Pod Insights Eye On A.I. #273 Benjamin Encz: How Ashby is Building the Future of Hiring with AI About this episode This episode is brought to you by Extreme Networks, the company radically improving customer experiences with AI-powered automation for networking. Extreme is driving the convergence of AI, networking, and security to transform the way businesses connect and protect their networks, delivering faster performance, stronger security, and a seamless user experience.
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In this episode, Craig Smith sits down with Benjamin Encz, co-founder and CEO of Ashby, the AI-powered recruiting platform that is transforming how companies hire.
Ashby is disrupting the $650B recruiting industry by combining automation, data, and large language models to streamline the entire hiring process, from job postings to resume screening and interview scheduling. With clients like OpenAI and Shopify, Ashby is setting a new standard for modern recruiting.
Benji shares how AI is boosting efficiency while keeping human judgment at the center, and what the future of hiring could look like as these tools continue to evolve.
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0:00 Speaker 00 With the features in Ashby, I can actually describe these criteria. So I can put in these things I'm looking for and then we'll have LLMs read the resume and it will indicate whether or not these skills are present. And once you start interviewing someone, we believe that is something that requires human judgment kind of almost all the way through. Huge part of the touch on Ululam is like this data piece where, you know, we help companies understand their process better and optimize it. It's a pretty recent thing that companies have really invested in automating the more complicated parts of the process. And we're definitely seeing more of it, but we're definitely not fully there yet.
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1:10 Speaker 00 I'm excited to get started. Thanks for having me here. My name is Benji, co-founder, CEO of Ashby. My background before starting this company was all in software engineering. And then in my most recent company, I was a director of engineering and pretty much spent all my time hiring engineers onto my team. That was really hard and competitive. It was back in San Francisco in like 2015 to 2018. It was very hard to hire engineers. And so I got really deeply involved in recruiting, recruiting tools or overall recruiting process. And I had a lot of issues with the tools we were using. There was like a lack of automation, a lack of data and insights. And that was kind of slowing us down on this really top priority.
1:53 Speaker 00 And so, yeah, that was almost six and a half years ago. And eventually decided that I think we should build something from scratch with more modern technology that's really focused on companies that are hiring top talent in really competitive spaces. And that brings us here. We're basically offering an end-to-end recruiting platform for in-house teams. So it covers everything from posting jobs, getting people to apply, sourcing passive candidates, moving everyone through the interview process and all the way through extending offers. So that's kind of the scope of the product today.
2:23 Speaker 00 And we serve about 2700 customers, a lot of them in the technology space.
2:28 Craig S. Smith? Yeah, yeah, that's right. I mean, there is, I mean, it's in Zuckerberg paying, offering $100 million salaries. I don't know how real that is.
2:42 Speaker 00 Yeah, I know. The rumors are, but probably not totally off from for all the data points we're seeing. Yeah.
2:47 Craig S. Smith? Yeah, I mean, certainly total comp, I guess, if you include options and stuff, but how, I guess my first question is what were the problems that you saw? You don't have to name the other platforms, but what were the problems that you saw that you that the other platforms weren't offering that you
3:09 Speaker 00 Yeah.
3:09 Craig S. Smith? you wanted to correct with Ashby?
3:12 Speaker 00 Yeah, one of the key ones was data and analytics. So I think for for some background there. You know, a lot of the other platforms were started around 2012 or before. And back then, recruiting teams weren't used to working with data in like a day to day basis. But as hiring has become more competitive, a lot of recruiting teams are now operating more like a good a market team, like sales and marketing, and they're increasing increasingly have an operations team supporting them. And with that, a lot more data is being used to make decisions, to optimize a process, to figure out, you know, what is our comp strategy? Where are we losing candidates? All these kind of questions. And we weren't able to answer these with these other products. It was a lot of manual work. And so reporting was one key piece.
3:55 Speaker 00 And then the second part was kind of automation across a lot of different areas of the process. This was like pre L L Ms, but still a lot of companies were trying to become more efficient at this like competitive hiring process. And so they were using an applicant tracking system, which is like the name for this kind of category of software in. But then they were adding separate products, one for like automating interview scheduling, which can be really time consuming and manual, one for automating like outreach to candidates that are you're trying to source for your company. And so the second opportunity we saw was kind of bundling all these products together. They bring like this core operating system, this app and tracking system, but then also add all the automation in a single product. So these are probably the two biggest insights we had back then.
4:41 Craig S. Smith? Yeah. And and the data, what kind of you're you're talking about
4:47 Craig S. Smith? data about the candidate pool or data about churn within the organization? Or what what kind of data is important in this case?
4:59 Speaker 00 Today, we're really focused on the recruiting process. And so if you think of that, it's like a funnel of a lot of different stages. And there can be drop offs at different stages. Like I remember when we were hiring for my engineering team, you know, we had certain channels where candidates were coming from, where we only had like a 10% offer acceptance rate, which is pretty abysmal. Like if you extend nine offers or 10 offers, you only get one candidate to actually join. You're spinning your wheels. And so it's really about all these metrics throughout the process. And that also includes like, how long does it take to schedule candidates? Where are candidates dropping off? Why are we losing out on offers? Where are good candidates coming from? How many referrals are we getting? All these operational questions in the recruiting process is what we're really focused on today.
5:41 Speaker 00 And it's kind of surprising if you're not in, if you haven't worked in a larger recruiting organization, there's a surprising amount of complexity in in actually scaling scaling a team.
5:50 Craig S. Smith? Yeah. And your customer base, are they small and medium? Or are they big corporations? Or a lot of them, are they tech companies or companies that are not in the tech space, but are hiring tech teams?
6:07 Speaker 00 Yeah, so we started more in tech because that's where there was the most competition for talent. And so that's where our product got the most uptake. Because if you have a competitive process, then you need more data to make decisions. And then today, really sort of, you know, one to above 10,000. So our bigger customers include Shopify or Snowflake, like large public technology companies. We also have OpenAI as a customer who's not huge yet, but growing extremely fast. And then we also have a lot of earlier stage companies to like ramp, kind of probably in the middle of the pack. But then also, we have a lot of like five person startups joining us too, because they know that hiring and talent is really important. And they want to start with the right product that can kind of grow with them over time. And then we are expanding it to non-tech as well, but tech is definitely kind of still
6:52 Speaker 00 the bigger portion of the customer base.
6:54 Craig S. Smith? Yeah, and and the this platform is used by the HR departments at companies. It's not a platform, sort of a platform that candidates, you know, post their resumes to and you know, like Indeed or something like that.
7:16 Speaker 00 That's right. Yes. So we use primarily by the recruiting teams within HR and then the hiring managers. But a lot of candidates know about us now. If they've been applying to a lot of tech jobs or AI companies, then they will see the Ashby brand out there quite frequently. But there's no candidate. The candidate only interacts with the company through through Ashby, but they don't have their own profile or anything like that. That's right.
7:39 Craig S. Smith? Yeah, because it, yeah, I mean, the the the question is really managing. The the process is one thing, but finding the candidates is increasingly difficult, isn't it? What do you do? Is there anything in Ashby that that helps hiring managers or HR managers identify candidates? Or is it once they have a candidate, then it Ashby takes over?
8:09 Speaker 00 There are a few different parts of the product, but in general, we are we support what is called inbound channels. So all the people that are applying to companies actually helps post these jobs and then collects all the people that are expressing interest in your company. Then there's also other modules, like we have a sourcing module. If I'm like interested in your background, you're not applying to my company. We can find your contact information. We can send you an email outreach and try to compel you to the interview with us. So we also have that outreach piece. In terms of finding candidates, most of our customers in technology are kind of using LinkedIn as a primary place to identify people that I want to reach out to if they're not already coming inbound. But the interesting thing in the
8:49 Speaker 00 job market over the last two or three years in tech is actually a lot more people are coming inbound than it used to. It used to be a difficult channel, but a lot of our customers are now making more than half of their hires from applicants that are coming into Ashby. And for applicants that are coming into the platform, we do help them use free AI, actually identify kind of the potentially best fit talent in what is increasingly a bigger pool of applicants. So we do help with that part.
9:13 Craig S. Smith? Yeah, and the applicants, and I'm interested mostly in in the AI sector.
9:19 Speaker 00 Yeah.
9:20 Craig S. Smith? Applicants coming in through whatever channel, are they primarily
9:29 Craig S. Smith? people looking to change jobs, or are they people coming out of master's programs or even PhD programs who are looking for jobs?
9:41 Speaker 00 Yeah, we have a mix of all sorts of talents, depending on the strategy of each of our customers. But it's usually a blend, you know, of like, tenure people and internships, new grads. So it's a pretty broad range.
9:55 Craig S. Smith? Yeah. And is there something on Ashby that helps a hiring manager evaluate the candidates? Or is it is it simply sort of tracking them through the process? I don't mean simply.
10:08 Speaker 00 Yeah, yeah, I totally get your point. So what we have deployed AI, and I know we're going to go a bit deeper in that. But one of the key areas we launched a little bit over a year ago now is at the kind of resume review stage. We can help the team identify the potentially ideal candidates. And so one example is I'm hiring product managers for my team today. I actually want people to have a product background. Ideally, they were a founder before and have a software engineering background. That's like pretty hard to find combination. And the last time we posted a role, we got about 2,000 applicants. But with the features in Ashby, I can actually describe these criteria. So I can put in these things I'm looking for, and then we'll have L L M's read the resume
10:52 Speaker 00 and will indicate whether or not these skills are present. Instead of reading every resume line by line, I can have AI help me kind of scan the resume, find relevant skills, and then let me kind of filter through the applicant pool that way. And that's something that really wasn't possible pre L L M's in a reliable way. But it's really changed how a lot of companies use kind of this inbound channel. Because now you can sift through like thousands of candidates more efficiently. And so we help with that part. Once you start interviewing someone, we believe that is something that requires human judgment kind of almost all the way through.
11:22 Craig S. Smith? Yeah.
11:22 Speaker 00 We have a lot of like, features that help manage the process, but we don't really take the judgment out of the process. We think that's. Human's job for sure, yeah.
11:33 Craig S. Smith? Yeah, that's interesting. I just saw an article and I my wife and I have talked about this a lot because, as I said, you know, we have a generation through our kids and nieces and nephews who ask for advice. And you know, I'm sort of old school. I tell everybody, yeah, the resume should all be on one page. And I saw an article recently saying that that's that's bad advice because all this stuff is getting read by by L L Ms now. And what what is the case? I mean, can you send in a five page resume? Is the L L M going to
12:17 Craig S. Smith? scan it? Is it to the point now where the more information you have, the better for that?
12:23 Speaker 00 That's a great question. Yeah, I definitely think you want to have your key accomplishments on that resume because otherwise it's hard for anyone to identify them. That said, in our case, our customers really do a combination of this AI assisted first pass of like trying to figure out which resumes to look at first, but then you also have a human looking at it. And so five page resume is probably not optimal either. So you know, probably one to two pages is still good if you can bring in like your your key relevant skills for that job. But if you have like closed hundred K dollar deals, or you have shipped code in a certain language, you want to have these details on the resume because that will help
13:04 Speaker 00 in case people are looking for these skills. So but it's definitely changing. Yeah, you have to kind of optimize right now for the machine and the human kind of at the same time ideally.
13:13 Craig S. Smith? Yeah, yeah. And and I guess the L L Ms these days are smart enough that it's not simply picking out keywords. It's it's understanding, you know, what's being expressed on the resume.
13:31 Speaker 00 That's been an exciting change. I think where, you know, recruiters were historically pretty good at keyword search, but keyword search is very liberal. And so if someone used a slightly different phrasing or an adjacent skill, they would be missed in this kind of pile of resumes. And now the L M actually has like, yeah, this kind of semantic understanding, which we've seen really helpful in terms of accuracy. And it's one of the areas that L M's are actually really, really good at. So that's why we decided to ship that.
13:59 Craig S. Smith? Yeah, and you know, a lot of jobs I hear, you know, there'll be a, an entry level job, or, or even more senior job, and companies will get 10,000
14:16 Craig S. Smith? applications, partly because now, you know, you don't have to lick an envelope and, and find a stamp. I mean, you can do it all online. So people sort of flood the zone. What percentage generally, is there some metric, what percentage of resumes make that first LLM pass?
14:37 Speaker 00 Yeah, that's a great question. I wish I had prepared a stat on that, because I actually have just data on our like trends report, but, and the way to think about it is like, our customers don't actually have the LM do the first pass, and then human is actually side by side. So, you know, let's say we get 2,000 applicants. We look at every resume, but we start with the ones that seem like the strongest fit. We go for them first with the mindset of like, these are probably people who want to move forward. Then we see the bottom people that don't seem to meet most of our criteria, they may just mass applied, then not actually good fit for all. And we can go through the mindset of these are probably not a fit. So it just speeds up the human review, but it's still always human going through and making a decision on each of the resumes.
15:16 Speaker 00 The interview rate, that's a good question. I think it probably hovers somewhere between like 1 and 10% on average. It really depends on the volume of interviews, but it's kind of roughly what you can think of. And you did touch on a point there where we are seeing more tools on the candidate side too, that make it easier to mass apply to jobs and that increases the numbers, but doesn't necessarily always mean there's a great fit.
15:39 Craig S. Smith? Yeah, that's correct.
15:40 Speaker 00 Yeah. But yeah, 10% is probably a good benchmark.
15:44 Craig S. Smith? Yeah, and, and when you say that it's done side by side, and it just seems...
15:52 Craig S. Smith? I've never hired at that level, but it seems that, uh, you know, if you get a thousand resumes and you would just shoot them all through the LLM first, and at least have them ranked them. Uh, is that what Ashby does?
16:08 Speaker 00 Exactly. We can rank them, but we don't do like what companies do in our space, which we shy away from is we don't say like you're an eighty seven out of a hundred and you're a fifty two out of a hundred. Instead, we take these objective criteria that you defined and we just say like, could we find on the resume? Yes or no? Um, and if yes, where's the evidence for it? And so it's really like an assistant reader that's very objective based on the criteria that you set out, which is a little bit different. Um, we think it's kind of right way to go about it.
16:37 Craig S. Smith? Yeah. And then, uh, uh, the, the, the humans then go through the top one hundred or something. If there are ten thousand resumes come in.
16:49 Speaker 00 Yeah, it depends on the team. There's a lot that go through every single one, but if you have the system to read resume, it can take like a second or two to validate like, okay, this person, they applied for a software engineering job, but they actually never worked as a software engineer and you know,
17:02 Craig S. Smith? Right.
17:02 Speaker 00 make a quick gut check and then you move forward. But there are different approaches. We do generally recommend that you even still look at every, everyone. Um, but yeah.
17:12 Craig S. Smith? Yeah. Um, so how, how has this, uh, industry and Ashby in particular, how do you think it's changing hiring practices? Is it making it easier to find uh, the right fit?
17:30 Craig S. Smith? I mean, is it, you know, it just seems like there's, you know, on the one hand, there are a lot of people that have, again, I'm talking about AI that have gone to, uh,
17:43 Craig S. Smith? Undergraduate or boot camps or something that that have a feel for AI that can code. Uh, and those are the kinds of people who are not finding jobs. And um, and the people like Meta are laying or Microsoft are laying off.
18:04 Speaker 00 Yeah.
18:04 Craig S. Smith? Right. Uh, because a lot of that can be automated, but at the top. There was kind of a shortage of talent. That's why you have talk of a hundred million dollar annual salaries.
18:17 Speaker 00 Yeah.
18:18 Craig S. Smith? Is uh, this, where do you see the market? I mean, are, are. Is it, is that top tier talent growing at all? And if it's growing, where is it coming from? Is it international students sort of, I mean, I'm talking about the US market primarily. We can talk about international, but uh, is it, uh, you know, people coming into the country? Is it uh, people coming out of graduate programs? And is there enough of a pipeline to meet the need? Uh, and what, what do the, the, the people that went to boot camps expecting to have a high-flying career? Uh, what do they do?
19:03 Speaker 00 It's a great set of questions and I'll, I'll try to take them one at a time. I think the interesting thing about this narrative of entry level jobs and AI and automation. I don't think it's perfectly accurate and I think a big part of what we saw in the last years is kind of a readjustment of the 2021, 22 period where a lot of these tech companies hired like literally tens of thousands of people in a single year. Um, and they pulled so many, they pulled so much talent into companies that are just overstaffed. Um, for the size they should be at, and that's even outside of AI or automation. There's like, if you talk to someone at Google or Microsoft, there will be stories of teams just being too big to actually move nationally. And so I think AI is like a good story of like why these redundancies are happening.
19:46 Speaker 00 But in practice, it's uh, often just like, we, we hired too many people and we need to right-size the business. Um, uh, so I think that's a big part. Then there is definitely still really strong competition for talent, but.
20:01 Speaker 00 It is more at the senior level. There's just a lot of supply of people of zero to two years' experience. So I think that's a challenge right now with this this location we had in a market. We're adjusted from this like big over hiring and now we're pulling back. Where people are pulling back the most is at entry level where, you know, people that join at that level, they aren't that productive. They're not that accretive to the business that. You invest in them for like future growth, but if you can also just hire senior talent instead at a higher price, then that's kind of what companies are opting for a bit more.
20:32 Speaker 00 In terms of practical advice, I think startups still have a hard time to hire talent, especially early stage ones. So if you have any hands on experience actually building something, even if that's on the side and you can demonstrate that, I think that's a great place to to to launch a career. You know, there are a lot of scrappy companies out there. They're looking for people who can actually build stuff. So especially if you have coding skills, putting projects out there, side projects, posting about them will give you a good amount of visibility.
21:00 Craig S. Smith? Yeah. And and that top tier talent, is it coming out of graduate schools or is it coming from overseas?
21:09 Speaker 00 There's a mix of all all sorts of channels, but I think in general, there's still a big shortage of software engineers that are meeting the bar for like a senior software engineer at any technology companies. You just can never like. Every every company, every startup, every founder I talk to has a hard time hiring as many engineers as they'd like. But a background can be all over the place. I mean, there are obviously a lot of the traditional, you know, Stanford grads or Harvard grads. But when it comes to software engineering, it's much more about the practical skills and there are a lot of people that have an untraditional background. And just have demonstrated.
21:44 Speaker 00 That it can code on the side. I may never even completed a degree. So there's a real, there's a real strong pattern there. Yeah.
21:53 Craig S. Smith? Yeah, and and does Ash be of, I mean, what do you do when someone submits a portfolio? For example, or is that later in the process?
22:05 Speaker 00 Tends to be later for design roles, it may happen early, but it's part of that human review. So, you know, the the LM again will read resume, will try to find if you have like the key criteria. And then you will take a look at that as kind of the first step. I think you asked this other question earlier that I also want to touch on of like, how do we help companies? Kind of solve these talent problems or get better at hiring and a huge part of the touch on early on is like this data piece. Where, you know, we help companies understand their process better and optimize it. And that can include things like you have hired, you know, 10 software engineers in the past. Here's how many applicants it took you to hire a single person. So if you post this job, you know, roughly how many applicants you need before you will make one hire. Then you can use that to decide how long you want to leave this role open.
22:50 Speaker 00 Then you may look at your process and you notice that you have a drop off at a certain stage. And then you can take a close look at that and see if like these particular interviews are too hard. Or if there are interviewers that are grading too harshly. And so a lot of the optimization that Ashley offers is around this kind of data and data analysis that helps you refine your process.
23:09 Craig S. Smith? Yeah, yeah, that's interesting. And how often do people?
23:15 Craig S. Smith? I mean, again, from your data and analysis, hire the wrong person? Is that a problem? I would guess it is.
23:26 Speaker 00 That is certainly a problem and it varies pretty highly. We actually don't have like perfect visibility into that because we we do end at like the higher process. We actually, we did roll out something about a year ago, we call quality of hire surveys. And that is something that will trigger a survey to hire manager at a cadence that you can choose internally. We do 30, 60, 90 days. So we started to collect this post hire data and we can see like where better people coming from, how does it correlate with the interviews? But yes, people definitely hire the wrong people, you know, interviews are highly imperfect. But that is a big area that we are helping our customers on is like collecting the data post hire. Then again, understanding which interviewers or which interviews are more predictive of performance, which channels tend to produce better candidates.
24:09 Craig S. Smith? Yeah.
24:10 Speaker 00 Can help with all of that. But yeah, that's a big problem for sure.
24:13 Craig S. Smith? Yeah. And the channels, what are the channels?
24:17 Speaker 00 Yeah. So inbound broadly speaking is one. And then within that, you can have different job boards, you know, it could be LinkedIn indeed. Depending on your industry, some of these will give you better fit candidates. And then outbound is another one. So again, if we have a team that's going to reach out to candidates that are not actively applying and interviewing and we source them, that's an important channel. Referral is the other channel that tends to be really important early on, where you're asking your employees to submit people that they have worked with in the past and obviously have like some pretty deep knowledge of their skills. And then internal mobility is another one. And that's actually increasing for our bigger customers, especially at a time when they aren't hiring as much externally. You see more people move internal into different roles.
25:00 Speaker 00 And then if we look at our data, internal mobility is obviously one of the best channels. So you already know, you already know so much about this person based in your organization.
25:10 Speaker 00 And then referral can be another generally good one if you have a good referral program in place because if you have strong talent under team, then the chance that they know strong talent can be quite high.
25:20 Craig S. Smith? Yeah, that internal thing is interesting. Does, I mean, organizations are big. Is, does it typically happen that
25:32 Craig S. Smith? one department will post a job company wide and people will apply? Or can you scan an organization? And say, you know, here are people with the proper skills. Here's their current job.
25:48 Speaker 00 Yeah.
25:48 Craig S. Smith? We could replace them with a cheaper person and move them over to this. I mean, Are there tools to do that?
25:56 Speaker 00 Yeah, that's a great question. So today is definitely more deformer where people are applying and expressing interest in roles. But we are rolling out AI functionality to generally find past talent that you've interacted with in Ashby. And so if you think about our customers, they've hired each like a couple hundred or a thousand people, but have, you know, sometimes millions of candidates that have some touch point with in the past. And what we can do now with AI is resurface these automatically. So if you're opening a new job, we can say like, hey, you're all these people that you've interviewed before or hired before, or they expressed interest in your company that you didn't interview, and revisit them. And that's something that's pretty exciting because it's something companies have been trying to do for many years, but pre-AI it was really hard.
26:37 Speaker 00 And every search kind of started from scratch, but now we can use all this historical data to surface people, including internal candidates. That could be a good fit.
26:44 Craig S. Smith? Yeah. And, and so I would guess,
26:50 Craig S. Smith? you saw the opportunity, you guys jumped into it, but a lot of other people are jumping into it. How do you see the market and how are you growing?
27:00 Speaker 00 Yeah, we are more than doubling year over year for the last couple of years. So I, I think can pretty confidently say that in our space, we're probably not a fastest growing vendor. A big advantage we had is coming into the space in 2018,
27:17 Speaker 00 when again, all these other solutions were five, six years older and the talent market really changed in, especially in software around like 2015 or so. There was like much more private capital. The competition again for like senior engineering talent really exploded, the number of startups exploded. And so we really built for this new way of acquiring talent and these more sophisticated recruiting teams that work with data. And so we have a pretty big like up, I think, because we had, we were lucky in the timing and we had it inside a couple of years before everyone else. And then we have been able to build this all in one product. So instead of buying like a lot of different solutions, we have a single platform for our buyers to kind of run this entire process. And that especially useful now with AI where we can use all these different touchpoints. It can be,
27:59 Speaker 00 we're about to launch call recording. So if you have conversations with candidates, all of these are recorded, all the emails that you're writing, all the feedback forums, all the notes. All this data is in a single platform and that creates some really valuable data that's hard to replicate. So, yeah.
28:14 Craig S. Smith? Yeah, uh, yeah, that's interesting. And so, uh, building the product out, I mean, you're doubling every year. Uh, do you have to, is there a lot of pressure to keep expanding the product? And what's the vision? Where do you see Ashby going?
28:34 Speaker 00 Yeah, it's a good question. I mean, our, fortunately, our customers are really happy with the velocity at which we're improving the product and shipping, uh, shipping features. Um, that's something we focused on pretty early. Both my co-founder and I have an engineering background and I spend a lot of time hiring engineers. So we were able to build a really strong engineering team. Um, the, so people are really happy with what we have today. We do definitely have quite ambitious plans to expand into areas that we're not quite talking about probably yet. But, um, uh, today we're really focused on, on this kind of recruiting challenge. And especially with AI, there's a ton of opportunity to rethink a bunch of areas of the product that we built a couple of years ago, pre L L Ms. So that's kind of our immediate focus.
29:17 Craig S. Smith? And is this, um, is it getting to the point that, that the hiring process, uh, all of the, the sort of tedious work is being automated? And, and so hiring managers are, um, can really focus on, on interviewing and, and the candidates? Or has that always been the case? It's just taken care of by back office people?
29:48 Speaker 00 Yeah, it's definitely not always been the case. So part of, uh, my reason to start Ashby was when I was in the hiring manager role, about half of my recruiting team was spending all their time coordinating interviews, uh, because it can be pretty complex interview loops. You know, we used to do like four hour on sites and we need people with specific skills and need to go in a certain order. And so we had, we're recruiting coordination team where all their time was spent just scheduling interviews. Um, that was one of my insights back then was like, hey, I think computers are really good at organizing calendars. So let's try to automate that. Um, but it's not, it's a pretty recent thing that companies have really invested in is like automating the more complicated parts of the process. Um, and we're definitely seeing more of it, but we're not, we're definitely not fully
30:30 Speaker 00 there yet. But the goal of Ashby is to get to the kind of core competencies which are How do you make your company attractive to people such that they want to express interest? How do you evaluate candidates because we think that requires humans? And then how do you, you know, close them once they have competing offers? That's really where we're kind of focused on. And we're trying to eliminate everything else, but even things like scheduling interviews today, we can automate eighty percent. You know, with L M's, we're going to try to get it to like ninety, ninety-five. But some of these problems are incredibly hard to solve in like a real world setting. So there's still a good amount of opportunity left.
31:07 Craig S. Smith? Yeah, that's right. I mean, things always sound easy in theory, but in practice are.
31:15 Speaker 00 That's also the interesting thing about AI. How L M's work is like, you see the news of like, you know, models solving some math Olympiad problems, but they can't actually be an executive assistant. Because they don't have all the context necessary. They're they're kind of smart on unknown problems they've seen before, but not very smart on things we need a lot of context. So.
31:35 Craig S. Smith? Yeah, well, and then actually, that's a that's an interesting point is, does Ashby do anything to build context?
31:44 Speaker 00 Yeah.
31:45 Craig S. Smith? You know, maybe the personality profiles of the people on the team that the candidate would be joining or something like that.
31:54 Speaker 00 Now, it's really more about all this data we collect and we started making that more accessible. So we now have this thing we call candidate assistant. So if I go to your profile and actually I can ask any question, the way would ask chat G P T a question. And we will pull from any kind of data source we have that can be past conversations, feedback, any anything. And that's been really helpful because I can go in and be like, okay, you know, why has this candidate left their most recent job? If I was an interviewer asking a question, historically, it would have been really hard to find it out. Now I can get an extra transcript with the conversation. And so we're collecting all this data and they're making it available to AI and to the end user. And there are more and more workflows where we can start embedding that kind of stuff.
32:35 Speaker 00 And then another thing we launched only a couple of months ago, which is really exciting for candidates. You know, a lot of people have experienced interviewing somewhere, maybe going for like three to four rounds of interviews and then getting a very generic rejection email. Which is like, sorry, we're not moving forward.
32:50 Craig S. Smith? Yeah.
32:51 Speaker 00 Because it's actually time consuming and hard to give compliant feedback on a process. But we made it pretty easy by introducing like a special email token you can include. In your email and we will automatically pull all the feedback, we'll sanitize it and we'll give you like a, a nice version of feedback that a candidate can take home and like we're just gonna do that in like two seconds where before it would have taken like thirty minutes per candidate to compile that. And so there's some really interesting stuff we can do with all that data to also humanize the process a bit more.
33:18 Craig S. Smith? Yeah. And, and hiring successfully, you were saying that one of the things that Ashby does is sort of track when people drop out of the process, why they drop out of the process. What are the reasons that, that hiring
33:36 Speaker 00 Yep.
33:37 Craig S. Smith? process would, would lose somebody?
33:40 Speaker 00 Yeah. So there are obviously two broad categories. One is you decided not to move forward with a candidate. And then the other is a candidate pulled out of the process. For candidates pulling out, speed is a really important one. Like the best candidates are not in a market very, very long. They'll have multiple conversations. So it's actually an area where actually with the product and all the automation helps, you know, we can bring someone through an interview process in like twenty days if you set it up really well. And if your competitor takes thirty or forty days, then you may have signed off before they finish the process with another team. So speed is like a huge area that we're, that we're focused on. Other things, compensation, you know, companies are getting better now about posting that upfront, having these discussions early, but that can cause misalignments and waste a lot of time for you. Or misalign the compensation at the last step of the process.
34:25 Speaker 00 And when it comes for you rejecting candidates, what we really try companies to focus on is try to eliminate candidates as early as possible because it's going to be more efficient, right? What you see in miscalibrated interview processes is a lot of people make it through all the way to the last round and then most candidates drop off there. And that means your team has wasted hundreds of hours interviewing candidates they didn't actually hire. So these are all the knobs that you can kind of tweak in the process.
34:50 Craig S. Smith? Yeah. And, and you know, as the economy becomes increasingly digital.
34:59 Craig S. Smith? Presumably, demand for this kind of thing will grow. How do you see the market developing?
35:05 Speaker 00 Yeah, I think, yeah, the way you can think about it, we are benefiting most companies and those that are either hiring competitive talent or are hiring at a large volume. So but especially competitive talent, as you can see with these like AI offers you touched on earlier. It's generally increasing. And in our customer based AI companies are the ones that are about growing the fastest. And I think the kind of return, the return to outlier talent is increasing, right? So people that are really good at their job are being able to be even better with AI and some of the new tools. And so I think we may see some lower hiring volumes in some industries, but the competition for that talent that is being hired is going to go up.
35:45 Speaker 00 And that increases the need for more sophisticated recruiting teams and more sophisticated recruiting processes. And so that trend we've been seeing kind of growing in our favor. And then this recruiting operations role that I touched on earlier is also growing in importance where the recruiting team used to just be recruiters, but now there's this operations layer that's really focused on tooling and data and automation to kind of get the most out of every single recruiter on the team and do more with less. And that's also in our favor.
36:12 Craig S. Smith? And are you guys broadening the product into, I mean, you said that that you cover a lot of industries. I mean, how horizontal is it?
36:23 Speaker 00 Yeah.
36:25 Craig S. Smith? Yeah.
36:25 Speaker 00 Yeah, there's nothing industry specific per se, but we are, we started in mostly technology venture backed companies. We now have a lot of financial services, professional services. So it tends to be like these more skilled workers, but then we have increasingly also, you know, large insurance brokerages or other companies that are hiring operations people, even some of our customers in AI are like hybrid where they have software teams, but then also have like field workers that are working on energy. For example, so we have like some really interesting companies. And if there's this volume hiring component, that's another area where AI and automation is really important because you're dealing with even more applicants and the process becomes really overwhelming if you don't have good tooling.
37:06 Speaker 00 So we launched things like texting to candidates for like this higher volume roles. And so we're seeing quite a bit of expansion across, yeah, pretty much every industry in the end is like hiring to some degree. What you don't do today is like, you know, McDonald's or Burger King or other like purely high volume. These tend to be like specialized products that are really focused on that. Yeah.
37:29 Craig S. Smith? Yeah. And how the market is primarily in the US?
37:36 Speaker 00 It's about, you know, seventy percent US, twenty percent EMEA, and then the rest is rest of world. So we do have customers in dozens of countries at this point. And EMEA, we we stumbled into a little bit. I'm from Germany originally, moved to US like twelve years ago, and we had some early customers there. And the market is actually quite similar, especially in the UK, the Netherlands, like the English speaking countries, there are a lot of technology companies that operate very similar to like US companies. And so we have a pretty big team there now too, and a big customer base.
38:07 Craig S. Smith? Yeah.
38:08 Speaker 00 Obviously, our European counterparts are more skeptic when it comes to AI usage. So that's probably relevant for this podcast.
38:16 Craig S. Smith? Yeah, yeah. And the are you guys venture funded? Are you bootstrapped? And how how is that journey going?
38:27 Speaker 00 Yeah, we are venture funded. And but we've been really efficient with our capital. And you know, we're kind of like in a control your own destiny situation now, which is quite nice. We have the opportunity to invest in a business and grow it, but we don't need to rely on additional outside capital.
38:47 Speaker 00 But yeah, I think we made it kind of fruity trough of twenty twenty one, twenty twenty two companies. Largely, it was still pretty early as a business. And then we've been growing really nicely over the last couple of years. So it's put us in a nice position.
39:03 Craig S. Smith? And and is the ambition then to eventually become an all in one HR platform? I mean, beyond hiring, or are you going to stick to the hiring?
39:16 Speaker 00 That's the part that you're teasing out of me that we don't talk too much about publicly. But there's definitely a bigger ambition
39:23 Speaker 00 across broader kind of talent management, if you will. And you know, my co-founder and I, we wanted to start this company to hopefully work on it indefinitely and build a large independent business. And so
39:34 Speaker 00 to do that, we'll have to expand over time. And again, we're not talking too much details, but there's definitely a bigger kind of ambition behind behind what we're doing.
39:42 Craig S. Smith? Yeah, and to keep up, I mean, you're an engineer yourself. So to keep up with with the advancements. I mean, it's as a journalist, it's hard to keep up. I can't imagine trying to build something. Every other day, there's a new model or a new capability. And you know, how do you manage that?
40:05 Speaker 00 Yeah, it's a good question. Yep.
40:08 Craig S. Smith? I was gonna say, is the product always like two steps behind, you know, the the cutting edge of the research?
40:17 Speaker 00 Yeah, that's a good question. I think the interesting thing is if you have a background in software engineering and you are deploying these L L Ms in practice. You develop a certain intuition for things. And the reality is in the space right now, there's a lot of noise, you know, it's like, again, this model won this benchmark and this model had this breakthrough performance on this benchmark. But a lot of these benchmarks are not one to one correlated to actual practical improvements on a real world applications. And so
40:49 Speaker 00 there's actually more noise than actual real progress or change in in the capabilities. And so what we are doing in practice is our engineering team experiments a lot with new tools, you know, if new models come out, we've built a way to try them out against our benchmarks. And we can quickly prototype things. But I'd say the progress has been slower than it seems, and outside of like material progress on what I'd call like general intelligence of these models. So but we're definitely behind. We're not doing cutting edge research. We're waiting to see which models are really good at a certain thing. And then we're carefully deploying it into the product. We hiring is a sensitive area. We don't want to,
41:27 Speaker 00 you know, we don't want to experiment with with that process. Instead, we're testing, validating, and then rolling stuff out that feels we're really confident in.
41:37 Craig S. Smith? Yeah. And and does the platform, you know, agents is what everyone's talking about these days?
41:45 Speaker 00 Yes.
41:45 Craig S. Smith? Does it have an agentic layer where, you know, the user can ask it to do something and it'll it'll go out and?
41:54 Speaker 00 Yep, not today. So again, we've been kind of careful. And when we roll things out, and we've been really focused on like this task level assistance, candidate assistant, A I app review, note taker, kind of this discrete tasks that L M's are really, really good at. We're not building towards an agentic layer and trying to see what is actually possible. There's some areas of the product where I think this could work really well. Interview scheduling is a good example where we we automate a lot of the pieces to get ready today. And what an agentic workflow would do is like glue these together and instead of a human doing like a small intermediate step so we can have the system do it. But agentic is another big buzzword out there and there's definitely some real progress, but also
42:38 Speaker 00 there's not that much stuff in production yet that's working really well on complicated, long running tasks.
42:43 Craig S. Smith? Yeah, exactly.
42:46 Craig S. Smith? Yeah, is there anything I'm not talking about that you want listeners to hear?
42:53 Speaker 00 Yeah, that's a good question. I think I tried around the fundraising question because I wasn't sure when this is going to air, but we are. By the time this is live, we will have announced a fifteen fifteen million dollar series D. So that's that's kind of exciting for kind of for the investment to our product and AI. And you can learn more about SHB at SHBHQ.com. We're hiring across all teams. We're still employing a lot of humans and paying them a lot of money to improve the product and the business. These are probably probably the most important things. And then maybe lastly, yeah, if you're on the job market and interviewing, I think it's helpful to have some empathy with the recruiting teams on the other side.
43:35 Speaker 00 And it's not an easy, not an easy job to sift through like a lot of people that are interested in joining really interesting companies. Yeah, I hope we could shine some light on both sides of the table today.
43:45 Craig S. Smith? And and beyond tech, what's the what's the strongest vertical that you guys have worked in?
43:51 Speaker 00 Yeah, professional services broadly speaking is probably the that area, you know, similar where the return to really strong talent is quite high and it's generally competitive to hire senior people. So that's kind of another industry and financial services is kind of adjacent to that.
44:07 Craig S. Smith? Just generally, maybe sum up with how the HR function or hiring practices are evolving and whether I, you know, will there come a time when?
44:26 Craig S. Smith? An HR function, when you can apply, give it the platform access to your social media or whatever. Maybe your past writing, if you've been writing papers and that sort of thing. And the platform will do sort of a holistic read of all these candidates. I mean, how deep a look can these platforms or do you think these platforms will be able to take at individual candidates?
45:07 Speaker 00 Yeah, it's a good question. I definitely think, you know, the models out there today already have like, obviously these like web search capabilities. And so that's something we're looking into like beyond a resume. Is there other data points that we can collect for certain roles that are kind of publicly out there? I don't, I do think some of the core problems about recruiting will stay the same, which is, you know, it's a matchmaking problem similar to dating or other matchmaking problems. And there's always an issue of like imbalance of supply and demand. And if the process gets more efficient on both sides, we may just end up with like more candidates applying to more jobs, more jobs getting more applicants. But we're still going to run into issue of like, in the end, we probably need humans
45:51 Speaker 00 to kind of figure out the best possible fit. And so we are, again, really kind of focused on how can we give companies the right tools to filter for these folks faster. But I think the core of the recruiting job probably won't change too drastically. And I don't think it will be automated away anytime soon. So it's just a matter of roles we talked about earlier, where we need a lot of context about the business. Because a lot of these roles are pretty specific to a specific team people are joining or the challenges the business is facing. So it's a role of the recruiting team. So I think there's a lot of change. Our goal is really get rid of the drudgery of the process and get people to focus on the core skills as much as possible. I think we're making some good progress.
46:34 Craig S. Smith? Ashby, where does the name Ashby come from?
46:36 Speaker 00 That is a great question. You know, we started a company. We wanted a name that's not specific to any one product. And so because we wanted to expand to multiple products over time. We wanted the domain to be available, the trademark to be available and something short. And so we brainstormed for a couple of weeks and this was actually a street name in in the East Bay of San Francisco where between my house and my co-founder's house. And we're like, this is fine enough, you know, and it's it's definitely grown on us over time and worked pretty well. That's kind of the Yeah. logical process that which arrives that does.
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