How to proceed when you run out of cash, but you still believe?

We’ve converted a human problem into a computational problem

One of our processes.

What’s our situation?

How should we proceed?

  • We’ll be reaching out to potential acquirers. This is the list of teams we are contacting or want to contact — if you can introduce us to the right people in those companies or other companies, it would help a lot. My email is;
  • Most importantly, with this post we want to publicly share our vision as loudly as possible. The best way to do it is to describe our assets and our team: our assets are a reflection of what we’ve considered truly important in fashion ecommerce.
  • Any other ideas? This is important for us.
This is the list of teams we are contacting or want to contact

In-Bedroom eCommerce, as an example

Our In-Bedroom Fashion Stylist.

#1 Asset: Our fashion ontology

Ontology of fashion products. Because fashion lacks a standard to classify clothes.
  • Products ontology. It is a 5-level ontology that describes products and subjective characteristics of products. Learn more here;
  • Outfits ontology. It is a 2-level ontology that describes outfits, mostly with subjective descriptors.

#2 Asset: A system to “classify clothes” automatically, with 175 million classified and correlated meta-products. This system allows us to automatically understand, manage and act upon any collection from any retailer (similarities, correlations, recommendations…)

A system to “classify clothes” automatically. Allows you to display products at your convenience, or your shopper’s convenience.

#3 Asset: A system to “understand people”, that builds a Taste Profile of each shopper based on the interactions of that shopper with fashion products and the retailer channels

Spotify, Netflix and the Fashion Taste API. Companies building Taste Profiles.

#4 Asset: One Fashion Taste Graph for each retailer

The first iterations of our Graph got the basic concepts right, but their understanding of shoppers and retailers was very poor. As a result, they were useless. In the image above, you can see a representation of our 2012 Taste Graph, it was pretty useless.

#5 Asset: Our Digital Closet

Once you have the data architecture and the interfaces in place, a digital closet is a simple concept.
  1. From an architecture of information point of view, a person’s closet is exactly the same as the collection of any retailer. This realization had two major consequences for us: (i) we could remove a lot of code and we love that; and (ii) any work we do on fashion products is applicable both to a retailer collection and to a person’s closet;
  2. Building digital closet tech requires interconnected efforts from very different disciplines. For example, it is fascinating how strong is the relation between the “closet interfaces” and the “data architecture”. Without the team structure we have, we couldn’t have built this tech. If you like complex interconnected software problems, I encourage you to read our description of the Smart Virtual Closet Technology.
  • View of the magical In-Bedroom Fashion Stylist: 100% built on top of the above infrastructure, you can install it from the Alexa Skills Store.
Our In-Bedroom Fashion Stylist — learn more here
  • Adding clothes to your digital closet (one of the mechanisms):
Adding a skirt to your Digital Closet, using QR.
  • Our In-Store Outfit Recommender
Our In-Store Outfit Recommender — learn more here
  • Our Digital Closet on iOS. You can install the iOS app directly from the App Store.
Our Virtual Closet Tech — learn more here
  • Smart Fitting Room
Our Smart Fitting Room — learn more here.

#6 Asset: Two key patents. Why do we patent?

#7 Asset: Our SaaS to build One Taste Graph for each Fashion Retailer

Old vs New personalization approaches. Continue reading here >


We work exceptionally well together
  • We are a team of 8. As you can see in the team bios, the team is: 1 person with long experience in automation and personalization, 1 robotics engineer specialized in fashion ontologies, 2 full-stack engineers, 1 iOS and 1 Android, 1 user-research and design, and 1 QA/product/community. Being a small team is a choice;
  • Being remote is a core part of our culture. Our main offices are located at Slack & GitHub & Whereby. We work exceptionally well together and are a bit obsessed with processes. We like having the right tools to do our job, you can see our stack here;
  • We absolutely love the problem and feel a strong respect for it. No one of earth knows more than us about the challenge of automating shopping and outfit advice;
  • The team has built and shipped the above infrastructure to production with an active community in search of fashion ideas, whose behaviour provides immediate feedback. Our community, which we are grateful for, is a great learning environment, few things beat shipping to production and getting feedback. We have an iOS app, an Alexa Skill, a Google Action and an Android app. We have received 376 messages per day from our community;
  • We have shipped an average of 3 releases per week considering no vacations. With an 8-team person doing all of the above, including one person devoted to iOS, we have built and shipped 204 different public releases to the App Store during 5.5 years and 919 pre-production releases. More on this below;
  • Our internal data portal. Our job requires a team with complementary skills, and we initially had difficulties understanding each other. We solved this problem by creating an internal data portal that exposes all data and data relations. It is a key asset because it provides a shared language and knowledge where everyone in the team can easily *access and see* the same information.
  • Here you have the team bios.

Are you interested?


iOS Subscriptions revenue

So many iterations trying to build a subscription business… distracting us from our core tech efforts.

We exposed our ontology to Google, and we ended up understanding the connection among SEO, conversational interfaces and ontologies

Exposing the ontology to Google (KPI = unique mobile users coming from Google)

Our iOS app: 3 releases each week during 5,5 years

  • With an 8-team person doing all of the above, including one person devoted to iOS, we have built and shipped 204 different public releases to the App Store during 5.5 years. We have built 919 pre-production releases, internal, releases. This is an average of 3 releases per week considering no vacations;
  • Our rating has always been 5 stars or very close to it, depending on the country. However, it started to decline when we shipped our subscription service. We’ve been featured as App of the Day in 140 countries by Apple, many times. We’ve been rejected by the App Store many times for many reasons;
  • Even today’s version of the app is simply a step into its evolution. This week we are releasing new clothes capturing mechanisms: from closet to digital closet to outfit suggestions, in milliseconds;
  • [Edit] If you want to learn about our approach to ASO, please read our Guide to Alexa Skills SEO — Amazon Alexa Skills Store Optimization.
Featured multiple times in 140 countries.

376 messages per day from our community

People’s feedback is the best.
  • In this 5,5 years, we’ve received 752,000 messages from our community, an average of 376 messages a day. These messages responded to lots of different questions we had, or they were simply general feedback. We’ve done tons of user tests, built groups to test 3rd party apps during long periods of time, and more. Now this feedback loop is activated when we need it. The majority of the messages have arrived via Intercom with 472,000 messages (Intercom later became too expensive for our growth), Typeform with 14,600 messages, and via email most of the remaining messages;
  • We’ve identified retention levers and anti-levers using behavioral cohorts. We run cohorts not only over the actions that people performed, but also over the value they received. Time to convert has been critical for us… you can read about all this in How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach;
  • Chicisimo is like that friend or mother who helps you decide what to wear, when you have nothing to wear” one person once told us. We couldn’t be prouder of really helping people.

Hey but… does a clothing purchase predict future clothing purchases?

Past drivers of purchases DO predict future purchases.

Last: A Basque song for you

Txoria Txori.



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