Eight startups graduate from Target Accelerator Programme’s fifth batchNeha Jain
These startups focus on applying artificial intelligence, machine learning, computer vision, natural language processing, analytics and digital experiences to retail.
Target India announced the fifth cohort of startups to graduate from the Target Accelerator Program on Wednesday through the ‘Demo Day’. A technology that makes it easier for shoppers to use self-checkout with fresh produce and an in-store digital shopping assistant powered by artificial intelligence were two of the unique solutions to emerge from the latest accelerator program.
The startups in the fifth cohort – the biggest batch yet – showcased the products and solutions they developed and refined during the four-month accelerator program. The startups focussed on areas that include artificial intelligence, machine learning, computer vision, natural language processing, analytics, and digital experiences.
The Target Accelerator Program, which was launched in December 2013, gives startups a unique platform to develop, scale and test their products in a live retail environment while also accessing Target mentors in India and the US. To date, 30 startups have graduated from the program and worked with Target teams across stores, marketing, finance, legal, merchandising, mobile, and digital.
“India has the third largest startup ecosystem in the world, and we’ve had the opportunity to work with some of the very best startups here,” said Rakesh Mishra, Vice President of Marketing for Target and the accelerator program’s executive sponsor. “Every year we have seen an increase in the diversity of ideas and solutions of startups entering the program. We are thrilled by the success of the fifth cohort, and we look forward to working with more startups through this program.”
All the eight startups will continue to test their products with Target beyond the program. The startups that presented at Demo Day included the following:
- Cogknit – A tool that automatically generates transcripts and closed-caption files enabling accessibility of online video content
- Cognitifai – A video analytics solution that uses computer vision for varied applications such as detecting the placement and removal of products for inventory tracking and creating “to-scale” digital images for online shoppers
- Hyperworks – A self-checkout solution for fresh produce that uses gray-scale images
- Jumper.ai – Enables shopping on social media through hashtags
- Light Information Systems – A conversational bot that can be used to answer employee questions
- Moonraft Innovation Labs – An interactive, in-store digital shopping assistant powered by artificial intelligence
- Streamoid – Enables personalised product recommendations and natural-language search that suggest outfits and helps improve product discovery
- vPhrase – Generates automated, natural-language insights from structured data
IoT product innovation from India
In addition to the accelerator program, Target is looking for innovative ideas from Indian startups for Target’s Open House, a connected-device concept store based in San Francisco. Open House gives consumers hands-on interactions with new IoT products and services, and provides the entrepreneur community a spot to gather and learn from one another and from consumers.
For the first time, a startup from India, Lumos, was selected to be featured at Open House. The startup was selected for Garage, a platform which serves as an area for companies with products that are still in protoype or early go-to-market stages, to showcase their smart products and get real-time feedback from consumers. Successful products from the Garage may be sold at Open House and eventually at Target stores or Target.com.
Gandharv S Bakshi, Co-Founder and CEO, Lumos Design Technology Pvt. Ltd. said, “We heard about the Open House through the Target Accelerator team and applied to be a part of it. Participating in the Open House helped us reach the right target market and gave us a lot of insight into how our product would perform in an (offline) retail setting. We had a dashboard view to the detailed feedback received from consumers, who interacted with the product in the store setting. Most hardware startups do not have access to this kind of data, this early in their product journey.”