How Ople CEO Pedro Alves grew Ople to $504K revenue and 3 customers in 2019.

just got done editing this interview you guys are gonna love it before i do that though i want you to know that i'm going to be in the comments for the next 30 minutes or so answering your questions if there's additional questions you want me to ask the ceo next time i interview them leave them below or if you're just loving the data points i get ceos to share click the thumbs up button below that's your way of telling me you're loving this stuff and i'll get you more of it additionally again i'll be in the comments answering any questions you have all right for 30 minutes enjoy the interview hello everyone my guest today is pedro alves he's building a company called opal which makes ai easy and valuable he's been in the industry for 18 years some of that time was in academia and some in the industry as a data scientist he's been struggling or he's seen the struggle that companies face when trying to get a return on investment with ai and that is why he created opal all right pedro you ready to take us to the top yes absolutely all right so what what does pedro doing are you guys a pure place ass model yes so i've like you said you know i've been my whole career doing ai and trying to get companies to actually get a return on investment besides just pr which is how most companies cai today and yeah our company is software right so it's software that enables a user that's not very technical a business end user an analyst to build models that up to today would take you know a team of phds to actually build and are you so is it pure play sas or is it a lot of consulting as well no no consulting okay so also all stats recurring fees yeah okay and give me a general sense of what you're working with here right so on average what's the company or customer going to pay you per year to use the technology so it's we try to um bill in a way that incentivizes people to build more models to do more with ai so initially investors really wanted us to charge per model built per project done and that way i think it goes back to the whole thinking you know the whole mentality of companies going okay we have these five projects we only have money for two which ones we choose we didn't want that so the idea is if we charge per user and say you know how many people are going to be using the software a simple you know type of a user license then they can build as many models and build as many projects as they want so most companies are going to be in a six figure yearly engagement with us just because of the number of people they have you got a few they're going to be in the five figure and then we have a couple that are in the seven low seven figure range yearly but those are a lot of users you know bigger bigger companies yep okay so you're charging per users like per seat is there any usage-based upsell no we want to incentivize the behavior of the more you do with ai the better for your company the better it is for us okay so charge per seat have some less than six figures some more than six figures but it sounds like a sweet spot might be caught 100 200 grand a year to use technology yeah for a lot of companies that's going to be the the size okay so let's the reason i asked that question is let's then go into your sweet spot so if i am paying you 100 grand per year to use your technology how many people are probably on my team how many seats am i probably paying for so uh in that range that's going to be in the three to six person team so that's still you know company could be a medium-sized company but you have a small little team of analysts or business developers um and and that's going to be the roughly given that range yep is there anything else you upsell against besides number of seats any feature-based upselling so we are starting to talk to some customers about some beta products that we have that we haven't really advertised on the on the website uh things to do with unsupervised learning and a couple of other technologies that are really new out there and so those are things that would be charged separately they're you know separate products within the platform if you will no that that obviously uh makes sense especially as a company matures so what is that i'm curious you know history here what does it look like when did you launch the company uh close to three years ago it was the beginning of 2017 so getting close to three years okay and how uh how between when you wrote the first line of code your first dollar revenue how much cash did you think of the mvp so the mvp we got to very quickly i had been thinking about this for a good five years before starting the company so when we hit the ground running officially i had done a lot of the leg work right so we started officially in the beginning of february and we had a big demo day with an accelerator at the very i think it was the last day of march or the very beginning of april so it was like two and a half months and we had i mean granted what it looked like back then and what it looks like today there's a big difference you know three people working on it for two and a half months versus today but but yes technically we had that initial demo that we could do with a fully functional piece of software within three months and so cash i always like to get mvp cost right so you're talking like a hundred grand into the platform pre-revenue or 300 grand or a million so i'm not measuring to to revenue and measuring to the like you said the mvp of the the software right the first that's my question pedro how much cash did you sink into the platform building it before your first dollar of revenue before the first dollar revenue so that first dollar revenue was still in the first year um so i want to say less than a million dollars uh it was in the six figures because the first check we got was in the first year uh by summer probably so i want to say let's ballpark it around uh 500 000. and most of that was developed you know these three developer salaries folks like things like that yeah that was the biggest cost uh we had a little few extra costs you know cloud costs uh we had you know conferences that we went to you know but the majority of it ended up being uh with salaries and then a little bit of hiring towards the end of the five months we started uh actually growing the team a little bit yeah so where did you get this 500 grand pre-revenue are you super rich you just found it yourself or you raised capital no i raised capital so i started off with a 300 000 check from an accelerator uh accelerator group in atlanta and then after what would they take for the 300 like a yc model like 100 150 for seven percent or what yeah it was a percentage based um the the uh interesting thing was one of my advisors told me that that initial check was too small to basically you want to get a check that gets you long enough that you can get the next check right and i had a fun conversation with him where i called him and he said look i've been thinking about it for one month i finally gonna give you an opinion he hadn't yet and he said i think you shouldn't take it you need to wait to get a bigger check because you're going to run out of money before you get your next investor to put money into you and i as he finished this whole thing and i said i actually called you to tell you i just signed the contract and i wanted to celebrate with you on the phone and we had a fantastic conversation because it was still super valuable because of that advice right from day one i knew i was gonna run out of money i trusted his advice and i said okay i'm going to start raising today day one and he was what was what was your burn at that time so you raised 300 grand how much were your total expenses at the time it fluctuated a lot because we kept cutting salaries uh in order to make it last so it that first you know we didn't raise again until uh i want to say end of may or june so it lasted about six months um that 300 so you're burning about 50 000 a month uh yeah i mean it lasted us about six months that sounds about right yeah okay good and then so how much total today have you raised for the company about 15 million okay one five or five zero one five one five okay so you got a funding track you obviously keep raising when was the last round so we raised uh series a last year at the end of the year okay and that was for how much that was an eight million dollar uh a round eight series a end of 2018. okay and why was eight million the right amount to raise for you at the time it was just doing the math of you know how many people we need to build a product to the stage that we need to get the product to so that we are selling um at a faster pace we're differentiated enough and then obviously trying to measure the runway of when do we think we can raise series b right what's the right valuation for series b how much uh air are we need and what we need to have with the product so it was just getting all these measurements of when we think we can...