Key facts
- Maven Robotics has raised $100 million in funding.
- The company's robots are designed for mixed palletizing tasks in warehouses.
- Maven's robots can move at 10 miles an hour and lift up to 30 kilograms.
- The startup aims to build 250 third-generation robots.
- Hamza Derbas, CEO and co-founder, previously worked at Apple on special projects.
- Maven's robots have achieved 99% or higher uptime in customer facilities.
Maven Robotics, a startup founded by former Apple engineer Hamza Derbas and his brother Khalid, has emerged from stealth after securing $100 million in funding from investors including RoboStrategy, LocalGlobe, Vine Ventures, and XTX Markets Ventures. The company specializes in developing robots for end-to-end automation of industrial tasks, particularly in warehouse logistics and mixed palletizing.
Derbas, who previously worked on special projects at Apple, co-founded Maven Robotics with the goal of solving complex operational challenges rather than focusing on single robot problems. The company's approach involves integrating with warehouse management systems to autonomously handle tasks from one side of a facility to the other. This strategy helped Maven win a deal with a large consumer goods company, beating out established competitors.
Maven's robots are built on wheeled bases, capable of speeds up to 10 miles per hour, and equipped with arms that can lift up to 30 kilograms. They are designed to create mixed pallets of goods from various factories for shipment to retail stores, a task currently performed by human labor. The company reports that its robots have achieved 99% or higher uptime while working 16 hours a day in customer facilities.
Unlike some competitors that focus on bipedal robots, Maven's wheeled design is seen by Derbas as more practical and cost-effective for industrial applications, emphasizing return on investment. The company plans to use the new funding to build 250 of its third-generation robots and begin development of a fourth-generation platform. Maven's long-term strategy involves collecting more data to train robots for broader material handling, automation, and fabrication tasks, aiming to tackle multi-billion dollar markets one problem at a time.