How Apres CEO Matt Waite grew Apres to $3.5M revenue and 12 customers in 2024.

hello everybody my guest today is matt wait he's the ceo and founder of apre an automated automation platform for ai training they focus on eliminating the complexity and cost associated with training artificial intelligence applications their application lets anyone build their expertise into a training module to generate high quality data necessary to create and maintain intelligent software matt you ready to take it to the top let's do it all right man so this is a concept that sometimes it's difficult to kind of wrap your head around so make it simple for us what's the company do and how do you guys make money yeah absolutely so really what we focus on is making the data that teaches machines how to see how to hear and what to do so let's say that you had a machine that was trying to recognize stop signs well first it needs to recognize that there's a stop sign in the image so that requires a certain level of data on top of the image let's say and we provide that data so we do that at scale and we actually train machines to be able to produce that data more efficiently than anybody else even even more than like a mechanical turkey kind of model yeah so what mechanical dirk is doing is really throwing humans at the problem we take a learning approach so we're actually training an ai model to produce the data for your ai model interesting okay so who's paying for this so companies with large deployments of intelligent software or databases that need to be updated continuously i mean the real problem that these companies are running into is that as your model runs into the edges of what it knows it needs sort of to relearn what it doesn't know and so that's where they interact with the software like ours so that we can re-educate the model can you tell me a concrete story is there a kind of a customer story you can share on how they used you yeah sure so there's a company called reply ai and really what they do is they provide uh chatbots as a service to enterprise cosmopolitan hotel is a great example of one of their implementations and really um let's say the cosmo's throwing a a party on the weekend that's outside of the lexicon that they would normally use or the dialogue scripts that they would normally be able to reply to but what they do is they link us into the flow so when their ai doesn't recognize a conversation pattern it links us now if we don't recognize it or i rai doesn't then we actually route it to humans who can do the labeling the performance analysis necessary to pump it back into their model so that they can recognize that behavior wait sorry what's a chatbot have to do with with data on top of an image yeah so we do both uh object detection as well as textual data so not only can you you know from a textual standpoint understand language through ai you can you know you can understand visuals as well we solve for both use cases got it okay and then help me understand how you make money right so are you pure place sas company so it's actually based on the data that we produce for you so we charge a fee for machined insights so that's a very low fee a fraction of a penny and then anything that touches human hands has um you know sort of whole sense attached to it but we really only charge the data that we process so each query would be a um a single charge so i'm coming you today to kind of sign up i'm another version of the cosmo uh what like am i going to try and guess how many queries you're going to do over a year and that's going to be my contract value to you or do you bill me at the end of each month based off number of queries like used or something yeah so we actually act like a cloud provider in the sense that we're you know it's kind of a pay as you go model and that's really what um what they need it's because you can sort of walk crawl running in a sense but also there's very elastic needs and so charging a subscription fee really doesn't work um and typically we're dealing in the millions of data points it's really scale implementations yeah um get the most views so so really that's so help me understand just because we don't have time to go on every customer cohort but on average what would you say kind of an average kind of user of your platform is going to be paying you per month and about how many data queries is whatever that payment is yeah so large enterprise customers over the course of a year will pay you know around a million dollars and um you know a smaller sort of sas based series a stage uh scalable company would be paying you know five to ten thousand dollars a month okay do would kind of the five grand a month thing be more of a fair average across your entire user base or no yeah i would say that if it's if it's more of a b2b sale then yes that's that's sort of where we sit but we also sit with developers as well just because it's a pay-as-you-go product we you know are pretty accessible if someone's paying five grand uh put a put a query number behind that so 60 grand a year i'm going to be doing about how many queries uh 60 grand a year you're doing you know half a million million queries those those are the results you're delivering back to me right exactly interesting okay cool put this on a timeline for me when you guys launched the company about a year ago is when we kicked off the first line of code and we're actually about to do a public launch in a week or so okay so are you pre-revenue right now no no we have revenue oh it's just a function of the fact that we haven't told anybody really that we're on the internet where were you so give me context a year and a half ago i mean what were you doing what what made you jump into this idea yeah so i was running a financial services company who um and i was the cpo of the company i had to build a lot of processes to you know translate data between humans and machines and felt like scaling out that utility was going to be a net benefit for the market so i spun out of that company and started you know a whole new one that's great so 2018 you launched called a year and a half ago and then how many customers have you scaled to today so today we have about 12 customers that are active on the platform okay that's more than i thought you'd say i thought you're going to say maybe three four kind of you know individual cases but you have you've got 12 kind of pre-launch that's great yeah yeah and we've had a couple hundred businesses that we've interacted with over small implementations or small you know uh sort of interactions but yeah really like core good use cases i would say yeah around it doesn't and how did you get kind of the the first you know two paying of those 12 well this is kind of a uh a business where you can you know it's almost like a mechanical turk you really don't know what's happening necessarily and so um the data itself can be human engineered a lot of the time and so we just cut our margins until it made sense for the market to say yes to us and we ended up not making money for a long time built out the technology components that we could widen our margins and so having those conversations with people really with cash as our only differentiator initially is really what kicked off the first batch of customers and we were just very how'd they find you i'm talking about specifically how they find you so i uh had the idea for the company and put it on product hunt and i got a got the first cohort and then started reaching out to ai companies um just with a few you know good case studies um b2b you know typical repeatable revenue kind of how did you reach out like were you looking up linkedin kind of job titles of certain companies you wanted to target or how did you do it yeah so a combination of things um there's uh there's a great networking app called shaper and so i used a few various you know linkedin shape or a few other networking applications and just try to get in touch with as many viable prospects as possible just with the angle of let's talk about who are they though i'm trying to dive deep here what is the viable prospect like what's the title of the person you're trying to sell to inside of an organization ctos and uh anybody around data science okay got it to connect so you're looking for like the c2 at like the cosmo this yeah the cto of typically ai driven companies or that have obvious ai components so how do you quickly figure that out um based on their technology profile i mean if they're selling sas ai software anybody that you've ever interacted with um they have this component built into their business so it's a known problem interesting okay and um have you bootstrapped the company or did you decide to raise yeah no i funded it and um we got a small check at the end of last year but we're about to go out and raise that's great okay wait so how much in the company to date how much raised i don't want to talk about that but not a lot of money okay like less than 100 grand yeah okay fair enough and then why are i mean why can't i always root for people to stay bootstrapped because then you like you're like betting on yourself and i think it's just lazy to raise so like why do you have to raise why can't you keep bootstrapping so this this is a it's a rich...