Venture with Grace
00:06:57 · Grace Gong, Austin Spiegel
[00:06:57] S0: I love that. I feel like that definitely sets it apart because of what you're constantly taking actions that it really puts out the signal in the universe. I know it sounds like we're right now, but okay. So I wanna talk about, like... So you got, like, 50 something SpaceX people to invest in your company when you guys first started. Right? [00:07:16] S1: Yeah. Well, so we actually have a... One of our first investor, a guy named, Scott Norman at Earthrise Ventures. He's a former SpaceX engineer. He left in 2012, went to go work in Silicon Valley. He worked for, one of Peter Thiel's Venture funds. He struck out on his own, I think, around 2020 or 2021, and he founded a venture fund with another ex SpaceX here. And I actually met him at a happy hour, for another software for hardware business. I pitched him this idea. He was really excited. He was immediately ready to write a check. Ultimately, I told him, like, why don't we go do some research and really prove that there's an opportunity here? But most of his LPs are actually x SpaceX folks. So, kind of by proxy, we've got a lot of ex SpaceX folks on the cap table there. And then of course, he's invested in subsequent rounds and then we've had some angels as well.
00:08:14 · Grace Gong
[00:08:14] S0: I guess, when you were thinking about pivot into a company, so obviously you guys have built the software or the cloud product within SpaceX before. What was the original idea look like? Did it do a huge pivot later on? Do you feel like you guys are still building the original vision? And how do you really test out the market? Because I feel in anything space, it sounds like it's going to be an industry that's like have a really long sales cycle. And then there's only five company that does nowadays, there's like a trillion people that does something in space. But I feel like it is a market that's more of like a... It's not like the SaaS market, which is everyone's buying every tool all the time, but nowadays it's like AI. What does a market opportunity look like and how do you validate the idea at the very beginning?
00:09:10 · Austin Spiegel
[00:09:10] S1: Yeah, for sure. I mean, looks a lot different now than I think it did four years ago. So the idea that we had four years ago was based on the tools that we had seen at SpaceX. And I would say it was very much around the verification and validation process of software built for hardware and then hardware itself, and then hardware operations as well. That vision is, that's what we built. That's what we've, I'd say, continue to build. Although at this point, we've basically built everything that we had at SpaceX and beyond. So now we have to continue to figure out what is the next thing going to be and where are these companies going. I think when we started the business four years ago, we were kind of pretty early, right, to this kind of re industrialization narrative. We were early to, you know, people even realizing that there's a lot that's going to happen in aerospace. I think this this, SpaceX IPO this year has really kind of, like, opened people's eyes to what's happening in space. But certainly now, we're really starting to feel that there's a pull here and there's an opportunity here. A lot of these businesses that were founded, hardware businesses that were founded, say, four years ago are finally really getting their legs, right? I think you've seen a lot of big contracts signed and now they're actually transitioning into a production phase where they have to deliver on those contracts. And I think that's actually where Sift can really help benefit our customers. So, I think there's a multi part answer here, it's to say we built the vision that we had initially, we continue to improve upon it. There's some really exciting things that we're thinking about, especially now that generative AI has become such a big deal. And then the market is just continuing to evolve to a point where I think our existence is even more justified than it was four years ago.
00:11:03 · Grace Gong
[00:11:03] S0: I wonder, maybe we could talk about the observability in this space department. What is the core difference between you guys and the current platform? Not current platform, but the legacy platform everybody is using. I also, I mean, I was like listening to our whole notebook out and like you guys also hired people from that company. And I guess like, is there, what would you say is like the core problem that the original solution couldn't really solve? And how do you think about like, know, ensembling the talent as well since, you know, I've heard you guys hired from like Palantir, Anduril and like a lot of these really robust, I don't think Palantir, but I'm pretty sure it was one of the executives from Anduril. And maybe you want to talk about how do you ensemble a team as well after Series B?
00:11:59 · Austin Spiegel
[00:11:59] S1: Yeah, yeah. Okay, good question. Let me start with the first question, which is like, what's the difference between what we built? So, I think there's kind of a couple of things to point out here. One is, if you look at observability software, which is software for understanding what's happening within a system, If you look at observability software prior to Sift existing, it's primarily software that's built for software engineers, right? So you think about Datadog's a good example, Honeycomb, Splunk, kind of all these solutions where you capture telemetry from software. So, everything from CPU usage, network IO, memory usage, and then of course you collect logs from software. These solutions are really built with that user in mind. So, any of the end applications are really about application uptime, traces through distributed systems. Then on top of that too, you care more about aggregated statistics in software. So, you think about what's the P99 response latency and you don't necessarily need to look at each individual response latency, right? That is a very... It's very different in hardware. So, hardware, one thing is the fidelity of the data really matters. So, if you think about testing a rocket engine, for example, a rocket engine has thousands of sensors on it, those sensors are emitting data tens of thousands of times per second, your test only lasts maybe sixty seconds. You need to capture every data point from that sixty seconds because something might happen in a nanosecond that you need to analyze. So an anomaly might occur, you need to understand why the software switched the rocket off, maybe there was an overpressure event, stuff like that. So the data platform needs to be different to be able to handle that frequency and volume of data. And then I think the second part too is just like the end applications, right? The way that engineers want or sorry, hardware engineers want to interact with the data is kind of fundamentally different. So, the types of analysis they want to run, the types of plots they want to create, these things differ from what a software engineer is doing. So, that's kind of why you couldn't use the old solutions. And then if you look at what existed on the market, it was basically a patchwork of tools.
00:14:18 · Austin Spiegel
So, if you think about how a software engineer is maybe looking at data from a simulation, maybe they're creating some files from that simulation like HDF5 files or CSV, then they have the Jupyter Notebook that they're using to kind of parse that telemetry and run analysis on it. If you think about like a hardware engineer, maybe they have a test bench that's using LabVIEW and they're capturing TDMS files from that. And then you think about production automation, they have a PLC that's creating data. So, you've got all of these different data sources and all the data is not stored in the same spot. So, it makes it really difficult to actually go and then when you want to look at a problem that maybe spans multiple stages of the hardware development lifecycle, you can't do that because the data is all disparate. So, that's kind of what existed and what the challenge was. And basically, point of Sift was like, how do we unify all of this data, put it in a single source of truth so that when something happens to a satellite on orbit, you can trace that problem back to the satellite during test. And then how do you basically provide hardware engineers and the variety of personas, it's not only hardware engineers, but it's technicians on a shop floor, it's embedded software engineers. How do you provide them with the tools that they need to diagnose problems and bring vehicles to market faster? So that answered your first question. Happy to go on to second question, does that make sense?