Siadhal Magos, Co-founder & CEO at Metaview: BREAKING: Metaview Raised $60M Series C

0:00Siadhal Magos:Hello and welcome back to Scaling Europe show. I'm Seb Tronson. Today on the show, we've got MetaView co-founder and CEO, Syle, with some very exciting news. What's going on? Take it away. Yeah, I mean, pumped to announce we raised another round of funding. So we raised$60 million, led by Insight Partners, joined by other folks like Intrepid, Growth Partners, GV, Google Ventures, Seedcamp, Plural. Yeah, a bunch of our favorite people. So yeah, excited to get back to building and yeah, pumped to be on the show as well. Thank you. It's an amazing roster of investors. And just over a year after your last kind of fundraise, so tell me what's happened, you know, what has happened in between this round and your Series B that has enabled this, that has got a bunch of new investors excited?

0:44I think there's a bunch of things that have happened, you know, in the world where people are increasingly understanding that we're moving to a future where AI does most of the work, right? That doesn't necessarily mean humans do less work, to be clear, but definitely most more work's going to get done. And a lot of it's going to get, most of it's going to get done by AI agents. That's like really clear to anyone that's living on the, on the frontier. Um, and what that, what, how that specifically sort of manifested for us is about a year ago, we, we had a bunch of traction and then we raised our series B, uh, off of that.

1:15And the sort of the story was, listen, we've got this point solution, um, that is making our customers very successful around how they essentially manage the data from within their hiring conversations. But that's always for us been the start of almost the baseline for a platform. And we actually think this is the most important context for other agents to be able to help you with recruiting. So for example, if you want to have the best sourcing agent on the planet, it's going to have to have access to the candidates you then speak to, in order to get better over time and monotonically improve and get smarter.

1:46And the same thing for every other part of the recruiting journey is better informed if it has access to the conversational data. So that was what we were saying a year ago. Now we can sort of prove that because we have the rest of the suites of agents on our platform and they are indeed the most accurate and most trusted agents that you can get in recruiting because they know all the things that you know. And so that's really what's changed for us in the last year is MetaView has become this sort of broader agentic recruiting platform. Obviously, we've also grown far, grown quickly and, you know, serving a lot more customers and, you know, on larger contracts and all those things that sort of show you're having more impact.

2:21But that's really what's changed for us in the last year and what we're sort of committed to pushing through on over the next few years.

2:27Siadhal Magos:So you've built this almost proprietary data set. Can you talk about how big that is? Like how many, I don't know, conversations you've had? Yeah, there's like a bunch of ways to, conversations is one of the sort of sets of data, like the proprietary data set we have for sure. Although again, 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 tools and the harness and the agents that work on top of it in really effective ways, but it is our customers' data at the end of the day. So it's more like they're making better use of their proprietary data, and they do that for a meta view.

2:58We get about five, six million 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 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 going to 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 you know 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.

3:48And 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 naturally by definition scarce. And so how you make use of that data becomes 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 want to do that by taking ownership of how, you know, if I have a hundred or a thousand or 10 ,000 people in my organization i want to understand the interactions they're having with the ai agents in our system and i want to use the the learnings from all of those interactions to make everyone in the company better at work uh in our case it's the work of hiring um but so really there's data from like every corner of the hiring process it's not just the conversations yeah that's interesting and and

4:34Siadhal Magos:so now you've built this much broader platform i guess and now that 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 going to use this new kind of chest of cash to do it's got to be both um i i we've always been um much as we're you know we started in europe i'm based in london um we've always been we've been global from the outset i would say many of our even early customers were based in the u.s so it's been international expansion from a boots on ground perspective but we've always had about 50 60 percent of our revenue from the u.s and that's continues to be the case.

5:11We opened up a San Francisco office last year. We're opening up a New York office now, basically. We opened up a couple of weeks ago. So there is definitely boots on ground and meet customers where they are with this transformational technology. But yeah, 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 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 uh obviously building a world-class team in order to to figure those things out um uh 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 mess of you which is really good fun as well we're um i think our marketing team is absolutely nailing it uh 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 yeah definitely this week you

6:38Siadhal Magos:you did the public launch of phil maury who's sort of like opening up 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 and 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 what's the impact theme yeah so i'll share some of the sort of technical aspects of what we launched first and then we can talk about like the like what does this mean for the market so you can think about um 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 meta views sort of the core meta view product is the thing we launched uh 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 co-worker 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 and it goes away and does the work itself.

7:56And 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 you meetings. So you engage with Fillmore because you want to fill more roles and you want an AI agent to do it for you. And so who this works really well, 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 long-time horizon, multi-step processes by itself.

8:32And of course, if it's a multi-step process and it's long-time horizon, it's less about having a workspace, right? You don't want to sit there and watch it the whole time. 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 tweak here and there what they're doing. I sort of let them get on with it and you get like high much high leverage results as a result of that because it can do far more work uh than you could ever keep up with um and so that's what film film war is it's a like highly autonomous sourcing agent whose sort of sole objective is to get you meetings with candidates that are fit for your role and it has it's it's rights for customers who give are happy to give it a high level of autonomy in making that happen of course it still understands guidelines it still understands your brand it still understands your tone of voice it understands all those things but it it can get super creative in the ways that it finds candidates and gets them in your calendar and we've been sort of amazed even even as the builders of the agent because you leave so much of the reasoning to the agent itself we've been amazed with some of the creativity that it's shown in in getting people booked in how similar do you see the role of ai

9:40Siadhal Magos:in how it's impacted engineering to the impact that it could have in recruitment you know everyone talks about like a 10x engineer that's like the thing that everybody wants and how AI can make the very best even better yeah do you see the world of 10x recruiters uh certainly it hasn't happened yet uh I think Fillmore is by far the closest I if I'm gonna like put a number in it I think you're probably in the four to five x range with Fillmore as opposed to the 10 I know I know people don't necessarily mean these things literally but it's funny how it ends up being actually quite accurate if you look at the prs by our engineering team like prs per engineer it has i think at the moment i think we're it's either 8 or 12x what it used to be so you know let's call it 10x on average but it's always they use because that job has been transformed right that is now an agent orchestration job now the funny thing is and the people that you know join metaview as you know as members of the team what they sort of get excited about is obviously people sort of put the the job of engineering on a pedestal a bit because it's just you know it seems such a high leverage like highly it's just like uh it's a high status role let's say certainly in the tech world the funny thing is it's actually harder to achieve that in in within the recruiting workflow and that's for like a very simple reason that the level of trust you need to have for a truly for a recruiting agent to do work on your behalf is much higher than the level of trust you need for a coding agent to do work on your path because if i get a coding coding agent to work on you know 12 features while I'm sleeping overnight, I can come in, realize that they're all bad and bin them all.

11:12And like, okay, I've wasted a bit of money on tokens, but no one's, you know, no one's been hurt, you know? Whereas if I'm ever going to trust a recruiting agent to do work on my behalf, it cannot do work unless it comes into contact with reality, right? The work of a recruiter involves communicating with other human beings. And you can't just say, oops, that was a terrible way to communicate. Let me do it again. You've burned that candidate, right? And so the level of trust is that much higher. And so I think that's why you have this slightly slower. That's why we haven't had it yet. But I think we're really getting there because now A, the AI is smart enough to understand all of that nuance and context.

11:47B, it can actually, again, do things over a long time horizon, like maintain a relationship or help you maintain a relationship with a candidate for multiple weeks or even months while you're trying to hire them. And just fundamentally, the models are smart enough. Sorry, the models are smart enough such that they don't make some of the mistakes they've made in the past.

12:07Siadhal Magos:You know, when you look at your product roadmap or designing or rolling out products, how much does that like sort of like high stakes nature of recruitment impact how willing you are to push things live or test things or, I don't know, and like how on it you have to be watching all these interactions and checking because you're right, it's really high stakes. There's a real relationship, it's real people that if you get it wrong or the agent get it wrong, that could be a relationship burn either for your you know yourselves your clients what impact does that have on the products you roll out and when you roll them out um it's always been because we've always 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.

13:00And some customers don't want to 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 take risk with some of these things for the upside. In this particular case, where essentially you have an agent communicating at scale on your behalf, there's a bunch of essentially calibration workflows the agent will go through.

13:40So this is designed by us. You can be so AI-pilled as to say, hey, we should let the agent decide if it should even bother calibrating. You know, we don't do that. We think people like to feel in control. People should feel in control of the agent, but you need to give it enough breathing room to work at scale. If you have human in the loop at every single stage gate, obviously you're never going to like reduce time to fill by 10X because it's just, the human's going to get in the way. And so we have this calibration process where essentially you go through a flow where you get comfortable with two things, the two most important things with the agent.

14:18One, does it understand what good looks like? for you for it 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 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 of how you like to communicate. And really, that's what, whenever you're doing hiring, like if 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 butt in seat, you're thinking about who are the right people and how do I want to speak to them?

15:00And if I could times that by 10 and have all of my people do it for me, then I would. And that's sort of what this is an answer to.

15:06Siadhal Magos:And what have been some of the early results? Because you've been running for more, I guess, in a sort of beta mode or with some existing customers for a little bit before you did the public launch. What's been the reaction for people who have been using it? uh it's been phenomenal frankly um and like the the real proof becomes when you start to see a lot of times recruiting functions um will look at their recruiting data um you know periodically let's say once a quarter and they look like oh and also like where what are our source what's our source of hire oh it turns out we did a lot of sourcing or we did a lot of inbound uh i.e.

15:41people came to us this quarter and the real proof in the pudding is going to 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 want to do. That's a very human thing. But when you start to see that change, that's obviously a game changer. And that's when people start to ask the more transformational question of like, is this the Claude Code moment for recruiting?

16:12Because there's like clearly highly economically the valuable work that got done that we didn't do and is now affecting how much we're winning at recruiting. So 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, you see that delight. So early results are we close about 15 to 20 % of the roles that are added to the platform in the first eight weeks when we onboard um 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 run small team of agent operators on our side Fillmore is working four or five hundred new roles every month.

17:08You know, you're talking about like that's sort of the scale of a very large agency at this point. Yeah. And what we the best sign of all is obviously customers that add more roles. Right. You might start with, hey, I'll try that. 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 Model ML, 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.

17:41Siadhal Magos:And so as you mentioned a bunch of metrics in there, when you are looking at the health of the recruitment processes of the, you know, the 7 ,000 companies that you're, you're working with, what are some of the most important metrics that shows you that things are going well, that either their recruitment process is going well or their user products well. Those are really like, this is the KPI, the North Star that we're looking at. Yeah. Well, we focus mainly on like, there's two questions there. Like, how do we know that essentially we're being helpful to our customers? We're helping our customers win.

18:13And 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 related, but slightly different. How do we understand, And what do we look for? We primarily look at usage. The reality is we could deploy MetaView to a tech company, and they could still hire without using it. Hiring has existed before MetaView existed. There's a way to do it without MetaView. It's not sort of, yeah, 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.

18:58So we primarily look at usage and we look at that across. Are you using multiple of our agents or just one? I mean, multiple agents is better because it means you're making better use of the context. And so that's what we sort of, that's what we know makes customers successful. It's a slightly different measure versus how a customer is measuring their own success at recruiting as a whole and what almost usage of meta you should add to, two, which essentially comes down to the things that 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 preferably even their butt is in that seat.

19:37Time 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 AI. And mileage varies on this. I don't want to act like MetaView sort of does it for you. This is still very much a 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 think it should become essentially, you know, as unrecognizable as an engineer's job is where they can, you know, close, you know, complete about eight to 10 X as much work.

20:13We 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 up their human attention in order to get either better or faster output and how how

20:31Siadhal Magos:does meta view compare on some of those metrics to the clients that you work with whether it's like time to get bum and see yeah we are um our customers are very high performing companies to be to be clear on on average but we're we're uh we're tracking at about i think a 23 day time to fill uh which is very solid that's like that's low basically um uh again varies across different job families most companies will will aim be aiming for or they'll see good as around sort of 30 to 45 days time to fill so we're especially with the impact of film or over the last three months or so we've been using film or internally for about three months um it's that's massively brought it down i mean i'm literally getting off once i get off this i've got a call with a candidate that i want to make an offer to that was a film more candidate uh i feel more found them and engaged with them uh and i was sort of going through our almost our communication history which you know i was aware of because obviously that's the way film works if you're aware of how it's going but you know it's the reality is there's this conversation that had occurred with this candidate that primed them to get ready to meet with me that was not like from my fingers to their ears you know or from my fingers to their eyes yeah so it's real game it just means you you're sort of like your surface area is much like anyway to answer your question uh we're top quartile uh times of hire isn't necessarily the type of thing you could have like a one day time to fill if you wanted to if you were willing to you know hire the first person you met but that's obviously not so so you want to be careful you want to counter measure to these counterweights to these um measures um but we're doing well amazing we'll look i'll let you go close that hire uh but Amazing.

22:07Siadhal Magos:You guys are absolutely crushing it. Thank you for taking the time. And yeah, I'm looking forward to covering the series D and E and the rest of your journey. So huge congrats and thank you again. Thanks, man. Appreciate it.