Nominal's $240M Series C Values Its Hardware Test-Data Platform at $3B as Five of Seven Top Defense Contractors Sign On

Nominal, which builds software for analyzing the test data of aircraft, satellites, robots and other complex machines, has raised a $240 million Series C at a $3 billion valuation. The company announced the round on November 9, 2026. Lightspeed Venture Partners led it and Insight Partners co-led, with participation from investors including Lux Capital, Sequoia, General Catalyst, Founders Fund, Avenir, Lockheed Martin Ventures, Salesforce Ventures and DE Shaw Ventures.

Nominal describes itself as a “hardware intelligence platform.” It is headquartered in Austin, with offices in Los Angeles, New York, Seattle, Washington, D.C., and London, and is led by co-founder and CEO Cameron McCord.

The problem it is selling against

Nominal’s argument is that hardware engineering data is scattered. In the announcement, the company says test data “remains fragmented across legacy systems and disconnected tools,” while the knowledge needed to interpret it “often lives in individual engineers’ experience.” Without the conditions, decisions and expertise behind a test run, it says, engineers struggle to build on earlier work, and AI systems lack a reliable foundation for high-consequence engineering.

The platform connects raw hardware data with that engineering context so engineers can investigate problems and make decisions, and so AI agents can work from the same record. It runs in cloud deployments, on-premises, in air-gapped environments and on edge devices, which matters for defense programs with strict security rules.

Agents on top of the data

The company’s first agent, Nominal Analyst, investigates engineering data and runs complex analyses, with answers “traceable to the underlying evidence,” according to the announcement. Nominal stresses that engineers keep control of the decisions, reviews and approvals that matter. “Engineers stay in command, with purpose-built AI helping them do their best work,” McCord said.

A developer layer, Nominal for Developers, exposes the platform through SQL, APIs and the Model Context Protocol (MCP), so teams can connect existing tools and build their own workflows and agents on top of Nominal’s data.

The new money will go toward more agents and applications covering a machine’s full lifecycle, from requirements verification and planning to scheduling and fleet health. Nominal also plans to move into new industries and regions, including through acquisitions. This year it acquired Fid Labs, whose team now develops Nominal’s agents, and Pull Systems, which extends the platform into automotive.

Customers and results the company cites

Nominal says more than 80 commercial and government organizations use the platform, including five of the seven largest defense contractors. It gives three examples, all reported by the company:

• At Anduril Industries, more than 300 engineers use Nominal across programs, and analysis that once took five to six hours now happens in near real time.
• At shipbuilder HII, a pilot cut certain analysis tasks from hours to minutes and reduced some production test steps by about half.
• Hermeus cut the time between uploading and reviewing data by 80% while designing and building its Quarterhorse Mk 1 aircraft in seven months.

The company also notes that the U.S. Air Force recently selected it for a $71.2 million Strategic Funding Increase to support Collaborative Combat Aircraft work. Nominal says that on large aircraft programs, test evidence often still moves between prime contractors, suppliers and government testers “through zip files and email threads,” and that its platform gives them a shared workspace while each organization keeps control of its own intellectual property.

Where Lightspeed puts the round

Lightspeed partners Guru Chahal and Sebastian Duesterhoeft said Nominal “earned that position by understanding the realities of hardware engineering and building software engineers use every day.” McCord framed the round around the next five years: companies that bring AI into engineering workflows, he said, “will be able to attempt more: more product lines, more configurations, and more performance from their assets in the field.”