SaaS Interviews with CEOs, Startups, Founders
Electric Companies Pay Them $250k To Track Forest Fires, $1.2m raised at $7m Valuation
06 Nov 2021
Chapter 1: What is the main topic discussed in this episode?
I think you'll have to go down a little lesser because it depends. Some customers are nonprofits and they're not that like, they don't pay that high. We give them huge amount of subsidies.
I see. I see. Are you guys past a half a million in ARR at this point? Do you know?
No, no.
You are listening to Conversations with Nathan Latka, where I sit down and interview the top SaaS founders, like Eric Wan from Zoom. If you'd like to subscribe, go to getlatka.com.
We've published thousands of these interviews, and if you want to sort through them quickly by revenue or churn, CAC, valuation, or other metrics, the easiest way to do that is to go to getlatka.com and use our filtering tool. 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 Abhilasha Parwar.
She's a Fulbright Scholar, Yale alum, having 10 plus years of work experience in private equity, big data analytics, product development, and environmental policy. She's now building blueskyhq.in, which is helping large brands play in the data set space using satellite data and AI. Abhilasha, are you ready to take us to the top?
Yeah, sure. Let's go for it.
All right. So this is sort of a big idea here. You have some good examples on your website about forest fires in India and quantifying those. Help me understand what you're building.
So we take satellite data, large volumes of it, about 30 terabytes plus and ground sensors. And we use this data to give more spatial and temporal resolution. So for instance, the forest fires that we have in India, you have in California, Siberia, Spain, Greece, everywhere on the planet. And we just don't know how much fires is happening, how much are the greenhouse gas emissions.
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Chapter 2: How does satellite data contribute to tracking forest fires?
For instance, if I'm doing like water pollution monitoring, it's very complex monitoring. So it's more expensive. Air quality is cheaper because it's like cheaper modeling the science or the engineering that's needed behind both of these models. It's like different.
Interesting. Okay. Got it. So, so, um, and are these people, are they paying for a one-time data set, a snapshot, or are they truly recurring?
No. So our data set is not static. It's dynamic. So once we deploy our algorithms on the cloud, that data set is being generated at a temporal frequency of that particular category. So it could be daily. It could be once in two days. It could be weekly. Usually our frequencies, temporal frequencies for everything is less than once a week. So you get like either daily or once in two or three days.
I see. Give me more of the backstory because this is fascinating. When did you write the first line of code for this?
So we started in January, 2019. We're going to finish three years. We started in my living room actually just there. Me and my brother and two of our first engineers, and we were all literally operating out of my savings because I used to work at a private equity firm in Connecticut before this. So we were just, you know, chilling on a computer table, eating pizzas and writing code.
And we got interested in satellite data because, you know, it's growing very exponentially, both the number of satellites that are going in the orbit and and the resolutions that they capture. It's same to our iPhones, you know, the resolution, you can now zoom your iPhones by like six X. This wasn't happening like five years ago. And that's the same thing with satellites.
So we are able to get snapshots of earth all the time for different locations, for different parameters. And we clean that data, crunch that data, make sense of all of this huge volume of the data that comes down. And we put it for climate action because we really need to know what's happening in our planet to be able to save our planet.
And Abhilasha, did you and your brother split equity 50-50 on day one or who kept more?
No, I think we were like one. So it's just basic rules of founders that there has to be one final decision point. So you never split equity 50-50. One person takes more majority and the other person takes less.
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Chapter 3: Who are the primary customers for environmental datasets?
So for founders, it's different because we're brother and sister. We just like, it's like, hey, you just take like 10% more.
Are you guys the only, have you raised capital or do you bootstrap?
Yeah, no, we raised capital. We raised a seed round last year for about 1.2 million. And we have gotten a lot of grants and a lot of prizes, like AI innovation prizes. So it took us up to like, I think $800,000 or something. Yeah.
And so when you did the raise last year for 1.2 million, did you do it on a safe or a price round?
We did a price round.
Oh, right out of the gate. Why'd you make that decision?
Um, I think, uh, you know, it depends on different markets in the U S market. You can do 1.2 million of safe, but in the Indian market, you know, it's not that easy. Like usually the rounds get priced also first time founder problems. Like, you know, we don't know, you don't know better. Somebody's coming like, Oh, I'm going to invest a million. It's like for climate change. Oh my God.
You should start a nonprofit.
So the valuation totally depends on your ability to tell a great story and sell the vision. So what valuation did you end up raising at?
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Chapter 4: What pricing models do they use for their datasets?
I see. I see. Are you guys past a half a million in ARR at this point? Do you know?
No, no.
Can you break it this year?
Uh, probably. So the, in India, our cycle is in March, March of 2021. So our, the, the financial cycle is not January to January, March to March. So I, we have six months left to go.
I see. So do you think over the next six months you can break a half million run rate?
That is what you're hoping. We are really, really trying very hard for it. We have some like customers that we're trying to work with right now. So like your big name clients, essentially, you know, and if we are able to crack one of them, then we will cross half a million.
Well,
When I look at your website, I look at your customer logos. I just feel like you guys would have customers that are like... I mean, didn't you say you had customers that were paying like a million per year for dataset?
There are customers. There are datasets that we are pitching for a million per year to different customers.
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