Remi: Remix & Reimagine Fashion

Project Overview

Client: UVIC | Project for Design Thinking Course in Barcelona, Spain
Timeline: 1 week ( May 2025 )
My Role: Lead UX Designer, Assistant Researcher
Team: Vivek Chanduri, Angel Nguyen, James Wang

Remi aims to help users overcome the feeling of having “nothing to wear” by encouraging mindful outfit creation using what’s already in their closets. To address this, we researched user habits, identified key pain points around wardrobe management, and designed a solution that promotes sustainable styling and reduces unnecessary spending.

Click to View Final Prototype

The Problem

Many young adults own full wardrobes yet repeatedly wear the same outfits. Existing fashion inspiration platforms generate ideas but rarely help users make better use of clothing they already own. This disconnect leads to wardrobe frustration, unnecessary purchases, and increased clothing waste.

The Challenge: How might we help people feel excited about their existing wardrobe while making outfit planning faster, more personalized, and more sustainable?

Research Methods

• Conducted 12 semi-structured interviews

• Participants aged 19–30

• Interviewed students and young professionals with varying fashion habits

• Explored wardrobe management, shopping behavior, styling habits, and sustainability

Key Insights

1. Decision fatigue outweighed lack of clothing

Most participants owned plenty of clothing but regularly defaulted to the same outfits because creating new combinations required too much effort.

2. Inspiration existed—but wasn't actionable

Participants frequently searched Pinterest, Instagram, and TikTok for outfit ideas, yet struggled to translate inspiration into outfits using clothes they already owned.

3. Sustainability mattered—but convenience won

Although many interviewees wanted to shop more sustainably, time constraints and styling uncertainty often resulted in buying new clothes instead.

4. Users wanted personalized guidance

Participants consistently asked for recommendations based on weather, events, mood, and their own wardrobe rather than generic outfit inspiration.

Design Opportunity

We identified an opportunity to create an intelligent wardrobe companion that transforms existing clothing into personalized outfit recommendations, helping users maximize their wardrobe before purchasing something new.

Design Goals

• Reduce outfit decision fatigue

• Encourage wardrobe reuse

• Personalize recommendations

• Promote sustainable shopping habits

• Make styling fast and enjoyable

Iteration & Feedback:

Usability Testing- Round 1

Participants: 12

What worked

• AI outfit recommendations

• Calendar integration

• Weather synchronization

• Pinterest-inspired interface

Pain points

• Wanted more personalization

• Questioned AI clothing recognition

• Wanted stronger Pinterest integration

  
  

Design Changes

Improved personalization

• Pinterest integration

• Better recommendation engine

• Expanded clothing categories

Improved flexibility

• Multi-use clothing tags

• More customizable wardrobe organization

Improved discovery

• Color-based filtering

• Better AI recommendations

Usability Testing- Round 2

Participants: 5

What worked

• Pinterest integration created a more complete source of outfit inspiration.

• Calendar and weather synchronization continued to be highly valued for simplifying daily outfit planning.

• Users appreciated being able to view their digital wardrobe in one place and revisit previous outfits.

• AI-generated outfit recommendations remained the most engaging feature, particularly when personalized to upcoming events.

Pain Points

• Wanted opportunities to share outfits and gain inspiration from friends.

• Needed more flexibility for clothing that could serve multiple purposes (e.g., scarf as a belt, dress as a skirt).

• Requested faster ways to browse clothing by color and coordinate outfit substitutions.

  
  

After two rounds of user testing and iterative refinement, Remi evolved into an AI-powered wardrobe companion that makes getting dressed easier, more personalized, and more sustainable.

The final design combines:

• AI-generated outfit recommendations

• Digital wardrobe organization

• Weather and calendar integration

• Pinterest-inspired mood boards

• Multi-purpose clothing tagging

• Color-based wardrobe filtering

By helping users rediscover clothing they already own, Remi reduces decision fatigue while encouraging more intentional and sustainable fashion choices.

Design Changes

Increased Community & Inspiration

• Outfit sharing concept for future iterations

• Community-inspired outfit feed

• Enhanced inspiration experience through Pinterest integration

Improved Wardrobe Flexibility

• Multi-use clothing tags

• Customizable clothing categories

• Greater support for creative styling and repurposing garments

Enhanced Outfit Discovery

• Color-based filtering

• Easier garment substitutions

• More personalized outfit recommendations

Reflection

This project reinforced the importance of iterative, user-centered design. While our initial assumption was that users simply needed better wardrobe organization, our research revealed a deeper problem: decision fatigue. Most participants already owned plenty of clothing but struggled to visualize new outfit combinations, organize inspiration, and make quick styling decisions. This insight shifted our focus from closet management to helping users maximize and rediscover what they already owned.

One of the biggest challenges was the project's timeline. With only one week to move from research to a finalized prototype, our team had to work quickly, make decisions with limited information, and prioritize the features that would provide the greatest value to users. While the condensed timeline limited the depth of research and testing we could conduct, it also pushed us to focus on rapid synthesis, efficient collaboration, and iterative decision-making. The experience felt similar to working within a fast-paced product environment where teams must balance time constraints with user needs.

What I learned

• Designing for sustainability means balancing user goals with business opportunities.

• Research synthesis uncovered that the real problem wasn't a lack of clothing—it was decision fatigue.

• Iterative testing revealed that personalization and flexibility mattered more than adding additional AI features.

• Working with an interdisciplinary team strengthened my ability to synthesize qualitative research into product decisions.

If I were to continue developing Remi, I would explore AI-powered clothing recognition, more advanced personalization, and community-driven features that allow users to share outfit inspiration and discover new ways to style the clothing they already own.