The Robots Are Coming For Your Wardrobe Posted on : Apr 29 - 2017

Artificial intelligence is rapidly changing just about every industry, with billions being poured into algorithms for everything from self-driving cars to detecting cancer to chatbots. Not so much fashion.

Mad Street Den, a three-year-old artificial intelligence startup founded by a husband and wife team in Chennai, India, is boldly going where few startups have gone before. The couple’s complementary career paths have helped. CEO Ashwini Asokan was previously with Intel Labs working on product design; her husband and CTO, Anand Chandrasekaran, is a neuroscientist who switched over to AI when he realized the technology wasn’t quite there yet to build silicon brains.

After living and working in the U.S for years, they returned to their country of birth in 2014 to build a startup, tooling around with a few different ideas before landing on fashion. Since then, they’ve developed over a dozen AI-assisted tools for online retailers, many of them using computer vision algorithms that can suggest pieces to finish an outfit or meet a customer’s tastes. The company says that in an internal study, online shoppers spent 72 minutes on websites where its software was deployed, compared to 25 minutes on sites without it.

Among the clients they’ve racked up are a mix of internet marketplaces, brands, and big box stores. Their international clients include Villoid, a fashion app cofounded by British model Alexa Chung; HipVan, a Singaporean decor and furniture brand; and Indian online platforms Tata CLiQ and The LabelLife. In the U.S. the company works with Thredup, a fashion resale website based in San Francisco, Shoprunner, and an iconic denim brand that they won’t name. (Its own mysterious name is a hodgepodge of high-tech and human, Asokan explains: Mad stands for “mind able devices,” den is what their first space was like and street comes from a night where one too many drinks were had.)

Investors are also taking notice. Mad Street Den recently raised an undisclosed Series A round of funding from Sequoia Capital India along with another infusion from existing investors Exfinity Ventures and growX Ventures. From launching with just one other cofounder and employee, neuroscientist Costa Colbert, they have grown to 45 employees with a headquarters in San Francisco, and offices in London and Chennai.

Fast Company: So what exactly does your software do for fashion?

Ashwini Asokan: The first one that we’ve made some fantastic progress on is the suite called Vue Commerce. It’s basically an end-to-end AI-assisted onsite or on-app set of products. So people are browsing a site or looking up on their app and we’re basically trying to understand what they are looking for, at the starting levels, of color pattern necklines, sleeve length, styles. We’re trying to look at user behavior, history, and their cohort.

We’re looking at it at a completely meta level. I think traditionally people have looked at it as big data, but we’re actually looking at it and even the visual aspects of every little thing that the user is doing. At the end of the day fashion is super visceral. It’s super visual. [But] we’ve pretty much stripped that entire experience off of our apps and our sites. We’re missing that tangible and almost emotional experience one has when you go and try something on in a store, when you actually touch and feel something. A big question that we were asking ourselves is, how can we use computer vision to change that experience online?

FC: What does that look like, specifically?

Anand Chandrasekaran: We have what’s called dynamic personalization, and that becomes relevant when a site has 200,000 dresses. Which of 200,000 should I show you? We can’t pick up all the cues immediately obviously. But in time we can show you 200 of the 200,000 dresses that are relevant to you. And it’s our job and the job of our AI to start picking up those kinds of things.

AA: So if you’re looking at, let’s say, a pink polka dot A-line dress. Chances are you like the shape of A-line dresses and like pink polka dots. But people like different types of pink. Maybe you like baby pink and I like fuchsia—”pink” is not pink. I might start with that polka dotted dress but I might go down a very different path.

Let’s say I’m only interested in A-line. And so I’m willing to go around very specific kinds of styles, while you on the other hand are only looking for specific types of pink. Both of us end up having entirely different experiences on that site. And whether it’s product page recommendations or ensemble creations, we do personalized recommendations with every single click that you make on a given app or a site.

FC: Do you think that the “if you like that, you’ll like this” approach could inadvertently keep us in our fashion bubbles, the way our algorithmically designed news feeds keep us in personalized filter bubbles and echo chambers?

AA: If you are always going to show me something I’m expecting to see, at some point I’m going to get really bored and move on. The question we ask ourselves is, how do we inject randomness at regular intervals? View More