Blue Sky Analytics Revenue, Valuation & Funding (2024)

[00:00] Hey folks. My guest today is Abhilasha Purwar. She's a Fulbright scholar, Yale alum, having ten plus years of work experience in private equity, big data analytics, product development, environmental policy. She's now building blueskyhq.in, which is helping, large brands play in the dataset space using satellite data and AI. Abhilasha, you ready to take us to the top? [00:21] >> Yeah. Sure. Let's go for it. [00:22] Alright. 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. [00:29] >> 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 are happening, how much are the greenhouse gas emissions. All of this information is needed by electric utilities for planning for future, [00:55] >> by climate insurance, by banks and everybody for making decisions, especially as we have the frequency of forest fires or any extreme climate events rise up in the coming ten years. [01:05] And who are the buyers, Abhilasha? Are these governments? [01:09] >> No. So it's majorly infrastructure companies because they have to take care of all of this large infrastructure that is supporting our life in the face of climate change and its insurances and investment firms. [01:21] Okay, got it. So insurance investment firms, when you say infrastructure companies, can you name one or two real ones? [01:26] >> Electric utilities, for instance. If you're an electric utility, you face forest fires, you also face the danger of your infrastructure causing forest fires. So it's like a very, very important thing. Mean, even like for instance, home insurances, in the year 2021, the forest fires in California affected 40 times more houses than the last ten years of fires combined. So You really see how this problem is growing exponentially. And then there's a lot of greenhouse gas emissions [01:55] >> that come from forest fires. And the other thing is with bluesky, we don't just focus on forest fires. We focus on not all environmental data, whether it's air pollution, water pollution, carbon emissions from industries, carbon emissions from forest fires, the whole spectrum of environment and climate data. [02:11] Interesting, Abhilasha. And and so what might an electric company pay you per month or per year to access your dataset or your software? [02:19] >> So our datasets go anywhere from like $10,000 to like a million dollar depending on the resolution. For instance, if you want something with one kilometer square resolution, it's cheap. But if you want something with like, you know, 100 meter resolution, it's expensive. [02:33] Okay. And so it's $10,000 a month or a year? [02:37] >> A year. It can go anywhere from $10,000 a year to, like, $1,000,000 a year. [02:42] What would you I understand that's a massive range here, but what would you say the sweet spot is? Is $10,000 a year a good average? [02:48] >> See, I really can't say the sweet spot because it depends on resolution, right? [02:53] I get that, but if you take all your customers, all your revenue divided by all your customers, you can get an average. [02:59] >> I think it'd be good to say that we do 100 ks a year. [03:02] Is it good? Okay. Yeah. Okay. And if someone's paying you 100 k a year, what resolution are they probably getting? Is that like 10 kilometers, 50? [03:09] >> No. No. No. No. You get more resolution with that. Get like 300 kilometers, 400 kilometers. [03:14] Yeah. Got it. And also [03:15] >> depends on what is the dataset. Right? 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 is like different. [03:32] Interesting. Okay, got it. Are people Are they paying for a one time dataset, a snapshot, or are they truly recurring? [03:40] >> No. So our dataset is not static, it's dynamic. So once we deploy our algorithms on the cloud, that dataset is being generated at a temporal frequency of that particular category. 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, sorry. You get like either daily or once in two or three days. [04:04] I see. Give me more of the backstory because this is fascinating. When did you write the first line of code for this? [04:09] >> 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 chilling on a computer table, eating pizzas and writing code. And we got interested in satellite data because it's growing very [04:34] >> exponentially, both the number of satellites that are going in the orbit and the resolutions that they capture. It's same to our iPhones, you know, the resolution you can now zoom your iPhones by like 6x. 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 [04:57] >> 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 on our planet to be able to save our planet. [05:05] And Abhilasha, did you and your brother split equity fiftyfifty on day one or who kept more? [05:09] >> No. I think we are like one so it's just basic rules of founders that there has to be one final decision point. So you never split equity fiftyfifty. One person takes more majority and the other person takes less. [05:21] Alright. So how much did you take? [05:24] >> I think I took the majority, but then we flipped last year because my brother initially started. He like, Oh, I'm going to help you for two months. And then he was like, Oh, this is so exciting. There's AI and cloud and APIs, and this is SOTEC. So he's my CTO. He's like, Okay, so this is a technology company. And I think we flipped our equities last year. So he's the majority shareholder now. [05:44] Got it. So you just sort of issued him more options, one year cliff for your vesting, and now he's a little more. [05:49] >> No, we are founders, right? So for founders, it's different because your brother and sister, just like, it's like, hey, you just take like 10% more. [05:56] Are you guys the only have you raised capital or do you bootstrap? [05:59] >> Yeah. No, we raised capital. We raised a seed round last year for about 1,200,000 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. [06:11] Uh-huh. And so when you did the raise last year for 1,200,000, did you do it on a safe or price round? [06:18] >> We did a price round. [06:19] Oh, right out of the gate. Why'd you make that decision? [06:23] >> I think, you know, it depends on different markets. In The US market, you can do 1,200,000 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, you know, we don't know. You don't know better. Somebody's coming like, oh, I'm gonna invest millions. For climate change? Oh my god. Know, when she started, they were like, oh, you should sell a nonprofit. [06:46] So the valuation is totally dependent on your ability to tell a great story and sell the vision. So what valuation did you end up raising at? [06:54] >> I think it's something around 7,000,000. I I have to look again. [06:57] Okay. And any plans to raise now where you can stay bootstrapped for a while? You can stay capital efficient for a while? [07:03] >> So we are capital efficient because we are like, you know, we are we are just a tech company of about 20 people, completely remote. So mostly it's engineering and data talent, but we are planning to raise more capital and actually flip to Netherlands because it's a very exciting domain for like space data and like climate change. So we are looking to raise about 5 to $6,000,000 more and just flip it over the, you know, in the [07:26] >> next year beginning. [07:27] Tell me a little bit more about customer growth. Tell me, how'd you get your first customer? [07:32] >> Oh my God. So we initially made the dataset and we thought that nobody would care about it, like nobody. And then the largest mobile company in India was like, oh my God, can we take your data and integrate it with like millions of mobile phones in India? And that was so exciting because, you know, we literally spent first seven months listening to people telling us, hey, air pollution, that should be done by governments or nonprofits. [07:54] This is Xiaomi, by the way? [07:55] >> Yeah, that is Xiaomi. So they took our data and like, you know, our APIs, the hits went from 10,000 to like a million like overnight. Also, our API gateway broke because we didn't imagine that much amount of like customers, that much amount of users taking interest in environmental data. Even today, the idea that Nathan would want to know how much is water in the reservoirs in the town that he lives in is very bizarre. People are [08:20] >> like, oh, Abidone's gonna pay for it. But I really feel that as the years go by, like three years, four years, five years later, climate data is going to be extremely valuable. [08:30] Yeah. I mean, you're seeing this in California right now. I mean, it's insane. Okay. So that's how you got your first customer, that that big telecom company. How many customers are you working with now today? [08:38] >> I think we have about eight to 10 customers now, different sizes, different now. I'm not even a sales team anymore, so I do not know much of information. Yeah. [08:46] No. You're good. So can I take, like, eight times a $100,000 average ACV? You guys are doing about $800,000 in terms of run rate right now? [08:54] >> I don't I think you should I think you'll have to go down a little lesser because it depends. Some customers are nonprofits and they are not that, like they don't pay that high. We give them high huge amount of subsidies. [09:04] I see. I see. Are you guys past a a 500,000 in ARR at this point? Do you know? [09:10] >> No. No. [09:11] Can you break it this year? [09:13] >> Probably. So in India, our cycle is in March, March 2021. So the the financial cycle is not January to January, March to March. So I we have six months left to go. [09:25] I see. So do you think over the next six months, you can break a mill a 500,000 run rate? [09:29] >> That is what you're hoping. We are really, really trying very hard for it. We have some, like, weak customers that we're trying to work with right now. So, like, you your big name clients essentially, you know? And if we are able to crack one of them, then we will cross 500,000. [09:42] Mhmm. When I look at your website, I look at your customer logos. I like, 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, 1,000,000 per year for dataset? [09:52] >> There are customers there are datasets that we are pitching for 1,000,000 per year to different data different customers, [09:58] but you have to closed. [09:59] >> Yeah. [10:01] Oh, I see. Got it. Okay. I'm just saying, yeah, because you bill off number of API hits. You have very good utility based, like, upsell metric because it's based off resolution on on kilometers. I mean, I feel like you guys should be closing enterprise deals like crazy. Right? [10:13] >> Well, we are really so one of the things is that we are trying to get more expansion in The US and Europe because that's where most of our customer base comes from. At the moment, we are a small team, only three people two people in New York and one in Netherlands. So we will be expanding out and placing more people in these geographies in the next two to three months. [10:31] Yeah. Wait. Sorry. I thought you said you had you had 20 people fully remote? [10:34] >> 20 people fully remote, out of which 17 is in India and three is outside of India. So we have two in New York and one in Netherlands. [10:40] I see. But they're everyone's full time. [10:42] >> Yeah. No. Everyone's full time. Yeah. [10:44] I see. I see. I see. Okay. Cool. So how are you I mean, with that many employees and you're still early on the revenue side, how are you managing burn right now? [10:52] >> So one of the thing is that when you're I think between America, Europe and India, the capital is very different. So for instance, in India, you can get like a lot of talent. Can also live a very high standard of living at a much different cost actually. So the cost of running a startup like us in The US and New York, we're probably like 10,000,000 a year. But for cost of running a startup like us here [11:13] >> is about 1,000,000 a year. [11:15] So what are your total headcount expenses per month right now? [11:19] >> I don't know. I don't think I can also review this information to you. [11:22] Why not? [11:23] >> I think it's like a little more confidential, right? [11:26] Well, I'm not asking for what you pay yourself or an individual employee, but generally speaking, the show is really about helping SaaS founders. And if there's clear arbitrage on developer talent in Pune or Bangalore, etcetera, you know? [11:36] >> There is a huge arbitrage on the developer talent. Like for instance, we I would say, let's say thirty thousand dollars is something like, you you can get really, really good Senior engineer. You can get senior engineers for like coding amazing stuff for $30,000. You get nothing in The US for $30,000. [11:52] Yeah. Yeah. Interesting. Okay. So 20 on the team, how many are engineers? [11:57] >> I think we have about eight people in engineering team, five in the data team, and then rest is all the other teams. [12:06] I see. And then why did you you mentioned you're not doing sales anymore. Most founders like that you hear general advice is, the the founder should really do the first million dollars of sales. Why did you stop doing sales? [12:14] >> I I focus a lot on the marquee client. So I chase after one client and rest of the team chases after all the clients. Like, I go very much like, I'm gonna close, like, one big name, Morgan Stanley right now in the next six months. So I have like some like, you know, individual one big client that I'm chasing after right now. [12:33] I see. What gave you the It sounds like you came from a private equity firm in up in Connecticut or the Northeast. What gave you the confidence to leave that? I imagine it was a cushy job, cushy salary. What gave you the confidence to leave that and start this? [12:43] >> Well, actually, you know, I think over time, it was you know, my boss was very, very encouraging. He was like, just go for it. If you fail, then just come back and join it again. [12:53] Which firm were you with? [12:54] >> I was in a firm called Golden Capital based out of Stamford, Connecticut. [12:58] I see. Interesting. Okay. Was it cushy or they weren't paying you enough so it was easy to quit? [13:03] >> No. It was pretty cushy. Actually, the other thing I was really interested in doing a PhD in economics on climate change induced financial depression. And I was working with Professor Gary Gordon in Yale. So I'm a Yale graduate in the master's program. And I really, really wanted to do that because I was obsessed with the great depression in the early 1930s. And I really saw a parallel coming in 2020s. So that was like my whole thesis. [13:30] >> And then instead of doing that PhD, my professor was like, well, you know, this is a good company because climate risk is going to be a very big sector for a private company. Why don't you go that? And if it doesn't work out, you can always do a PhD. So I have a PhD in Yale, a good job in private equity as backup. Also coming back and working with my brother has been so much fun. I [13:52] >> think it was the best call ever. [13:53] I love that. Okay. Let's wrap up, Abhi, here with the famous your famous vibe. Number one, what's your favorite book? [13:59] >> My favorite book? Oh my gosh. I love this book called The Worldly Philosopher by I'm forgetting his name. He's a it's a book about a bunch of economists and how they thought about the world. [14:11] Number two, is there a CEO you're following or studying? [14:14] >> Oh, yeah. Lots of CEOs. But I do really like the guts of Elon Musk. I mean, I do not completely follow him. I'm not a fan, but I really love the fact he takes a problem which is so fucking impossible. I'm sorry for my French. So impossible. And, you know, climate change is the most impossible and the most difficult problem. So seeing him go head to head with everybody saying you're going to fail is something which [14:38] >> does give a lot of courage. [14:39] Number three, what's your favorite online tool for building blue sky? [14:43] >> Notion. Notion. Notion, guys. If you're not using Notion, if you're on Google Drive, please come to Notion. Google Drive. [14:49] Did you switch? [14:51] >> Yes. Completely. Like, you know, in 2019, my brother was convincing me to join Notion. He was one of the first 100 users or something. And I was like, no, no, no. I can't use this tool or something. Like, I'm gonna do Google Drive. And we had a big fight also. But now now I'm the biggest proponent of Notion on, like, Twitter, every platform. [15:09] I love that. Number four, how many hours of sleep do get every night? [15:12] >> I take eight hours of beauty sleep, man. The girls gotta sleep. [15:15] I love that. And what's your situation? Married, single? Married, single, kiddos? [15:20] >> I was single for the last four years, which was a very, very bad side effect of being a founder. I was kind of annoying, but I recently started dating. Actually, my first crush from 2008. So it's kind [15:32] of But not married yet? [15:34] >> No. Okay. No kids. [15:36] Right? No. Okay. [15:38] >> And how can I ask mean mom? [15:42] I ask I'm not saying go have kids. I'm just curious. And Abhi, can I ask how old are you? [15:47] >> I'm 31. [15:48] 31. Last question. [15:49] >> What's something you wish you knew when you were 20? [15:52] >> What's something I wish in my twenties? Oh my God. I wish [15:59] >> actually, I don't. I think my twenties are pretty good. [16:02] It's not something you would change, it's just something you wish you knew, it's not a regret. [16:06] >> Something I wish I had gotten on the path of software development just a few years earlier. It was something I was just mightily scared of, even though I had so many friends around me who were doing it. And even though I myself was like, you know, a computer, I used to code when I was 17 years old actually. And I got like really good grades in computer science, but I don't know why I stopped doing it. [16:29] >> I don't know why I stopped doing it. But it's been super exciting to pick it back. Yeah. [16:33] There you have it. Blueskyhq.in. Their customers are electric utilities, insurance companies, and infrastructure companies who pay them for data, satellite data. They upsell based off the resolution, based off kilometers. They have customers paying as little as called a $100 a year chasing million dollar a year deals as well. They just passed eight paying customers and called about a $500,000 run rate, hoping to break $500,000 in terms of run rate in the next six months as they [16:56] look to scale, raised $1,200,000 last year at around a $7,000,000 valuation. A a Abi and her brother team brother sister team working together growing this bad, but we'll see what happens next. Abi, thanks for taking us to the top. [17:08] >> Thanks so much. Have a good one, Nathan. [17:12] 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 [17:37] Central. Additionally, remember these recorded founder interviews go live. We release them here on YouTube every day at 2PM Central. To make sure you don't miss any of that, make sure you click the subscribe button below here on YouTube, the big red button and then click the little bell notification to make sure you get notifications when we do go live. I wouldn't want you to miss breaking news in the SaaS world, whether it's an acquisition, a big [17:59] fundraise, a big sale, a big profitability statement or something else. I don't want you to miss it. Additionally, if you wanna take this conversation deeper and further, we have by far the largest private Slack community for B2B SaaS founders. You want to get in there. We've probably talked about your tool if you're running a company or your firm if you're investing. You can go in there and quickly search and see what people are saying. Sign up [18:21] for that at nathanlatka.com/slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode and if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, but I do it so that we can all learn. We have to counter those people. We [18:40] got to push them away. Click the thumbs up below to counter them and know that I appreciate your guys'support. All right. I'll be in the comments. See you.