FabricMatch

Inspiration

FabricMatch was inspired by students and everyday shoppers who struggle with knowing what to wear and often feel overwhelmed in malls with too many choices. We saw an opportunity to turn malls and stores into guided, personalized shopping experiences rather than trial-and-error browsing.

What it does

FabricMatch is an AI-powered styling assistant designed for mall kiosks and in-store use. It analyzes a shopper’s body and facial characteristics using computer vision, combines this with personal style preferences, and recommends both complete outfits and individual items available in nearby stores.

How we built it

We built FabricMatch with a React and TypeScript frontend optimized for kiosk and in-store displays, and a Python backend using computer vision, machine learning APIs, and web scraping. The system connects visual analysis and user preferences to a custom styling engine that matches shoppers with real, in-stock clothing from participating retailers.

Accomplishments that we're proud of

We created a working prototype that demonstrates how AI-driven styling can function in real mall and store environments. Successfully integrating body analysis, personal preferences, and live retail data into a single kiosk-based experience was a significant milestone.

Built With

• fastapi
• opencv
• python
• react
• typescript
• yolo