Foundry Robotics Raises $100M to Expand AI-Powered Assembly
The startup has opened a 12,100-square-meter California facility where robotic systems and manufacturing specialists assemble complex industrial equipment.

EMERYVILLE, Calif. – Foundry Robotics has secured $100 million in Series A funding to build out its AI-driven assembly capabilities at its newly opened factory in California, in which Foundry plans to build different kinds of industrial equipment with flexible robots.
The startup disclosed the funding on Oct. 28, disclosing Marlinspike Partners as its lead investor. The companies that joined the round include Khosla Ventures, Red Glass Ventures, SE Ventures, Wing VC, Axiom Ventures, Hanabi and Zero Shot Fund.
The capital will be used for developing the factory, building out AI models and recruiting engineers and factory workers in the areas of robotics and manufacturing. Foundry has shifted to an expanded 12,100 square meter facility in Emeryville, expanding its manufacturing presence twenty times.
Different from traditional vendors that manufacture robotic systems and sell to customers, Foundry runs its own manufacturing facilities and offers assembly services to its customers.
The manufacturing process at Foundry includes AI-enabled robots and skilled technicians. It uses FoundryOS software to manage its robotic equipment and factory operations.
The company claims that its technology enables robotic cells to undertake assembly tasks on varied products without having to set up a distinct production line for each individual product. Data obtained through production is employed in the enhancement of the AI algorithms of the firm.
Foundry aims to produce complicated electromechanical devices such as wiring harnesses, telecom infrastructure, data center hardware and robots.
The company has been founded by Adarsh Kulkarni, previously a robotics lead at Scale AI, and Emmanuel Lamacchia, who was an executive in manufacturing at Tesla.
Foundry has not revealed its clients or production capacity, nor verifiable figures to demonstrate how efficient its approach is in minimizing costs, assembly time and human involvement.
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