Nominal raises $240M Series C to bring AI to hardware engineers in aerospace, defense, and energy

Nominal raises $240M Series C

Nominal builds software for hardware engineers across aerospace, advanced manufacturing, and energy. The company's $240 million Series C is effectively a bet that this corner of the economy, long underserved by enterprise software, is ready for AI.

Bryce Strauss frames the product around a simple premise that the industry undersells: testing never stops. Engineers test when they simulate, when they first assemble, and at every station on the manufacturing line. The core question at each stage is whether the thing is working as expected, or whether it's being built correctly in the first place. Nominal sits inside that loop.

“We are announcing our $240,000,000 Series C for Nominal. We build software purpose built for the engineers building in the physical world — across aerospace systems, advanced manufacturing, and energy. A lot of this fundraise is really about helping this discipline, the industrial sector and engineers, adopt AI. We're announcing a lot of things with it.”

Why AI hasn't reached hardware engineers yet

The obstacle is structural. Manufacturing floors and aerospace programs often lack reliable Internet connectivity, whether for IT infrastructure reasons or national security constraints. That's a large part of why the cloud never fully penetrated industrial environments. Strauss says Nominal has a team of 15 people dedicated exclusively to compute infrastructure, figuring out how to make the platform run in environments that don't look anything like a San Francisco data center. The clearest illustration he offers: waterproof deployment on a Navy ship.

That constraint shapes the model strategy directly. When frontier models are accessible, Nominal uses them, with an emphasis on auditability so engineers can trace how a decision was reached. When connectivity is unavailable or restricted, the platform needs to run locally. The goal isn't building a frontier model for margin reasons, Strauss says. It's building a model that works under the conditions hardware engineers actually face.

The aerospace manufacturing moment

A thread running through the conversation is aerospace learning volume manufacturing for the first time. The sector has historically built to precision, not to scale. Nominal's pitch to those customers borrows from automotive's playbook: how do you build not one thing that goes five times the speed of sound, but a hundred of them, quickly, reliably, and in a way you can maintain?

The industrial supply chain framing extends further than prime contractors. Strauss describes customers across the full stack: actuators, batteries, subsystems, and all the components that have to converge before a rocket, plane, or vehicle exists.

2026 growth

Strauss describes 2026 as the year the government business came into focus, with Nominal moving to serve government customers directly as procurement diversifies across more vendors. He frames the broader macro as a tailwind: the industrial world is being reinvented, customers want to accelerate, and there's nothing else well-suited to serve hardware engineers at the AI layer. The engineers who build the physical economy, mechanical, electrical, aerospace, and chemical, have watched every other discipline get purpose-built AI tooling. Nominal's argument is they're next.