OSARO Achieves Year-end Milestones, Including Series B Investments from King River Capital, Mark Cuban, and Others
“OSARO achieved significant milestones in 2020, which included a new OSARO subsidiary, OSARO GK, in Tokyo, and grew our team by more than 20 percent adding ten new engineers, and deploying 18 software systems”
OSARO Inc., a leader in machine learning software for industrial automation, has closed its Series B extension, with participation from Darco Capital, The Mark Cuban Companies, King River Capital, Alpha Intelligence Capital, Founders Fund, Pegasus Tech Ventures, GiTV Fund, and existing investors, bringing total funding to $37 million from investors on four continents.
OSARO's robotic piece-picking software has improved performance and efficiency in e-commerce order fulfillment and intralogistics for multiple customers, including top material handling companies globally. OSARO focuses on industrial automation for the electronics, apparel, grocery, pharmaceutical, and other industries.
King River Capital: "The pandemic has driven global behavioral shifts that have dramatically accelerated the rate of growth in e-commerce and logistics," stated Megan Guy, OSARO Board Member and Partner and Co-founder of King River Capital. "Warehouse automation technology is essential to scaling the capability of these supply chains to safely and efficiently meet society's needs, and King River is proud to back OSARO as a leader of this transformation."
Mark Cuban: "It's more important than ever for retail and e-commerce companies to meet their end customers' delivery expectations. Meeting the increasing demand for e-commerce being fueled by the COVID-19 pandemic is becoming more difficult than ever. During the past 12 months, I've been impressed with OSARO's ability to meet this challenge. OSARO builds machine learning software for legacy and new robotic systems to automate picking and packing — through what was once considered the most labor-intensive and repetitive processes in the supply chain. Grocery, retail, and other e-commerce distribution centers can set up OSARO software in a quick and effortless integration at lower costs than we've ever seen. With OSARO, customers can get their products picked, packed, wrapped, and delivered faster than ever, with high satisfaction. In addition to their new product features and increased machine learning accuracy and automation, it was an easy decision to increase our investment in OSARO's most recent Series B round."
"OSARO achieved significant milestones in 2020, which included a new OSARO subsidiary, OSARO GK, in Tokyo, and grew our team by more than 20 percent adding ten new engineers, and deploying 18 software systems," stated Derik Pridmore, co-founder and CEO of OSARO.
In a performance evaluation for automated warehouse picking, OSARO's machine learning software achieved a 99% successful pick rate and 99.81% successful placement rate. Another independent performance evaluation validated OSARO's lead in computing vision for warehouses and industrial use cases. Across 1400 unseen and adversarial objects, OSARO had the highest accuracy across the full range of products.
In November 2020, OSARO and Innotech announced a new "Piece Picking Station" and demonstrated its advanced capabilities at INTERPHEX JAPAN. This new collaboration produced a smart general-purpose Innotech mobile robot that uses OSARO piece-picking software and machine learning models. Innotech is marketing the mobile robot to logistics and factory automation customers serving food, medical, and cosmetics businesses.
OSARO is the first company to develop a software-only approach to robotics. The company is also the only full-stack robotics company that offers a vision-only solution. OSARO automation software brings together advanced machine learning for object recognition with powerful control software for motion to deliver industrial automation productivity. OSARO software uses visual intelligence that adapts to customer data and environments and is tightly integrated with every major industrial arm and gripper.