A stylist who can understand and predict your preferences, take into account external factors such as the weather in the city that you live in, and help you make more informed fashion choices; a stylist you can summon with just a few swipes, and even carry around in your purse or pocket. These are some of the features of the AI stylist designed by Streamoid, a Bengaluru-based startup.
Streamoid has been helping the fashion-conscious intelligently navigate their way through multiple buying options and the retail industry gauge the pulse on consumer tastes. Using technologies such as Computer Vision and Artificial Intelligence (AI), the company’s offerings for fashion intelligence are right on the mark.
Led by Sridhar Manthani and Rajesh Kumar, Streamoid was founded in 2013 when, in the words of the founders, “they had a breakthrough in image recognition technology and saw the opportunity to build intelligent solutions for fashion retail”.
Streamoid uses AI across the value chain in a number of flagship products and solutions for the fashion and retail industry.
Their catalogue management solution Autoscribe helps mitigate grunt work. It auto-tags product images with relevant attributes, increasing the accuracy and consistency of data. This dramatically brings down the cataloguing costs and time-to-market, and also keeps the catalogue up-to-date by augmenting trending keywords to products.
Refine-by is a search tool that helps customers widen or narrow their search based on all relevant attributes and quickly zoning in on the products they want to buy.
Streamoid’s Outfitter is among the first AI styling engines that can take any item and automatically build an outfit/look around it. It comes pre-programmed with styling nuances for different seasons and geographic regions and can be customised as per the brand’s style sensibilities. Outfitter helps make online visual merchandising seamless in real time with existing inventory.
Then they have Stylebot, a chatbot that doubles up as an expert stylist and offers real-time insights into a retailer’s inventory as well as fashion trends. With advanced natural language understanding based on an extensive ontology, and Computer Vision trained for the fashion context, Stylebot quickly figures out what customers want and has meaningful conversations with them.
Sharing how their solutions have made a difference in the retail industry, Rajesh says, “In e-commerce, the relevant tagging of images is crucial to product discoverability within the website as well as for SEO optimisation. Through our solutions, we have automated this manual task, that not only saves time but also increases the accuracy of search. With product images enriched with relevant attributes, we are able to filter products in more meaningful ways. For example, we have categories such as ‘brunch wear’, ‘party wear’, etc., which helps improve the product’s discoverability.”
Even as their solution was creating a significant impact on the retail industry, when Target approached Streamoid to join the fifth cohort of their Target Accelerator Program (TAP) in 2017, the founders were excited. Set up in Bengaluru in December 2013, the Target Accelerator Program has been instrumental in helping identify startups in the technology space with the potential to make advances in the retail industry.
Explaining why they were keen to join, Rajesh says, “We wanted to test some of our solutions in the U.S. Target did an A/B test with aesthetic filters such as “brunch wear”. The results were much beyond what we expected. We were able to achieve this with support from the Target Accelerator team and the constant involvement of the business mentors.”
The Streamoid CTO adds, “We got solid support from the Accelerator team to take our product live for A/B testing. Following that they supported us all the way, got us visibility with senior management and gave us the opportunity to win a larger contract.” In addition, he says, the programme helped them get access to key business leaders and decision makers at Target, and mentoring to fine-tune their presentation and make an impact in front of Target leadership.
Today, they have brands from the Aditya Birla Fashion and Retail Ltd. group and Spain’s large retailer among their customers. When asked how participating in the programme helped them take their business to the next level, the duo maintain that the case study they built with the results from the A/B test conducted by Target were good for proving the efficacy of their product. In addition, participating in the Target Accelerator Program got them visibility within the startup ecosystem and led to engagements with leading business consultants.
According to them, future plans include exploring how they could use AI to go beyond customer experience and create more AI-based solutions that can add value across the fashion value chain.
“Everyone wants to look good. With our AI stylist, everyone will have the opportunity to buy trending products that suit them. The retailers can improve the efficiency of their systems and can free up styling resources for more creative and complex jobs,” says Rajesh.