| Years | Role | Scope |
| 2025 - Present | Technical Consultant | Product Strategy, Technical Architecture, AI Integration, Mobile Development, Growth & Go-to-Market |
The founder wanted Stylegen AI to be a strong mobile and web product, the app people open to answer one question: what do I wear today? The hard part was delivering a polished, AI-powered experience across platforms on a limited budget, with a small team and no room for waste.
I joined like a technical co-founder and built the technical and product roadmap the team needed, then shipped it fast with the right modern technologies. The bet was simple: make capturing a wardrobe almost effortless, then let AI do the deciding. A clean, cross-platform architecture spans mobile and web, photo capture with automatic background removal turns a real wardrobe into a clean digital closet, and a styling engine pairs garment data with generative models for daily outfits and virtual try-on. Every layer feels instant and personal while staying cheap enough to run lean.
Plan what to wear, day by day
Map out your looks across the week from the clothes you already own. Planning ahead for work, trips, and events turns the daily 'what do I wear' question into a decision you make once, calmly, instead of every morning in a rush.
Daily AI outfits, planning, and virtual try-on
From there the AI takes over: daily outfit suggestions built only from what you own, a calendar to plan looks ahead for trips and events, and a virtual try-on that shows how a combination looks before you commit, turning 'I have nothing to wear' into a two-tap decision.
I worked like a technical co-founder, owning both the product and the engineering. I planned a clean architecture, chose the frameworks that gave us the most leverage for the budget, and solved the hardest problems in the journey the way a CTO would, from AI cost and latency to effortless wardrobe capture. On the product side I sharpened the core bet, shaped the onboarding and retention loop, and kept AI suggestions personal and controllable, all while learning the consumer app journey end to end.
Leading the architecture on a tight budget taught me that the best technical decision is the one that buys the most value per dollar. A clean, cross-platform stack let a small team ship a real mobile and web product without waste.
The hardest problem in an AI styling app isn't the model, it's the input. Making wardrobe capture effortless mattered more than any algorithm, and watching the full customer journey, from first photo to daily habit, taught me where a consumer product really wins or loses.
Let's build
something
together.