 weeks to 2 weeks. Partners operate end-to-end without BD dependency.
Designed and deployed a multi-agent agentic AI system synthesizing 40+ live data sources — delivering in 30 minutes what a 3-5 person analyst team needs 2-3 hours to produce manually.
Shipped a full 3D browser game — with product strategy, monetization model, and community roadmap — in 48 hours. Pure browser, no backend. Platform concept with POI system for brand spots and collectibles.
Ten products that span applied AI, real consumer apps, human-centered design, and a consulting framework. Each one answers a different CEO question.
Every card is something that shipped — or proved a concept worth building. Click any card to go deeper.
Not less. The organizations replacing people with AI are about to learn something expensive.
Organizations are discovering that replacing experienced judgment with AI output creates a different problem: no one left who can tell when the AI is wrong. I've watched this cycle from inside enterprise AI transformation. The first round of regret is already happening.
"The companies that gutted their design and product teams are now rebuilding the human judgment layer."
AI is spectacular at pattern completion. It cannot do problem discovery. It cannot tell you which problem is worth solving. It cannot navigate organizational politics, read a room, or know when a product decision needs a human champion. That's a different kind of intelligence.
"AI has all the wings. You still have to choose which one to fly."
CEOs and leadership teams are drowning in AI vendor noise. New models every week. New frameworks every month. No one with the cross-functional fluency to cut through and say: here's what actually moves your business, here's what's a distraction, here's where to start.
22 years of product and UX judgment. 2 years of deep AI building. 60+ prototypes across 10 industries. I've built the map others are still being handed directions to. I can walk alongside your leadership — not selling AI, but making your organization genuinely AI-capable.
"Application is the new education. I've done the application."
Speed isn't the point. The fact that I can go from idea to something running in hours proves depth of pattern recognition. When I explored MiroFish the same day I heard about it — that's how you know someone truly understands a technology.
MiroFish: Same day I learned about it, I had a full 3D spatial canvas running. Explored it, documented it, moved on.
TaskForce: Concept to deployed multi-agent site in hours — including the domain boringtopic.com. The point was the insight, not the timeline.
Finding Penguin: Full 3D browser game with product strategy and monetization model — 48 hours. No backend. Pure insight as constraint.
Director-level AI product and design roles where human judgment and AI capability need to be brought together — and where the work actually ships.
contact.appsparrow@gmail.com →