00:12:38 · Siadhal Magos
[00:12:38] S1: It's always been... Because we've always... We... We're, like, very committed, and I think it's worked out really well for us. We're very committed to being on the bleeding edge, and you cannot say that and not incur some risk. Now within our... Literally how we operate, we then sort of segment our customers, usually in conversation with them about how they're, like, thinking about their AI adoption and that sort of thing. And some customers don't wanna be right on the bleeding edge, and that's totally fine because we have some really mature products that clearly are an appropriate and best practice almost way to use AI within recruiting. And then you have other customers who are seeking every advantage they can and are willing to take some of that risk with us. Now that doesn't mean we totally push risk to the edge and make it their problem, but, you know, there is tolerance in the market to to take risk with some of these things for the the upside. In this particular case, where essentially you have an agent, you know, communicating at scale on your behalf, There's a bunch of essentially calibration workflows the agent will go through. So, you know, this is designed by us. We we sort of... We... You can be so AI pilled as to say, like, hey. Like, we should all... We should, you know, we should let the agent decide if it should even bother calibrating. We we don't do that. We think people like to feel in control. People should feel in control of of the agent, but you need to give it enough breathing room to to work at scale. If you if you have human in the loop at every single stage gate, obviously, you're never gonna, like, reduce time to fill by 10 x because it's just... The human's gonna get in the way. And so we have this calibration process where essentially you go through a a flow where you get comfortable with two things, two... The two most important things with the agent. One, does it understand what good looks like for you, for a for a for a candidate profile? So there's, like, a very specific tailored flow we take you through to get that level of calibration that you feel confident this this agent is getting closer and closer almost to being a second brain. And then the second thing is, does it communicate like you? And, again, we have... There's a bunch of context you can... We sort of take...
00:14:36 · Siadhal Magos, Sebastian Johnson
We take you through a flow to share the right context with the agent such that it essentially knows how to communicate in a way that is representative how you like to communicate. And, really, that's what whenever you're doing a hiring, like, you're a founder, if you're a hiring manager, if you're a recruiter, if you're out there trying to get the right button seats, you're thinking about who are the right people and how do I wanna speak to them. And if I could times that by ten and have all of my people do it for me, then I would, and that's sort of what this is an answer answer to. [00:15:06] S0: And what have been some of the early results? Because you've been running for more, I guess, like, in a sort of beta mode or with some some existing customers for a little bit before you've the public launch. What's been the reaction for people who have been using it?
00:15:18 · Siadhal Magos
[00:15:18] S1: It's been phenomenal, frankly. And, like, the real proof becomes when you start to see... A lot of times recruiting functions, will look at their recruiting data, you know, periodically, let's say, once a quarter, and they look at... Oh, and also, like, where what are our source... What are the... What's our source of hire? Oh, it turns out we did a lot of sourcing or we did a lot of inbound, I e, people came to us this quarter. And the real proof in the pudding is gonna be when you... Is is when you start to see the number of hires that are attributed to fillmore, like, become significant, and it becomes significant quite quickly. There's usually about a six to eight week delay after adopting the product to starting to see actual hires made as a result just because there's still interview process and general sort of courting of the candidate you wanna do. That's a very human thing. But when you start to see change, that's obviously a game changer. And that's and that's when people start to ask the more transformational question of, like, is this the Claude code moment for recruiting? Because there's, like, clearly highly economically valuable work that got done that we didn't do and is now affecting how much we're winning at recruiting. So so that's that's like the big payoff. What you see though is as soon as people start adopting the product and they start to get meetings in their calendar, you know, despite not having to sort of toil away themselves, that you see that delight. So early results are we we close about 15 to 20% of the roles that are added to the platform in the first eight weeks when we onboard. Well, currently, we're on track to onboard about 350, maybe 400 roles per month, and this is it from quite a small team. So, like, when you think about the sort of the the multiple you're getting on human effort, essentially, it's a small team small team of agent operators on our side. Fillmore is working four, 500 new roles every month. You know, you're talking about, like, that's sort of a scale of a very large agency at that... At this point. Yeah. And what we... The the the best sign of all is obviously customers that add more roles. Right? You might start with, hey. I'll try that...
00:17:24 · Siadhal Magos, Sebastian Johnson
Try this out on a couple of a couple of roles to get started, and then you start to see these, you know, world class companies like Replit or Luma AI or ModelML, and they decide to upload 10, 20, 30 roles because they realize this is... This should just be a part of how they make every hire. [00:17:41] S0: And so, Siadhal, you mentioned a bunch of metrics in there. When you are looking at the health of the recruitment processes of the club, you know, the 7,000 companies that you're you're working with, what are some of the the most important metrics that shows you that that things are going well, that either their recruitment process is going well or they're using their products well? Those are really, like, this is the the KPI, the north star that we that we're looking at.
00:18:03 · Siadhal Magos
[00:18:03] S1: Yeah. Well, we focus mainly on, like... There's two questions there. Like, what do we... How do we know that, essentially, we're being helpful to our customers? We're helping our customers win. And the second is, how would a customer understand it relative to their peers if they're actually doing... If they're winning at recruiting? Those are those are related but slightly different. Yeah. How do we understand... What what do we look for? We primarily look at usage. The reality is you could... We could we could deploy Metaview to a to a, you know, a tech company, and they could still hire without using it. Right? There... The hiring has existed before Metaview existed. There's a way to do it without Metaview. It's it's not it's not sort of... Yeah. The the the the evidence that we're helping you achieve your goals, we think the truest form is that people are essentially opting into going to this thing to do the work instead of other systems. So we primarily look at usage, and we look at that across, are you using multiple of our agents or just one? And we... Multiple agents is better because it means you're making better use of the context. And so that's what we we sort of... That's what we know makes customers successful. It's it's slightly different measure versus what... How a customer is measuring their own success at recruiting as a whole and what what almost usage of Metaview should ladder to, which essentially comes down to the things that we we care about on their behalf there are time to fill. So how quickly do we go from creating a role saying, hey. We have this need that we need to solve by bringing talent into the company to having someone at least with an accepted offer and pref... Preferably even their their butt is in that seat. Time to fill, we think, is really what many of our customers already do, but we think that's the thing that should change most as a result of of AI. And mileage varies on this. I don't wanna act like Metaview sort of does it for you. It's... This is still very much as workflow where the humans involved is super important, but we see averages around 25, 30% reduction at the moment in time to fill. We think there's a lot more headroom there. We think we could bring that down. We we think it should become essentially, you know, as unrecognizable as an engineer's job is where they can, you know, close...
00:20:10 · Siadhal Magos, Sebastian Johnson
You know, complete about eight to 10 x as much work, we should be seeing similar. You know, recruiters should be running 20 roles at a time instead of five or seeing, you know, multiples more as many candidates as they are per role. There's some variance where they should be able to spend more attention. Yeah. Their human attention in order to get either better or faster output. [00:20:30] S0: And how how does Metaview compare on some of those metrics to the clients that you work with? Whether it's, time to get bummed seat.