StyleAi

Where Your Closet Meets the Marketplace

Comment

Inspiration

Fashion is deeply personal and ever-changing — yet most digital platforms offer static suggestions, generic filters, and no personalization. We wanted to build StyleAI, a smart stylist that lives in your pocket. Inspired by the idea of a personal wardrobe assistant and the fusion of fashion with AI and AR, StyleAI aims to help users digitize their wardrobe, try on outfits virtually, and get curated suggestions based on trends, occasions, and body types.

What it does

StyleAI is a subscription-based, AI-powered fashion assistant that helps users:

  • Upload and digitize wardrobe items (shirts, jeans, accessories, etc.)
  • Get AI-driven outfit suggestions for occasions like casual, formal, work, or travel
  • Receive smart recommendations for missing or trending items from marketplaces like Amazon, eBay, Etsy, Walmart, Zara, and more
  • Use 3D virtual try-on technology to preview clothing on a photorealistic avatar
  • Participate in style challenges and share looks with the community

How we built it

Frontend

  • React + TypeScript for SPA development
  • Tailwind CSS for mobile-first responsive design
  • Three.js + React Three Fiber for real-time 3D avatar rendering
  • Framer Motion for microinteractions and transitions
  • Service Workers to enable PWA and offline support
  • React Context API for state management with optimistic UI updates

Backend (Serverless)

  • Express.js on AWS Lambda via API Gateway for scalable APIs
  • DynamoDB for fast and flexible NoSQL storage of user data and wardrobes
  • S3 + CloudFront for secure and performant image storage and delivery
  • JWT Authentication for session management
  • OpenAI + other AI services for: Outfit recommendation Trend prediction Visual similarity (CV-based) Color harmony analysis

Challenges we ran into

  • CORS management between S3, CloudFront, and Lambda endpoints
  • 3D avatar optimization for low-bandwidth mobile devices
  • Integrating AI-based personalization while keeping response times fast
  • Balancing real-time updates with serverless cold starts
  • Ensuring seamless try-on UX across different screen sizes

Accomplishments that we're proud of

  • Achieved photorealistic try-on experience using Three.js and custom shaders
  • Built a highly personalized AI engine that considers trends, weather, and user wardrobe
  • Integrated multiple marketplaces into a unified fashion suggestion engine
  • Made the entire experience serverless and scalable, with performance optimizations
  • Created a community-based gamification layer for fashion challenges

What we learned

  1. How to blend fashion heuristics and color theory with deep learning models
  2. Efficient use of AWS serverless services to scale without infrastructure headaches
  3. Practical implementation of real-time recommendations using AI APIs
  4. Advanced 3D rendering with React Three Fiber and Three.js
  5. Optimizing media-rich webapps for performance with CloudFront caching, lazy loading, and provisioned Lambda concurrency

What's next for StyleAi

🤳 Virtual Fitting Room with camera-based try-on using ARKit/WebXR 👗 Outfit Planning Calendar with suggestions for each day based on your wardrobe 🛍️ Marketplace Plugin SDK so other retailers can plug into the StyleAI recommendation engine 🧠 Custom AI Model Training using user style preferences and historical outfit ratings 🛡️ Advanced Privacy Controls to let users manage how their wardrobe data is used

Built With

Share this project:

Updates