00:02:27 · Sebastian Johnson
[00:02:27] S0: So you've built this almost proprietary dataset. Can you talk about how big that is? Like, how many, I don't know, conversations you've had?
00:02:33 · Siadhal Magos
[00:02:33] S1: Yeah. It's it's it's... There's like a bunch of ways to... Conversations is one of the sort of sets of data, like the proprietary dataset we have for sure. Although, again, would I would make it really clear. The conversational data is our customer's data. We are... What we do is we have the... We process it. We have the the the the the the tools and the harness and the agents that work on top of it in really effective ways, but it is our customer's data at the end of the day. So it's more like it's more like they're making better use of their proprietary data, and they do that for Metaview. We get about five, 6,000,000 conversations, all hiring conversations per year through the platform. We have obviously millions and millions of interactions with our agents either to analyze those conversations or to source candidates based on some of the data from those conversations or to review applicants. So it's really hard to sort of put in... Put a number on, like, how many agent interactions you have. But, essentially, it's this very unique... Put it this way, intelligence is becoming abundant. Right? It's very clear that models are gonna get smarter and smarter over time. They're also getting faster and faster over time. In fact, the iteration cycle on the models, like, I sometimes struggle to keep up with Yeah. You know, the the the time between, like, Soul and Astra was very short. Right? So, obviously, the intelligence is getting better and better all the time, enough beyond us. What's getting... What that does is the thing that's scarce becomes more valuable, and the thing that's scarce for an organization that's trying to win is their own proprietary data. Right? No one else has that, and that's... So it's naturally, by definition, scarce. And so how you make use of that data becomes the the game. And you start to think about as an organization, and we obviously think about this on our customers' behalf, how can we produce more scarce proprietary data? And you wanna do that by taking ownership of how... You know, if I have a 100 or a thousand or 10,000 people in my organization, I wanna understand the interactions they're having with the AI agents in our system, and I wanna use the the learnings from all of those interactions to make everyone in the company better at work. In our case, it's the work of hiring. But...
00:04:27 · Siadhal Magos, Sebastian Johnson
So, really, there's data from, like, every corner of the hiring process. It's not just the conversations. [00:04:33] S0: Yeah. That's interesting. And and so now you've built this much broader platform, I guess, and now that you've you've raised this round, you've done a bit of international expansion. What... What's next? You know? Is it is it more product development? Is it more expansion? What are you gonna use this new kind of chest of cash to do?
00:04:48 · Siadhal Magos
[00:04:48] S1: It's gotta be both. I I... And I... We've always been... Much as we're... You know, we started in Europe. I'm based in London. We've always been... We've been global from the outset. I would say many of our even early customers were based in The US, so it's been international expansion from a boots on ground perspective, but we've always had about 50, 60% of our revenue from The US, and that's... Gets... Continues to be the case. We opened up a San Francisco office last year. We're opening up a New York office now. Basically, we opened up a a couple of weeks ago. So there is, like, definitely boots on ground and meet customers where they are with this, you know, transformational technology. But, yeah, the the the the the real key is I sort of feel in many ways to build a world class AI first company. A lot of it is actually about the always on figuring it out. Because as I mentioned, the models are actually changing all the time, and even user expectations around how they are willing to or want to interact with AI is changing all the time. And you sort of need to be in the game and have your fingers on the dials as as maximally as possible in order to actually figure out what's next. And so those are the two focuses for us. We're scaling what clearly... You know, we have a we have a product or a platform whose time is now, and at the same time, there are new capabilities that are emerging that we need to make sure we figure out before anyone else. And the way we solve for that is by, obviously, building a world class team in order to to figure those things out, and that's the the... That's essentially what we're doing with the cash. I mean, there's also a bunch that we can do to spread the word about Metaview, which is really good fun as well. We're... I think our marketing team is absolutely nailing it at the moment and getting the word out in really sort of evocative and fun ways. So that's a that's a that's a bit of fun use of the cash as well.
00:06:36 · Sebastian Johnson
[00:06:36] S0: Yeah. Definitely. This week, you you... The public launch of fillmore, you're sort of, like, opening up to to to everybody. That was everywhere. Right? And it was like very distinct. I probably saw like a dozen posts about it on LinkedIn and they were all... You could tell that they were all talking about the same thing just by glancing for half a second, which I think is like an amazing strength of brand. Yeah. But it was like... It was almost like a new activation, but it very instantly became recognizable and dominated. Tell me about that launch. Right? So so what have you launched, and and what's the impact been?
00:07:06 · Siadhal Magos
[00:07:06] S1: Yeah. So I'll I'll share I'll share some of the sort of technical aspects of what we launched launch first, and then we can talk about, like, the... Like, what does this mean for the market? So you can think about Metaview's core product as an AI native workspace. So you go you go to the workspace to do the work of recruiting alongside an AI, and it's still a big part of our future. We do believe people will continue to want to go somewhere to do, like, lean in work, and they want to be augmented by AI while they're doing it, and that's what Metaview's sort of... The core Metaview product is. The thing we launched earlier this week, fillmore, is almost a different form factor. Same thesis, but different form factor, and the form factor is more... It's like it's a coworker. So you don't actually go to it to do work. It either comes to you where you are, which is usually in Slack, or you sort of, you know, give it... Throw it a task while you're in Slack, it goes away and does the work itself. And who this is really great for, and the work, just to be clear, is researching where to find candidates, finding those candidates, engaging with them, I. E. Communicating with them to say, are you interested in this role and sort of following up and scheduling and all those things and booking new meetings. So you engage with fillmore because you wanna fill more roles and you want an AI agent to do it for you. And so who this works really well, the the sort of reaction this is is much as the workspace makes a ton of sense for this lean in work, the fact is AI has moved on to the point where you can trust it to do, like, long time horizon, multistep processes by itself. And, of course, if it's a multistep process and it's long time horizon, it's less about having a workspace. Right? You're not gonna... You don't wanna sit there and watch it the whole time. It... You you expect it to happen in the background. Just like when I delegate a task to a member of my team, I don't sort of watch them the whole time and, you know, tweak here and there what they're doing. I sort of let them get on with it, and you get, like, high... Much higher leverage results as a result of that because it can do far more work, than you could ever keep up with. And so that's what fill... Fillmore is.