FieldAI, headquartered in Irvine, California, was founded in 2016 by Dr. Ali Agha, who brings nearly two decades of experience developing AI and autonomy algorithms across diverse robotic platforms, including work at NASA's Jet Propulsion Laboratory. The company's mission is to deliver robust, general-purpose autonomy for robots that must operate in unstructured, unpredictable real-world conditions, often without GPS, prior maps or human teleoperation.
At the core of FieldAI's platform are Field Foundation Models (FFMs), a class of 'physics-first' foundation models built specifically for embodied intelligence. Unlike vision or language models retrofitted for robotics, FFMs are designed from the ground up to handle uncertainty, risk and the physical constraints of the real world. FieldAI's EDGE platform presents this as a general-purpose 'robot brain' that works across different robot embodiments, tasks and environments, and it incorporates a Belief World Model that lets robots reason about uncertainty and act safely, a capability the company calls risk-aware robotics AI.
FieldAI has emphasized real-world deployment, reporting operations across three continents and partnerships with leading robotics and compute organizations including Boston Dynamics and NVIDIA. By targeting hard environments such as construction sites, industrial facilities and other GPS-denied settings, the company aims to prove that a single foundation model can replace brittle, site-specific autonomy stacks.
In 2025 FieldAI announced that it had raised more than $405 million across two consecutive rounds, reaching a valuation of about $2 billion. Its investor roster is unusually deep, including Bezos Expeditions, BHP Ventures, Canaan Partners, Emerson Collective, Intel Capital, Khosla Ventures, NVentures (NVIDIA's venture arm), Prysm and Temasek, with prior backing from Gates Frontier and Samsung. FieldAI competes in the embodied-AI foundation-model arena alongside Physical Intelligence and Genesis AI, differentiating through its physics-first modeling and a long track record of fielding autonomy in demanding real-world conditions.