FieldAI Builds the Intelligence Layer for Robots

FieldAI is building a software brain for robots that have to work where maps go stale, terrain changes, and people keep moving. Its Field Foundation Models are designed to help different kinds of machines navigate and act in unstructured environments without relying on predefined routes, GPS, or a purpose-built autonomy stack for every body.

That hardware-agnostic approach is the strategic center of the Irvine, California company. FieldAI is not betting that one robot wins every job. It is betting that the same intelligence layer can travel across quadrupeds, humanoids, wheeled platforms, and industrial vehicles, then improve as those machines gather experience across real sites.

The company matters now because that thesis is moving through deployment, partnerships, and capital at the same time. FieldAI announced $405M across 2 financing rounds in 2025, says its systems operate across hundreds of customer sites on 3 continents, and has since announced work with Caterpillar, NVIDIA, Boston Dynamics, Certis, and construction company Big-D. A reported 2026 term sheet to raise another $700M at a $10B valuation adds financial attention, but the operational question is more important: can one autonomy layer become dependable infrastructure across industries and machines?

About FieldAI and its Field Foundation Models

FieldAI's core product is a family of physics-first, risk-aware models built for embodied intelligence. The company says its models combine data-driven learning with physical reasoning and uncertainty awareness so a robot can adapt when it encounters conditions that were not scripted in advance.

That distinction matters outside a controlled demo. A construction site can change by the hour. An energy facility may contain hazards, narrow passages, and limited connectivity. Security operations have people and equipment moving through the same environment. In those settings, a brittle navigation plan becomes an operating constraint.

FieldAI positions its Field Foundation Models as an autonomy layer across hardware. The same core intelligence can be applied to different robot forms while the customer chooses the machine suited to inspection, mapping, material movement, monitoring, or another workflow. The company still has to integrate sensors, site data, software, and operating procedures around each use case, but it is trying to keep the most valuable learning reusable.

Why FieldAI matters right now

Industrial robotics has often scaled through tightly structured environments and repeatable tasks. Physical AI is pushing toward the opposite conditions: sites that are unstructured, dynamic, and expensive to redesign for automation.

FieldAI's 2026 partnerships show where that shift is being tested. A Boston Dynamics collaboration pairs FieldAI's models with robotic platforms for construction and other changing environments. Its NVIDIA work connects real-world robot operations with Omniverse libraries and simulation workflows. A Certis partnership extends the model into multi-site security operations.

In September 2026, FieldAI announced a commercial collaboration with Caterpillar after real-world validation across Caterpillar sites. The companies plan to combine Caterpillar's industrial expertise and operational data with FieldAI's models for inspections, situational awareness, and operational optimization. These are company and partner claims, not independent proof of deployment economics, but they show an increasingly specific route to market.

The deployment flywheel is the product strategy

Robotics companies face a difficult loop. Customers want reliability before they expand a deployment, while model quality improves through the varied data generated by more deployments. The company that earns enough trust to enter real workflows can collect more useful edge cases, improve the models, and reduce the burden of the next rollout.

FieldAI is building around that loop. Its NVIDIA collaboration describes a path from live site data to high-fidelity digital twins and simulated environments. Its work with Big-D Construction describes robots moving from individual job sites toward broader adoption after more than 2 years of field deployments. The company says each workflow contributes to a larger operating data base across machines and sites.

That flywheel is valuable only if learning transfers. A model that performs well on one robot at one construction project but needs extensive custom work everywhere else behaves like a services business. A model that carries useful capabilities across hardware and customers starts to look like a platform. FieldAI's long-term differentiation depends on proving the second outcome without hiding the field engineering required to get there.

Leadership built around field robotics

Founder and CEO Ali Agha spent 7 years at NASA's Jet Propulsion Laboratory, where he led autonomy work for projects including the DARPA Subterranean Challenge, off-road vehicles, and Mars exploration. President and Chief Science Officer Shayegan Omidshafiei previously worked on large-scale AI models and reinforcement learning at Google and DeepMind.

The broader leadership team spans field autonomy, research, federal applications, and finance. David Fan has led engineering work on DARPA robotics programs. Sebastian Scherer combines his FieldAI role with research at Carnegie Mellon University's Robotics Institute. Eric Krotkov leads strategy and business development for FieldAI Federal, and CFO Duncan McIntyre brings operating and finance experience from technology companies including Delivery Hero.

That background matches the product challenge. FieldAI is trying to connect foundation-model research with the less glamorous work of deployment: reliability, site integration, data operations, customer support, and safety boundaries.

Hiring is an execution signal

FieldAI's careers page lists hiring across model development, product and user experience, infrastructure and software, field robotics, and business operations. Current public job listings also span robotics hardware, mapping, simulation, product engineering, quality, strategy, and forward-deployed engineering.

The breadth is the signal. Scaling physical AI requires researchers who can improve models and operators who can make them work on a customer's site. Forward-deployed and quality roles suggest the company is investing in the handoff between laboratory performance and repeatable operations, while simulation and infrastructure hiring supports the data loop behind the models.

The hiring pattern also raises the execution bar. A fast-growing team can widen capability, but it can create coordination costs across research, product, deployments, and commercial commitments. FieldAI has to preserve a common platform while serving environments that resist standardization.

What FieldAI must prove next

FieldAI has the ingredients of a consequential physical-AI company: experienced leadership, substantial capital, a hardware-agnostic architecture, a growing partner set, and deployments in markets where autonomy could remove dangerous or repetitive work.

Its next proof points are operational. Customers need to expand from pilots into recurring deployments. Models need to transfer across robots and sites without turning every rollout into a bespoke engineering project. Safety and reliability need to hold over long missions, not only demonstrations. The commercial model needs to convert deployment activity into durable revenue and margins.

If FieldAI can meet those tests, the company could own a valuable layer between robot manufacturers and industrial operators. The bodies will keep changing. FieldAI is trying to make the intelligence portable.