SaaSEngineering case study

AI Stylist

Mobile wardrobe-management and outfit-recommendation product. Users create wardrobes, upload clothing photos for AI-assisted category, tag, and description analysis, and generate one-time, daily, or weekly outfit suggestions that can use precise local weather; recommendation history is stored locally in SQLite and designed to sync with the cloud.

My role

Mobile and backend product engineering across wardrobe management, AI-assisted image understanding, recommendations, location, local persistence, and OAuth.

Product scope

A mobile wardrobe product where users organize clothing, upload item photos for AI-assisted analysis, and generate one-time, daily, or weekly outfit recommendations that can incorporate precise local weather.

Engineering focus

Built the Expo/React Native product around wardrobes, clothing-item detail, photo upload, outfit generation, and recommendation history.
Designed AI-assisted clothing analysis for category, tags, and descriptions, with recommendation workflows that can use device location and weather context.
Used SQLite for local recommendation history with a cloud-sync model backed by Python, SQLAlchemy, Alembic, and PostgreSQL.
Designed authentication around Google and Apple OAuth with secure app-managed token storage rather than password authentication.

Product evidence