Helm.ai Signs $70M in Contracts to Advance Physical AI and Autonomous Driving

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Helm.ai, the foundation-model software company for physical AI, announced that it has signed $70 million in commercial contracts over a 12-month period, spanning global automotive OEMs, Tier 1 suppliers, and industrial automation companies. The company is on a path to operating breakeven as it carries production-bound automotive programs toward the start of production. This combination of commercial scale and capital efficiency differentiates Helm.ai from other high-end autonomy software providers.

Commercial momentum, by the numbers:

• $70 million in commercial contracts signed over a 12-month period
• Commercial partners spanning perception, full-stack autonomous driving, automated data labeling, and generative simulation
• Production-bound vehicle programs in deep integration with next-generation vehicle architectures ahead of start of production
• Production-track deployment beyond the road, including perception for heavy industrial equipment in open-pit mining
• On a path to operating breakeven — high-end autonomy delivered on a software company’s economics, not a fleet operator’s

The autonomy industry’s dominant approaches carry structural costs that compound with scale: fleet-heavy data collection, monolithic end-to-end models with soaring compute requirements, and premium in-vehicle silicon. Helm.ai’s foundation models are trained with the company’s unsupervised Deep Teaching™ methodology to master the structure of the physical world itself. This separates the problem of understanding an environment from the problem of acting in it. The result is a system that learns from a fraction of the data, generalizes to environments it has never encountered, and deploys within the compute constraints of real-world physical systems — from vehicles, to robotics, and to industrial equipment. That shift is why Helm.ai is positioning its technology as foundational infrastructure for physical AI broadly, converting autonomy from a capital problem into a software licensing decision.

One platform, many embodiments. Helm.ai’s foundation models learn the structure of the physical world rather than the particulars of any one machine, so the same core intelligence transfers across embodiments: passenger vehicles, industrial machines, and robotics platforms. The company’s software already spans L2+ through L4 development automotive programs, production-track perception in heavy industry, and expanding robotics development — all built on one model lineage, one training infrastructure, and one training and validation stack. Every deployment strengthens the platform that serves the next one.

“Capital efficiency isn’t a constraint we manage, but rather a property of the technology. We’re on a fundamentally different accuracy-versus-cost trajectory, not the same capability built cheaper, but a different curve entirely,” said Vladislav Voroninski, CEO and founder of Helm.ai. “We built foundation models that learn the structure of the physical world with radically less data and compute, and the commercial results now speak for themselves: production-bound automotive programs, paying customers across driving and industrial AI, and a business on a path to breakeven in a category known for burning billions. Autonomous driving is our first market at scale. The same intelligence is already going to work in robotics — and the economics that won in automotive travel with it.”