How Sapia CEO Barb Hyman grew Sapia to $35M revenue and 50 customers in 2024.

hey folks my guest today is barb hyman she's been working 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 s a p i a sapien dot a i barb you ready to take us to the top i am so excited to be here thank you so much nathan all right 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 i think the big question is how many data scientists we have or data scientists 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 they're not then there's not really ai going on that's right to where where are you guys so we are headquartered in australia we have an amazing uh team which we call phi labs who are really our innovation machine they're a group of phds in machine learning and ai and they're full-time or consultants no no no it's all full-time we only have uh 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 so we 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 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 you know capability and our continued innovation i want to give you some time to defend that because most people listening are going to wait how does this lady i'm just hearing on nathan's show have more data than google so defend that a little bit how have you gotten unique data that google doesn't have yeah so there's a lot of discussion around you know do you actually need to have large data sets in order to create impre you know 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 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 you know a well-resourced organization 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 structured interviews that's now at about 800 million words it'll be at a billion words fairly soon and that is our across how many interviews 800 million words how many candidates 1.8 million each of these okay got it over what period of time across 47 countries and around about three years so it took us 18 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 you know 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 cv data and they tried to build a predictive model of that the issues are firstly when you're hiring of your incumbents you risk amplifying existing biases and secondly when you're using cb data you're very likely to amplify existing devices 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 and use ai in a safe way for people decision making oh what's going on there youtube good to see you guys now imagine this you love watching these interviews with sas founders but imagine if we took all of the valuation data out from over 2807 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 founder path check this out i'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for valuation this year now the secret evaluation is there's many different ways to value a sas business so the reason you're going to see three or four different valuations inside of your frowner path dashboard this is all free by the way is because depending on who's doing the buying of your sas company you're going to get a different valuation a vc is going to pay a different valuation private equity firm is different if you're going to 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 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 1.2 million seed round 3.7 raise they sold 22 percent of their business go in here and filter by the event maybe you only want to 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 going out right now and you're raising your seed round well 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 data set of sas valuations than what you can get now inside of founder path and we're thrilled to bring it to you all right we're going to go back to the youtube video here in a second but if you want to check this tool out if you want to jump in and sign up you can check it out for free to get your valuation at this link this link founderpath.com forward slash products forward slash evaluations or if you go to founderpath.com and hover over products click on get your evaluation 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 all right let's jump back into 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 yes so we have um typically multi-year annual contracts we're not a monthly subscription because we're enterprise focused you know we work with uh businesses that have big pain which are normally meaning 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 just like fifty thousand hundred thousand dollar acvs or what would you say the averages yeah yeah it's around about 100 to 150 we might have significantly higher than that but um you know somewhere around about 100 would be typical okay do you have any customers that are by themselves paying you more than a million per year no can you get there quick in next year or two um look there are different ways to drive revenue depth you know you can build more product um to get there but you know 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 you know a handful of customers here erickson air canada's north america obviously we've just won a couple of others um and our ambition is to obviously take the incredible product market fit in australia we work with most of the trusted consumer brands their aquinas group will west group bunnings you know anyone who's on the asx is uh aware of us if not using us we want to bring that to the u.s market so our focus is to be really really focused and we see growth coming from expansion you know 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 our new product um we have around 50 enterprise customers at the moment across australia the us and the eu got it interesting and so how many i guess do you upsell against number of interviews is that the right metric yeah typically the pricing is based on hiring so you're really aligned in terms of you know performance if you like you're not charging based on applicants you're charging based on the number of highs and when you think about the difference right so if you're using an agency i don't know what it's like in the us but in australia you know you might be paying 1500 2000 per hire for an rpo you know you're paying 20 bucks for higher without technology so the cost savings are more expensive than that i mean recruiters in the states you're paying 20 30 a first year salary yeah disrupting to that to that sector and the rpo sector mm-hmm so you're charging based off number of of completed hires okay so how...