Vertical Machine Learning

A Vertical Machine Learning approach provides a competitive moat around your business


We at the Fashion Taste API follow a Vertical Machine Learning approach to fashion. We believe in the unique advantage of having a profound understanding of the consumer problems, end to end.

With a focus on the consumers, we’ve applied machine learning to automate the two processes that we’ve considered game-changing:

  • Capturing shopper’s taste data (including what clothes they have in their closet);
  • Building the data infrastructure that allows us to understand input automatically, and to deliver personalized output also automatically.

Read our Manifesto on the Future of Fashion Retail.


We believe that fashion retailers have a clear advantage over newcomers: they are the domain experts. The specific benefits, however, need to be captured:

  • Fashion retailers have a profound understanding of the fashion consumer;
  • Shoppers generate a unique body of data around the retailers properties. Retailers can choose to start capturing that intelligence and build their moat;
  • Physical stores are a unique layer of experimentation to test omnichannel solutions and receive fast feedback.


At some point, newcomers will offer In-Bedroom Fashion Stylist or Smart Mirrors to end consumers.

Apart from the increase in market share that this new category will generate, In-Bedroom Fashion Stylists will have two very relevant consequences:

  • Data: The amount of incoming taste data will grow exponentially;
  • Learning: The speed of learning will accelerate thanks to better shopper feedback.

And the current competitive moat around traditional fashion retailers will be gone.


The more digitally powered experiences you offer, the more taste data you will capture. The more data you capture, the better you can automatically serve your shoppers. The better you serve your shoppers, the more adrenaline you generate in your teams, the more ambitious they become.


Foundational patents in capturing and understanding fashion taste

Patents expected to provide a competitive advantage


We’ve been analyzing fashion taste for more than a decade.

As a result, we own foundational patents in the online fashion market. An independent review of the Fashion Taste API portfolio uncovered relevant market adoption related to some of our patents.

This intellectual property powers our Digital Closet Technology, our In-Store Outfit Recommender, our In-Bedroom Fashion Stylist and other solutions.


We are all starting to see fashion images with dots on top of them, these dots enabling you to find and buy the products in the picture. This system is gaining popularity since 2016, and it was patented by us in 2013.


In our prior work with music, we extracted relations among songs by analyzing the sequence in which people would listen to music, and by analyzing our interpretation of items. Apple later bought the systems and patents for Apple Music. We then built similar systems for personal finance, and for content-agnostic input.

With this background, we thought that an outfit was like a playlist of songs: a way to communicate that it makes sense to consume several items together. This simple concept allowed us to conceptualize the first version of our Social Fashion Graph. In 2013, we protected a system to capture and understand how people mix and match clothes in outfits and closets.

Today, the technology has evolved significantly. It captures any input in our our Ontology, which includes any concept relevant to fashion, and assigns descriptors to products and to people. We can extract all type of relations among analyzed items.


Our initial interest was related to social tagging of images. Then, we invented more complex mechanisms to assign descriptors to clothes, outfits and people. Our IP protects a system to search outfit ideas and fashion images.

It could be a simple query such as “give me ideas to wear jeans” or a more complex one such as “give me ideas to wear my pair of red pants with my floral shirt, to go to the office in winter”, or “give me clothes that are “similar” grouped by your concepts of similarity”. This is strongly related to our Fashion Taste Graph Technology.

The Fashion Taste API patents are expected to provide a competitive advantage.


Our fashion ontology

Our fashion ontology is our tool to understand products and people, and we built it to respond to very specific needs in our mobile apps.

Fashion ontology
Multilevel fashion ontology to describe clothes and user needs

Why we built our fashion ontology?

Fashion lacks a standard to classify clothes or to refer to the variety of concepts that describe products, styles, and personal fashion preferences.

At some point, we found ourselves receiving a lot of data via our consumer apps. We were receiving millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear

Looking at this data, we only saw unorganized data, so chaotic that it was impossible to manage or build on top of it.

Our users could not really express their needs precisely in a ways that we could understand them, we couldn’t even describe the content displayed in our apps, in a way that could be found by those in need of it.

Our iOS app, in action

The backbone of our Taste Graph

Today, our fashion ontology is the backbone of our Taste Graph technology, and it is core to our development process.

We divide our ontology into two parts:

  • Products ontology. It is a 5-level ontology that describes products and subjective characteristics of products;
  • Outfits ontology. It is a 2-level ontology that describes outfits, mostly with subjective descriptors.
Our Alexa app 🙂

Our fashion ontology today

Our fashion ontology is our tool to understand products and people: it goes well beyond assigning descriptors to clothing. It has a very strong emphasis on describing people.

Our ontology today is the classification of descriptors needed to define a fashion product and a shopper’s need, in terms that are relevant for shoppers, for retailers and for data scientists. It contains the vocabulary used by people and by fashion retailers, with an understanding of how these descriptors are used together. Our ontology covers all aspects related to fashion purchase drivers: product properties, occasions, brands or influencers, or trends: It is formed by +2500 unique concepts and a few hundreds of thousands of descriptors.

Lots of interesting processes in this video – all related to our ontology

What we’ve built on top of the ontology?

All our Chicisimo products have been built on top of our Fashion Taste Graph, and the ontology is it’s backbone. This is what the ontology has allowed us to build:

  • Digital closet
  • Taste Graph
  • Search tech
  • Recommendations is all forms and personalization
  • Clothes classifier
  • The ontology has also provided a unique common language for the team to communicate, and work, more efficiently.
Another one of our processes

How can I fashion ontology help your business?

Well, that’s a question you’ll have to answer prior to building one. You might already have a fashion taxonomy, and you’ll expand it little by little as your product team demands more tech strength.

In general, your fashion ontology will help you describe products and shoppers in a way that allows you to:

  • Understand products and shoppers;
  • Manage products and shoppers.

Your ontology will help you manage your catalogue with new browsing methods, humanized categories adapted to different types of shoppers or situations. It will reflect how your organization wants to talk to shoppers, always retaining total control over your voice while understanding the voice of your shoppers.

It will guide your data-attaching mechanisms to attach the right descriptors to products and shoppers. Data-attaching mechanisms include your taste graph, your editors, your deep learning algorithms, and other sources of product and shopper descriptors.

Characteristics of our ontology

Again, it will depend on the state you are at. Your ontology or fashion taxonomy is a tool that contains the organized and structured descriptors that define your products and your shoppers, in the terms that are relevant for your organization and your shoppers.

  • Your ontology includes clothing and non-clothing descriptors;
  • The ontology includes descriptors used by your organization, and by your shoppers;
  • Descriptors are classified in categories, types, properties and more;
  • We capture the correlations among your descriptors as defined by your products and your shoppers.

How can you use your ontology?

The Fashion Taste API main focus has been about the search for structure, in the unstructured world of clothing classification.

Once we learnt about clothing classification, we learnt that purchase drivers go well beyond clothing taste, and we gave a structure to those as well. So, believe us, we love clean and structured data that our algorithms can work with.

Despite of the above (or because of the above), we have found amazing value in shoppers’ unstructured input. This input are descriptors that you would not expect your shoppers to be using when interacting with you.

We’ve learnt that people refer to their fashion needs in ways that are very different to how retailers do. Also, the type of taxonomies that fashion retailers tend to have cover mostly product descriptions. But drivers of purchases are related to other factors: products, occasions, influencers, brands and trends. You can read about it here.

Need help?

Contact us


Our Fashion Taste Graph Technology for Omnichannel Personalization


Through our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear.

In 2013, with so much consumer data we built a Taste Profile for each person, and a Taste Graph to serve the right products to each individual based on her specific interests. It is important to note that a profile in fashion is not only driven by taste, but also by other triggers. You can read about it in the Taste Profiles section.

  • Described outfits: We capture this type of data from millions of described outfits and we also capture how other people interact with those outfit creating more relations among items and allowing the Taste Graph to assign further descriptors to the outfits and the Taste Profiles.
  • Described clothes in millions of closets: We also receive the described clothes in millions of closets from where we can extract a lot of meaning in terms of correlations and in terms of our own interpretation of the products.
  • Queries: We also receive hundreds of millions of what-to-wear queries from people trying to decide what to wear and what clothes to buy, for any occasion you can think of. Each of this queries informs the Ontology and the Taste Graph.

That’s the origin of our technology. The Taste Graph Technology receives data and correlations. It also assigns the correct descriptors to clothes, outfits and to people. It is the tool on top of which teams can effectively work in personalization. Delivered via a website, in a physical store, or delivered via Alexa in a bedroom or closet.


Our Fashion Taste Graph Technology for Omnichannel Personalization is the engine that allows us to assign descriptors to shoppers. It allows us to understand the taste of each shopper, and provide them with personalized omnichannel recommendations.

It is the infrastructure and data that contains, manages and understands the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. The behaviour of each individual, the aggregated behaviour of everyone, and the behaviour of any subset we’d want to target.

The backbone of our Taste Graph is our fashion ontology, which you can read about here.


We build your Taste Graph, your tool to understand the taste of each individual shopper.

Read about it here.


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.


Our consumer fashion apps bring unique impact to our learning process.

They allow us to see the future with more clarity, by solving the problem end to end and receiving direct feedback from the end-consumer. Iterating a consumer app is a unique learning experience, and it contributes in a great way to the definition of the Taste Graph and of course of the Ontology. You can read about or approach to growth in our consumer product in this Medium essay.

Then, we from Using that data to build an automated personalization engine lead us to build our Taste Graph, and patent it in 2013. It has proven to be a unique personalization omnichannel technology, and a great internal tool for classification and categorization of clothes.

Read our Manifesto on the Future of Fashion Retail.