Nominal raises $240M Series C to bring AI to hardware engineers in aerospace, defense, and energy

data platform that powers it. And we have our next guest in the waiting room. We are ready to move on to uh some funding news.

Here he is.

Welcome to the show. How are you doing? Thank you so much for taking the time.

I am doing fabulous. Um happy Monday.

Happy Monday. uh chaos or chaos in the uh in in in the Wall Street Journal, but fortunately your business is going smoothly.

Chaotic growth.

So all good news.

Violently violently up and to the right.

That's good. Yeah. Tell you right before us. We got we got the real economy as a good theme for today.

Yes.

So nothing but optimism in the real economy.

Tell us about that.

Yeah, guys. It's

I would love to. It's good to see you again. Um, yeah. We are announcing our $240 million series C for nomination.

Come on. Big time. Um,

blew my ear drums out.

Honestly, mine, too. I'm like, I'm going to I need to bring it down a notch, but

it's a good for a long time. It continues.

I know. Really really really well done. I think you like you really nailed the suspension apparatus to like maximize long-term vibration, which

you can do with nominal.

Yeah, there we go.

Clean head with the lights.

It was warmed up from earlier in the show.

That's right.

Yeah. Actually, I think I think we just came up with I think we need to like measure the vibration of that gong and keep you like a live tracker in the back on your alltime gong hits.

Oh, that would if we had a we had a video play like a monitor in here that we could actually measure that the power Oh yeah, of

the hit. That would be excellent. We can do a lot more than power. This is exactly why why nominal was created goes ding

to solve this problem.

Yes. Yes, definitely. Uh yeah, jokes aside, talk about the growth of the business. Uh three and a halfx year to date, huge. What's driving it? Uh what's on the product development side? And then uh where's the customer adoption been?

Yeah, totally. And just just to reintroduce folks to nominal think, you know, we build software purpose-built for the engineers. who are building in the physical world. So think um you know across aerospace systems, advanced manufacturing and energy all over the place like you know we are very much in this moment of like the industrial world is getting just reinvented in front of us. Um and you know nominal has been just an unbelievable beneficiary through all of that change and and this has been a very just particular interesting year. Um both like the title wave of like the change in the market. You know I think we've had a a lot of really awesome success. We've announced in our government business and you know what it has looked like to serve the government directly as they make sure the complex things they buy for more vendors than ever work. Um but you know we're also serving all around the you know the supply chain now. you know, people developing in systems, rockets, planes, trains, and cars, and all of the subsystems that go into them, actuators, and batteries, and all the different things that have to come together in a supply chain to, you know, help you go build those things. And we're just in this moment where like people want to accelerate. Um, and people need products like nominal to do it, and they're just so dramatically underserved by anything else available to them. So, 2026 has been awesome. Um a lot of this fund raise is actually really about one of the one of the really important parts of this year has been um we have really just I'll just say realized and step on stepped into this role of helping this discipline helping the industrial sector and engineers in the industrial sector adopt AI. Um, and so you hear about it everywhere else. And of course, like it's changed the life of every software engineer, you know, and every banker and lawyer and marketer, you know, everyone kind of has um, you know, either the purpose-built thing for them um, or they have like figured out a way to go build some kind of crazy set of tools around them. But this is a discipline where like there's just a ton of consequence and a ton of history and a ton of reality. Um, where it's hard for agents to make contact with reality in a way you can trust it as a hardware engineer. And that is really what our series has been about is is stepping into that moment. We're not announcing a lot of things with it.

Um but uh yeah, it's it's really just all about that very particular

intersection.

Walk me through uh the like a hypothetical case study. So uh Audi recently just uh announced that they're establishing their first US production site in Georgia. They're investing $4 billion creating 4,500 jobs. That's a production site. Doesn't sound like a lot of R&D there. Is that an opportunity for nominal to plug into uh to a manufacturer an automaker at that scale or is this just something where you might try and plug into their R&D hub internationally? Uh what where where is the line? Where's the biggest value these days?

Yeah, I think we we did oursel both a wonderful justice in the market but maybe a disservice to the broader tech industry by saying we help people test things. Yeah. Um, what people don't realize is that testing never stops in the life of a piece of hardware. You simulate something, you're testing it. You put it together for the first time, you're testing it. And by the way, as you build it on a manufacturing line, every single step, you're having to walk yourself through the core question of is it working in the way I expected it to?

Um, or am I building the right thing in the first place? And so, you know, this was really about us positioning for our customers how to adopt us. But like very much like yes that is like

manufacturing use cases are amazing for nominal because we can come alongside them and help them make sure you know every station is doing what it is supposed to but probably more importantly is it getting better. Um you know we're in a moment where like

think like aerospace hasn't had to think about like volume in a really long time. Um and so much of our breadandbut business is in aerospace where like you know we are coming in and teaching them a lot of like here's like what's happening at the bleeding edge of automotive. Um, here's how you can think about not just making one thing go five times the speed of sound, but a 100 things go five times the speed of sound and build them really quickly. Um, and build them in a way that you know you can trust them and maintain them and um, and of course manufacture them. And that

how are you guys

should What's the best mental model for your guys' approach? Is it like a a cursor where you are leveraging uh models from the frontier labs and then and then making your own you know post-training you know open source models are you guys buying data yourselves like where where is the focus I think it's like a interesting sort of resource allocation problem because you guys are going to have the trust and the end relationship with these like organizations that need to have um uh a yeah again like a huge amount of trust because there's national security implications. I can I can see how you guys can vend in a bunch of different solutions. But it's interesting like do you spend the mo do you spend all this money and time trying to make your own models when you have to by this point you you should assume that all the frontier models are just going to keep getting better and better and better and in sort of like weird spiky ways that that weren't always perfectly predictable like let's say like a year ago, two years ago.

Yeah. Yeah. Yeah. Totally. I mean think we we are a purpose-built company. We purpose build for hardware engineers. And so what that what that how that comes to life is it is not just about models but like everything we build we are thinking about how do we bring that technology the last mile into the way that they can adopt it. So you know what informs an AI strategy is thinking about like well what constraints are on a hardware team. Um constraint number one connectivity sucks in the real world. you very rarely have a constant and reliable internet connection even if you're on a manufacturing line floor. Um sometimes that's because of IT infrastructure, sometimes that's because of some national security constraint. Um but it doesn't matter if you're building an aerospace or automotive or energy really anywhere in the industrial sector like um connectivity is a really big important problem and it's a lot of like why the cloud didn't make its way here. um they use it for simulation and they use it for these variety of things but like they are full stack in the truest of senses you have to think about how computers and compute infrastructure is going to work in my organization. So how that informs phenomenal is like if we are going to build agents for you know for these customers for them to be able to trust them. Yes, sometimes we have great access to internet and we should absolutely be using a frontier model in that moment and giving them a really clear picture of you know how that model is helping them make a decision and how do you audit that model and give them perfect responses coming back from it. that does look a lot like Harvey and Rogo and Lori and all of these companies building these like intelligence platforms like for a discipline. Um but nominal has a really unique thing where you know we have a team of 15 who is only thinking about computer infrastructure. How do we take everything we build and make sure it can work in a

how about a waterproof on a Navy ship? Um you know so like

that tells you what you need to do with models. So like you think our model strategy is not just about like we're going to go build a frontier model because we need good gross margins. It's like we're going to build a model that works waterproof on a Navy ship. Oh by the way we're also going to go figure out what it looks like to work on a manufacturing line floor when you have gigahertz level data streaming into this thing and you have to make a decision or 30 years of records then we have to make this warranty call. Like there's just so many intricacies that come in hardware but it is bigger than just you know asking a chatbot what to do. It's like managing a ton of context and helping them like build in the realities of the compute infrastructure they have.

Well, congratulations. Thank you so much. fascinating position and uh opportunity to make sure that intelligence is accessible

underwater maybe a little bit

at least accessible to you know if you're not in

if you're not in New York or San Francisco and you ask someone what they do and they answer engineer the vast majority of the economy that is mechanical electrical aerospace chemical engineers people that build the real world and we want them to benefit from AI too

I love it I love it well thank you so much for hopping on the and we'll talk.