Lightspeed Leads Nominal's Series C to Launch AI Agents for Hardware Engineering
Nominal secured a Series C led by Lightspeed and unveiled AI agents designed to break down data silos in hardware testing, aiming to accelerate R&D cycles in aerospace and defense.
Venture capital firm Lightspeed Venture Partners announced on November 9, 2026, that it has led the Series C funding round for Nominal, a software company specializing in hardware test data. Alongside the financing, Nominal launched three new AI agents—Analyst, Execute, and Plan—intended to bring artificial intelligence into traditional hardware engineering workflows.
Hardware engineering has long suffered from "data silos." Unlike software development, where code changes can be tested instantly, data from rocket or satellite tests often resides on offline test stands, USB drives, or individual engineers' Python scripts, making it inaccessible to modern AI models. Nominal's core strategy involves building a unified data layer that aligns high-rate sensor data, video, and logs on a common timeline, creating a foundation upon which AI agents can operate.
The newly released agents target different stages of the testing process: Analyst visualizes data, reviews specifications, and debugs failed tests; Execute controls instruments to run tests and automatically aborts them if anomalies are detected; and Plan helps engineers decide which parameters to test next to maximize information gain. Lightspeed noted that approximately 600,000 engineers in the US work in industries driving most R&D, representing about $100 billion in annual labor costs. Even optimizing a fraction of this time presents significant economic potential.
Nominal's customer roster includes aerospace and defense technology companies such as Anduril, Shield AI, and Hermeus. In its announcement, Lightspeed claimed that Nominal helped Hermeus achieve hypersonic flight faster than any program since the 1960s and secured a sole-source contract worth up to $53 million from the Air Force Test Center. However, these specific outcomes rely primarily on vendor and client statements, with no independent third-party efficacy assessments currently available.