AI as invisible helper, not main feature

It would've been easy to make "AI-powered" the hero of the app — lots of onboarding explaining how smart it is, dashboards showing learning progress, settings to tweak the algorithm.

I went the opposite direction. The AI should be invisible. Users don't care how it works — they care that it works. The app suggests an outfit, you accept or reject it, done. Every interaction trains the model, but you never have to think about it.

This decision shaped the entire UI: minimal controls, no complex settings, confidence in defaults. The smartest technology often feels like no technology at all.

Wardrobe digitization as gradual process

A common failure mode for wardrobe apps: they require you to photograph and catalog your entire closet before you can do anything useful. That's a massive barrier. Nobody wants to spend a Sunday photographing 80 items of clothing.

I designed the onboarding to be incremental. Start with a few key pieces. Add more over time, whenever you want. The app works with 5 items or 500 — it just gets more accurate as your digital wardrobe grows.

This "progressive onboarding" approach was inspired by how the best products let you start getting value immediately, then deepen engagement gradually.

Context-aware suggestions

An outfit that works for a home office day doesn't work for a client meeting. What's comfortable in July is wrong in December.

I integrated contextual signals into the suggestion logic: weather data, calendar events (if connected), time of day, and user-defined "outfit modes" (casual, work, going out). The app doesn't just suggest clothes — it suggests clothes that make sense for today specifically.

This was the insight that elevated the concept beyond a simple "random outfit generator."