CLOSIQ
My flagship project: an AI stylist for the clothes you already own
A mobile wardrobe and styling prototype that catalogs garments from photos, builds occasion-aware outfits from your own clothes, and connects saved looks with weather and event planning.
PROJECT OVERVIEW
CLOSIQ is my flagship project, centered on a simple question: what can I wear from the clothes I already own? The primary app is built for mobile and combines a garment collection, AI stylist, weather-aware Today screen, saved outfits, event planner, and a personal style profile. Users can add clothing from the camera or photo library, review garment details, request a look for an occasion, swap one piece, and save the result.
THE PROBLEM
Choosing an outfit involves more than matching colors: it depends on the occasion, weather, available garments, and personal preferences. CLOSIQ focuses on a person’s existing wardrobe so recommendations can be grounded in items they actually own rather than a shopping catalog.
TECHNICAL APPROACH
An Expo and React Native client talks to a Node.js backend that proxies Gemini garment analysis and outfit generation. Supabase handles authentication, user-scoped Postgres records, and garment-image storage; AsyncStorage provides an offline read cache. Outfit requests use wardrobe garment IDs and personalization context, while Open-Meteo supplies weather information. The app offers manual entry when image analysis fails and clearly labels its deterministic local styling fallback when AI is unavailable.
PROJECT OUTCOME
Built a working prototype connecting wardrobe management, outfit generation and swapping, saved looks, planning, and personal preferences. The mobile app is the main surface; an earlier React/Vite web prototype remains alongside it. Broader physical-device validation and production deployment are still pending, and the app’s fallback behavior makes unavailable AI explicit.