# We raised $240M to build the intelligence platform for hardware > Nominal raised $240M at a $3B valuation, led by Lightspeed Venture Partners and co-led by Insight Partners, to give the engineering teams building the world’s most advanced machines a platform they can build on. Source: https://nominal.io/blog/series-c 1. [Blog](https://nominal.io/blog.md) 2. [News](https://nominal.io/blog.md?category=news) # We raised $240M to build the intelligence platform for hardware Nominal raised $240M at a $3B valuation, led by Lightspeed Venture Partners and co-led by Insight Partners, to give the engineering teams building the world’s most advanced machines a platform they can build on. NominalNov 09, 2026 The next generation of machines will be built by engineers working with increasingly powerful intelligence. They’re designing aircraft that fly farther, robots that do more, energy systems that generate and store power more efficiently, and spacecraft that take us places we’ve never been. That future is taking shape today inside some of the most ambitious engineering teams in the world. They will solve problems that are difficult to imagine today, using tools that are only beginning to emerge. But getting there will take more than better AI models. It will take a new foundation for how hardware is designed, built, tested, and improved. We believe hardware engineering needs its own intelligence platform. One created specifically for the complexity of the physical world, where intelligence can connect to real systems, draw on engineering knowledge, and help people make better decisions with greater confidence. Today, we’re announcing a $240 million Series C at a $3B valuation. For four years, we’ve built the infrastructure, connections, and engineering context hardware teams need. This round turns that foundation into the intelligence platform engineers build on. ### Physics gets a vote AI can make engineers dramatically more capable. But more capable models alone won’t produce better machines. In engineering, physics gets a vote. A design can look right on paper and still fail in the real world. Machines must be built, tested, and proven. Unexpected results must be investigated. Every conclusion must hold up against evidence. Engineering also depends on context that rarely lives in one place. Data is scattered across instruments, files, simulations, internal tools, and teams. Understanding an unexpected result might require connecting a sensor reading to a hardware configuration, a previous experiment, or a decision someone made months earlier. A general-purpose model cannot reliably bridge these gaps on its own. It needs access to the right data, the context around it, and tools that let it investigate rather than simply generate an answer. And the human engineers responsible for the outcome need to understand the evidence behind its conclusions. Bringing intelligence to the physical world takes more than powerful models. It takes a foundation that connects intelligence to the systems, data, and context of engineering. ### We’ve been building toward this moment Nominal began with a specific problem: hardware teams needed better ways to work with test data and make decisions from it. Testing is where plans and predictions meet physics. Engineers compare what they expected to happen with what actually happened, investigate the difference, and use what they learn to determine what comes next. Yet the software supporting this work has often left engineers stitching together disconnected systems, rebuilding analyses, and manually piecing together the evidence they need. We set out to change that, and over the past four years, we’ve built software that understands the realities of hardware development: complex, high-volume data; diverse physical systems; demanding deployment and security requirements; and workflows where the details matter. That work has given us more than a starting point for AI. It has helped us build the underlying infrastructure, connections, and engineering context that intelligence needs in order to actually be useful in the physical world. And it has put us alongside the teams doing some of the hardest work in engineering. We’ve learned from the environments where their systems are developed, the problems they encounter, and the evidence they need to move forward. The opportunity now is to build on that foundation and to help frontier organizations meet the AI moment. This is an evolution of our original mission to help hardware teams build better machines, faster and with greater confidence. AI creates the opportunity to accelerate that mission and, as a result, accelerate the mission of the humans who build on Nominal. Realizing that opportunity depends on the foundation we build beneath them. ## Four layers, one platform A Hardware Intelligence Platform brings together the systems, data, intelligence, and applications that hardware engineering requires. At Nominal, we think about this foundation in four connected layers. Each solves a distinct problem and, together, they make intelligence useful across the hardware lifecycle. ![The Hardware Intelligence Platform](https://cdn.sanity.io/images/xupoa92h/production/e2c44c22830c0c9464f449642d34e21084de109d-1369x1200.png) #### 1\. Connected Control: Connect intelligence to the physical world Intelligence needs a connection to the systems engineers are building. Connected Control links Nominal to physical hardware and the systems used to operate and test it. It enables teams to command equipment, capture data where it is generated, and connect engineering decisions to real-world results. This connection matters because the physical world is not static. Systems change, tests reveal new information, and decisions lead to the next experiment. A platform that can connect to those systems can help engineers learn from what happens, not just analyze it after the fact. #### 2\. Data Infrastructure: Build on what your team has learned Hardware generates enormous amounts of data, but volume alone does not create understanding. Engineers need to bring together signals, files, configurations, documents, and other evidence from different tools and environments. They need to know what a measurement represents, which system produced it, and how it relates to earlier work. Nominal’s Data Infrastructure is built around these realities. It gives engineering data a common structure while preserving the details and context that make it meaningful. It also provides the foundation for teams and software to work with that data across systems, deployments, and workflows. Without this layer, intelligence is limited to whatever information happens to be available in a given moment. With it, engineers and AI systems can build on a richer, more connected picture of the work. Every test adds to what the team knows. #### 3\. Intelligence Layer: Turn context into useful action Models are becoming more capable, but their usefulness depends on what they can access and what they can do. The Intelligence Layer connects models and agents to engineering context, tools, and workflows. Rather than relying on a model to produce a plausible response from a prompt alone, it enables systems to investigate questions, run analyses, compare results, and assemble evidence that engineers can inspect. Nominal Analyst, our first purpose-built AI agent, is an early example of where we’re heading. It helps engineers work through complex analysis by drawing on the data and context available through the platform. The goal is to extend engineering judgment: helping engineers explore more possibilities, spend less time on repetitive work, and focus their expertise on the questions that matter most. #### 4\. Engineering Applications: Build on Nominal The value of a platform is ultimately measured by what people can accomplish with it. Engineering Applications turn the capabilities of the underlying platform into useful workflows across development, testing, manufacturing, and operations. Some are built by Nominal. Many are built by customers and their teams, using SQL, MCP, APIs, and Nominal for Developers to connect their workflows and develop custom tools. Because every application shares the same foundation, it draws on connected systems, consistent data, and engineering intelligence instead of rebuilding them for each use case. That means a team can solve an immediate problem and extend the platform as their needs evolve, carrying knowledge and capability across workflows instead of starting from scratch each time. The four layers are most powerful together. Connected Control links the platform to the physical world. Data Infrastructure gives that world’s evidence structure. The Intelligence Layer reasons over it. Engineering Applications put those capabilities into practice. This is how a platform becomes more than a collection of tools. It becomes a foundation that teams can build on. ### From better answers to better machines The potential of this foundation goes beyond making individual engineering tasks faster. When data, context, and intelligence work together, the results of one experiment can inform the next. A finding from one team can help another avoid repeating work. An unexpected result can become a source of knowledge rather than an isolated investigation. As engineers learn more, they can carry that understanding forward. Over time, this can change the pace and ambition of engineering itself. Teams can investigate more hypotheses, learn from more experiments, and make decisions with greater confidence. They can spend less time reconstructing what they already know and more time discovering what they don’t. And as models improve, the platform can make those advances useful in the context where they matter most: real engineering work, connected to real systems and grounded in evidence. The engineer remains central. AI expands what people can do, and the platform connects that capability to the physical world. We’re already seeing what ambitious hardware teams can accomplish with better tools and more connected engineering workflows. We believe the next generation of intelligence will let them go further still. They will build machines we can barely imagine today, using tools that are only beginning to emerge. Our ambition is simple: give engineers the tools to build what comes next. That future will be built on Nominal. - [Physics gets a vote](https://nominal.io/blog/series-c.md#physics-gets-a-vote) - [We’ve been building toward this moment](https://nominal.io/blog/series-c.md#we-ve-been-building-toward-this-moment) - [From better answers to better machines](https://nominal.io/blog/series-c.md#from-better-answers-to-better-machines) ## Further reading [All posts](https://nominal.io/blog.md) Read next NewsNov 06, 2026 ### Nominal Acquires Pull Systems Every car on the road is teaching its engineers something. The hard part is acting on it before the lesson gets expensive. Henry and Manny built Pull Systems to solve that problem for automakers, and they're joining us to bring that work into the Nominal platform. [Read the post](https://nominal.io/blog/pull-systems.md)