FieldAI-powered robots operate mapless autonomy across a construction and industrial site for monitoring and inspection.
Undisclosed, August 21, 2025
FieldAI announced $405 million raised across two rounds, including a $315 million tranche, driving its valuation to about $2 billion. The company develops Field Foundation Models (FFMs) that enable robots to operate without maps, GPS, or pre-defined travel paths, cutting deployment time and cost. FFMs are hardware-agnostic and run on humanoids, autonomous vehicles and other platforms. Training mixes real customer-site data with synthetic data from thousands of simulations. Early deployments focus on monitoring, surveying and inspections. The new funding will accelerate engineering, global expansion and hiring to roughly triple headcount as FieldAI scales commercial operations.
A robotics software company announced it has raised $405 million across two funding rounds, including a $315 million investment that closed earlier this month. The new cash lifts the company’s value to about $2 billion, up from roughly $500 million a year ago. The company plans to use the proceeds to speed engineering work, expand internationally and double its headcount by the end of the year.
The firm builds software that helps robots operate in real-world industrial places such as construction sites, factories and other busy, changing environments. At the center of the offering are what the company calls Field Foundation Models, or FFMs. These models are built specifically for robot control and are intended to work across many types of machines without heavy customization.
The platform runs on humanoid robots, autonomous vehicles and a broad set of other machines straight out of the box. It can be trained using data gathered on customer sites and with synthetic data produced by thousands of robot simulations. Those simulations are powered by an open-source robotics lab tool from a leading graphics chipmaker.
The software includes features that estimate how confident a robot is in a given decision and can stop or change behavior when that confidence is low. Customers can deploy multiple robots in the same facility and set them to coordinate their work, which can reduce the time and cost needed to put robot systems into operation.
One major selling point is that the models can operate without prebuilt maps, without GPS and without detailed, user-defined travel paths. That approach removes a common barrier to automation: creating detailed maps for every deployment, a process that can take weeks and raise costs. The company says its models make it easier to deploy robots in fast-changing and messy sites where maps are often unavailable.
The company reported that customers have already deployed its software in hundreds of industrial settings. Typical uses include monitoring construction projects for plan compliance and inspecting production equipment on factory floors. The software is designed to let firms use a variety of hardware options, which helps reduce dependence on a single robot maker and can cut costs by letting customers pick more affordable machines.
The larger of the two recent investments was a $315 million round led by a group of strategic backers. Other participants across the two rounds include venture units of major chipmakers and several venture investors and strategic funds. The company has now secured a total of $405 million in the two rounds combined.
The company will use the money to accelerate product work across locomotion and manipulation, to hire engineers and other staff, and to expand sales and support overseas. It expects to grow headcount sharply from its level at the end of last year toward a substantially larger team by year end.
The company says its models combine learned behaviors with physics-aware components so robots can better judge and manage risk in unpredictable settings. Synthetic training plus real-world site data from deployed robots aim to create models that handle variation in surface conditions, moving people and changing layouts while lowering the chance of navigation errors.
The funding and product focus signal a push to bring more robots into everyday industrial work, especially for tasks that are repetitive, risky or hard to supervise. The combination of model design, simulation-based training and support for many hardware types is meant to lower the cost and time needed to roll out automation at scale.
It raised a total of $405 million across two rounds, including a $315 million infusion that closed earlier this month.
The latest funding values the company at about $2 billion, up from roughly $500 million a year earlier.
Field Foundation Models are AI systems built specifically for controlling robots in physical spaces. They are designed to work across many kinds of machines and to manage risk in changing, real-world settings.
No. The models are designed to operate without prebuilt maps, GPS, or fixed travel paths, which helps in fast-changing sites like construction areas.
Training combines real data from customer sites with synthetic data produced by thousands of robot simulations. Those simulations use an open robotics lab tool from a major chipmaker.
Proceeds will support engineering, international growth and hiring. The company plans to roughly double its staff by the end of the year to speed product work and expand global reach.
| Feature | What it means | Why it matters |
|---|---|---|
| Field Foundation Models (FFMs) | AI models built for robot control across different hardware | Reduces need for unique autonomy software per robot type |
| Mapless operation | Robots can navigate without prebuilt maps or GPS | Saves time and cost, works in fast-changing sites |
| Synthetic training | Thousands of simulated robot runs generate training data | Speeds model development and covers rare scenarios |
| Confidence-based risk control | Software estimates decision confidence and can limit actions | Reduces navigation errors and operational risk |
| Hardware flexibility | Runs on humanoids, vehicles and other platforms | Lets operators choose cost-effective machines |
| Funding and growth | $405M raised; $315M in latest round; $2B valuation | Enables hiring, product development and global expansion |
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