Aug. 17, 2026 — Series A: SoftBank is investing $200 million in Gravis Robotics. As the largest Series A in construction robotics history, this funding accelerates Gravis’s mission in physical AI — scaling autonomous heavy machinery across jobsites globally.
Global infrastructure is in the middle of a massive expansion. Scaling the energy networks and data centers required for the AI economy is driving unprecedented demand, and the need for housing, transit and climate-resilient infrastructure is equally urgent. Across every sector, construction has become the primary bottleneck, and we cannot reshape the physical world fast enough to keep up with demand. That shortfall stems from a severe labor crisis, but fixing it isn’t just about hiring more people — it requires augmenting the workforce through automation. Heavy construction remains one of the least automated major industries in the world, still run largely on machines that operate exactly the way they did 50 years ago.
Making this future a reality requires solving some of the hardest challenges in deep tech: translating advanced artificial intelligence into precise, real-world physical execution. That’s the problem Gravis exists to solve.
The Gravis approach: AI built to change the world, not just navigate it
Gravis was founded in 2022 to bring software-defined intelligence to an industry that has relied on purely mechanical hardware for decades. Spun out of ETH Zurich, we have spent the years since building on that technical foundation to turn heavy iron into commercial robotic systems.
Most physical AI operates in static worlds. Self-driving cars steer around obstacles on smooth pavement, while humanoids and robotic arms move objects across fixed countertops or warehouse floors — leaving their surroundings completely undisturbed.
An excavator does the exact opposite. It works by intentionally crashing into the environment, breaking apart varying soil with hidden rocks, and reshaping the earth with every pass of the bucket.
Our AI doesn’t just find a clear path through a static world; it actively takes that world apart and puts it back together. We solve this challenge with models that are enriched by vast simulated experiences, and that efficiently bridge the sim-to-real gap. This synthetic training lets us move billions of cubic yards of virtual earth — from soft clay to rock-filled soil — and enables our software to bring factory-floor precision to historically unpredictable civil jobsites. Rather than simply imitating individual operators, our AI world model incorporates the distinct performance characteristics of a wide range of manufacturers, making it generalizable and adaptable in a way single-machine systems can't match.
"Skilled operators read the earth through subtle physical feedback — listening to the engine strain, sensing the machine vibration and reacting to hydraulic resistance. Our AI takes that same physical input and grounds it in machine telemetry, responding to varying subterranean forces and soil mechanics at microsecond speeds. We didn’t try to simplify the world for our software; we gave it the physical intuition to handle real jobsites with precision that goes beyond what any human can feel from inside the cab" — Dominic Jud, CTO and co-founder of Gravis Robotics
How Gravis works
The heavy equipment market is highly fragmented, with roughly two-thirds of global demand sitting outside the top-three manufacturers. Contractors choose machinery based on deep regional service relationships and existing fleet investments — they shouldn’t be forced into a closed, single-brand ecosystem.
Acting as an operating system for mixed fleets, our software runs on the Gravis Rack — our autonomous control kit — which has been installed across an unmatched range of machinery brands including Caterpillar, Case, Develon, John Deere, JCB, Hitachi, Sumitomo, Yanmar, Volvo and more. Because our learning-based models excel at adapting to the nuances of machines, the same core software can drive a compact excavator or a machine five times its size without custom reprogramming.
This deep understanding of the nuances of individual machines and how they interact with the ground has allowed us to squeeze productivity from these systems — delivering up to a 30% boost in jobsite productivity compared to peak manual operation, while simultaneously improving overall worksite safety.
Our software stack is built for ultimate operational flexibility, offering a full spectrum between AI-augmented manual control and full autonomy. With Gravis Copilot, operators stay in the cab, utilizing real-time 3D guidance and hazard detection to elevate their daily performance. When full autonomy is engaged, those same operators can step away into safe and social environments to supervise entire robotic fleets at once. Regardless of the mode, every Gravis-equipped machine acts as a continuous site sensor — automating surveying and hazard mapping in the background as the work gets done.
The road ahead: What today’s funding unlocks
With systems already deployed alongside global infrastructure leaders across four continents and proven across diverse real-world machinery and sites — we have both the technology and capital to scale rapidly.
This investment powers a global rollout, bringing physical AI directly to construction sites and embedding our autonomous systems into heavy machinery fleets across a trillion-dollar market bottlenecked by labor shortages. Building on this momentum, Gravis was recently selected to lead an $8 million UK government-backed CAM Pathfinder project in partnership with Flannery Plant Hire, the country’s largest provider of operated heavy-equipment rentals. Together we are retrofitting excavator fleets with the Gravis Rack — giving contractors turnkey, on-demand access to autonomous machinery to help accelerate Britain’s $716 billion infrastructure pipeline.
“To build the future, we need to change the world, literally," said Ryan Luke Johns, CEO and co-founder of Gravis Robotics. "Whether we are building housing, modernizing energy grids or scaling data centers, every project starts with moving earth. That foundational work has been the bottleneck slowing down the entire built environment. Our machines are built for the messy, unscripted reality of live jobsites that breaks traditional automation. With SoftBank’s backing, we can hire the best builders and engineers, put Gravis-powered autonomy on every major jobsite, and scale faster than anyone thought possible."
Dai Sakata, managing director of SoftBank Group, echoes this vision: “Physical AI is central to SoftBank’s vision for the next phase of AI. Gravis is bringing AI-powered autonomy to construction, helping build smarter, more efficient infrastructure, and we look forward to supporting its mission.”
