Nominal raises $80M at $1B valuation to bring hardware testing out of the Excel era

Summary

Nominal, a software platform for hardware testing and operations, has raised $80M at a $1B valuation in a round led by Founders Fund. The raise was preemptive. Nominal had closed a Sequoia-led Series B just eight months earlier and was not actively fundraising. Founders Fund has backed the company since the seed round, with partners Delian and Trey involved throughout, and the new check followed direct visibility into Nominal's recently announced partnership with Anduril. The company now has 135 employees and 60 customers spanning aerospace, defense, automotive, nuclear power, and satellite operators.

What the product does

Nominal replaces Excel, MATLAB, and legacy PDF-based workflows with a cloud-native, multiplayer platform for managing hardware test data. Cameron McCord, Nominal's CEO and a former submarine officer, argues that industrial software stagnated for two decades while the consumer internet absorbed engineering talent, leaving the hardware sector on tools from the 1990s. He describes the platform as doing for engineering data what Datadog does for software infrastructure, providing continuous monitoring, anomaly detection, and traceability across physical assets.

In practice, as components come off a manufacturing line or arrive from suppliers, engineers run them through quality and functional tests. All sensor readings, pass/fail judgments, and metadata live in Nominal. If a batch of batteries starts showing anomalous voltage readings in the field, an operator can trace that signal back through the supply chain and identify every other unit from the same supplier lot deployed elsewhere. Nominal argues its legacy competitors cannot perform that correlation at scale across thousands of assets and millions of data points.

A key architectural feature McCord highlights is taking single-player tools like MATLAB, standard in every mechanical engineering graduate program, and making them horizontally scalable in the cloud. At a company like Anduril, which is approaching 10,000 people, moving from single-player to multiplayer engineering is a material productivity gain on its own.

AI test agents

Much of the new capital is earmarked for Nominal's internal AI team. The near-term product direction moves away from deterministic test matrices, where a battery is validated against a fixed set of voltage thresholds run sequentially, toward AI-driven test agents that determine the next most informative test point in real time. Rather than stepping linearly through a predefined matrix, the agent operates across a multidimensional state space, updating its model of the hardware after each result and selecting conditions most likely to surface failure modes. McCord frames this as knowledge maximization rather than checklist execution. Physics-based simulation runs alongside this as a parallel input, letting teams pre-screen conditions in software before committing physical hardware to a test run.

Defense and on-prem deployment

Nominal supports fully air-gapped, on-premises deployments from day one, a deliberate engineering investment McCord acknowledges would be unusual for a typical enterprise SaaS startup. The company ships physical servers to customers when needed, or deploys its stack inside a customer's own VPC to inherit their existing security and credentialing infrastructure. It also supports classified work, holds a facility clearance, and a portion of its engineering team holds security clearances. McCord describes the compliance and certification process as ongoing but notes the company is well past the early days of having to explain basic signal-processing math to skeptical government customers.

Competitive positioning

On the SaaS commoditization question, McCord argues that Nominal sits at a defensible intersection of proprietary hardware telemetry data, enriched in real time by human engineering judgment and accumulating inside its platform. As foundation models commoditize software generation, the moat shifts toward data access and the domain-specific reasoning built on top of it. Cross-customer compounding matters too. What Nominal learns from testing a Pratt & Whitney jet engine at Hermes translates into pattern recognition that benefits Anduril, and vice versa. No individual customer building its own tooling would replicate that cross-customer knowledge base.

McCord says Nominal has spent three and a half years dragging the hardware testing industry from 1990 to 2022. The $80M is intended to bridge it to 2026 and beyond.