SaaS Interviews with CEOs, Startups, Founders
Camera Analytics SaaS Hits $6k MRR, Raised $1.5m at $4m Valuation
28 Nov 2021
Chapter 1: What is the main topic discussed in this episode?
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?
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.
It's like a big Excel sheet for all of these podcast interviews. Check it out right now at getlatka.com. Hey folks, my guest today is Tavi Tamiste. He has a passion for finding actionable value using data all by applying machine learning. He's doing this building FIMA.AI right now. We're going to jump in today. Again, no code AI computer vision tool. Tavi, 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. Prima by itself is currently targeted at the 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 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 and people walking in front?
Yeah.
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.
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Chapter 2: Who are the primary users of the no-code AI computer vision software?
Give me the backstory 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'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. And this idea actually got started from when I was sort of bootstrapping my own AI consultancy business. And we were doing a
a project for a city here in Tallinn, Estonia, where they wanted to measure cars on 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 infrastructures 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 FIMO, you can do it in 20 seconds, basically. 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 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 look at everything.
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.
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 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 ongoing. So we have a lot of traction that has sort of recently come up there.
So Tabi, 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?
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Chapter 3: What datasets are used to train the AI model?
So basically that money went into mainly into research and development, getting the cost down of 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 when 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 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 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 you're pre-revenue, how do you come up with the valuation to raise that?
Well, it 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 helped us put together our valuation.
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 by heart, but I think it was around 5 million.
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Chapter 4: How much do customers typically pay for the technology?
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?
Well, we have a pretty decent runway still to go. So depending on when we will raise and what we will raise, I can answer that question once we have a couple of projects and let's put it like this.
Tell me more about your team. How many full time today?
I think at the moment, the entire team is around 12, 13 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 that part. How many engineers? I think engineers.
So software, I think four and data science, I think three or four.
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?
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're turning this into pedestrian street for the summer. Let's measure how traffic works there. That has happened on two occasions, basically.
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Chapter 5: What challenges do customers face with billing and usage?
I love the story, Tavi. Let's wrap up with the famous five. Number one, what's your favorite book?
What's my favorite book? Recently, my favorite book is actually President Obama's book. I really like that one.
Number two, is there a CEO you're following or studying?
No, there isn't. And I don't do this on purpose.
Number three, what's your favorite online tool for building FIMA?
My favorite online tool for building FIMA? Slack, definitely.
Number four, how many hours of sleep do you get every night? Six. And what's your situation? Married, single, kids? In a relationship. Okay. Not married. Any kids? Nope. Okay. And how old are you, Tabi? I'm 32. 32. Last question. Something you wish you knew when you were 20? Something I wish I knew when I was 20?
I think I wish I knew at 20 that university knowledge is not the same as real-world knowledge.
Guys, there you have it. FIMA 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.
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