Helm.ai Reaches $70 Million in Signed Commercial Contracts for Foundation Models

Helm.ai has announced that it has signed $70 million in commercial contracts for its foundation models for physical AI over a 12-month period. These contracts spanned global automotive OEMs, Tier 1 suppliers, and industrial automation companies.

The Redwood City, Calif.-based company considers the automotive industry as its flagship deployment area, involving deep partnerships. It also has partnerships with customers in mining and construction. Founded in 2016, Helm.ai's software spans SAE Level 2 through Level 4 autonomous vehicle programs, production-track perception in heavy industry, and expanding robotics development.

Helm.ai trained its foundation models using its 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, resulting in 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.

Vladislav Voroninski, founder and CEO of Helm.ai, stated:

"Those kinds of projects coming to that level of maturity are super exciting for us, because it demonstrates that there's a real demand for the technology that we've built. It's clearly crossing that threshold into actually being deployed in the real world."

"Also, as a company, we're now on a path to break even, which is rare in this space, and it's a testament to the capital efficiency of our approach," he said.

Voroninski compared the company's approach to how a teenager learns to drive, noting that a teenager does not have to drive for millions of hours to experience every possible scenario. Instead, they can already perceive everything and make predictions about what other vehicles or people might do without relying on driving data.

"That's a pretty critical thing, and that gives us data efficiency, which is really important in the autonomous driving space, but it's actually even more important in robotics. I would say it's essential in robotics," Voroninski said.

He explained that for robotics applications, no one has a large fleet of robots yet, so there is little data available to train that way. Helm.ai factors its stack into perception and everything downstream from perception, which plays an important role in data efficiency as well as safety certification.

Voroninski said an important aspect of Helm.ai's technology is being environment-agnostic. The company's roots in autonomous driving already require generalizing across a range of environments, from busy city streets to desert roads, providing a foundation for generalizing across more environments.

"We've shown that our technology can generalize to entirely different application areas. We can take the same perception stack and use it for autonomous driving purposes, an open pit mine, or other kinds of industrial environments," said Voroninski.

So far, Helm.ai has projects bound for production in autonomous vehicles, mining, and construction. It has also applied its AI technology to delivery drones.

Voroninski discussed using Helm.ai's technology across autonomous vehicles, drones, humanoid robots, and more. While it may seem difficult to generalize to many robot form factors, he said these different form factors all come with similar problems, starting with the sensors being used and how they are configured.

"It's essentially the same problem, just different definitions for what you want to detect, what you want to localize, and the kinds of behaviors you're going to care about," he continued.

Looking ahead, Voroninski said Helm.ai is interested in working with robotics companies across industries and embodiments.

"Where robotics is now is almost where autonomous driving was 10 years ago. It's really now entering its breakout moment, so we're just super excited to take things that we've learned from the commercial traction we've achieved, and apply that to many different areas," he said.