Our credentials

Experience. Approach. Technology. Dataset


During the last 20 years, we've solved specific problems in car manufacturing, music and finance by introducing technologies to understand specific behaviours and automating very concrete processes.

  • In music, Apple acquired the technology and intellectual property for Apple Music.
  • In finance, Barclays, Deutsche Bank, ING, BBVA and other 700 banks still use the technology that our team built before we moved to fashion.
  • Since 2010, we are exclusively focused on fashion.


In fashion, our success comes from 3 elements:

  • Focus on the shopper. We build final solutions to understand the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases.
  • Understand fashion with depth. Facing the challenges with a Vertical Machine Learning has allowed us to build a unique our Ontology and the Fashion Taste Graph Technology that we patented in 2013 and we have perfected since then (read more about our patents here).
  • Empower hybrid teams. We provide tools for business and tech teams to participate in the decision making process with a common understanding and sense of control. Our Digital Closet Technology is an example of this.


The Year 2000 brought a horizontal approach to AI that has proven ineffective.

These are the main differences between Legacy personalization vendors and Next-Gen Omnichannel Personalization, and about why we don't believe in black-box technologies, or in helping a shopper without knowing her taste.

OUR experimentation layer

Technology serves product and business teams. Not the other way around.

We've analized millions of closets that we receive via our consumer apps, millions of described outfits, and hundreds of millions of what-to-wear queries from people trying to decide what to wear for any occasion you can think of.

With this data and the experimentation layer it has provided, we've built our Taste Graph Technology as a tool to serve the business. Our experimentation layer has provided us with a learning process and some of its outputs are our In-Bedroom Fashion Stylist, the Smart Fitting Room and the In-Store Outfit Recommender.

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