- With a fully remote team of 8 people, we built an amazing app and an effective learning model:
- We built a digital closet app that allows women to easily digitize most of their clothes in less than 2 minutes. The closet then tells you how to combine your clothes providing (i) outfit suggestions, and (ii) showing you outfits of real women wearing the same clothes you have in your closet.
- A mobile app women love, with 5M installs from word-of-mouth and non-paid acquisition. We have built and shipped 204 different public releases to the App Store during 5.5 years. Our rating has always been 5 stars or very close to it, depending on the country.
An unsupervised learning model
- Chicisimo apps are built on top an unsupervised learning model that automatically classifies clothes and understands people’s taste. We developed this model by automatically learning from users’ closet & outfit data, their queries and interactions. We had to develop different skills, as the closet was a combination on ontology, taste graph, obsession around interfaces and incentives, the correct data infrastructure to interpret user input, and a strong product culture. All Chicisimo tech is described here.
If you are interested in what we’ve built at Chicisimo, here you have a few links that will provide a nice overview:
- On December 2019, we stopped fighting as a standalone company and started seeking an acquirer and the final Chicisimo Post-Mortem;
- How we grew to 4M women with our vertical ML approach
- Read how Apple describes Chicisimo to its users when they feature us;
- If you want to see the consumer products in action, please watch our In-bedroom fashion stylist 46-seconds video, our digital closet video (57 seconds) or our in-store outfit recommender video (37 seconds);
- The technology we built is described in this website.