Sapia Revenue & Funding (2024)

[00:00] Hey, folks. My guest today is Barbara Hyman. She's been working in her career in career executive HR roles. She realized that companies weren't able to unlock the true potential of their people simply because they didn't have bias free insight on everyone's strengths. She's now trying to solve this via human learning and machine learning at Sapia, sapia.ai. Barb, you ready to take us to the top? [00:21] >> I am so excited to be here. Thank you so much, Nathan. [00:24] Alright. Well, anytime anyone mentions AI or machine learning, I always just cut right to it and go, what's your total team size and how many engineers? [00:33] >> I think the big question is how many data scientists we have. [00:35] Four data scientists. [00:36] >> Lot of talk about what is AI and one quick check that I suggest to businesses is go out on LinkedIn and see whether there are any data scientists. If there are not, then there's not really AI going on. That's right. [00:47] Where where are you guys? [00:49] >> So we are headquartered in Australia. We have an amazing team which we call Phi Labs who are really our innovation machine. They're a group of PhDs in machine learning and AI. [01:00] They're full time or consultants? [01:02] >> No, no, no. It's all full time. We only have full time. We're about 54 people. We've been product led from the beginning because you need to be when you're building technology that's used to support human decision making. We So have engineers, machine learning engineers, everything we do is proprietary so we don't use any open source algorithms or products. We effectively are a fully vertically integrated machine learning system that has built a capability to understand you Nathan [01:28] >> from a short conversation. So it's really new science. It's something that IBM tried to do with Watson for a couple of decades but couldn't because they didn't have the data. And even though Google has 10,000 PhDs working in NLP, they can't do it either because they don't have the data. So there are some elements that we've got that are pretty unique and that's what's really fueled our capability and our continued innovation. [01:51] Wanna give you some time to defend that because most people listening are gonna wait. How does this lady I'm just hearing on Nathan show have more data than Google? So defend that a little bit. How have you gotten unique data that Google doesn't have? [02:00] >> Yeah. So there's a lot of discussion around, do you actually need to have large data sets in order to create impressive and accurate predictive models? You don't because you need to look at the context in which you're using it. So what we are is we've scaled the science of a structured interview. So if you think Google and Amazon, for instance, they take you through these laborious interview processes that are very rigorous where you're being asked the [02:23] >> same questions and you're all measured against the same rubric. In their case, it's the leadership principles. Now you can use humans to do that, which they can afford to do because they're a well resourced organisation, but most can't. How do you actually maintain that level of rigor but remove all the human bias by using technology? That's what we're doing by chat. The data that we have that's first party and proprietary data is the responses to those [02:45] >> structured interviews. That's now at about 800,000,000 words. It'll be at a billion words fairly soon and that is our [02:52] Across how many interviews? 800,000,000 words, how many candidates? [02:54] >> About 1,800,000 interviews. [02:56] Okay. Got it. Over what period of time? [02:57] >> Across 47 countries and around about three years. So it took us eighteen months to build a product. You can't just hire engineers and suddenly have a machine learning product. You actually need to capture the data, do the research, most importantly do the bias testing And we started with what we call machine learning models where you take a hired signal. Like if you think about what Amazon did wrong all those years ago in 2018, that they took [03:24] >> CV data and they tried to build a predictive model of that. The issues are firstly, when you're hiring off your incumbents, you risk amplifying existing biases and secondly, when you're using CV data, you're very likely to amplify existing biases. We don't do that. The data set we're using is clean. It is just words. It doesn't have any demographics. It doesn't even have the question in it. And that's what makes this a pure way to understand people [03:49] >> and to use AI in a safe way for people decision making. [03:53] Oh, what's going on there YouTube? Good to see you guys. Now imagine this, you love watching these interviews with SaaS founders, but imagine if we took all of the valuation data out from over 2,807 interviews I've done manually. Saves you a lot of time. Well, we've done this. We've built it into the beautiful interface inside of Founderpath. Check this out. I'll show you how you can access this in a second, but you log in, you connect [04:16] your Stripe account, you see your valuation real time. You can see what it changed over the past eighty eight days and even set goals for valuation this year. Now the secret evaluation is there's many different ways to value a SaaS business. So the reason you're gonna see three or four different valuations inside of your Founderpath dashboard, this is all free by the way, is because depending on who's doing the buying of your SaaS company, you're gonna [04:41] get a different valuation. A VC is gonna pay a different valuation. Private equity firm is different. If you're gonna do a minority sale, that's different. And if you sell the whole business, that's a different valuation. You can see all those when I hover over here, Right? So the teal is what a VC would pay. Yellow is what private equity and red is if you sold the whole thing outright. Now what's cool about this is this is [05:03] not built off random data. Again, you guys hear these interviews on YouTube. All these datas are built from real time valuation data points founders share with us on the show. So traction, 1,200,000 seed round, 3.7 raise. They sold 22% of their business. Go in here and filter by the event. Maybe you only wanna see companies that have sold the whole business. Well, here are a bunch that have been acquired, the valuation and the multiple. Maybe you're [05:28] going out right now and you're raising your seed round. We'll go in here and look at all this recent seed deals that went down, what they raised, what valuation they raised at and what percent that they sold. There's never been a larger dataset of SaaS valuations than what you can get now inside of Founderpath. And we're thrilled to bring it to you. All right, We're gonna go back to the YouTube video here in a second, but [05:50] if you wanna check this tool out, if you wanna jump in and sign up, you can check it out for free to get your valuation at this link. This link, founderpath.com/products/valuations. Or if you go to founderpath.com and hover over products, click on get your valuation here, and go ahead and sign up to give it a whirl. Again, all that valuation data live right inside the platform. I hope to see you there. Alright. Let's jump back into [06:16] the interview. Okay, Barb, I understand the product, this is great. What are companies paying on average per month or per year to use your technology? [06:25] >> Yes. So we have typically multi year annual contracts. We're not a monthly subscription because we're enterprise focused. We work with businesses that have big pain which normally mean that they're big. So we're not at the point where a mid market or a small business can use us, right? It's [06:41] just So like like 50,000, $100,000 ACVs or what would you say the [06:44] >> average Yeah, yeah, it's around about a 100 to a 150. We might have significantly higher than that but you know, somewhere around about a 100 would be typical. [06:53] Okay, do you have any customers that are by themselves paying you more than 1,000,000 per year? No. Can you get there quick in next year or two? [07:01] >> Look, there are different ways to drive revenue depth. Can build more product to get there but right now for us, we're about, we have a number of really large customers that are pretty close to that. And part of why we're coming into The US, we've got a handful of customers here Ericsson, Air Canada, North America obviously, we've just won a couple of others. And our ambition is to obviously take the incredible product market fit in Australia. [07:27] >> We work with most of the trusted consumer brands there, Qantas Group, Woolworths Group, Bunnings, anyone who's on the ASX is aware of us if not using us. We wanna bring that to The US market. So our focus is to be really, really focused. And we see growth coming from expansion, continuing to deliver this value in our technology to customers like the ones that we've been serving well in The UK and Australia rather than start to build [07:52] >> out new product. [07:53] How many total customers today? [07:55] >> We have around 50 enterprise customers at the moment across Australia, The US and The EU. [08:02] Got it, interesting. And so how many, I guess, do you upsell against number of interviews? Is that the right metric? [08:08] >> Yeah, typically the pricing is based on hiring. So you're really aligned in terms of performance if you like. You're not charging based on applicants, you're charging based on the number of hires. And when you think about the difference, So if you're using an agency, I don't know what it's like in The US but in Australia, you might be paying 1,500 to 2,000 per hire for an RPO, you know, you're paying $20 per hire with our technology. So the [08:32] >> cost savings are [08:33] >> pretty It's more expensive than that. [08:34] Mean, recruiters in The States, you're paying 30% of first year salary. [08:38] >> Yeah. Yeah. [08:39] >> Okay. So we're disrupting that sector and the RPO sector. [08:43] Mhmm. So you're charging based off number of completed hires. So how many completed hires, successful placements did you have last year? [08:51] >> About 80,000. [08:53] Oh, wow. Okay. And how do you do you make sure track attribution on those? Can can people go outside of sort of your system or do you know every time your system lands to a hired candidate? [09:04] >> So when we integrate, we're enterprise, we normally are delivered through the system. So a workday or a SuccessFactors or an HRIS. And when you do the integration, you're obviously feeding in data which is not just us pushing data to you in terms of here's the score, here's the profile, but actually we're getting data back. So we have a closed data loop which gives us further opportunity to retrain our models, which we do for our large customers. [09:30] >> So you get to higher and higher accuracy at the time. [09:32] And so last year 80,000, what about monthly last month, how many total hires? [09:39] >> I didn't track it on a monthly basis because we've got quite a lot of cyclical. You know, if you think about retail, you might have 20,000 in one month like our largest [09:48] What do think you'll do this year? [09:49] >> In terms of hires? Yeah. This year? I'd say it would be close to 200,000. [09:56] Wow. Across how many total interviews done since we get a placement rate? [10:00] >> Yeah. It's normally about 2%, 3% yield. So we expect, yeah, we're likely to get to three, four million interviews this year. [10:10] Well, would be, I mean, 200,000 against three, four million would be higher than 3% placement, right? Mean, that's like six, seven, 8%. [10:17] >> Yeah, it's you know, for some, for the really high volume players, it's a very small number in terms of placement for when we work with tech companies, obviously it's higher. But typically if seeing that you're hiring less than 10% of your applicant pool, it makes perfect sense for you to be using technology to give you leverage in that automation process. [10:37] Of course, of course. This is okay, what a great story. So we understand that product, we understand some of your customers. Put this on a timeline though for us, when did you launch the company? [10:45] >> So we launched the company in 2018 is when I started. But as I said, we had to build the product and get to product market fit that took us a good eighteen months. And we have basically had 200% plus growth year on year since then. We're, you know, in The US, I'm here in Seattle right now and have been in San Fran just meeting with VCs and getting to know that market because our business has really [11:10] >> been funded. I've put in my own money, know, I've put in $500,000 because I'm a huge believer. [11:15] Barb, is that a lot for you? Are you [11:16] super rich from a past decade or is that a lot of money for you to make? [11:19] >> I'm just a regular person. I'm a regular person. [11:22] So that was all your savings, your hard earned savings. That's a lot of money. You have to make this work. [11:26] >> Yeah. Yeah. No. That was a that was a second mortgage. Right? That's not money that's sitting in a bank. And then we've had some amazing, really high net worth people in Australia that have, you know, funded my vision and really backed me and believed in me, you know, so much. [11:41] When was that seed round pre seed round? [11:44] >> So end of last year, we had some investors coming in. And so what we want is to now figure out who we wanna partner with. You know, I really wanna partner and a set of partners that can help us grow in The US. So we're we're doing the rounds of VCs over here. [11:59] How much did you raise in the pre seed last year? [12:02] >> I'm probably not privy to disclose that. [12:05] Oh, okay. You don't wanna share that? [12:07] >> No. No. But it's what I would say is that we're unbelievably capital efficient. Our return [12:14] I know that's what I'm saying. Why wouldn't you wanna It brag about [12:17] >> was a six month payback which for an enterprise business is pretty impressive. We won't have that for this year. It might be more like a twelve month. But you know, that's just information I'd prefer to keep private. [12:27] Okay, fair enough. Let's jump into some of the team today. So you said 54 full time, how many engineers? [12:33] >> We have 16 engineers, and then we have another three PhDs and we have two ML engineers. [12:41] Okay. And you mentioned you're in The States right now trying to meet VCs, find the right partner and raise. Do you have a target in mind in terms of what you're trying to raise? [12:48] >> Look, I'd say between 10 to 15 US, you know, we wanna really, we've got a team of seven sales people. We were pre marketing until December. So as I've said, we've been incredibly focused on the product and the product is not just what the engineers do, it's what sits underneath in terms of the NLP and the bias testing and the bias governance and model cards and all that kind of good stuff. That's been a key part [13:12] >> of building out the product as well as the IO component that goes into our assessment. So what we want is to build a sales team here that's bigger than three which is what it is right now and start to invest a bit in the brand marketing side. We've just rebranded which is exciting and we need to put a bit of money behind that so between ten to fifteen. [13:32] >> We're not in any urgency because we've got runway until the middle of next year. So for me it's about really taking the time to find the right partner and [13:42] >> obviously for me, I'm very focused on building the story about our impact here to share what we've achieved for our customers elsewhere. [13:50] And 50 customers at the average ACV you shared earlier about a 110,000 per year would put you today at about a $6,000,000 run rate. Is that generally accurate? [14:00] >> I'd say that's about right. Yeah. [14:01] Okay. And 200% year over year growth would mean you're doing you know, you're doing about a $170,000 a month last year, about a 2,000,000, 2,500,000 run rate last year. [14:09] >> Yeah. That's about well, a bit more than that. Yeah. We're growing a bit more than 200%, but yeah. [14:14] Fair. Very cool. Well, this is great. I mean, this is this is very I mean, look, I don't know what you raised in your pre seed round, but I'm I'm guessing it was less than your current ARR. And anytime I see that ratio, it's a fantastic ratio and very capital efficient. So congratulations. [14:27] >> Thank you. Yeah. It's been hard work. [14:30] Why go give up that and you go do a $10,000,000 raise, you have a three person, five person board now, you've got board meetings, you give up control. There's, you know, you sure that's a step you definitely wanna take? [14:42] >> Look, I mean, think I'll probably end up moving to The US but there's a lot that we don't know about this market. There's not a lot of localization we need to do in the product which is great because fundamentally people are people and our ability to understand different languages is really strong now with the dataset that we have. But for me, it's getting advisors, getting experts to help us who've been there before. So that's why having [15:05] >> a VC partner who's been a founder, who's figured out go to market, who's found ways to cut through the noise. It's a very noisy market HR tech like that. That's kind of gold. I'd feel really privileged if I could find people like that to help us out. So it's an exchange of that obviously needs to deliver in terms of accelerated growth but that would be my motivation. [15:26] Jazz HR, you know, PE backed K1, iCIMS PE backed Vista. There's a lot of M and A happening in this space and you are the perfect target because there's not VCs that they have to negotiate with and give the VCs a 100 x control. Someone might think they could buy you for 20 x and get a good deal. Are you in The US talking to any acquirers? [15:44] >> I get asked about that a lot, but I I just feel we're on the very early stage of our journey in terms of what we can do with this capability. It is world changing. Like we are basically raising the collective self awareness of humanity with our tech because it helps you learn about yourself and your strengths and where you can go with your career and like I just think, wow, imagine what you could do with that. [16:05] >> So I'm not ready to kinda sell out. Know, I feel that there's still a lot of my vision to bring to life through through the product development. [16:12] I mean, speaking though of wow, imagine what you could do with that 60,000,000 on cash upfront would be a nice offer. [16:18] >> Yeah. I'm not in the market for sales for for selling really honestly. Yeah. [16:23] I just don't that's the right answer. You're giving all the right answers. I just don't believe you. [16:27] >> Yeah. No. I'm serious. Like, I'm in a I mean, [16:30] everyone has a number. Right? Everyone has a number. [16:32] >> Yeah. But, you know, for me, this is like the best job I've ever had. I've had amazing jobs, but it's so creative. You get to work with incredible people and every day I'm learning, you know, and I'm surrounded by people who are smarter than me and like, why would I wanna bring that to an end? [16:48] Guys, there you heard it. You're seeing it here on YouTube on iTunes. If you read in the press in like a week, say, Barb sells to I'm gonna ask for a 100,000,000 all cash upfront. You know where to find her, comment below. No, Barb, this is a great story. I guess last question before we wrap up. You know, net dollar retention is really key when it comes to SaaS valuations, which is key for your next raise [17:08] and, you know, minimizing dilution. It sounds like you have pretty healthy net dollar retention, above 110, 120%. [17:14] >> We've had no churn, no defection. We have deals with RPOs who are like agency partners and they've been kicked out and we've had to go along with them. But our renewal rate is from our direct customers is 100%. We've had customers who have been with us for three years. It's incredibly sticky. [17:33] What about expansion? [17:35] So are they growing? [17:35] >> Product expansion, yes. So the product expansion, well, we are the full stack of assessment if you like, if you're thinking about recruitment. So in terms of expansion, we need to build more product in order to get more expansion because when [17:47] you can You can upsell on number of hires, right? Same product but more hires. [17:51] >> Yeah, but different kind of hiring requirements. So if you think about when you're hiring for white collar, there are other things that matter other than just your capabilities. So your technical skills matter. And so then you're one part of a piece. So right now we just wanna stay focused where we are a 100% of the stack when it comes to hiring for a particular customer and just more and more of those. So we have the ability [18:13] >> to go and expand and that's certainly happened in some, but we're focused more on revenue growth through taking the existing formula, which is where we own the entire assessment stack for a large volume player and rinse and repeat again and again. So, you know, Home Depot, Walmart, HEB, you know, Albertsons, you know, all those kinds of players here. Aesop is just about to go live in The US. [18:40] I'm I'm gonna cut you off. We're just short on time just to be clear. HEB, if they hire a thousand people through your platform last year and this year they hire 1,200, they should be paying you more. There's 200 extra hires, but you're saying they don't. You don't drive expansion that way. [18:53] >> Oh, no. No. That that definitely plays into it. Absolutely. [18:55] That's what I'm asking. That's what I'm asking. So what's the expansion revenue from more usage over the past twelve months? Is it 120, 130% on average? [19:02] >> Yeah. Been about 20%. Been about 20 Yeah. On average. [19:06] Dollar retention, if you have no churn, that would be about a 120%, which is really healthy. Yeah. Very cool. Alright. This is great. Anything I missed you wanna touch on before we wrap up? [19:15] >> No. Just great to connect and look forward anyone who wants to contact me. You can find me on Twitter, BarbHyman1 or [19:24] >> find me on LinkedIn. We'd love to hear what you think. [19:27] All right, Barb, Famous [19:27] >> But don't come with any offers, I'm not selling. [19:30] Famous Five here, let's wrap up number one, last book you read. [19:35] >> So I'm reading the book now, Founder Brand, which I'm absolutely loving. And so that's partly why I'm driven towards going back onto Twitter, which I got off. I was just on LinkedIn. So I'm loving that book. [19:46] Number two, is there a CEO you're following or studying? [19:52] >> Satya Nadella for me is inspiring because one person with a different set of attributes can fundamentally transform a culture and that's what he's done. [20:01] >> Microsoft, LinkedIn, LinkedIn recruiter, M and A deal, who knows? [20:05] Number three, [20:06] >> what's your He's a natural owner of our technology. Absolutely. [20:10] Alright. Number three, what's your favorite online tool for building the company besides your own? [20:15] >> Look, I'm a bit obsessed with Loom. Yep. I I you know, in a global team and the ability to connect at a human level, I have become a bit obsessed with Loom. Yeah. [20:25] Alright. Number four, as you chug your Starbucks like me, how many hours of sleep are you getting every night? [20:30] >> Five to six is a good night. [20:31] Alright. Fair enough. [20:33] >> I'm not great. I'm always on. [20:35] And Barb, what's your situation? Married, single, kiddos? [20:38] >> I have three kids, older kids, and I have a beautiful partner who has no kids and a dog. So it works really well. We live separate. [20:44] Amazing. I love that. [20:46] That's incredible. Okay. And do you mind me asking how old you are? [20:49] >> I'm 52. [20:50] Oh my god. You look amazing for 52. Last question. Something you wish you knew when you were 20. [20:56] >> I wish I knew that I was so freaking good at sales because I would have gone into sales and made a lot more money than where I am right now. [21:04] Guys, there you have it. Sapia.ai redefining what it means to do an interview, to hire recruits, to diversify, and do it at scale. They processed, call it last year, over one Oh, sorry, total over the last three years, 1,800,000 interviews. Last year, 200,000 or sorry, this year, 200,000 total hires projected from 3,000,000 interviews. These are the HEBs of the world using the platform. They've just passed a $6,000,000 run rate, growing over 200% year over year. Looking [21:29] at raising right now to really put fuel on the fire, maybe a $10 to $15,000,000 round. We'll see what Barb ends up deciding. But Barb, thanks for taking us to the top. [21:37] >> Thanks so much, Nathan. Thanks for having me. [21:40] One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers, they try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU, CAC, LTV, you name it, they share it and the buyers try and make a deal live. It is fun to watch every Thursday one [22:05] p. M. Central. Additionally, remember these recorded founder interviews go live. We release them here on YouTube every day at two p. M. Central to make sure you don't miss any of that. Make sure you click the subscribe button below here on YouTube, the big red button and then click the little bell notification to make sure you get notifications when we do go live. I wouldn't want you to miss breaking news in the SaaS world, whether it's [22:26] an acquisition, a big fundraise, a big sale, a big profitability statement or something else. I don't want you to miss it. Additionally, if you want to take this conversation deeper and further, we have by far the largest private Slack community for B2B SaaS founders. You want to get in there. We've probably talked about your tool if you're running a company or your firm if you're investing. You can go in there and quickly search and see what [22:48] people are saying. Sign up for that at nathanlatka.com/slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode and if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, but I do it so that we can all learn. We have [23:08] to counter those people. We got to push them away. Click the thumbs up below to counter them and know that I appreciate your guys'support. Alright, I'll be in the comments. See you.