Chicisimo

  • 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.
Chicisimo app.
Increasing Chicisimo app retention, as a result of the team iterations
Impact of introducing a new functionality.
Chicisimo’s tech in your bedroom – an Alexa skill fully shipped.
  • 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.
Chicisimo has been featured as App of the Day in 140 countries.

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.
One of Chicisimo processes. Extracting correlations from outfit elements (and more) to create a learning model.
Fashion lacks a standard to classify clothes or to refer to the variety of concepts that describe products, styles, and personal fashion preferences. Our ontology solves the problem.
The Fashion Taste Graph of a retailer is a brain. Like the brain of the “Chief Stylist” who knows precisely each product and shopper and the retailer editorial line.

If you are interested in what we’ve built at Chicisimo, here you have a few links that will provide a nice overview: