Rewear: Context-Aware AI Styling with What You Own

Three questions to the project team

What was the particular challenge of the project from a UX point of view?
One challenge was defining the target audience, as outfit needs vary by age, gender, and fashion interest. This made ethical considerations and edge cases complex. However, user research revealed a shared behavior: most people consider the weather when getting dressed. This insight centered the experience on weather-based outfit suggestions using items users already own—offering universal relevance and reducing personalization friction.

Another challenge was cold-start scenarios and the lack of universal style or occasion tags. I addressed this by reducing friction during onboarding with curated style pickers and support for image/video uploads. This enabled the AI to learn preferences early while still supporting filtering later.

What was your personal highlight in the development process? Was there an aha!-moment, was there a low point?
The idea began with a personal frustration: I had plenty of clothes but still felt like I had nothing to wear—especially for specific occasions. Styling new looks was time-consuming, and I often repeated the same outfits. I hoped AI could help re-style what I owned. But early research showed similar apps already existed—a low point that made me question the value of my idea. Then, through user interviews and surveys, I noticed weather data was underused. Most apps ignored deeper factors like wind, or “feels like” temperature—yet users said weather shaped their choices. That became my aha moment. It led to a focus on weather-based daily and travel outfit planning, which gave the project new clarity and momentum.

Where do you see yourself and the project in the next five years?
I see this project evolving into a personalized AI style assistant that promotes sustainable, conscious fashion. Beyond helping users plan daily outfits and reuse existing clothing with sustainability scores, it will include a “Donate Unworn Items” feature—connecting users with charities or resale platforms to reduce waste. Looking ahead, the platform could integrate with smart closets, travel tools, sustainable brands, and resale marketplaces—building a connected, responsible fashion ecosystem. Its long-term value lies in daily utility, personalized insights, and features that encourage reuse and sharing. Personally, I aim to keep designing human-centered AI experiences that simplify everyday decisions.