How Zeff CEO Ben Taylor grew Zeff to $360K revenue and 3 customers in 2019.

hello everybody my guest today is ben taylor he's the co-founder and chief ai officer at a company called zeph.aizf.ai previously worked at intel and micron at a hedge fund and a sequoia startup called higher view and then founded a deep learning auto machine learning platform back in 2017 ben you're ready to take us to the top yeah so was that company was that auto machine learning platform in 2017 that you found that that is zeff correct yeah it was called ziff now it's zeff yeah story yeah no we'll just stick to zeff but we'll jump into that stuff maybe later but tell us what the company does and kind of what the revenue model is how do you plan to make money so we wanted to make deep learning easier for the enterprise and so we we chased automl a lot of people are doing that but we focused on unstructured data so image audio video and text so those are the data sets that we're focusing on the revenue model is a subscription-based platform so people pay an annual subscription to get access to the platform to build these models and you have an idea kind of what you're going to price against is it seat based or number of images scanned per month or what's the pricing going to look like so the entry level pricing we started a quarter million dollars a year and then we have a transaction-based scalar that goes up from that our pricing could come down in the future so we do want to grow with our customers and as we find more more opportunities inside the business we want to be able to grow those contracts what's a transaction based scalar though like transactions of what give me an example so that would be um normally the way we build out the sas model is we give them a very healthy ceiling where we don't track transactions but if they for example if they exceed i can't remember off the top i had the numbers but they exceeded maybe 10 million images per month or a million video um maybe 10 000 video hours per month then we would start um we would start tracking that and billing them for overages um which is pretty interesting so you never want to tax innovation so that's one of the challenges with ai is you want to reward discovery and so we don't want to charge them for training and r d experiments we want to charge them for consumption real consumption or there's real utility output yeah we did look at charging them per model early on the issue with that is the first model might be high value it's easy but what if you want to do a second model that's mediocre value and it causes issues because they don't want to experiment because you have to charge them yeah and i'd rather just have you have all models in production that you can think of even if some of them aren't very useful or valuable so when did you launch so when you guys write the first line of code for the company uh two and a half years ago okay so called maybe three is a side yeah so um so three years ago i would put us into the class of what you'd call a entrepreneur where they're employed full-time but they think they're an entrepreneur and then it's not until you cut the strings you burn the ships where your personal paychecks depend on your ability to hustle uh so we did that two years ago we've been we've been bootstrapped that's great so still bootstrap today and how many customers have i scaled to so it's interesting we scaled up um on on our customer counts i think we hit five or six and then we've scaled back um to three and one of the interesting things with ai um so so two quick points one point with a sas startup and this is true not just for us for other people you'll land into vanity brands so think about like a facebook or some some top fortune 50 company that's sexy they might come across your plate and you'll be tempted to entertain them as a customer the issue is you're always one feature away from closing them and honestly if you don't have a team of 10 plus you're not at a stage where you can support like a monster customer like that so we we definitely made some mistakes chasing a few of those alpha accounts um so we've scaled back and really focused on the verticals that we're chasing right now they're insurance and stuff uh insurance and any audio or video assessment so there's other things we support with our platform but it's very important to have a go to market strategy because ai is such a complicated sell you don't want to start the conversation over with every new prospect you want to kind of under you know you want to understand their domain if your insurance i want to come talk to you about loss or fraud so i mean can i take the three customers you have and they're quarter million dollar your accounts are 20 grand a month you're doing about 60 000 a month in revenue right now um so the early customers they definitely had some discounts uh the customers we have in the pipeline right now they're the ones that were kind of going through procurement and final contracts they will come in close to the full amount yeah what the just but if you take it just revenue last month on an mrr basis are you more like 20 or 30 grand what's that at yeah be more like 20 or 30 grand and honestly anything to have some revenue is great but until we cross a million revenue i would say all of that early revenue is just market research like um and until you have a customer because our customers right now they're kind of scattered like we have a product customer we have a marketing customer until you have repeat customers in a vertical i would argue you have not found product market fit and so that's what i'm hoping to have the next six months is i can say look we have two insurance customers we've got two assessment customers these are real verticals that are are growing yep what's a team look like today how many people um we've got five people uh we're pretty proud of the seniority of the team but this compared to most of the startups i think that you interview i would say we we just found product market fit it's two years of market research how do you i would argue you haven't found that yet just because there's not enough customer volume i would say you're you're still very much exploring but that's also a very back of the napkin kind of thing yeah so the the funny thing with ai you know ai hype is insane right now so we're we're doing deep learning as a service and the exact count doesn't matter but you know whether it's four or five acquisition offers we've got a lot of acquisition offers and you might say yes they're aqua hire offers but it's crazy like just the the potential upside that people are teased with doing ai sas startups right now is is just mind-blowing just the hype that's out there for these companies that well yes i mean no i mean no you're right uh but the problem is is it doesn't matter if you have the best team if you don't know how to communicate that you have the best team it almost doesn't matter because the people who think are the best team will then like become the best team because of you know they'll get more customers and they'll use that money to go buy real engineers yeah yes uh yeah you definitely have to ship value but owning the mic has been very helpful mm-hmm what is the so i mean how aggressively being right now in terms of like burn are you operating at cash flow positive or how much money are you guys burning each month uh we're we're being aggressive with burn because we have we really need engineers and the engineers we have to hire are not they're all principal level engineers we can't hire kids out of school and expect them to ramp on um enterprise level deep learning which requires big data needs high performance computing needs um yeah so we're every new contract we close we hire yeah i mean how much are you burning right now are you talking like 30 grand a month 100 grand a month um no like 30 grand a month and who's feeling that uh so the the founders have definitely you know paid you know we we've kind of paid our our share you just keep putting in more money yeah but it's interesting because you talked to i know you know some of the utah startups pretty well but talking to companies like weave and degree that are doing really really well right now you if you had talked to them three years ago their vcs were telling them to close up shop and founders were maxing out credit cards so like you know we know what we signed up for yeah by the way i i think i think there's anything wrong with that i like founders that keep their companies and don't raise any vc i think that's a smarter way to do it if you can manage burn yeah um something that's funny when you talk to entrepreneurs is when you're employee you're a pet when you're an entrepreneur you worry about the the payroll like can i make payroll this month and if i'm talking to you and you say man it's a hard month i'm going to go without pay this month my my or my reaction is like wow well like bravo like wait until you do that for you know months and months and months like wait wait until you do that a year before it means anything who cares about you specifically like you cares about do you have any non-founders i think all five are founders right no we have two co-founders okay but we kind of see four of us are seen as kind of the core leadership team we've been we've been together for four of us for a year now and i i think i talked...