When shoppers interact with a fashion retailer, they generate taste-based data. We encourage fashion retailers to retain that intelligence and use it to build a competitive moat around their business.
We help fashion retailers build that type of infrastructure. Because we are certain that the future of fashion will be build on top of it.
FashionTasteAPI was born when we at Chicisimo opened our API to 3rd parties.
Chicisimo has built a smart virtual closet that allows women to easily digitize most of their clothes in minutes. Then, the virtual closet tells you how to combine your clothes (i) giving you outfit suggestions, and (ii) showing you outfits of real women wearing the same clothes you have in your closet, or very similar ones.
Our two largest consumer products were our iOS and Android apps, with a combined 5M installs and a 5-star rating average. You can learn How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach.
- Smart Virtual Closet Technology >
- In-Bedroom Fashion Stylist >
- Physical-Store Outfit Recommender >
- Online-Store Product and Outfit Recommender >
Our consumer products are built on top of an unsupervised learning model that automatically classifies clothes and understands people’s taste, by learning from users’ closet & outfit data, their queries and interactions.
A basic enabler of our tech is our ontology which is the backbone of our Fashion Taste Graph.
- Fashion Ontology >
- Fashion Taste Graph for Omnichannel Personalization >
- Clothes classifier >
- Taste Profiles of your shoppers >