Opentrons Raises $40M in Series E to Expand Its Robotics, Software, Data Infrastructure
According to the company, the funds will support the next phase of its growth in enabling autonomous science in labs and biopharmaceutical companies using its AI and robotics systems. The post Opentrons Raises $40M in Series E to Expand Its Robotics, Software, Data Infrastructure appeared first on GEN - Genetic Engineering and Biotechnology News .
Opentrons, a company specializing in robotics and lab automation, has secured $40 million in Series E funding to expand its offerings. The investment, led by healthcare investor Avego and including participation from BroadOak Capital Partners, will fuel the development of Opentrons' robotics, software, and data infrastructure tools.
The primary focus of the funding will be on advancing the integration of artificial intelligence and robotics, aiming to enhance the design, execution, and learning from scientific experiments. This includes bolstering AI-driven experimental execution, expanding automation capabilities, and venturing into new markets and geographical regions.
James Flexner, managing partner at Avego, highlighted the increasing trend of drug developers and AI companies moving towards automated workflows that generate usable data. He sees significant potential for autonomous science to converge around Opentrons' platform.
Opentrons' CEO, James Atwood, emphasized the company's commitment to making lab automation accessible for any lab and its systems designed to run experiments directly with AI. This strategic move has led to Opentrons deploying over 11,000 robots across more than 80 countries, serving a diverse clientele including academic institutions, diagnostic labs, and global pharmaceutical companies.
Earlier this year, Opentrons announced its collaboration with Nvidia’s Isaac and Cosmos AI platforms to generate training data for developing and deploying physical AI-enabled laboratory robotics.
Written by urgent.news from GEN Biotechnology's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.