How Fyma CEO Taavi Tammiste grew Fyma to $117.9K revenue and 10 customers in 2024.

hey folks my guest today is dave damiste he has a passion for finding actionable value using data uh all by applying machine learning he's doing this building fima dot ai right now we're gonna jump in today again no code ai computer vision tool tabby are you ready to take us to the top yeah of course thank you for having me first of all you bet okay so it's a no code again ai computer vision software who's paying for this and how are they using it so the idea basically got started that it's really difficult to build computer vision applications um fiber by itself is is currently targeted at the commercial real estate developers anybody who has physical environments uh that have movement in them that people need to track and also out of our advertising companies so people who have billboards people who want to understand what's going on their billboards but the technology itself has potential to be used everywhere but that's the first two verticals that we're tackling at the moment and do they basically attach this to their cameras right the commercial real estate or the billboards to track metrics yeah people walking in front that's that's the that's the idea basically so we're one of the first companies out there who doesn't deal in any hardware we take your existing cctv cameras push the stream to our ai brain and give you data out and it really is as simple as that and there's no integration fees there's no nothing you just press couple of buttons and you start gathering data so toby your software enables these cameras to recognize that's a pedestrian that's a car that's a male that's a female what data set did you purchase to fuel and train your model or are you built on something that's open source so both basically we uh we have our own data sets so we're gathering data ourselves as well uh we did use uh open source uh data sets like cocoa data sets to to start but we're we're a long way from that so i think our data sets now are above 200 000 300 000 images at the moment so yeah it's growing growing by the day basically what does someone who owns a commercial shopping center for example pay you on average to use your technology each month it depends because because the solution is so easy to use we charge per month per camera so it depends on how many cameras they want to look at and yeah and it can vary from day to day as well so at one point you know you you have new covet restrictions or whatever and you want to measure everything so you know you pay for 50 cameras and the other day you know you just want to see business as usual and you pay for 10 cameras so so it's it depends a lot actually people are changing their ceiling every single day well it's it's a software as a service so you can do that you can click you know i use a bunch of i use a bunch of sas tools there is none of them where i go in and i upgrade and downgrade every single day based off a usage metric yeah the thing with the commercial real estate is that they very often want to run experiments in measurements so for example think of it like this i have you know a shopping center i have a parking lot outside my idea is i'm going to order 10 food trucks there and i want to see what happens for those 10 food trucks i'm going to take 10 cameras outside attach them to 5 and measure something so these types of experiments can be run but yes you are correct in the sense that most of the cameras we are are like 24 7 running at the moment i see so what do you charge per camera per month so right now we charge 200 euros per camera per month okay so you call it 225 us dollars or something like that and the average logo the average brand that signs up for you how much cameras is the onboard on day one so it depends um i think the last average i looked is around 20 cameras uh but it can be one it can be 50. yep so can we take i mean again i'm looking at a sweet spot here 20 cameras 225 is about is about 4 500 per month when a new brand signs up is that about right exactly okay interesting give me the back story here how did you get the idea when did you write the first line of code for this so we got started around two years ago and i was i've been working with applied machine learning for about eight years now and i i've been working with large corporates and i've been trying to run my own ai consultancy business and this this idea actually got started from when i was sort of bootstrapping my own ai consultancy business and we were doing a project for a city here in thailand estonia where they wanted to measure cars and city streets basically sounds easy enough right but what ended up happening was that that project from start to finish took six months so we had to find data science resources we had to gather the data we had to train the model we had to put that model to run on some infrastructure somewhere and then we had to visualize the data with some software so it took six months to actually get something out of it and now with fiber you can do it in 20 seconds basically so the trouble of sort of building custom ai ai applications is where this pain got started from now it's a lot easier to do it basically but there is a there is a very important part in this is that you can only do it in a specific niche and in computer vision we're doing it in the cctv camera angle because you can at least not yet make an ai algorithm that can you know look at everything yep okay interesting i love the niche focus commercial real estate makes a lot of sense to me um talk to me about how many customers you're working with now today so right now uh i think we're working with about 10 to 15 customers uh but we are running a bunch of pilots in the uk at the moment so the customers we can talk about is the queen elizabeth park area in london in the upper east side of london that's where the 2012 london olympics were set up so we have like 30 cameras set up there and yeah basically inside london outside london in exhibition centers trials are go ongoing so we have a lot of traction that has sort of recently come up there so toby last month how many paid cameras were on your network how many paid cameras i think around 50. around 50 total okay across 10 customers is that right yes 50 across 10 customers okay and i mean so if we take 50 across 10 customers at 225 a camera that means you guys are doing about 11 000 bucks a month in revenue right now not that much because the thing is that most of these are still in pilot phase so we're doing around half that i think okay got it so about six thousand dollars a month in revenue but still yeah everyone has to start from zero so congrats on your first six a month now have you bootstrapped all this or did you raise no we have raised two rounds so we raised our pre-seed round of 250k i think in 2019 and we raised a seed round of 1.55 million euros december last year and and walk me through sort of what you use that money on so basically that money went into mainly into preseason development getting the cost down of the running the gpus running the machine learning models and the neural networks uh testing out different verticals because we didn't start with with commercial real estate we started in various different ways where we saw a problem but this is where we sort of ended up as in our sweet spot and then taking the uk market that's been a big one as well so entering that market very cool okay so you raised the 1.5 and again you're doing about 6 000 a month right now in revenue what were you doing exactly a year ago do you remember that's a good question i think a year ago we were launching our first version of the the web application and we didn't have any paying customers got it so zero back then nice so interesting so when you go out and raise the 1.5 million but your pre-revenue how do you come up with the valuation to raise at um well it depends basically because we did have a a very good idea we did have an understanding of how the market work and what the total market size would be in computer vision applications uh how the consulting work consulting market would look like we had a very good lead investor who helped us figure this out as well to be honest uh so they have helped us put together our valuation so so that's how it came together basically and so what valuation did you end up using don't quote me on this because i don't remember my heart but i think it was around 5 million okay and did that feel right to you or in the right range i think it felt right in the stage we were at and and also in the sort of area we were at as well and when you say we did you have a co-founder here yes i do i have a co-founder called karen burns so she's actually the ceo of the company i'm the cto did you guys split equity 50 50 at the start yes okay got it so so now it's what like 40 40 and then investors own 20 something like that something like that we don't really know the capital by heart but yeah no no no just curious now you've had some growth i mean you've grown revenue since your last round are you planning on raising anytime soon uh well we have a pretty decent runway still to go so depending...