Sequoia Leads $75M Series B Into Nominal | Alfred Lin Joins Board

Well, we're here to congratulate you on a massive round that you just closed. A $75 million Series B. This brings your total funding to just over $100 million. Who led this round and what was the process like?

Thank you know, we're, we're super excited to be here and share this news with the world around. Series B led by Sequoia specifically. Alfred Lin is a partner there who will be joining Nominal's board. We're super excited about that. And the round is also co led by Lightspeed Venture Partners and so Guru Chahal and Connor Love from the Lightspee. You know, Anduril had all the resources in the world, really, really, really smart people, but still was sort of limited by the ability for that software to perform because you either had scenario A where you would use really old outdated software to do modern test engineering and it's really bad and it doesn't work. And most of those technologies were built in a pre cloud era or you, you know, have modern technologies that were generally built in a software only world with like no consideration for hardware limitations or constraints. Really like my time at Anduril, I kind of left there shocked that a product like Nominal didn't exist on the market.

Cameron and Steven, welcome to Sorcery.

It's awesome to be here. Molly, thank you for having us.

Thank you.

Molly, thank you for the hat. I love merch and this is pretty nice. Look at this Nominal hat. Not bad. Well, we're here to congratulate you on a massive round that you just closed. A $75 million Series B. This brings your total funding to just over $100 million. Who led this round and what was the process like?

Yeah, thank you. We're super excited to be here and share this news with the world. The round Series B led by Sequoia, specifically Alfred Lin is a partner there who will be joining Nominal's board. We're super excited about that. And the round is also co led by Lightspeed Venture Partners and so Guru Chahal and Connor Love from the Lightspeed team.

That's great. Was it a long process? How did you guys meet?

Yeah, we, it was a very short process, which is great. It honestly from, from starting the. Yeah, from starting sort of formal, you know, pitches and conversations to signing a term. She was about 10 days.

What?

Yeah, which is, which is awesome. And it was not a lackadays goal. 10 days. It was very intense. 10 days. I think the Sequoia team in particular did an insane amount of diligence in that time period which Got us excited. Like when investors really digging in and sort of seeing the world the same way you do as you're going through that process, it was amazing. I, it's been a very exciting period for, for me. I, on the personal front, got married.

Congrats.

Thank you. Went on a honeymoon. Did actually get to like celebrate and check out for a little bit as much as one can when running a then series A company. But basically as soon as I got back from that, had sort of let investors know that we were having a lot of traction in the business and we're going to be doing another financing. And so as soon as I got back full focus and attention on, on the financing and, and yeah, it went, it went fast. It was very much oversubscribed and a really good outcome that we're excited about.

So it's fantastic. I think I heard Pat Grady speak at the Upfront summit and mention that they do so much preliminary research that once they get to the deal, it's like you really impress the founder with how much research you come to the table with.

Yeah, we, uh, in our case like they, I think the Sequoia team spoke to, I think a hundred percent, I think they spoke to every customer of ours, um, which was amazing. Um, and just watching them go through and like build their kind of conviction and come to their own, you know, conclusions was, was really impressive. You know, part of the conversations we were having during the, during that process was they actually kept kind of telling us like, oh, you guys actually might be underselling some of the TAM here as they were like having more of these conversations. And like when you hear investors like, you know, doing that, it gets you really, really excited. And yeah, the Sequoia grow team is no, you know, no joke. I, that's sort of like final. We had a couple of two, two different partner meetings, one with like a smaller group with the growth team and then one with the whole, you know, the whole partnership. And they, you know, they had, they had receipts, they had done a lot of work and were able to really impress us with um, what they had sort of learned from, from a lot of these conversations. So it was awesome.

How did Lightspeed manage to co lead with them?

No, it was awesome. As is always or as is so often the way in venture, you know, we, we were lucky to have a pretty fast series A process as well that was led by general catalyst Paul Kwan. That was, you know, about a year and a half ago now. And two of the people we were having conversations with early then were Were, you know, Sequoia had chatted with Alfred and then with the Lightspeed team. And so it often happens then they're the first people to, to really move fast and get a lot of conviction. So that was how that, that happened. We love the Lightspeed team, particularly Guru there. I'm always partial to folks that have an operating background. And so Guru is, you know, led, founded two different companies, one in the hardware space, one in the software space. So he just gets what we're doing really, really well. And then, you know, Connor Love is awesome and is like just a growing name I think in the DoD venture world particularly. And we've had Connor like overdenomenal to speak at our all hands to like contextualize like the moment we're in with defense tech and what's going on right now. So Connor's awesome. And the combo that they, they bring as a pair of it was too good. Too good to not have.

So, yeah, super cool. Well, Cameron and Steven, you both have pretty big names in the hard tech community. Cameron, you specifically, you worked at Anduril. You were in the US Navy, which we'll get to later, and Applied Intuition, Saildrone and Lux Capital and various other government agencies. So how, what brought you to this problem and how did you gain conviction?

Yeah, really. Well, thank you, Molly. First, it's very kind, but no, I mean nominal for, for me is really a, like a, a converging of like a lot of like seeing this problem from a ton of different angles. And what really made it come to life, it was the time that I did at, at Andrew. You know, I guess going back, like started off my career as a submarine officer, so spent eight years on a submarine, which I know you want to, you want to talk about, we'll talk about later. Talk about that. It could be the whole podcast, but it won't be. Don't worry. Started off as a submarine officer, so just basically, you know, eight years of operating with the best technology that the U.S. navy and the Department of Defense has to offer straight out of, you know, 1980. And so just like a lot of empathy frankly, for what the war fighter, you know, goes through when they're trying to do their job. I left that world. I was very fortunate. I had met some of that early the Anduril team. I had met Trey and Brian Schimpf through some mutual connections and so ended up at Anduril relatively early on and was really fortunate to end up on this, the Anduril Counter UAS team, which is a team that Will I'm sure will go down in history as a ragtag group of folks. But you know Gokul who's now functionally like the one of the senior vice presidents of Mandurah and sort of leads essentially all engineering, you know, was an engineering lead there. A lot of people know Scott Sanders who now works at Forterra. Scott was, was leading a lot of the growth efforts, worked with like a lot of those, those folks in the early days and what I ended up spending a lot of time doing in addition to sort of product efforts on this autonomous, you know, tower anti drone system was actually thinking through and architecting a lot of the software stack that was going to power the ability to test really rapidly, test iteratively, look at data after test events as quickly as possible. And Anduril had all the resources in the world, really, really, really smart people, but still was sort of, you know, like limited by the ability for that software to perform because you either had scenario A where you would use really old outdated software, things like Win Plot people know but a lot of Matlab is use Python analysis, Python scripts, some of the national instruments tools, things like that to do modern test engineering and it's really bad and it doesn't work. And most of those technologies were built in a pre cloud era or you have modern technologies that were generally built in a software only world with no consideration for hardware limitations or constraints. And so you might have new technologies like people use Tableau, right Or like Looker or Grafana or Datadog or other things like that that aren't built for hardware engineering. You certainly wouldn't wake up and be like oh those are the technologies I would want to use if I were testing a kinetic kill drone system that's going to get computer vision lock and be deployed in, in a Syria where it's like 120 degrees and like there's batteries that need to be like you wouldn't, you wouldn't think of those. And so really like my time at Andrew, I kind of left there shocked that a product like Nominal didn't exist on the market. And then the journey you mentioned places like Luxor, Applied Intuition, like really it was okay. How can I actually go out and really educate myself on the market opportunity here? Some thesis that there's a product that needs to be built here but like was there a real business to be built? And so a lot of the time spent after that was watching at Applied Intuition like amazing in this case modeling and simulation software be built and be sold at scale and in the Case of Lux, you know, an opportunity to basically speak to like hundreds of hard tech companies and ask them a series of questions around like what software they were using for this, this problem and really build conviction like there's a massive market opportunity here.

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Yeah, when I was getting ready to leave Anduril really the two main things for me, one was a very small, extremely high caliber team and I had a pretty high bar coming from Anduril and then before that SpaceX and meeting the initial folks at Nominal. It was evident from those conversations that this team was above and beyond even those groups I had worked with at Anduril and at SpaceX. So that was extremely exciting. Checked that box immediately and obviously I knew Cameron as well from our Anduril days. And then I was looking for a product that I just really believed in and really could passionately develop and sell and be a part of that process. And I wasn't actually at the time very picky about hardware versus software or whether it was SaaS or something else. But got the product pitch, saw the demo and immediately tied it full circle back to my sort of career path which was starting at more of a Prime Sierra space, a small group within that organization. Organization having to build a lot of this software tooling myself and doing it also in air gapped environments for secure testing. And really there was just no buy option. I had to go and build it with some teammates over multiple years going to SpaceX, they've invested millions in their tool chain, everything from ERP to fleet management, to data analysis obviously. And so experiencing kind of, I saw the light of what a hardware development program could look like and how fast it could move with that really first class tooling. And then went to Anduril and kind of went back very early into the dark ages there where as Cameron described, there were a lot of good tools being built internally. And particularly on the engineering development side, there were good tools being built. But my organization at Anduril, the technical operations group responsible for the global forward deployed operations of all these robotic systems, we really struggled to leverage those internal tools and really for two reasons. The first was a lot of them were not designed to go deploy behind these security boundaries, out to customer locations, downrange behind air gaps. And even if we could get the tool there, then my team had to go and train on it. And these were tools built by engineers to do really tactical, important things. And so we had a lot of inefficiencies trying to get folks who were on the front lines, often quite literally being able to analyze data from these systems. And then concurrently we're going through lrip scaling, manufacturing for different products and those same tools are starting to break. And so seeing Nominal's product, I was sold pretty much immediately and came aboard.

Amazing. Well, it sounds super comprehensive and also in an incredibly untapped market. I'm curious, you mentioned a little bit earlier, but your investors had a hard time finding what the TAM was. Maybe you have the same problem, but exactly how big is the market for this in aerospace and defense and other industries?

Yeah, it's, we, we kind of, we joke internally a little bit that like the tan is like infinite tam, because I really think, I really think it is massive. And so the, the challenge becomes like how do you, how do you focus and how do you sort of step by step build the product suite that's going to unlock that tam? So we've been very focused initially on, we're a dual use business. So we have aerospace and defense commercial customers, more broader industrial commercial customers. We work with energy companies, transportation, advanced mobility, even just more traditional manufacturing. And then we obviously serve the Department of Defense directly, so we've started there. But I think as you look out at a lot of the fun conversations we were having with investors during this round and a thesis we really believe in is our vision is continuous hardware tests. That's what we talk about internally, that's the language we use. And it is really that the world is moving from this type 1 hardware development where you will work really hard, you'll Iterate on a prototype or a design of something, a widget, a machine. And then you will sort of set that design, you'll lock it and you will print manufacture 10, 100, a thousand, a million of those. Sometimes you won't even do it. You will just outsource it to someone else to do it. And the world is very much like Steven mentioned, SpaceX. But even now Anduril and other places have sort of Tesla flipped that paradigm where the power in hardware development just comes from like how tightly you can iterate between those two things. And so that, that's, that's what to us, continuous hardware test is. How do you allow the people that are doing this to, to link those two? And it's really linking testing, R and D development and experimentation to deployed operations of hardware. And so when you think about a lot of the customers we work with in the early use cases are in testing, you know, it's, it's R and D, it's like early and it's a fun place to start because there's like a high risk appetite. People need to build new products. There's always a budget for it. There's always like a way to get started. But really like the poll that's been exciting for us is customers that think the same way we do that say actually the software suite that I and the tools I use to do developmental tests is exactly the same stuff I want to have when my system is deployed and it's in sustainment. And I want to like monitor a fleet of those assets. Or we joke also internally we have a phrase, we say like, you know, every asset, every drone, every plane, every, you know, ground vehicle, every uuv is a test asset. Fundamentally it's generating, you know, telemetry sensor data logs just like you would a hardware in the loop, you know, test stand or a test cell or a wind tunnel. And so our whole vision is like that the software stack should, should spread between testing and operations completely.

There's certainly a ton of market shifts and trends that or helping companies like yours and others in the industry, things like re reshoring, manufacturing, reindustrialization, $1 trillion defense budget, hardware, hardware, hardware, humanoid robotics, drones, all of these things. What are you bullish on?

Yeah, no, I can start there and then you should jump in to Stephen. I think in like, you know, macro trends that get us really excited. I think the test world, the test community, if you will, testers, it's like love, we love testers. Like those are our people. Their job is getting like harder and harder and harder. So that's just like a trend that we see. Just like. So, you know, these systems are more and more software defined. They're just generating more data than they ever have. And that's not, it's not happening overnight, but it's been a steady rise over the last decade of just like software defined hardware. So there's more data that's being generated. And then I think talk about the DoD context. You know, the DoD is like shifting the way that it's thinking about a lot of these larger programs trying to get away from 10, 12, 20 year program development lifecycles like let's build the next exquisite, you know, bomber or exquisite plane or submarine. Right. Those things are massive capital investments. They take a ton of time. And we're trying to much more get to you know, distributed systems, smaller, autonomous, attritable, like all these words that you, that you hear. But what that fundamentally means from a development perspective is hey, we want to things that used to take a decade we want to do in 24 months, but we still want to do the same amount of testing. Oh, and by the way, there's more data. And so that community in general has felt like, I think a crunch and a crunch where we feel like software is a really big lever. And so we're trying to really just help, help out there. So, you know, we work with a lot of organizations within the dod. Air Force Test center is like the organization at Edwards Air Force Base that is basically in charge of and has a mandate to test and validate, you know, all Air Force, you know, weapon systems and new, you know, new planes, et cetera. And in groups like the Test Resource Management center, trmc, like that's an organization we were really excited to work with. They have a mandate at the OSD level just to look out at all test resourcing for the US physical infrastructure, but also software. And so I think that's something that we are bullish on is that test is having a moment. It's having a renaissance, if you will. And we want to be God that's such a test is having a renaissance. But that's what we believe. So I think we're very bullish on that. I think another thing that we talk a lot about and a point that I try to make is like this is not, this is a coordinated app. Like re industrialization is like, it takes so many different players. And I think what we think about is like, I think a lot in bottleneck theory, but basically just if we solve, we meet amazing founders Building like rare earth magnets and rare earth supply chain production, manufacturing, you know, additive, all of these things. Like those are real problems that we need to reshore in the US and sort of just modernize the industrial base. But we believe like that that is happening and people are solving a bunch of those problems across this, like this chain. But again what that's going to do is just like put more focus on the ability to test and validate these systems. And so we were playing in that space and excited too. But you can talk about maybe some of the technology trends that you've seen in this world. I think it'd be, yeah, I was.

Going to add just to the onshoring piece, doing that as an integrator. The large part of the problem is obviously just solving the supply chain and finding an alternate vendor, whether they're stateside or in an allied country or otherwise. But once you find that vendor, the next thing that happens is testing. If I'm, you know, previously was buying my brushless motor from China and now I'm going to buy it from a US vendor or a Czech vendor or whoever it might be, that is a test campaign that needs to go. And maybe it's, you know, relatively short, maybe it's longer for a larger system, but I need to go and validate that, that the performance is the same, understand how to validate every new unit that arrives and that is happening across. You think of the bill of materials for an aircraft or a uuv. That is just an immense amount of qualification work that needs to happen and then that needs to keep happening in perpetuity. And so we're seeing this at some of our customers. And then as Cameron mentioned, all of these new suppliers that are cropping up in the US and elsewhere, they are also potential nominal customers as they need to do their own validation as the supplier. So really, I mean on both sides of this, it's just there's an immense amount of opportunity to flip it.

What are you bearish on?

I mean, I can do what I'm bearish on also. And then I have one that's like maybe a contrarian take, which I always just, I think it's like an approach. But so what am I bearish on? Yeah, I mean, I think I'm excited at like a trillion dollar, I think sort of defense budget. You know, I think it's probably a positive thing for nominal. But I think my sense is this, the current administration now is not in the business of as much as we talk about cutting budget and cutting programs And DOGE or whatever, those have been small cuts in nature, their efficiency gains. The new administration I don't think is in the business of cutting big programs. And so unless you cut really big programs, I don't think there's going to be. There are efforts that are being like really well funded right now. Golden Dome is one missile defense agency like that that's exciting. And I think that a lot of companies realize that, see that there's a lot of money and effort obviously going to southern border like efforts like that that the administrations I think kind of made clear. But I think like when I look at the sort of overall budgetary picture, like there's just not, there's not enough of these big program cuts, I think for all of the amazing new companies building out there to like really get the big programmer record wins that they want over the next five years. So. So not everyone, you know, is going to on the nominal lens, like I flip and say, like I think the administration is definitely looking for efficiency gains in existing programs. And we think a big part of the efficiency gain is helping people, you know, test A contrarian take that I will make in this ecosystem broadly is like, you know, there's a lot of anti, anti prime rhetoric that people like to talk about. And I think like we take a pretty vocal like, different approach. Just like the primes play a like really key role in national defense. They have resources, they have capabilities. They can do things frankly that you know, say essentially no other startup can do. And so we need to treat them accordingly in that way. And so we nominal, we love the partnerships we have there and we take a very like. I think as an American I want to take a very optimistic approach to what modernizing those companies can do because they really are national assets. It's just the incentive structure is warped a little bit. But we need those companies to succeed and do well. What are you bearish on?

I think you nailed it. Maybe this is partially bearish, partially contrarian, but there's an immense amount of hype and focus around vertical integration. I think a lot of companies moving through that process are realizing that there's a huge amount of power in outsourcing to suppliers and you don't need to reinvent the wheel everywhere. And R lens into that often is the software side, right? You can buy, there's, there is an offering off the shelf nominal to go and address your, your software and telemetry management needs. But it's true on the hardware side as well. And I think, you know, maybe it's this like misconstruing of companies like Anduril and SpaceX that have espoused this idea of vertical integration. But they have massive supply chains.

Right.

Space X is always going to look and see first if they can go and buy something before they go and build it themselves. And flipping that around can get you into a really dangerous place. And so I think things are trending generally in a natural direction. But as the supplier base for all these different hard tech industries grow, I'm excited to see the buy decisions being made in a lot of places and helping to support those companies as well.

To go further into where nominal software fits in, could you just explain out the end to end hardware development process?

Yeah. So starting on the hardware development process side, generally our customers are going to fall into one of two buckets and sometimes if they have multiple products, they're in both. So on the one hand you have the zero to one development program and you're starting from pure simulation data to prototyping to initial qualification testing. Qualification testing can take months, often takes years. It's a very arduous process. And then into production testing, which is kind of. Things are rolling off the line and we're just making sure that everything works. And our software product overall is focused on this entire spectrum of test cases. So we have customers that use nominal only for simulation data and customers using it all the way on production, manufacturing lines and out to their fleet. And all of this data is relevant to pull into a single consolidated database, be able to manage it, present it back to engineers, to technicians, to VPs in an easily consumable way. And then the second piece is in the hardware development process. Right. You need to scale 1 to N and that means bringing up a production line. But to Cameron's point about everything is a test asset. You also need to be aggregating all the data from your fleet back into the same telemetry management and analysis tooling that you were using for development to be able to close that loop. And so the platform that we're building, both from a deployability standpoint, a usability and a feature standpoint, is trying to work across this spectrum and unify that tool chain. I think in general we've seen a lot of really exciting success both with small startups as they're moving through that maturation process, as well as coming into an existing pretty mature program and really transforming the way that they think about test and the way that they do engineering.

Where do current tools and workflows fall short in this.

I mentioned. Yeah, one earlier is it's not an intractable problem to build a tool chain for a single product. For internal development only. You can cobble together matlab and Jupyter, Notebook and Grafana and two or three other things and kind of get by. The places where that is going to fall down is as you scale production and move into customer operations, that tool chain is not going to work either at that scale or with that new user base. Your users are no longer the experienced discipline engineers that actually built the platform. They are field technicians, flight test engineers, customer operators, technical operations engineers, like at Anduril. And so your bar for usability moves very, very quickly. And then the second piece is as companies are bringing in their second, third and fourth product, they might have built a tool chain that worked very well for product one. But almost always what we're seeing is that it's typically over tailored to that system in terms of the data types it supports, the analysis workflows, how commoditized it is. And then they go and decide, hey, we need five more widgets in our product line and that tool chain cannot just be extended into those. And you start doing a new zero to one process for building entirely new tool chain. Or you're looking at hiring 5 to 10 really experienced software engineers across a pretty wide range of skill sets to be able to build a platform like what Nominal has. And so those are generally the points where we're coming into customers from a sales motion standpoint as well. And they're seeing this kind of coming down the tracks for them and we're just kind of presenting this very easy end to end solution that we can drop in and really get running on very often. Day one, within hours of starting with a new partner, we're delivering value.

Stephen, can you talk about Delta Qual? Yeah, I just.

Stephen, Whoa, what is that?

No, no, just it's. Stephen was sort of articulating it, but like it's a thing he, you know, like people have a thing that they just say internally a lot. Steven says deltaqual a lot right now and it's catching on.

Okay.

But yeah, it's a, it's a, it's a articulation of what makes Nominal powerful, I think.

Yeah. And this was something that originally experienced at SpaceX. I was working on the Crew Dragon program. I was working in the test automation group specifically who were sort of productionizing all of these processes for building Crew Dragon. And we talk a lot about mission criticality at Nominal. I don't know if there's a more critical mission than the first flight of a human rated spacecraft with people aboard. And so my role there was looking at these different test assets and processes and ensuring that a we were meeting these third party requirements levied upon us by NASA or by the faa, or if it was an Air Force account by the Air Force, but also meeting our own internal bar and determining what that was. And the Delta quell process is, let's say I have my asset out in the field and I determine I want to make a change to it, a design change or change the supplier for a part or make a firmware change. If I've already qualified that system, I don't need to go back and do my multi month qualification test campaign all over again. I don't need to do all the vibration testing, thermal testing, performance lifecycle vendor qual. What I do need to do is demonstrate that that one small change that I made is not going to invalidate all the previous work that I did. Where you get this acceleration, if you're a SpaceX or an Anduril, is how fast can I identify an improvement I want to make and then convince my customers that that is a safe improvement and that I can deploy that into the field, whether it's new software or a new part. And so a lot of the testing activity at SpaceX kind of falls into this general bucket of Delta qualification and how quickly you can provision that test, collect the data, analyze it, build that body of evidence to go present back to your customers, your regulators. That is what it means to do continuous test and continuous validation and to do iterative development in an agile way on a hardware product. And SpaceX is very, very good at that. And we're helping a lot of other companies become very good at it as well.

Delta qual.

Delta qual. Yeah, it's a big, I mean, I'll say, like, I think it's the thing we see, you know, Stephen was sort of talking about it in the commercial context, but it's, this is a, I think a huge area for the DoD broadly as well. And what happens often is, I mean these programs are massive, really big budgets, very, you know, very, very costly from just the budget perspective, but also the human beings that go into these, these programs. And so being able to help, you know, in the DoD setup, like it has services, right. And so it's like there's intentional ways of, you know, the Navy version of the F35 versus the, you know, Air Force version. Right. What we see is like there's a lot of retest that happens that probably doesn't need to happen. Right. And so this concept of Delta Call, like, our vision would be to be able to provide the tools to determine exactly what does not need to be retested. You know, if I'm doing the variance between the Navy and the Marine corps for the F35, for example, like, very small, some significant differences, obviously, in what the operating picture and, like, what the vehicle is supposed to do. But a big swath of that is the same. And you shouldn't need to, like, re. You know, retest a lot of that. But often that's what. That's what's happening.

So to get deeper into the products, can we start off with the unified operating platform?

Yeah, absolutely. We have two products that we'll talk about today, and then there are some more in the works, but the two products that are out kind of on the market now, Nominal Core, that's what we started with. I'll talk a little bit about, like, what. How that, you know, got. Got off the ground. And then we're really excited. We released just, honestly a month or two ago, Nominal Connect, which is our second product, which has been really exciting. And we can dig into that a little bit. But I think it's just telling the story of where we got started. We got started specifically building a product that would. This testing and validating hardware is massive process, very complex. We wanted to do a single thing really, really well. And so that was this data review process. So if you're an engineer and you're building hardware, like, everyone knows what that term means. It gives you, like, an emotional connotation of just like, oh, God, I have to do data review. But making it just really easy for engineers, like, after a test has happened to collaboratively interrogate their data, have it organized, make sense of it, do both, like, ad hoc exploration. So, like, find, you know, data query channels, just, like, have a really interactive picture of their data, and then also start to build out sort of validation logic and rules that can automate that process because it's highly manually intensive. Like, a thing we hear a lot is you have really expensive engineers, very smart engineers, that with the way the software is sort of the status quo, they're spending a lot of time doing, like, very manual processes of their data. Things that can be done by software, can be done by automation, but it requires a really powerful interface for those humans to be able to sort of, like, enrich that data and make sure that it makes sense. So we started with data review, and the product has obviously grown. That was three years ago now. Has grown extensively since then into this beautiful product that is nominal core. Stephen, I'll kick it to you to give the sort of core overview and then talk about Connect.

Yeah, it's like you said, really a unified platform. And the way that I generally break it up when talking about it is these three main workflows. The first being data management. Second is data analysis, more of a manual process. And then the third is the automated validation. You kind of want that second piece to flow into the third over time, where as you're learning more about your system and doing that analysis in nominal, in nominal core, that you're starting to move those learnings into the rule set to automatically evaluate it. The data management side though, is very, very important. And it's something that ironically is, you know, we want it to be invisible to the average user. We don't want a nominal user to be thinking about what is the particular file I need to look at today? Where is it in my file system? What's the right version of the tool to go and analyze that? And so when you look at the makeup of our engineering team, about half of the headcount is backend software engineers and just brilliant people from Palantir, from financial industry, from big data folks that are used to these really heterogeneous, complex data problems. And so when we say data management, it's sort of two pieces. One is how do we go and be able to upload or ingest all of the relevant data into our platform, whether that's sensor data or video data, High rate, low rate, regardless of the source, be able to pull that in and do that very, very quickly. And we're very opinionated about meeting our customers where we are. And so rather than just kind of throwing them some documentation and saying, we do have a really good documentation, it's beautiful. But rather than just throwing them some documentation and saying, figure it out, we're actually going and building what we call these first class integrations with these data types. And this is one of the benefits of being focused on hardware testing is that that's not a super long list. We can go and build out all the necessary file upload routines, streaming data connections that we see across these industries so that we can get running, like I said before, within hours on day one of starting an account. And then the second big piece is presenting that data back to the user and doing it performantly. The performantly piece is very hard. Hundreds, thousands of nominal engineering hours into ensuring our backend storage and compute systems are highly optimized, highly scalable but then as a user looking into that data, it should be presented to me in a way that is representative of what was happening in the field.

Right.

And so we think about runs being these test events and so a lot of nominal customers, that just enters their vernacular where they're talking about nominal runs and they're referencing tests that are happening on the production floor, or flight tests or customer operations. So the run is kind of the real thing that happened, bound in time. And then the asset, like Cameron mentioned, the asset is the actual physical device producing the data that could be an entire aircraft, or you can make it as granular as a single motor or a single circuit board on your system. And so that data model maps very well to the way that your average engineers are going to think about their systems and what they're trying to evaluate. The data analysis piece, briefly, it's really just kind of merging these Grafana and Matlab esque workflows together where Grafana is a very good tool for extremely agnostic cloud data and really just looking at time series data. And so at a company like Anduril, very early on they might implement Grafana for fleet monitoring.

Right.

And then you have Matlab at the other end of the spectrum, which is very, very engineering focused, very hardware focused, that has all these toolboxes for different specialized analyses, but is locally deployed. Right. I'm running it on my personal laptop and if I want to share the results with somebody, I can't send them a link, I need to take a screenshot of the plot or send them 4,000 lines of MATLAB code to run. So we're lifting both of those workflows into core, into the analysis platform so that people can do this collaborative, really deep data analysis all in one place, right next to where their data is stored. And then the last piece, like Cameron mentioned, is the automated verification. And so whether that's validating that a particular component performed well on the production line, or running just a continuous evaluation of data coming off of a fleet of assets, you can create those rules in nominal using what you learned in the analysis platform and deploy it everywhere. And so that's the unification piece there. Whether it's production or simulation or customer operations, consolidating that single source of truth for what write looks like in your, in your data.

What about Connect?

Connect is extremely exciting and close to my heart because my work at SpaceX and my work at Sierra Space before that was very, very close to the hardware right at the edge, as people would say. And the things that make Connect really special. The first is this is a desktop native application. Web frameworks are wonderful. Core is a fantastic performance scalable product. But when you are out in a propulsion test facility and you need to be able to abort a test within 5 milliseconds if something is going awry and you need to go into a safe condition, a web framework is generally not the architecture that you want for that. Traditionally you're looking at something entirely custom or you're calling in a third party provider to implement a PLC based architecture. So there's this huge market for deploying an application at the edge. And Connect has been just a fantastic rollout over the last three months and a fantastic time to value and really what it is under the hood.

Right?

You have this very, very performant data backplane for pulling in data. But this can look a little bit different than Core's lens into data, right? Connect, you're thinking more about PLC pub sub networks, extremely high rate, potentially frequencies up into the megahertz. So pulling that data in, organizing it again, presenting it back to the user and doing that in a commoditized way. So we're building out all of these different drivers for different hardware systems, whether it's national instruments or you know, more hobby systems like labjacks for example, or PLC protocols to be able to plug in quickly. And then on top of that, we have this great sort of widget based architecture for running these tests.

Widget based architecture.

And that's everything from I need to run a checklist to I just need to look at some data on a plot to I want to actually upload data to Core to the mothership, as we would say, the mothership. And so between this driver layer that's connecting into the hardware and then all of these very easy to use GUI widgets, we can walk into a production line or into a test facility. And our Connect team, our instrumentation engineers, can get to Value in less than a day. Right. They're kind of looking at this smorgasbord here of different things to pull in from our Connect framework, implementing it and then giving it to the customer such that they can, if they want to continue supporting it themselves. We're not giving them this black box of like spaghetti labview code, which is the case for a lot of these solutions. We're giving them something that's more modern, more transparent, and so that they can help extend and build it if they wish as well.

I'm just super excited about Connect, so I'll hit on a couple things too, which is so Connects being like kind of run and managed by Jack Palmer, who we hired almost a year ago now. But Jack, most people know Jack as the former founder and CEO of Plotly, so one of the largest open source visualization libraries and kind of companies, extremely successful, is used by many, many customers in our industry. But Jack built that business, ran it for a decade, got back to his roots and was sort of, you know, building software in the hardware world for a little bit. And we kind of came across paths and decided to join, to join forces. We hired Jack and as Steven said, like we, we broke ground on, kind of got around the table as a, you know, executive team and sort of Jack had this really strong thesis that this product needed to exist. We'd heard a lot of poll from customers and slapped the table in December and you know, in January it was, you know, MVP and was already like selling on contracts. Right. And so like that, that was really exciting for us. It's now, you know, doing really well and we're, we're sort of doubling down on, on that team. We're here, We Nominal just raised a Series B. But I always talk to Jack like we kind of made a seed bet for Connect and like now I want to preempt the A. It's like Jack, oh, like go really, go really scale this because it's, it's working really well. We often get asked, you know, a question investors would ask or sometimes people just ask in general. They're like, you know, you've got this infinite tam, but like how generalizable can you make a product like Nominal to serve all these use cases? Like how could it be useful for someone that's doing flight testing and also someone that's just doing manufacturing and then someone that has building a nuclear reactor or like a, you know, fusion. All of those are nominal examples of nominal customers. Connect is kind of like our demonstrative answer to that, which is by actually building an application framework. Like we have very sophisticated, very technical users that are proficient in Python and can build a lot of this like last mile work themselves. And so I think it's been like super powerful to see our users and addition to sort of our instrumentation team start to build up these applications in our framework and just do that last mile to get Connect valuable for them, but then also to get data to the mothership, as we kind of said. And then the third thing is Connect is exciting because it's also kind of like a direct replacement for existing spend categories. So my, my business hat on National Instruments, LabVIEW, Siemens Massive Software, industrial, software conglomerates that have had, you know, billion dollar businesses here for, for decades. Generally there's like a very low NPS for those products. Like people don't like them, they're not very good. And you know, core is this like vision, it's sort of this light like on the, like you know, shining light on the hill that's like, here's a better way of doing organizational test data management and hardware data management for your organization. And Connect is sort of this like extremely practical. Like you are already using a similar tool like this right now. Nominal is cheaper, better, faster, like you already have the line item approved in your budget, like you should just swap out. And so it's really, it's helped us really go fast from a business perspective there too. And I think, you know, Steven made a point earlier about the sort of supply chain broadly. It's been really cool to work with customers that have one of the first questions or second questions they often ask us when they're using Connect is, you know, oh my God, like I have a supplier who I work with across the country and they're maybe a 200 person company and we rely on them. They make a widget or a component or a servo or a pump and like I need to interact with that data. Can you extend a nominal license to them? And so we've done that model where we've sort of given Nominal Connect to those companies, companies that would have probably, like, we just naturally would not have probably come across unless we were, you know, actively trying to seek those companies. So there's this huge like industrial, I say almost middle market that I think Connect is helping us sort of break into in a, in a really exciting way. So needless to say we're bullish on.

It, but actually talking about spending smarter and moving faster. Sorcery is sponsored by Brex and they're all about performance. So in this next question I want to ask about performance. So you're entering a legacy industry, one that's super complex. Each scenario, each case that you're working on is unique. How do you guys manage performance for your customers?

Yeah, Daisy?

Yeah, I think I'll hit it from a couple of different angles. The first is and maybe pushing back a little bit on the uniqueness, one of the things that's made Nominal possible now, or say three years ago, is that there is this crunch happening across all of these disparate industries in terms of the data types that they are using where 20 years ago, if we were going in and trying to integrate Nominal either Connect or Core. At n different companies, we'd be looking at n different potential data integrations because everything was so bespoke. But as everybody's migrating to more common data models, and you have also this Diaspora of SpaceX and Anduril engineers going out into the world and bringing these best practices to industries like nuclear, like maritime, we can go and plug into these previously fairly unique workflows in an equitable amount of time. And then everything downstream from that also looks quite similar. The data management problems are the same, the analysis problems are largely the same across industries. And we need to be a little bit focused. And if you read some of our previous media, we've been very focused on flight test, for example, and recently posted about test facilities as well. And so we are kind of marching through and thinking about for the analysis workflows for visualization. Let's make sure we really nail these one at a time as we move through the different industries. But from a performance standpoint, the additional thing I'll add is there's a lot of really fantastic open source or even licensed tooling that's available for us to leverage. And so again, 10 years ago, if we were building this platform, everything would be from scratch. There just was not the level of performance in the openly available systems to go and integrate. But we can do our own sort of buy versus build decision. As we're looking in and saying, hey, we need to set up this particular functionality in our cloud stack or in Connect, is there something out there that we can pull in and leverage and build upon ourselves? We're doing that and making sure that we're using these industry well known best in class tools to pull in. But then simultaneously we're building an immense amount of custom code and just the nominal proprietary magic sauce. And a lot of that has to do with hormones, horizontal and vertical scalability. And if we bring in a system that works very well in isolation, we might need to do additional engineering work to ensure that we can scale it to meet either surge demand from one customer or the introduction of 10 times or 100 times more nominal customers over time. But then also just continuously testing our own stack.

Right?

We don't want to wait for a customer to push an immense volume of data to us to discover that our ingestion or compute is insufficient. And so we're getting ahead of that, ideally months in advance, and doing synthetic testing of our stack, pushing representative data loads through it and just exercising it both discreetly as well as continuously as we're making code changes. So that we are not keeping up with our customers, but we are well ahead of where they are in their data scales. And generally I think we've been very successful.

You're not secretly building nuclear reactors in the back just to test it?

No comment.

No comment.

Yeah, we do have a really wonderful hardware lab. We posted I think about it at some point, but we've bought all of this great data acquisition hardware, these different sensors. And so we have our own we call HITL Hardware in the loop lab where we are continuously streaming data into our platform from systems that are representative of what our customers are doing, which A is great for testing, but B is really valuable for our own engineers to kind of get hands on with a lot of this hardware.

So I do want to go into some case studies because I think this will be so fun actually applying it to some of your customers. Maybe if we could lay out three different kinds of industries and how nominal is being applied to those specific problems or solutions.

Yeah, awesome. There's three great ones we can talk about and have some public case studies on too. But we can dig even deeper here. I think one is, you know, aerospace, traditional. We talk a lot about flight testing, but we a customer we love and have worked with for a while now, SHIELD AI so we can dig in there. I think another one Stephen kind of referenced and you talk about we're not building a nuclear reactor, but we're trying to help people. And so a company we work with is Antares, which we love and then a third one we talked about kind of the multi domain aspect. But, but Vadden Systems builds, Builds Submarines Small UVs, near and dear to me. Obviously that one's just a personal one that I love. But no, I'll start by talking about SHIELD for a bit and then Simon, you can cover and we'll kind of go through. But yeah, this was. SHIELD is really exciting. It's been over a year now for the partnership. We came across at a really exciting time where they had actually just brought in a new, I think then, you know, sort of VP of hardware engineering. But this gentleman by the name of Armor Harris, very well known, I think in the Space X kind of community. But he's coming over from Space X. I think he's now the senior vice president of aircraft. So all of the aircraft sort of development for SHIELD but he came over, I think he had experienced like the vertical integration benefits that that SpaceX had, had, had invested in. And so we hit him at a moment where he was coming in and I think rethinking and thinking about how S.H.I.E.L.D. was going to sort of move into this next moment for the V Bat, which is their vertical takeoff and landing drone. Really awesome product. And I think SHIELD had sort of won some big and exciting contracts for themselves. And so they're thinking about, you know, how are we going to go through all of the testing? Kind of Steven's like, delta cool. Like, how are you going to go through that exercise as you produce new variations, new variants of this V Bat drone and try and just do it as fast as possible. It's been, it's been really exciting. You know, they. Nominal is sort of become kind of de facto piece of software for experimental flight tests, developmental flight test, how they're going about iterating on the V Bat. And it's been really exciting to go from, you know, a mode where I think just given this sort of some of the software tools that were available to them, they were using a lot of Python scripts and matlab and then sort of bridging into this world where you have Nominal, whereas everything's really collaborative and you can just get kind of cross functional engineers that mechanisms or, you know, avionics or GNC or flight software or even more just like mechanical engineers, access to flight data. That was just very arduous for them to kind of get access to before. And what that does from an organizational perspective is like it just allows you to test faster. And so with them it's been cool to work through. And this is very much a, it's less the customer, it's like an actual kind of partnership. And they've been really awesome in pulling our product development forward, but the outcome we're able to deliver. Like why I think they're, you know, been excited about Nominal is testing like more tests a day. And when you think about that from a 6, 912 month, you know, program development life cycle, that's when you can take a step back and be like, what if, you know, using Nominal and investing in Nominal as an organization instead of taking nine months to get through the development and validation, it was eight months or it was seven months. Like what does that do to your top line and bottom line as a business like shield? So that's been really. Yeah, that's been a really exciting one that we, yeah, we, we love the, we love the SHIELD team. It's been great. So you want to talk about. Yeah. Antares and Madden.

Yeah, Antares was an early partner of Nominal, one of our, one of our earliest customers. And I think they, as we do, saw the light very Quickly that this was a tool that they wanted to put in place early. And we'll often hear folks arguing for, oh, we'll wait a year, we'll wait six months, rarely the right decisions. You want to get these platforms in place as soon as possible. And Antares took that bet and we rolled out the platform and have been kind of growing with them, which is very exciting. We had a fully functional and working product when we started with them. But as they're scaling their production of modular reactors, nuclear modular reactors, and we're scaling our product and introducing Connect, they've been this fantastic partner and really just a thought partner for building out features, getting feedback. And I think one of the major pieces is just the usability. And one of the hard things when you're hiring very quickly as a small company is as new folks are coming on board, you need to onboard them to all of your systems. And for most engineers those are analysis tools, data management tools, and one of the big values that we provide to all of our customers. But it's most apparent, I think at these earlier stages is they're trying to get people in and being able to do analysis and deliver value as disciplined engineers as quickly as possible. And if you're spending two weeks learning about the tool chain and learning about where to find the right data, that is a ton of lost time when you're a 10 person or 20 person team. And so as we've seen their group growing, bringing folks into the platform and seeing our focus on usability really just kind of coming to fruition or manifesting here, as they log in, they start doing work, maybe they have a question or two and they're just going right. There's no onerous training thing that we have to go through as they bring in new folks. So it's been cool to kind of scale alongside of them. And like I said, they've just been really, really great partners. And then on the maritime side with Vatten, similar smaller firm scaling up, one of the things that was really interesting there was again the speed that we were delivering value and doing that roughly six months after Antares. Antares was maybe a year ago and VATN started eight months ago or so and being able to integrate their data quickly, get those pipelines up in the matter of a few days and they lifted and shifted their analysis workflows into our platform very, very fast and just kind of did this cutover. And it was a moment for me looking at this and realizing like this is going to scale and we are breaking free of this world of a multi week or multi month implementation challenge that you see in a lot of other really heavyweight platforms. The other piece that was interesting with Vatton, well a, their product is awesome and I got to go out to their facility and meet the team and go out on the boat, which was very cool. But what we're seeing, you know, maritime, nuclear, aerospace, is that we're not getting bombarded with a million different maritime specific feature requests.

Right?

We're seeing a lot of commonality across these industries. Folks want the same answers, they want to have the same processes. And so in a very good way, right. They are using the product that we have built for everyone. And you know, they'll for example, VAD and came in and said, hey, we need to have bathymetric maps. When we're looking at our data, looking at the location of our assets, it doesn't, it's not very helpful to look at a satellite map of the ocean. We want to actually see the bathymetry and what are the contours of the earth underneath the water. And so that we went and built and rolled out and it's benefited our other maritime customers as well. So there's these little unique things that we need to do. But in general, the universality of the telemetry management analysis world has just been very exciting for us to, to build upon and, and yeah.

How much time and cost do you think you save your customers?

Yeah, it's a good, it's a good question. We, you know, as the company's gotten more mature over the last three years, like I kind of think about, I'd say like there's like three value, you know, conversations that we get to have. One is often the easiest, which is, you know, a quote I'll often hear is like, oh yeah, we, we hired a really good software engineer to build, you know, our internal version of nominal. And then like, whoa, I didn't realize like two years passed and there's now like 15 people on that team. And so we hear that time and time again as companies, you know, scale and it just like there's a lot of overhead that goes into maintaining a productionized quality version of, of something like nominal. I say like nominal because like they won't, won't be as good, but, but it's costly nonetheless. And so I think like we can often have a conversation there which is just like how valuable is it for you to not have to staff up to four or five, six people?

It's like minimum, a million in overhead.

Yeah, exactly. Fully burdened you know, engineering employee, like. And so that is the value conversation we have for sure. I think like, the next layer up is probably that sort of like incremental time savings. And so if you're, you take a flight testing customer and you can say, like, in a given day, you can test six times when prior you were testing three times, like three times a day. And often it's like these, this is human. Sometimes it's literally like a bus full of humans that are busing like three hours away to a test site or a test range, you know, performing test operations. Like, think about the salaries and all the ops overhead for like, those humans. And when you, like the moment when they come back and realize, like, oh, that one test, like, didn't actually get the data we needed to or like those types of incurrences. So there's like all of that just efficiency spend of what we're delivering for folks often, you know, hearing things like things that used to take 24 hours, taking like under 10 minutes. Right. And so like when you stack up those processes in an individual engineering layer, like you said me, 24 hours to just get to look at this piece of data, and now it's 10 minutes. And then I'll go back to the stuff that's exciting for me is then when you actually get to go to a executive or a vice president and be like, if you had confidence that you maybe could beat your competition because you're moving faster on the month time horizon, or you could increase even incrementally by a small number of percentage points the probability that your system is going to work when it needs to on a tight timeline. Those are, those are, I think, value discussions that are measured in the like, percent of contract. And so if we go to a program and they say, hey, we just want a 10 or a 50 or a $300 million program, and nominal is able to sort of make the case that if you invest alongside us as a partner, you're, you're like, you're having like a very exciting, I'd say, value, value conversation at that point.

So, yeah, so you guys are creating immense efficiencies for your customers. We're in a rapidly changing AI world and environment, and alongside that, you're growing a company. So how do you manage innovation internally with these huge macro factors?

Yeah, I mean, I'll give a. I'll kick it to you, Steven, for some of the specifics, but I think a general philosophy that we have at Nominal is like always thinking short, mid and long term and a Lot of companies say that, but one of our core company values is cultivated vantage. We talk about it all the time. And so I think we know an, at nominal, this isn't a quick adventure for anyone. We're building a generational company in this space and it takes time. And so I think we think and invest in long termism whenever we have the opportunity to. So I think what that means is like we look at this space of software for hardware broadly, I think of it as one where, you know, AI has not yet taken a foot and I honestly think it's going to be a little bit of time before it really does. There's a lot of, on the government side specifically, I think there's been a lot of like false goods that have been sold, predictive maintenance, prognostic, you know, type things that when you actually get down to it, the data, the telemetry and the sensor data and the logs, it's so unstructured and so messy and so disorganized that it's not really possible to actually do proper machine learning or just train inference in general on that data. So we see nominal as actually a necessary stepping stone there. And so I think that's a big area. But I do get excited as I look at more agentic workflows. I think that's an area where the workflow of what an analyst or a test engineer goes through. Steven has gone through this thousands of times in his career looking at test data and going through that process. I think there's a lot of power you actually can prompt humans with to help make their life easier. Once you sort of build this like, this base of like what it, what it's like to go through that workflow as a test engineer. So I definitely, we're far from being anti AI and we're actually, I think we're working on some exciting stuff there, but we think that it's definitely a crawl walk, run kind of process. You want to add anything? I don't know.

Yeah, I think you nailed it. The, the introduction of AI into a lot of these robotic systems is itself a market for us to be able to go and validate what a lot of people see as black boxes. And if you were to go on top of that and say the nominal validation software is also leveraging AI to go and evaluate the AI enabled robotic systems, you start to probably raise some eyebrows here. Like at some point you need the.

Human, it's become self aware.

The human tranche has to be doing.

The due diligence testing itself.

Yeah, yeah.

But there's good opportunities for us to bring that into our own platform. Things like natural language queries, things like anomaly identification. And the latter to me is more like the clippy paperclip in Microsoft Word. It's not doing it for you necessarily in the analysis workflow, but it's saying, hey, something looks odd here, you should go check it out. Here's why I think it looks odd. And then there's still a human in the loop to do that actual due diligence.

I'm curious because you have such a wide set of customers, what exactly is your go to market strategy across them and are you, your dual use or.

Yeah, we're definitely dual use on that point in the dual use nature, I think we've been really excited. The exact same product serves commercial and federal customers. So there's zero differentiation with either core or Connect there. It's been a big north star of how do you not drive yourself crazy from a resourcing perspective? The same product works there. The go to markets are totally different obviously. And we I think have been fortunate to invest in just like a killer federal BD team. Folks that are very experienced. Like that's an area that I'm super passionate about and, and have a good, good depth of experience. And then on the commercial side, yeah, I think just getting folks that span the, you know, span the gamut of like very technical sales. And so we have folks that have sold, you know, databasing technologies and like they, they love the technical sell. And honestly a lot of it we see is like be read about the Challenger sell. Like a lot of it is sort of, hey, why like, hey, you know, company that's doing this, like why can't, why can't you do this better? Why can't you do this faster? But done not in an antagonistic way, but done in a collaborative way. Like there is a better way, like let's show you and let's work together to get there. That opens a lot of the doors and it's totally different. Again, if you're serving a legacy prime where, you know, it's, it's. How can you navigate 15 phone calls before you like get to a decision maker that has like some, you know, some input or some, you know, some authority. So you, you add anything to that, I think it's great. Yeah, I think like, I'd say it's hard. Like we're setting up multiple go to markets and kind of routines at once. But you know, I think there's nothing, it's very simple. It all kind of boils down to I think like pincer movement. That sounds very aggressive, but I think it's just like building the technical alignment in base case with end users is like a thing you always have to do and then being able to have this sort of like executive level alignment over what value is actually being delivered. You know, if something like nominal is adopted, that is true across all of the instances.

So yeah, I'd love to go deeper into the nominal team. You guys have attracted tremendous talent from really big names. So how many employees do you have and where are they coming from?

Yeah, right now we're, we're about 60, a little over 60. Growing pretty rapidly. We are hiring. Check out the shops on the website and they'll probably be posted in this thread as well. We're hiring for pretty much, pretty much everything. Hiring for everything, for everything. A role that is like very. We love and we cannot get enough of is mission operations. And so that's for us that means it's kind of like one third account manager for our, you know, for our customers. 1/3 like product management and I think one third like technical sales. Um, and so it's very exciting. We get, we have folks who came from robotics backgrounds, folks who did flight testing, you know, folks who were systems engineers at NASA and also just like love engaging and talking with customers. And on the pitch I can say like in a single day you might be able to work with a company like SHIELD AI and Antares and Madden, which is pretty sweet. You could be out on a boat like cruising around with UVs and looking at a new generation small, you know, small modular reactor and watching autonomous V BAT fly around like it's pretty, it's a pretty sick job. So we're hiring, we're probably grow to, I'd say probably over 100 this year, this calendar year to just meet, you know, meet customer demand and yeah, I think like the secret, the secret sauce or a lot of what we think about is so much of nominal or really world class software engineers from diverse backgrounds. And then there's this perfect ratio of the Steven archetype. So someone who spent a lot of time operating hardware because the beauty comes and we talked about, we have this little hardware lab. The beauty comes when you can expose these amazing software engineers to the brutal world of where software meets hardware. And just people who've done it, who've been at disconnected environments at test ranges all over the world. And so that's been like a big part of the nominal culture is like getting all those folks under, under, under one roof. But yeah, it's team is a big, a big thing and I think it's, it's definitely one of the things like I personally am most, most proud of it. What we do have assembled a nominal and it's just, it's just getting started.

So what about the existing team? Any shout outs?

Feel like I've given, you know, Stephen shout outs, but yeah, Stephen, you want to give some.

I think, you know, I reflect back on some of the initial folks that I met who are all still at nominal and you know, in very senior positions now leading the team. These really fantastic engineers from Palantir, Helen, Mark Ross, Andrew, just this great initial cohort of absolute hitters. When you talk about talent, I mean there's like the real, recognizes, real moment when people come in and they, they meet these folks and them having spent between five years and a decade dealing with really complex data problems has just been absolutely immensely valuable for us. And then we're supplementing those backgrounds with folks from a dozen other different industries, two dozen different companies and you get this really great mishmash of different lenses into these problems. And so I think about our folks from Applied Intuition, Nick and Drake coming from the autonomy side from a very customer focused lens, excellent engineers. Michael from Relativity Space standing up his own data architectures there.

So.

It continues to floor me that every new person we bring in is just up leveling us in a new way, bringing new talents and it's just an incredibly smart and a team that just works together very well. It's very easy to interact between sales and product and design and C Suite. There's a lot of transparency across the org. I think people even see that in the interview process when they come in.

So I'll give a couple more because I love you in shout outs. I'll give a shout out to Joe, Joe Holliday, who's our head of mission. So he leads the mission operations team. Joe's phenomenal. Um, Shout out Joe. Shout out Joe. Yeah, Joe, you know, is it was a Palantir for a little over seven years and sort of led a lot of the commercial pilot efforts there. And so if there's like a place, there's a dojo that you would have wanted to train in in terms of like big, high stakes, complex deployments of software. Joe has done that at scale and so is able to. It's just a symphony watching him like work with our customers and get them value and nominal like rapidly. And then. Jen, shout out Jen. We have a very often, sometimes When I contextualize the federal side of the business and like the federal footprint, people are like, wait, where's the other 10 people? Just given the results, I think we've been able to deliver with a very, very small team. Jen is like almost a one woman show there and she's been amazing in, in leading a lot of the, the DOD efforts. Um, so no, we, we have an amazing, amazing team and we're looking to, to really double down as we grow.

Love the shout outs. Very necessary.

If you, I, I could just shout out the whole company. It would be, but people would be like, what?

We'll, we'll keep it. Yeah, we'll keep it at that.

We'll keep it brief.

Um, but all of these team members have helped you build an incredible product scale and then also raise over $100 million in total from some of the biggest names in Silicon Valley. Yeah, Sequoia, Lightspeed, General Catalyst, Founders Fund, the list goes on. What have you learned from your fundraisers to date and how do you think about milestones going forward?

Yeah, I think now it's a really good, it's a really good question. Obviously this, we, we started at the beginning like this fundraise happened quickly, you know, 10 days kind of from start to finish. I think one thing that like a new learning going through that process was we, we have a, a pretty killer, you know, team and we, we were putting together these like very comprehensive response memos. You get a lot of RFIs, you get a lot of like questions. You can tell when an investor is like really serious and digging in and we almost, it almost became like a game. We're very serious about it. But like we would put together 10 page responses overnight. Just. That's amazing. Yeah. And it's like, it's, I mean I think it, I think if you're an investor and I've been an investor on the luck side, like it shows that that person's serious. And I think being able to have this like trove of information that you can sort of expose to people, I think was, was one we were. You have to be always careful. Like customers, our customers are busy and so you want to protect, ruthlessly protect their time. But I think the willingness we showed also to get our customers on the phone, I would always just be like, you should just talk to one of our customers. It will make sense to you if you do that. I think that willingness sometimes when people are going through this process, they kind of don't want people to talk to their customers. We should always be Scratching your head and being like, I wonder why. We were just like, please let me get you on the phone with our customers because it will make sense after that, I think is a big one. And then yeah, maybe we're just more general thoughts here is you. You want to give. They want to give investors data points that they can make sense of. Like they're going to be investing. You know, this is a Sequoia led this round. It's the growth team. They're investing in growth. Right. And so you need to give them some data points and they will infer is the slope here, is the slope here. And so just making sure that you're having those conversations before it comes prime time for a financing. So they know like, oh wow, this team says they're going to do something and they will. I love the base episode. So I'm going to make a reference to violently execute. I think that was it. Yeah. Can this seem violently execute and just like go do the things they're going to say they could do? So, yeah, I think those would be the three. Three takeaways.

Violent execution.

Violent execution.

Setting the gold standard.

Yeah, that's great.

So, just a couple questions left. First, I want to know where do you see nominal in the next five years? What's your future outlook?

Yeah, I mean, right. Right now, I think with this, this new, you know, pot of capital, we're focused on, you know, a handful of things immediately, I think kind of 4 of those to talk through. But one of those is like really expanding more and more into these larger enterprises. And to do that at scale requires a large team. It requires a lot of focus. And so we have a lot of really good traction with some more of these legacy prime providers and we're going to move really fast to scale there. I love this headline, you know, and I worked at Applied Intuition, they had this incredible slogan that I think it was just 18 of the top 20 automotive OEMs. In their case, trusted Applied Intuition used Applied Intuition like crazy stat. They literally had 18 of the top 20. And so I want. Where we want phenomenal is I want that to be the case in Certainly the big five primes, but the same 18 of the top 20. I want 20 of the top 20. But you get the point to be loving nominal and to be using it and to be sort of ubiquitous with what it is to. To test in a modern manner and field hardware in a modern manner. So expanding to the enterprise, I think that we're really excited about is talk a lot about the US Lens Here. But this is a global opportunity. Massively we have international customers today. We have successfully sort of done that motion. And so we're excited to double down and expand internationally. That looks like Nominal offices overseas and just saying yes to more and more customers, which is really exciting. A big thing. We talked about core and connect. But to win really big here, Nominal is a multi product, multi product business and multi product strategy. We're keeping tight on a couple of those, but you'll be hearing more, more about them as the year unfolds. But delivering 5, 6, 7, 8 products that all work together in this sort of hardware, testing and operations domain I think has been really exciting. And then I give a shout out to Jen on our team, the one woman DOD show. We're going to be expanding and doubling down on the federal side. We've been really lean there and frankly the ROI has been really, really high. But we're bullish on a lot of those trends. And so we're going to be staffing up, opening a formal D.C. office later this year and really just building out, you know, that team. That is certainly what we're thinking about in the next 12, 15, 18 months. And then I think where, yeah, where nominal goes on a, you know, the five year time horizon I think is just becoming, I always say, like the North Star for us is when a hardware engineer, generally speaking, someone says, like, what is, what is it that you do? Like, what is your job? Their friend might visit them and be like, what do you do all day? They would open up nominal and that's like the thing, that's the vessel that they would use to explain like, oh, this is like what it is to be a hardware engineer. Like, I have to use all these data sources from my machine. I have to visualize them. And tons of it's automated. But there's some things I still am an expert in. So I need to like double check or explore this data and then I like build these, these rules and validation logic that governs my hardware system when I deploy it. Like, that's, that's the North Star for us. And so we will just be building towards that vision.

Well, to close out, one final question, I want to bring you back to your roots and this, you both can answer this, but this is a tr. This is a very hard question. All right, I'm setting the stage. Okay, you're in the Mediterranean. There's a severe storm. You're trapped on a very large yacht. Okay, maybe the, a rogue wave comes by. What do you do? Do you take A helicopter or do you take a submarine?

When you said the Mediterranean peace side. Now I have this like is it Wolf of Wall Street? Is that when. Yeah, I'm thinking of like Wolf of Wall Street. I feel like you like got into would I take a helicopter or a submarine? I've been, I'd say I've been on a submarine a lot. So my inclination would be to take a helicopter. But you said it's stormy and so submarine fun fact included. When you're on a submarine very deep underwater, you generally have no idea that it is stormy and so it's very calm. With the exception. This is a double fun fact because I've done this and submarine people have as well. But if you are in a submarine underneath like a hurricane, you can actually feel the swells down to like hundreds of feet deep.

Have you experienced that?

Yeah, yeah, yeah. Normally when there's like a hurricane. I was stationed in Norfolk, so I was on the east coast. Like hurricanes come and they like sound the alarm bell. And like the safest place for submarine to be is not in the pier, it's out in the ocean. And so you like scramble and get ready and you go out and you go deep. But just showing how powerful hurricanes are, like you're still jostling around at like 500, 500ft underneath, whatever. So it's pretty well.

But that's crazy.

That's a long worded answer. But I think I would, I think I would take a submarine. But I wonder what Steven has to say about this.

I think I'm switching my answer because originally I was team submarine. But I'm considering that first I have to get onto this rescue vessel and the odds, I've never been described as sure footed like the odds that I will make it onto the surface of the submarine and not just like slip off and disappear into the deep.

That was the end of Steven.

Yeah, yeah, I'll be gone. Like I'll be gone in an instant. And so I'm going to take the easier, you know, hop into the basket of the helicopter.

Yep.

And then I'll probably go down 30 minutes later in a blaze of glory.

Very morbid answers. We have to.

I hope not.

Yeah.

Because you're getting rescued.

Yeah.

Wait. Okay, so going back to your job as a submarine officer, could you just explain that a little bit further?

Sure.

We skipped a lot of details.

Yeah, we skipped a lot of details. Yeah. I first part of my career as a submarine officer. So what does that mean? Was there really did two major jobs and almost all Submarine officers start as an engineering officer. So the day to day is operating the nuclear reactor provides propulsion for the submarine. When I say operating as an officer, you're not actually touching the things as much as you are comprehensively sort of managing the state of the reactor plant and giving orders and receiving information. So telling others how to. How to operate. That's what I did in the sort of engineering side for a while. And then the other job I had is I was our kind of assistant weapons officer. So after you've done the engineering for a bit, you go forward is what it's called. But it's literally like forward half of the submarine. The back half's engineering, the forward half is operations and weapons and tactics. And then I was a weapons officer. So torpedoes, mines, Tomahawk missiles, etc. But just having visibility over all of that. If we needed to use them, thankfully we submarine did not, did not ever use them. But that's a lot of the job. Submarines do a lot of what's called isr, intelligence, surveillance, reconnaissance. So the mission set is mostly being places where others don't know you're there because you're very quiet and very sneaky and collecting useful pieces of information that others don't think that you're quitting that the, in this case, the, the US can use to. To get an advantage.

So many questions. How long have you been under the water for?

I always, I say this and then people are like, what? I've been underwater 484 days, not consecutively, which most people don't see. You. You did it too.

Um, it's still impressive.

No, no, it is. Um, yeah, thank goodness. Not. Not consecutive. That's my total, like underway time. Um, but longest consecutively was 84 days, which is still a lot of time. Yeah. And most people don't really realize, like you, you generally don't communicate when you're under. So, you know, you learn things. Like if you don't get an Internet sync with Spotify after 30 days, it just. All of the songs that you thought you downloaded are not actually downloaded. They go away. Like you learn these little tips and tricks that. So it becomes very sad after a while when you have no Internet connectivity and no, you know, comms with the outside world. But.

Got to bring a tape.

Yeah, got to go.

Did you see any monsters? Did you see any large squids?

I did not see any large squids. But you actually do hear a lot of things. So you hear whales, you hear dolphins, you hear shrimp. Like, you submarines, all. It's all sonar based. And so you do hear a lot of sea creatures. And normally it's like fun because you're like, oh, it's like it's a whale or it's whatever, but it can get. They're very loud and you're, you might be trying to listen to like another submarine or a ship or something that you need to. And you're kind of like UFOs. Yeah. You like Pacific Rim. I'm thinking of Pacific Rim. And you may be trying to listen to stuff that's like productive. And then you're like, these animals are like so loud because you can't hear. What? You mean quiet down? Yeah. Yeah, exactly. Yeah.

Okay. Well, I've, I've taken up a lot of your time and I very much appreciate it. Congratulations on your 75 million dollar series B. Massive. And thank you so much.

Thank you, Molly. It was awesome to be here. We loved it. Thanks Molly.

This is great.

You're welcome.

Thank you.

Hey, it's Molly. If you enjoy our interviews, check out our newsletter Sorcery vc where we deliver a once a week top deals and tech headlines. Email and also go deeper on our podcast interviews. Subscribe to Sorcery Today and don't forget to subscribe to the podcast on YouTube, Spotify, Apple or wherever you listen. Link in description to sign up.