Alex: Your AI Recruiting Partner

1. 0:00 – 0:35

   Intro

   1. NDNicolas Dessaigne0:00

      [upbeat music] Today I'm joined by Aaron Wang and John Rytel, the founders of Alex. They just announced a 17 million Series A with Peak XV. Congrats, guys. That's awesome.

   2. AWAaron Wang0:15

      Thank you.

   3. JRJohn Rytel0:15

      Thank you.

   4. AWAaron Wang0:16

      Excited to be here.

   5. NDNicolas Dessaigne0:16

      Can you first tell us what Alex does?

   6. AWAaron Wang0:19

      Alex is your AI recruiting partner that helps companies and staffing firms interview everyone and ultimately hire the best people. Alex has access to all of your favorite recruiting tools and can conduct phone screens, video interviews, scheduling, sourcing,

2. 0:35 – 2:00

   What Alex Does for Recruiters

   1. AWAaron Wang0:35

      uh, updating the applicant tracking system, completely autonomously.

   2. NDNicolas Dessaigne0:38

      So what's wrong with recruiting today? Like, what are you trying to fix?

   3. AWAaron Wang0:41

      Sure. So applicant volume has tripled in the past three years, and time to hire is at an all-time high. It's about 60 days. I mean, we're here sitting in San Francisco. You can close a house in San Francisco faster than you can hire your next engineer. And so the hiring market is incredibly inefficient today, and it's never been more infish- inefficient in history. And the main bottleneck is, uh, one, bandwidth of the recruiters, uh, and two, uh, matching, right? How can I actually match the best candidates with the best opportunities? So Alex solves both of those problems by allowing any company or any employer to interview everybody at scale, right, and get incredible insights on these candidates. And two, be able to match those candidates to the right opportunities because we have access to all that data.

   4. NDNicolas Dessaigne1:25

      Can you give us a sense of, like, the scale, uh, of the operations here? Like, how many interviews have you performed so far?

   5. AWAaron Wang1:31

      We're doing thousands of interviews across all of our customers every single day. Just since we launched last year, uh, Alex has helped hire thousands of people, and today Alex is, uh, filling tens of thousands of active job roles.

   6. NDNicolas Dessaigne1:45

      Who are you doing that for? Like, is it mostly, mostly, like, companies or staffing agencies, or how does that work?

   7. AWAaron Wang1:51

      What's really interesting about kind of the, working in the recruiting space is that, uh, it's an incredibly large market. Every employer

3. 2:00 – 3:50

   From College Projects to Starting Alex

   1. AWAaron Wang2:00

      hires by definition, right? And so the question is, where is that niche edge of the wedge? Where do you get started?

   2. NDNicolas Dessaigne2:05

      Mm.

   3. AWAaron Wang2:05

      We s- found a lot of pull from the staffing agencies. Our incentives are highly aligned. If we do a really great job-

   4. NDNicolas Dessaigne2:12

      Because they hire more people? Is that the main-

   5. AWAaron Wang2:14

      Exactly right.

   6. NDNicolas Dessaigne2:15

      Okay.

   7. AWAaron Wang2:15

      They concentrate the pain point, so it's a true hair-on-fire problem. If we do a great job, we also increase revenue for them. That's how they make money.

   8. NDNicolas Dessaigne2:22

      Mm.

   9. AWAaron Wang2:22

      And so, uh, staffing is one of our larger segments. We do also work with large, some of the largest employers in the world. Um, for example, the largest US supplier of, let's say, nuc- like, nu- nuclear components, right?

   10. NDNicolas Dessaigne2:34

       Okay.

   11. AWAaron Wang2:34

       We have a huge, like, electron deficiency with, uh, AI and training.

   12. NDNicolas Dessaigne2:38

       But what roles are you hiring for here?

   13. AWAaron Wang2:41

       Yeah, it's across the board, everything from highly technical, uh, staff software engineer roles to senior accountants to, for that nuclear company, they're a publicly traded nuclear company, we help them hire nuclear welders, right? And that's much more blue-collar.

   14. NDNicolas Dessaigne2:55

       Wow. And so the AI, do you test the technical skills, too? It's like stays kind of like first screen kind of interview?

   15. AWAaron Wang3:03

       Yeah. That's exactly right. So it tests for skills on that first call or that first interview.

   16. NDNicolas Dessaigne3:08

       Okay.

   17. AWAaron Wang3:08

       What's really interesting is nuclear welding is a very niche, uh, discipline, right?

   18. NDNicolas Dessaigne3:13

       You bet. [laughs]

   19. AWAaron Wang3:13

       But with, uh, Alex, right, with an AI, you're able to really dig deep into that discipline and really better understand if that particular candidate is qualified for, for that role.

   20. NDNicolas Dessaigne3:25

       And how do you make sure the interviewers, the interview, the AI interviewer knows about welding? [laughs]

   21. AWAaron Wang3:30

       Yeah. So, um, first Alex has access to your applicant tracking system-

   22. NDNicolas Dessaigne3:36

       Mm

   23. AWAaron Wang3:36

       ... or your HRIS. And so, uh, Alex will be able to see the types of people that you've hired before, will be able to see the job description and any intake meeting notes from the hiring manager. And so Alex will learn to be the best, uh,

4. 3:50 – 5:40

   Why Recruiting Is Broken Today

   1. AWAaron Wang3:50

      employer brand ambassador for your company. Alex, uh, can also be fine-tuned on particular roles like nuclear welding, um, such that, uh, it will find and, and, and ultimately qualify the best people for the job.

   2. NDNicolas Dessaigne4:03

      Let's go back to the early days of Alex. You're both relatively young, right? I'm not sure you did a lot of interviews yourself. [laughs]

   3. JRJohn Rytel4:10

      [laughs]

   4. NDNicolas Dessaigne4:11

      How come you picked that idea?

   5. AWAaron Wang4:12

      We've been candidates a lot longer than we've been employers, actually. Um, I think that ends up being a really unique advantage. I think for a lot of, uh, the companies that have tried to build in the recruiting tech space today-

   6. NDNicolas Dessaigne4:24

      Mm

   7. AWAaron Wang4:24

      ... I think where they haven't succeeded is in building, uh, a candidate experience, uh, that, you know, really puts job seekers for- first. An experience where, "Hey, I applied for this job. I want to talk to, you know, an AI. I want to engage with this employer," right? And so I think that gives us a very unique advantage. You know, John and I met in undergrad. We met at Brown. Um, we had built a hiring, uh, tech company, uh, before this, um, and that was a lot of fun.

   8. JRJohn Rytel4:51

      Learned a lot, and yeah, um, it really kind of helped us better understand the market, what, like, the pain points were for ca- uh, for candidates, and optimize for the candidate experience, 'cause ultimately, um, that's kind of what's more, most important, uh, in what we're building.

   9. NDNicolas Dessaigne5:05

      So how long have you been working on Alex? I mean, like, a few years ago, like, you couldn't even imagine using AI for that, right? I guess now, now you can make that conversational. You can have a real conversation with an AI. When did that become possible?

   10. AWAaron Wang5:19

       It really got started, uh, in, uh, the end of, uh, 2023 with the launch of G- GPT-4 Turbo. We ultimately were building at the start of 2024, launched towards the end of the batch, I believe it was in April of 2024-

   11. JRJohn Rytel5:32

       Uh-huh

   12. AWAaron Wang5:32

       ... um, and quickly got our first customer, and then, uh, continued to, to scale from there.

   13. NDNicolas Dessaigne5:37

       What were the biggest challenges? You know, like there is that uncanny

5. 5:40 – 8:00

   Automating Busywork in Hiring

   1. NDNicolas Dessaigne5:40

      valley where it's so weird to speak with an AI. It looks like we're past that. How did you get there?

   2. JRJohn Rytel5:45

      Yeah, at the end of 2023 is when this kind of building a voice agent that was, had low enough latency, um, where candidates were comfortable talking with it, um, that's when that really became possible. Um, and there were no other voice tools out there to kind of build this like you have today, the VAPIs, the retails. So we really owned that orchestration platform and being able to connect all the different models succinctly to be able to have a low latency, high performant, um, reliable agent. And over time, models just kept on getting better and better, so we made, you know, incremental progress on latency, on voice quality, transcription quality, and that really helped kind of optimize the, the candidate experience.

   3. AWAaron Wang6:25

      That's actually one reason we named the AI recruiting partner Alex-Was because the transcription [laughs] models weren't quite there yet in, uh, in even in 2024. And so, like Alex was like a, it's a very, uh, easy name to understand that the trans- that the transcription models -

   4. NDNicolas Dessaigne6:39

      [laughs]

   5. AWAaron Wang6:39

      ... could like, uh, understand and say like, "Alex, okay," like that's very obvious that he's, he or she is Alex.

   6. NDNicolas Dessaigne6:43

      The speech-to-text was, would always get this right.

   7. AWAaron Wang6:45

      Exactly right, it would always get it right. Um, and so, uh, that, that's kind of the origin of the, [laughs] of the name.

   8. NDNicolas Dessaigne6:51

      The conversation during the interview can lead the AI in some kind of like corners or corner cases, but at the end you want to have some score. How do you make sure you, uh, you get back, get the conversation back on track when necessary?

   9. JRJohn Rytel7:03

      That's just a matter of prompting. Um, and a lot of kind of, um, really seeing these candidates do, do these interviews, evaluating your agents at, at scale. It's just making sure that there are guardrails in place so that kind of, um, the questions, all questions are asked, follow-ups are asked appropriately, and making sure that, um, the, yeah, candidate has no opening for, for trying to, to take advantage of, of Alex. Even though we've had a lot of candidates try, try doing that.

   10. NDNicolas Dessaigne7:31

       Yeah.

   11. JRJohn Rytel7:31

       No successful attempts yet.

   12. NDNicolas Dessaigne7:32

       Do you have any, uh, good stories of candidates, uh, trying to hack the AI?

   13. JRJohn Rytel7:35

       We've definitely had, uh, interesting attempts, you know, candidates, especially so- software engineers who are kind of trying to break and see what, what the, um, weak points are of, of the agent. Um, we've seen, you know, people try to do that, and usually at the very end we, you know, we ask for, "Hey, do you have any feedback? Like, what's the inter- how'd the interview go?" And they're like, "Yeah, I'm, I'm impressed. I wasn't able to break it."

   14. NDNicolas Dessaigne7:55

       [laughs]

   15. JRJohn Rytel7:55

       Um, so we, we've definitely had a lot of, yeah, people who apply to, for our software engineering

6. 8:00 – 10:10

   Customers: Fortune 100s, Restaurants & Big 4 Firms

   1. JRJohn Rytel8:00

      roles to, to try and then-

   2. NDNicolas Dessaigne8:01

      So do you score them higher in the [laughs]

   3. JRJohn Rytel8:03

      [laughs]

   4. AWAaron Wang8:04

      We've had candidates like try to talk in like XML or in Markdown-

   5. NDNicolas Dessaigne8:08

      [laughs]

   6. AWAaron Wang8:08

      ... to try to prompt inject. Unfortunately for them that, that hasn't worked. Um, but it's, it's funny. An interesting phenomenon here is that, um, you know, when you build, you know, agents in a particular vertical, for this one, we're in the job market, it's a market, right?

   7. NDNicolas Dessaigne8:22

      Mm-hmm.

   8. AWAaron Wang8:22

      And so one clear dynamic of a market is that you have, uh, adversarial effects, right? That is, you know, candidates are gonna do what they can to try to get the job.

   9. NDNicolas Dessaigne8:31

      Of course.

   10. AWAaron Wang8:31

       Right? And, you know, the job is a scarce resource. And so recently we've seen a lot of uptick in candidates using, you know, their own AI to, uh, mass apply to jobs-

   11. NDNicolas Dessaigne8:41

       Ah

   12. AWAaron Wang8:41

       ... but also during the interview have, you know, transparent, you know, overlay like a, a ClueLee or something like this to try to cheat on, uh, interviews. And so that's something that we've had to build out, building kind of cheat detection to make sure, hey, this person is legit, right? We've had, we've had to build models for detecting deepfakes, right? 'Cause now there are like live deepfakes that you can stream into your device. Um-

   13. NDNicolas Dessaigne9:03

       Yeah, because you see the video of the candidate or?

   14. AWAaron Wang9:05

       Exactly, right. Because these interviews are oftentimes done through like a video conferencing platform that we actually built from the ground up.

   15. NDNicolas Dessaigne9:11

       And so, yeah, like interviews that end up like being an ag- a candidate agent and then the recruiter agent, and they speak to each other? [laughs]

   16. AWAaron Wang9:17

       Yeah. [laughs] That's, that's, it's, it's funny, we get that a lot, and we'll see where the, the future goes, right? Like, that's actually pretty interesting, um, that the, the two sides of the market, the employers and, and job seekers will, will always challenge each other. But ultimately I think it's, it's, it's actually good, right? Because that means that the market's evolving, right? You know, we believe in a world where, you know, jobs should be much more accessible, right? Today, you know, we see knowledge is democratized, right? I can go on Google, I can go on ChatGPT. Education is, is, uh, a lot of education is democratized, right? But still, like the job market opportunities are not, right? I'll apply to hundreds of jobs as a candidate, and I'll be lucky if I get a handful of interviews. We believe in a world where if you apply for a job, your dream job, you should be able to get that interview, right? And it shouldn't be about if you, you know, went to an Ivy League or, you know, whatnot, it should just be, "Hey, can you tell me about the skills you bring to the

7. 10:10 – 12:40

   Why It Takes Hundreds of Applications to Get Hired

   1. AWAaron Wang10:10

      table and the knowledge that you w- w- can bring to me?"

   2. NDNicolas Dessaigne10:12

      It's not like, uh, at least they get that first interview.

   3. AWAaron Wang10:15

      Exactly.

   4. NDNicolas Dessaigne10:16

      At least no interview.

   5. JRJohn Rytel10:16

      Every candidate gets a first round interview.

   6. NDNicolas Dessaigne10:17

      And then they can, um, unearth like some, uh, overlooked candidates that would not have had an interview.

   7. AWAaron Wang10:22

      Yeah.

   8. NDNicolas Dessaigne10:22

      Like, do you have like any, uh, examples where you actually ended up helping a company hiring a candidate they would never have interviewed?

   9. AWAaron Wang10:28

      Yeah. I, there are several examples. I was actually just on the phone with one of our customers yesterday. Um, so COBOL is this very old programming language. Um, it's used by some of the largest banks and financial institutions in the world. I think it was started in 1959. Not a lot of COBOL developers left in the world.

   10. NDNicolas Dessaigne10:46

       [laughs] Yeah, that's it.

   11. AWAaron Wang10:46

       Right? And so one of our customers is the staffing firm that hires for all those COBOL developers. When they launched Alex, um, Alex found, um, looked through their database of their own app, through their own applicant-

   12. NDNicolas Dessaigne10:57

       Mm-hmm

   13. AWAaron Wang10:57

       ... tracking system, found people that seemed relevant to this COBOL developer role that they were hiring for, automatically reached out to them, interviewed them, and found 11 people that they automatically just submitted, and they got placed with their clients right away.

   14. NDNicolas Dessaigne11:09

       And they were lost in the, the database.

   15. AWAaron Wang11:10

       They were lost in the database, right? Whether that database is your own applicant tracking system-

   16. NDNicolas Dessaigne11:15

       Mm

   17. AWAaron Wang11:15

       ... or your own applicant pool, or sourced from LinkedIn or Indeed, you know, these pools are just getting larger and larger and larger, and much more harder to handle with, you know, manually. And so our, our, our aim is to really be that recruiting partner that says, "Hey, well, you know, there is that diamond in the rough. Let's use AI to help you find it."

   18. JRJohn Rytel11:34

       You know, you might have already talked to the perfect candidate, you know, years ago. And, you know, with the current technology, it's, that person is just lost. But with Alex, you know, you're able to create opportunities that just previously, you know, you wouldn't have been able to.

   19. NDNicolas Dessaigne11:46

       Yeah. Is there any specific metric you are tracking closely, like kind of any evidence, for example, that's really working for, for your customers?

   20. AWAaron Wang11:53

       Yeah. There are several. Um, I think one of them that we typically see in pilots is can Alex actually, uh, given that they interview all these folks-

   21. NDNicolas Dessaigne12:02

       Mm

   22. AWAaron Wang12:02

       ... Alex stack ranks them. Will Alex's stack rank be similar or even better than the stack rank that, uh, a traditional recruiter would look at, right? So, you know, is that best candidate really better than the second-best candidate, that's really better than the third-best candidate, right? And I can say that because, you know, I've, I have the video interview, I can actually pull quotes from that interview and say, "Hey, yeah, this person scored a 95 on their, uh, let's say their Python knowledge, because, you know, they have, you know, 10 years of experience. They said this, they did that, and I tried to dig deeper and they, you know, kept pulling out evidence from their experience, and this person's fantastic." And so that's

8. 12:40 – 15:00

   Candidates Cheating with AI Tools

   1. AWAaron Wang12:40

      one way we look at it. Other things that we're seeing, um, are increases in retention rate.Right? So these candidates that are getting hired are actually staying longer at their jobs. And also for a lot of our staffing from customers, again, they generate revenue, right, from placing people. And so if they- the client hires them and then they don't end up staying, they don't, they don't, uh, get any incremental revenue. And so that's another way that we track success for, for our customers. And again, it really ties back to actually the candidates. So the first thing we really look at is, are candidates enjoying, you know, interviewing with Alex-

   2. NDNicolas Dessaigne13:10

      Yeah

   3. AWAaron Wang13:10

      ... meeting Alex?

   4. NDNicolas Dessaigne13:11

      How do they feel? How could they feel like, like, I guess the first time was, and still there is a lot of first times today, right?

   5. AWAaron Wang13:16

      Yes.

   6. NDNicolas Dessaigne13:17

      A lot of people have never spoken to an AI. How do they react?

   7. AWAaron Wang13:20

      I think my favorite part that I hear from candidates is that we really eliminate ghosting, right? Because in a world where, hey, if you apply to this job, you get that opportunity to at least be heard, right? You get to be updated on your, your, your candidacy-

   8. NDNicolas Dessaigne13:34

      Ah

   9. AWAaron Wang13:34

      ... right?

   10. NDNicolas Dessaigne13:34

       Like, versus the company where they never hear back. At least they get the first interview, even if it's an AI.

   11. AWAaron Wang13:40

       Exactly, and they can also ask questions during the interview. They have Alex's phone number, and so they can text Alex at any time and say, "Hey, you know, actually, I had this question about your PTO policy," or, "Hey, are there any updates to my application? Has this application been filled yet?"

   12. NDNicolas Dessaigne13:54

       Mm. So it's more of that interview. It's the whole candidate experience.

   13. AWAaron Wang13:57

       That's exactly right. Yeah, we're really supporting the candidate throughout their entire journey at any particular recruiting or hiring process that, that they're in.

   14. NDNicolas Dessaigne14:05

       Is there any things that Alex actually is looking for in candidates that, uh, human interviewers are overlooking?

   15. AWAaron Wang14:11

       There's an incredible amount of data in, within a video interview, right? And a lot of it is, is missed, right? If you think about a video interview, 20, 30 minutes, you know, that could be, you know, gigabytes worth of data. You know, with a traditional recruiter, that's oftentimes boiled down to handwritten notes, right? Um, but with, with Alex, that's all recorded. You know, you have much more in-depth notes. Um, and Alex is able to both analyze, hey, your technical skills, your hard skills, requirements for the role, but also things like soft skills, right? If I'm hiring for, let's say, a salesperson, I want to make sure that they are a concise seller and they have concise communication, right? That's something Alex is able to test for because Alex has that, that video data, that audio data, and will remember that, that interview.

   16. NDNicolas Dessaigne14:55

       [laughs] Okay. Awesome. Let's talk about the news you are announcing. So

9. 15:00 – 17:30

   Scaling One of the Fastest Growing Recruiting Startups

   1. NDNicolas Dessaigne15:00

      $17 million Series A, uh, that you, uh, you just closed. Uh, where will the capital go? Why raise now? Like, tell us more about the, the round.

   2. AWAaron Wang15:09

      Yeah. Now is an incredibly exciting time, I think, to, to build, um, a company in particular. Um, but we've seen incredible market pull across all of our customers. A year ago, I think we wouldn't have been able to say that, you know, especially, you know, hir- selling to some of the largest companies in the world. You know, you know, HR is one of the areas that is, you know, h- you know, they've really been burned by technology that hasn't worked for them in, in the past. And so a lot of what we've been doing recently has been building that transparency, building that confidence, and building that relationship with, with some of the largest employers in the world, and that's really allowed us to, um, one, uh, sell into these organizations, and two, continue to build a great product for them. And so we're gonna use this $17 million to really make sure that, hey, we have the best product when it comes to, uh, AI and recruiting today.

   3. NDNicolas Dessaigne15:56

      Mm-hmm.

   4. AWAaron Wang15:56

      Uh, and we want to make sure that we continue to be best-in-class in supporting, uh, our customers in, in that way.

   5. NDNicolas Dessaigne16:02

      All right, so just hiring a bit our team. [laughs]

   6. AWAaron Wang16:04

      Yeah.

   7. NDNicolas Dessaigne16:04

      Growing the team.

   8. AWAaron Wang16:05

      That's right.

   9. NDNicolas Dessaigne16:05

      Okay. Any- anything non-obvious?

   10. AWAaron Wang16:07

       Anything not obvious? You know, I, I think a lot of it actually is relatively obvious. I think something that is less obvious is being in San Francisco, you're around a lot of companies that, uh, hire a lot of engineering and technical talent and will oftentimes, like, outweigh the go-to-market side of things. I think for us, um, go-to-market is extremely important. You know, things like marketing, design end up being incredibly important because, again, you know-

   11. NDNicolas Dessaigne16:33

       Mm

   12. AWAaron Wang16:33

       ... of course you need to have a great product, right? You need to create value for your customers, or you need to make something people want. But in addition to that, when you have enterprise sales, right, relationships also matter, right? Building a brand also matters. Go-to-market, at the end of the day, matters a lot. We'll likely see kind of more go-to-market hiring than probably the traditional, uh, Silicon Valley company.

   13. NDNicolas Dessaigne16:54

       You're also announcing a, a rebranding, right? Uh, your name was not Alex. Alex was the name of the interviewer.

   14. AWAaron Wang17:00

       That's right.

   15. NDNicolas Dessaigne17:01

       Like, was that creating some confusion, or why did you choose to go from Apriori to Alex?

   16. AWAaron Wang17:06

       Yeah. Alex was, uh, the name of the, an AI recruiting partner-

   17. NDNicolas Dessaigne17:10

       Yeah

   18. AWAaron Wang17:10

       ... and a lot of our customers were already calling us Alex. Um, and so it made a lot of sense to reduce any type of confusion and just rename, uh, the company to, to Alex. Um, we had already seen companies do a good job with this anyways. You see, saw this with Accodium, right?

   19. NDNicolas Dessaigne17:27

       Mm.

   20. AWAaron Wang17:27

       Turning into Windsurf. And the particular naming the company

10. 17:30 – 21:15

    AI Agents and the Future of Job Hunting

    1. AWAaron Wang17:30

       actual name made a lot of sense. You're seeing a lot of success with that with, like, Harvey, for instance. W- we want Alex, we want the company, we want AI to be approachable and, and grounded, right? It's not just another piece of abstract AI software. It's really, you know, your partner.

    2. NDNicolas Dessaigne17:45

       Have you seen people react differently to that n- that new name?

    3. AWAaron Wang17:48

       Yeah. Well, they can certainly, you know, pronounce it a lot easier.

    4. NDNicolas Dessaigne17:51

       [laughs]

    5. AWAaron Wang17:52

       Uh, and share it a lot easier. But, uh, we've, we've had nothing but, you know, great things or heard nothing but great things from our, from our customers and, uh, so far. And so we're, we're really excited about the name change.

    6. NDNicolas Dessaigne18:01

       Looking ahead, like, what do you think will still be, uh, like, kind of five years from now? Will there still be human interviews? Like, where are we going?

    7. AWAaron Wang18:09

       We're not looking to replace recruiters. Um, and recruiters won't be replaced. They're gonna be supercharged, right? A lot of their time today when it comes to interviewing is, you know, look, you're asking the same, you know, five questions to these, these, you know, hundreds of people. There's a lot of scheduling. You're updating your pieces of software, right? Adding notes into your applicant tracking system. Um, and we want to give that time back, right? A lot of those administrative tasks can be, uh, pulled out, and you can reinvest that time into more strategic roles. For instance, uh, building a relationship with your hiring manager, helping close that candidate that's on the fence, right? Or spending more time with the candidates that you know are qualified, and you really want them to join the team, right? Those are things that I don't think AI will be able to replace, right, those human elements. And good, they shouldn't. But those more administrative tasks, we certainly want to be able to pull them out.of that and, and help them do more.

    8. NDNicolas Dessaigne19:02

       And speaking of, uh, hiring, so you are expanding the team. Like, any role you want to, uh, to pitch to, uh, to the audience?

    9. JRJohn Rytel19:07

       Yeah. Uh, we're hiring, I mean, on every front. So engineers, um, both r- full stack, front-end, back-end, designers, uh, PMs, um, go to- lots of go, go to market as well.

    10. AWAaron Wang19:18

        Yeah. If you wanna join a company that's not replacing people, but helping hire them, right? I think that's, like, very exciting, and that's the future that we believe with AI, and the future that we wanna build towards.

    11. NDNicolas Dessaigne19:30

        That's awesome. And before to conclude, is there anything you, you wish you knew when you started, looking back in time two years ago?

    12. AWAaron Wang19:38

        You know, we get this question a lot, especially when we come back to YC and, and h- talk with the current batches. Things that stick out to me are, I think at some point you need to, you know, put your foot down and have really high conviction in what you're building. There's a delicate balance between, you know, listening to the market and, and, and build, make something people want, and saying, "Hey, look, we know that this is going to exist in five years." Question is, like, in what form? We came out of the batch, we launched, um, we knew that there was going to be some use case for an AI recruiting partner. We weren't sure if that was gonna start with employers, or with tech companies, with, you know, the S- you know, Fortune 100s. You know, they're a few of our customers today, with staffing agencies. And so I think you need to be really obstinate about that vision that you have, but kind of flexible on, on actually how you're gonna get there.

    13. NDNicolas Dessaigne20:23

        That's awesome. Any other advice for founders from your journey?

    14. JRJohn Rytel20:26

        Yeah. I mean, you just need to be extremely versatile and be able to grow. Uh, kind of we, we live in an age where technology is changing and maturing so quickly. And, you know, there's new models, new, new use cases coming out. So just being able to really grow with the technology and say, yeah, just kind of have a clear long, long-term vision goal, but be flexible on how you achieve it and kind of what, um, what tools you use. You can really learn anything in a very short amount of time, so possibilities are endless, and you just need to be super, super, um, excited to build.

    15. NDNicolas Dessaigne21:01

        Aaron, John, thank you so much for joining us today. It was awesome to catch up.

    16. AWAaron Wang21:05

        Awesome. Thanks, Nico.

    17. JRJohn Rytel21:05

        Thanks, Nico. [upbeat music]

Episode duration: 21:15