Fyma Revenue, Valuation & Funding (2024)

[00:00] Hey, folks. My guest today is Tavy Tamiste. He has a passion for finding actionable value using data all by applying machine learning. He's doing this building fyma dot ai right now. We're gonna jump in today. Again, no code AI computer vision tool. Tavy, you ready to take us to the top? [00:16] >> Yeah. Of course. Thank you for having me, first of all. [00:18] You bet. Okay. So it's a no code, again, AI computer vision software. Who who's paying for this, and how are they using it? [00:26] >> So the idea basically got started that it's really difficult to build computer vision applications. Fyma by itself is currently targeted at commercial real estate developers, anybody who has physical environments that have movement in them that people need to track and also out of home 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 [00:56] >> we're tackling at the moment. [00:57] And do they basically attach this to their cameras, right, the commercial real estate or the billboards to track metrics Yeah. Of people walking in front? [01:05] >> 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 a couple of buttons and you start gathering data. [01:26] So, Tava, your software enables these cameras to recognize that's a pedestrian, that's a car, that's a male, that's a female. What dataset did you purchase to fuel and train your model, or are you built on something that's open source? [01:38] >> So both basically we have our own data sets, we're gathering data ourselves as well. We did use open source data sets like COCO data sets to start, but a long way from that. So I think our data sets now are above 200,000, 300,000 images at the moment. So, it's growing by the day basically. [02:06] What does someone who owns a commercial shopping center, for example, pay you on average to use your technology each month? [02:12] >> 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 it can vary from day to day as well. So at one point, you know, you have new COVID 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 wanna see business as usual and [02:34] >> you pay for 10 cameras. So so it's it depends a lot actually on that. [02:38] People are changing their billing every single day? [02:41] >> 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 [02:48] SaaS tools. There's none of them where I go in and I upgrade and downgrade every single day based off a usage metric. [02:54] >> Yeah. The thing with the commercial real estate is that they very often run to run experiments and measurements. So for example, think of it like this, I have, you know, a shopping center and 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 to fyma and measure something. So these types [03:18] >> of experiments can be run. But yes, you are correct in the sense that most of the cameras we have are like 24/7 running at the moment. [03:25] I see. So what do you charge per camera per month? [03:29] >> So right now we charge €200 per camera per month. [03:32] Okay. So call it $225 US dollars or something like that. And the average logo, the average brand that signs up for you, how much cameras do they onboard on day one? [03:42] >> So it depends. I think the last average I looked is around 20 cameras, but it can be one. It can be 50. [03:50] Yep. So can we take I mean, again, I'm looking at a sweet spot here. 20 cameras, two hundred twenty [03:54] >> Twenty five cameras. Is about is about [03:56] 4,500 per month when a new brand signs up. Is that about right? [03:59] >> Exactly. [04:00] Yeah. That's correct. Give me the backstory here. How did you get the idea? When did you write the first line of code for this? [04:07] >> 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've been working with large corporates and I've been trying to run my own AI consultancy business. 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 Tallinn, Estonia, where they wanted to measure cars on city streets [04:32] >> 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. It So took six months to actually get something out of it. And now with fyma [04:56] >> you can do it in twenty seconds basically. So the trouble of sort of building custom AI applications is where this pain got started from. Now it's a lot easier to do it basically. But there 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, [05:21] >> look at everything. [05:22] Yep. Okay. Interesting. I love the niche focus, commercial real estate makes a lot of sense to me. Talk to me about how many customers you're working with now today. [05:31] >> So right now, I think we're working with about 10 to 15 customers, 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 twenty twelve 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 [05:59] >> go ongoing. So we have a lot of traction that has sort of recently come up there. [06:04] So, Tabby, last month, how many paid cameras were on your network? [06:08] >> How many paid cameras? I think around 50. [06:11] Around 50 total. Okay. Across 10 customers. [06:16] Is that right? Yes. 50 across 10 customers? Okay. And and I mean so if we take 50 across 10 customers at 225 a camera, that means you guys are doing about $11,000 a month in revenue right now? [06:31] >> 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. [06:38] Okay. Got it. So about $6,000 a month in revenue, but still, everyone has to start from zero. So congrats on your first $6,000 a month. Now, have you bootstrapped all this or did you raise? [06:48] >> 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 €1,550,000 December last year. [07:05] Mhmm. And and walk me through, sir, what you use that money on. [07:11] >> So basically, that money went into mainly into research and development, getting the cost down of the running the GPUs, running the machine learning models and the neural networks, testing out different verticals because we didn't start with commercial real estate. We started in various different ways where we saw a problem, but this is where we sort of ended up in our sweet spot. And then taking The UK market, that's been a big one as well. So entering [07:41] >> that market. [07:42] Mhmm. Very cool. Okay. So you raised the 1.5 and again, doing about $6,000 a month right now in revenue. What were you doing exactly a year ago? Do you remember? [07:51] >> That's a good question. I think a year ago we were launching our first version of the web application, and we didn't have any paying customers. [08:01] Got it. So zero back then. Nice. So interesting. So when you go out and raise the 1,500,000, but your pre revenue, how do you come up with the valuation to raise that? [08:13] >> Well, depends basically because we did have a very good idea. We did have an understanding of how the market worked and what the total market size would be in computer vision applications, how the consulting market would look like. We had a very good lead investor who helped us figure this out as well, to be honest. So they have helped us put together our valuation. So that's how it came together, basically. [08:38] Mhmm. And so what valuation did you end up using? [08:43] >> Don't quote me on this because I don't remember by heart, but I think it was around 5,000,000. [08:47] Okay. And did that feel right to you or in the right range? [08:51] >> I think it felt right in the stage we were at and and also in the sort of area we were at as well. [08:58] Mhmm. And when you say we, did you have a co founder here? [09:02] >> Yes, I do. I have a co founder called Karen Burns. So she's she's actually the CEO of the company and I'm the CTO. [09:08] Did you guys split equity fifty fifty at the start? Yes. Okay. Got it. So so now it's what, like forty forty and then investors own 20%, something like that? [09:18] >> Something like that. We don't really know the capital by heart, but Yeah. [09:22] 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? [09:29] >> Well, we have a pretty decent runway still to go. Depending So on when when we will raise and what we will raise, I can answer that question once we have a couple of projects end. Let's put it like this. [09:43] Tell me more about your team. How many full time today? [09:47] >> I think at the moment, the entire team is around twelve, thirteen people. Most of it's technical team. We've recently been putting a lot of effort into sales and marketing as well because we see that's a big part of how you get the name out there. But, yeah, most of it's basically software development, data scientists, data labelers, you know, all of that part. [10:09] How many engineers? [10:12] >> I think engineers so software, I think four, and data science, I think three or four also. [10:20] Got it. So about seven there total on the engineering side. Nice. And then what about churn? Have you had anyone pay you and then stop paying you? [10:29] >> Yes, but only for short term customers who came in as short term projects. So we have customers like, hey, I'm turning this street that's usually meant for cars, we'll turn this into pedestrian street for the summer. Let's measure how traffic works there. That has happened on two occasions basically. So churn currently has only been for short term projects that were meant to be short term projects, but no one has actually churned from being a customer to [10:57] >> not being a customer yet. And the thing with this is that while our sales cycles tend to be pretty long because it's, you know, enterprise sales and it's, you know, commercial real estate. Once a customer comes with us, they really don't have a reason to leave because, know, they're gathering data for a year, two years, ten years. And then the product actually gets better on the back end as well because it's a software product instead of [11:20] >> a hardware product, right? [11:21] Very cool. Hey, this makes a lot of sense. I love the story, Tavy. Let's wrap up with the famous five. Number one, what's your favorite book? [11:28] >> What's my favorite book? Recently, my favorite book is actually President Obama's book. I really like that one. [11:35] Number two, is there a CEO you're following or studying? [11:41] >> No, there isn't. And I don't do this on purpose. [11:45] Number three, what's your favorite online tool for building fyma? [11:49] >> My favorite online tool for building fyma? Slack, definitely. [11:55] Number four, how many hours of sleep do you get every night? [12:00] >> Six. [12:01] And what's your situation? Married, single, kids? [12:05] >> In a relationship. [12:06] K. Not married. Any kids? [12:09] >> Nope. [12:09] Okay. And how old are you, Tabby? [12:12] >> I'm 32. [12:13] >> 32. [12:14] Last question. Something you wish you knew when you were 20. [12:18] >> Something I wish I knew when I was 20. [12:26] >> I think I wish I wish I knew at 20 that that university knowledge is not the same as real world knowledge. [12:34] Guys, there you have it. Fyma launched back in 2019. They're helping folks like malls or folks that look at traffic patterns use cameras to understand where pedestrians are, where cars are, run estimates and experiments. They were doing no revenue exactly a year ago, now doing about $6,000 a month in revenue as they look to scale. They did a $1,500,000 seed last round at a $5,000,000 valuation, now scaling with our team of 13, trying to move more [12:56] >> of these pilots into paid customers. [12:58] Tavi, thanks for taking us to the top. [13:00] >> Thanks for having me. [13:02] One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers. They try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU CAC, LTV, you name it, they share it and the buyers try and make a deal live. It is fun to watch every Thursday 1PM [13:27] Central. Additionally, remember these recorded Founder interviews go live. We release them here on YouTube every day at 2PM Central. 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