An AI personal stylist built around the clothes you already own.
Early development · Web application
LEBOSET turns your wardrobe into a starting point for everyday styling. Bring your clothes into a digital wardrobe, describe the occasion or look you have in mind, and discover outfit combinations shaped around your preferences.
The goal is simple: make it easier to decide what to wear and find more possibilities in the clothes you already have.
The first release is being designed around:
- A digital wardrobe — Add photos of your clothes and keep your collection organized.
- AI-assisted item recognition — Review and refine suggested clothing details before saving them.
- Natural-language styling — Describe an occasion, a mood, or a specific look in your own words.
- Personalized outfit suggestions — Explore combinations drawn from your wardrobe and preferences.
- Visual outfit browsing — Compare looks using photos of your own items.
- Saved looks and feedback — Keep your favorites and help shape future recommendations.
- Build your wardrobe. Upload a photo of each item and confirm its details.
- Describe your plans. Tell LEBOSET what you are dressing for and what you want to wear.
- Explore your options. Review outfit suggestions and save the looks you like.
- Make it personal. Share feedback to refine future suggestions.
“I'm going out with friends tonight. I'd like something relaxed and dark, with my black jacket.”
LEBOSET is designed to turn requests like this into outfit ideas using items from your own wardrobe.
Start with what you own. Recommendations begin with your clothes.
Keep you in control. You can correct item details and express what you want from each look.
Adapt to the occasion. Your current request guides the suggestions alongside your preferences.
LEBOSET is in early development. A local backend foundation is available for wardrobe management and outfit refinement. The complete web application and AI features are still in development.
On Windows, run start-dev.bat to install dependencies, run checks, and open the local API. See the backend development guide for details.