ClosetAI
AI-powered wardrobe recommendation platform that uses computer vision and LLM embeddings to reduce decision fatigue and improve outfit discovery.
Context
ClosetAI came from a familiar daily friction: people own more clothes than they actively use, but getting dressed still takes attention, memory, and a little emotional energy every morning.
What I built
I designed an AI wardrobe product that can identify clothing items, store the catalog, and generate outfit recommendations from the wardrobe instead of from a generic style feed. The aim was to make the system feel like a personal assistant rather than a search engine.
Why this approach
Computer vision handles item recognition, embeddings support style-aware matching, and natural language queries make the product easier to use than a strict category filter UI. FastAPI and PostgreSQL gave the backend a clean, structured shape while Next.js handled the interface.
Key features
- Clothing ingestion and recognition to build the wardrobe catalog.
- Style-aware outfit recommendations based on embeddings and ranking.
- A UI that reduces decision fatigue instead of adding more complexity.