Radar, a New York-based company that integrates Radio Frequency Identification (RFID) with computer vision to assist retailers to automate inventory management and more, has secured USD16 million in a funding round. The round was led by Ashton Kutcher’s Sound Ventures, NTT Docomo Ventures, Align Ventures, Colle Capital, Beanstalk Ventures, Founders Fund Pathfinder, and Novel TMT. According to the company, a couple of its stealth customers, two undisclosed billion-dollar retailers, also invested in the round.
The RFID tags have long been utilized by retailers in warehouses and distribution centers to accelerate and automate the process of tracking and counting goods. Additionally, the tags also can be utilized to check boxes or pallets into a store or storage facility and even to track individual items at any point in their journey. Unlike barcodes, RFID relies on low-power radio waves which don’t necessitate line-of-sight to recognize items. However, the report noted that RFID isn’t a perfect solution for every scenario like it may not be able to tell users the accurate location of an RFID-tagged item that could be indispensable in future retail outlets. Radar cofounder and CEO Spencer Hewett stated that RFID technology was originally designed to help retailers improve inventory management, but most solutions remain highly manual, limited in capability, or too expensive to deploy.
Moreover, the further retail store is a very different beast from that of years gone by. For instance, Amazon is doubling down on its cashierless Amazon Go outlets. Another instance of Kroger and Microsoft partnership that recently teamed up for connected, data-driven stores. Ahead of this funding round, Hewett further added that the rise of Amazon and direct-to-consumer brands has created unprecedented consumer expectations around speed and convenience. Founded in 2013 in New York, Radar says that its RFID technology is different from traditional RFID, as the company builds everything from the ground up using proprietary signal processing methods and location algorithms that improve the ability to identify an RFID tag in three dimensions.