How Chillwall CEO Michael Campanelli grew Chillwall to $328.9K revenue and 4 customers in 2024.

hello everyone my guest today is michael campanelli he is building a product called chihuahua personalization everywhere which uh is built around a concept called betty chihuahua's ai-powered desire prediction platform their licensing technology sits on top of business partners network pushing personalized experiences that help turn a curiosity into a and into a desire to experience showa ai focuses on real human interactions and desires michael you ready to take us to the top i am thank you nathan you bet so so what is this really i mean uh there's a lot of like kind of buzzwords there and what it does but tell me a customer story on how they're using you absolutely uh first of all i wanted to say greetings from toronto ground zero the ai revolution and secondly i want to say it's a real honor to be on your your podcast i our stories and some of the stuff that you talked about and we're going to talk about today's podcast are actually very very similar so for all those people out there listening we are proof of what we're going to talk about of what nathan is telling you so so yeah thanks for that now tell me about the company yeah i'll tell you a little bit about chill so chill ai is a deep learning uh startup um we are the creators of betty which is a um it's a desire prediction artificial intelligence which really focuses on you know turning that curiosity that you have in your mind and turning it into desire to go and experience and in particular life um and i have actually a very exciting announcement to make on today's uh podcast that we're also launching in the next a couple of weeks a complementary we call it code name alpha 5 semantic search engine which is going to go out and make that full experience so much better so really what is the value what are we what are we really trying to do what we've done is we've created a virtual concierge and this virtual concierge uh can sit on top of uh there's two kind of target industries we're focused on right now obviously transportation and it brings the pers the occupants uh personality and embeds it with the vehicle or uh tourism and hospitality where it actually there's no app it actually sits on the app and it lets you go and discover without searching um so what is your is i mean would you consider this a sas company or who's paying yeah it's absolutely a sas company so it gets white labeled um and so basically the model is as follows there's a development small fee of development and then there's an ongoing monthly licensing fee to use the technology okay so on average on the monthly fee what are companies paying to use it yeah exactly so again it depends on the customization but i'll give you a typical example in the tourism industry it would be around it's it's a 10 000 plus ar that they would pay so about a thousand dollars a month something like that yeah yeah exactly and what you got to remember nathan is that you know like this is kind of going back to kind of the idea of what you would kind of talk about which is really invest in the underlying like really invest in supporting technology that makes the service in this case hospitality tourism discovery of a city um so much better so what we've done is we focused on it it's it's kind of behind the scenes the concierge um and it obviously does have a front end the front end looks like the the hotel or the concierge uh as an example and what it does is it really starts to supercharge revenue for that for that vendor okay again so i understand the product put this on a timeline for me when'd you launch the company what year yeah a great question so we launched the company in 2015. and then fast forward to today how many customers have you scaled to yeah so i want to just get into uh just to get into this so short answer to that right now is automaker we currently have we just launched the uh virtual concierge um with uh we pre-sold um to a tourism and now we're scaling on that one so we're scaling on both products and the reason i say that is what we launched in 2015 wait michael sorry what's the how many customers are you serving and then you can fill it in with the back story yeah yeah exactly so on on the auto we we have a kind of our first core client and on the tourism we have our first core client and now we're scaling okay so just to be clear you've got essentially two clients paying about a thousand dollars a month right now so no not not at all our annual ar last year was 225 000. so we have one client that's like so in the auto industry it's actually um they're paying us significant development fees to develop the product yeah i don't care about i don't care about one-off development fees i'm talking pure sass so in 2018 your pure sas revenue december 2018 that month what how much in that month was pure sass yeah i would i would go back to probably in that month it would it would probably because we don't have the development of the automaker online yet it probably would be about 2 000. okay about 2 000. and today and today pure sass what are you at per month in the mrr yeah the mri would have been about 2000 in december and today what are you at six seven months later yeah we're probably at about three thousand four thousand okay now we're scale we're scaling so so the auto has the auto and the auto tech hasn't come online so the tourism is the piece that we're just starting to scale and that's kind of around three thousand kind of uh right now but um obviously we're rolling out and scaling the the um the virtual car so you have two customers right now one paying a thousand bucks a month one paying three thousand bucks a month on your two separate products for four grand a month right now on revenue up from two grand a month six months ago correct yeah okay wonderful and help me understand so launched in 2015 have you guys decided to bootstrap the company or decide to raise capital yeah we bootstrapped but we also were supported by i'm the ontario government the research um uh how big was the grant yeah grants around 800 000 okay um and so that's why nathan i just want to give you a bit of a backstory because you know ai is a really great buzzy word right now and it it it sounds good but essentially what we had to do was we we launched chillwill.com our consumer site um almost a million user interactions between 2015 and today to train the algorithm it's a great event network on the consumer platform side of it make a little bit of money on the revenue on the ad side of it but really what it's to do is it helps to train that that desire prediction but michael you don't have a big cohort that has gone through this site like when i go to chillwall.com it's ranked number one point two million on alexa i mean mylaca.com is like 100 000. so like you're getting no traffic through this thing yeah exactly so because because that was just a training site we weren't driving lots of traffic to that site now that we've kind of got our um it's a great question now that we actually have our our consumer product we're focusing our sas company that's why i just wanted to mention to you that in ai what typically happens is a lot of people do annotations um and what we did is we built it off real interactions nathan and then now that we have a built now we're turning and now we're starting to scale the business up with the virtual concierge which can be launched in less than 30 days uh across any of the tourism so what's the link for the virtual concierge where do i go to learn about that yeah you can actually go and you can so we're going to be launching our chill ai um website okay so there's nothing up chillwall.com is the main thing right now yeah but what you can do is you can actually go to customer i'll give you an example of a customer if you're interested nathan no no no i think i think i got it um i think it's interesting you've chosen to try and be a consumer play to capture data and now pivot to b2b enterprise play most most ai companies are machine learning companies in the b2b sas space that are doing that they are buying their data from data sources not trying to go be the thing that collects the data that's very very difficult and very different business model exactly and it's also not based on real it's basement annotation it ours's was based upon our training of our models in the building of the semantics was based on real customer use usage what's your team look like today how many folks for me yeah the team today is five okay um it's it's five plus one phd candidate um who's actually originally from your home state by the way virginia or austin yeah he comes from virginia tech i love that and i started at virginia tech virginia beach it's five people today they're one um one phd two master's degrees myself who is a uh i mean more of a marketing and mba but you know kind of i've been able to hire a lot of the really um great talent and experts better than i that than i am michael you raised 800 grand in funding you're not doing much revenue right now so imagine you're burning cash right now to invest in the product about how much are you burning per month [Music] yeah uh so we keep we keep your costs very very very low so i'm gonna say we're probably burning 25 uh around twenty to twenty five thousand fair enough and and uh and five people you're just now kind of scaling revenue what do you hope to...