Nominal Raises $240M Series C at $3B Valuation to Scale AI for Hardware

Nominal has raised $240 million in a Series C round at a $3 billion valuation, the company announced on November 9, 2026. Lightspeed Venture Partners led the financing, Insight Partners co-led it, and the capital is earmarked for expanding Nominal's AI platform for hardware engineering.

The money will fund deeper products for engineers and engineering enterprises, expansion into new industries and regions, and strategic acquisitions. Lux Capital, Sequoia, General Catalyst, Founders Fund, Lockheed Martin Ventures, Salesforce Ventures and DE Shaw Ventures are among the other investors named in the announcement.

The round lands on top of an established base. Nominal reports that more than 80 commercial and government organizations use the platform, including five of the seven largest defense contractors, spanning aerospace, defense, robotics, energy and advanced manufacturing.

Why did the company spend four years on data infrastructure first?

A companion platform post explains that Nominal spent four years assembling the data infrastructure, equipment connections and contextual information on which its platform depends. The company began by helping hardware teams work with test data: comparing expected behavior with measured results, investigating differences and deciding what to do next.

Its broader argument is that AI needs access to evidence and context instead of producing answers from isolated prompts. Engineering evidence sits scattered across instruments, simulations, files, internal tools and teams — interpreting an unexpected result can require connecting a sensor reading to a hardware configuration, an earlier experiment and a decision made months earlier.

Nominal's answer is a four-layer Hardware Intelligence Platform designed to bring those inputs together without stripping away their context:

• Connected Control links the software to physical hardware and test systems, letting teams command equipment and capture data at its source.
• Data Infrastructure gives signals, files, configurations and documents a common structure while preserving what measurements represent, which systems produced them and how they relate to earlier work.
• The Intelligence Layer connects models and agents to engineering context, tools and workflows.
• Engineering Applications extend the platform across development, testing, manufacturing and operations.

Together, the first two layers supply the organized evidence used by the platform's AI components.

What can the AI agents actually do?

The Intelligence Layer enables systems to investigate questions, run analyses, compare results and assemble evidence that engineers can inspect. Nominal describes Nominal Analyst as its first purpose-built AI agent; it investigates engineering data and runs complex analyses, with answers traceable to the underlying evidence. Engineers retain control over decisions, reviews and approvals.

Deployment options cover cloud and on-premises infrastructure, air-gapped environments and edge devices. Through Nominal for Developers, customers can connect existing tools and build custom applications, workflows and agents on the same foundation.

Where does the $240M go next?

The roadmap extends beyond test analysis. Nominal is developing agents and applications for requirements verification, planning, scheduling and fleet health across a machine's lifecycle. Co-founder and CEO Cameron McCord said the goal is to put decades of accumulated engineering knowledge to work while leaving engineers in command.

That positions the Series C as a bet on lifecycle breadth, not just test tooling. The company's 80-plus customers across aerospace, defense, robotics, energy and advanced manufacturing give it an established base for the broader products it plans to build.

The financing also ties Nominal's growth plans to a demanding proposition: AI must reach physical systems, work from structured engineering evidence and expose the basis for its answers. Extending that approach into more workflows, industries and regions will test whether the company can broaden its platform while preserving the traceability and engineer control required for high-consequence work.