Atlan: Leveraging Founder-Market Fit to Build a Global SaaS Brand

Introduction

Building a collaborative workspace for ‘the humans of data’

Dewi Fabbri: Roy, Prukalpa, welcome to Moonshot. First of all, Prukalpa, can you tell us about the genesis of Atlan and how does it solve the collaboration issue for data teams?

Prukalpa Sankar: So, a little bit of a backstory. Prior to this, my Co-Founder Varun and I, we had founded a company called SocialCops that did a lot of work in the data science for social good space. We were [solving] large problems like national healthcare, poverty alleviation; they didn’t use data, and it felt like they should. So, we said, “Let’s go do something about that.” And very quickly, our model sort of turned and we became an extended data team for our customers. We were doing work with folks like the United Nations, the Gates Foundation and the World Bank. Obviously, they didn’t have data teams or technology teams, and so we became their data team, which is really where Varun and I learnt everything that we’ve learned about building and running data teams and how complex and chaotic they can get, because of the kind of work we were doing; we were dealing with a wide variety and scale of data.

At one point, we were processing data for over 500 million Indian citizens, billions of pixels of satellite imagery a day. All that sounds really cool, but the reality for us as data leaders was, I mean, it was a nightmare. I feel, as a data leader, I have seen it all. I have had Cabinet ministers call me at eight in the morning and say, “Prukalpa, the number on this dashboard is broken.” And you know, I’ve scrambled to open up my laptop and realised that something is clearly off and not known how to answer it. And so I have called my project manager, who calls my analyst, who calls my engineer and it takes us eight hours and four people to figure out what went wrong. I have sat on the top of our terrace and cried for three hours because an analyst quit on me exactly a week before a really important project was due, and he was the only one who knew everything about the data. And I was like, “How am I going to deliver this project to my clients?” [We] doubled our team this one quarter; and thought it would solve all our problems only to realize at the end of the quarter that actually our productivity had dipped significantly, and we were way worse off than we were when we actually started.

The unique thing about data teams is that they’re very interdisciplinary. To make a data project successful, you need an analyst, an engineer, a scientist, a business, like 22 different kinds of personas that need to come together and collaborate effectively. But each of these people have their own tooling preferences, and their skill sets, and their limitations, almost their own DNA in the way that they were working.

And, so we hit this breaking point as a team ourselves, and we were like, “We can’t continue to scale like that.” And, so we actually started an internal project for ourselves to make our team more agile and effective. Over a couple of years, we built tooling that made our team over six times more agile. And that’s when we realised that, “Hey, we’ve probably built something that could help data teams around the world.”

Data teams were starting to become a function inside organisations. So, we said, “Hey, you know, can we help every data team in the world to become maybe just 25-30% more agile. What could we do with the world if we were able to do that?” And that’s basically how Atlan was born. So, the high-level on us – we see ourselves as a collaborative workspace for a modern data team. Every time there’s a function in an organisation – the engineering team has GitHub, the sales team has Salesforce. As data teams start becoming a function inside an organisation, we’re trying to say, “How do we build that collaborative layer knowing that the only reality in this ecosystem is going to be diversity of the kinds of people in this team?”

Unifying data teams on a single platform

Dewi Fabbri: Right, so how does Atlan solve for that? Do you bring them all on a single layer? How do these different teams talk to each other? And how do they access each other’s data?

Prukalpa Sankar: So, just a little bit about the product – we basically sit on top of existing data tooling. So, this is typically a data warehouse like Snowflake, BI (business intelligence) tools like Looker or Tableau, your ETL (extract, transform, and load) and your modelling tools like DBT (data build tool). And what we do is – the entire product is sort of built on this premise that we call a data asset.

So, the way we think about a data asset is; it used to be just your data, it used to be just your tables, but it’s not just your data anymore, right? It’s your code, your models, your BI dashboards, your DAGs (directed acyclic graphs), your pipelines; all of these are data assets and should be treated and maintained like a data asset. And so, around these data assets, what Atlan does is we create this layer that we almost call a data asset profile. The best analogy is what a GitHub code repo is. When you onboard an engineer, you just share a link to a GitHub repo. It has your code, your documentation, your revisions, everything in the same space, right? It’s almost your single source of truth for your code. So, we said, “Hey, if you had to create a similar kind of single source of truth for your data assets, the questions that users are asking are things like, where does this data come from? Who else is using this data? Can I trust this? What does this column name actually mean?”

The interesting thing about data, though, is that all of that truth is actually very dispersed. The truth about who’s using it in the BI tool, the truth about where it’s coming from, is in the ETL. And so, what we’ve done is we’ve basically built a platform that integrates into essentially all the three different tools. It creates this proverbial single source of truth – data asset profile, which gives 360 visibility into your ecosystem. And around that, we’ve basically built an ecosystem that we call embedded collaboration. So, when I request access to a data asset, it should be as simple as someone getting a message on Slack – approve/reject. Every data asset in Atlan has a unique URL, unique hashtag, which makes it referenceable. I can basically bring that same data asset into a BI tool like Looker, I can reference it in environments like Slack, I can create a Jira [software] issue using it.

So, we basically tried to say that, “Let’s look at all of the tools that users are using, and if you had to create the ideal user experience for these diverse teams where they have a 360 visibility, trust, transparency and collaboration, how do we sort of build that in?”

Dewi Fabbri: So, it’s almost like you’ve taken the hassle out of collaboration and made it efficient for teams to work together. So, Roy, what excited you about Atlan’s mission, and how do you see this space shaping up in India and globally?

How empathy can be a huge competitive advantage

Anandamoy Roychowdhary: That’s a very good question, Dewi. And I think, you know, when we first intersected with Atlan, with Prukalpa and Varun, it was clear to us that there were two major trends, right? The first trend is, of course, the rise of big data. We’ve basically seen how the big data ecosystem evolved and how big data was exploding in organisations. So, when you combine that with the fact that, as more and more people start to work with data, the inevitable need for collaboration tools is going to rise.

Now unlike most of the founders, we saw a very strong founder-market fit in this case, which is because both Varun and Prukalpa have essentially been trying to solve this problem for themselves for the last eight years. The solution they had built was perhaps the sleekest demo that we had ever seen. So, it was very clear to us that the founders really got it in terms of the problem. They had lived the problem for many years themselves, and so the solution that they were building was very authentic, and it had a very high degree of success in our view. So Prukalpa, one of the things that really stood out for me and one of the reasons we chose to partner with you was the founder-market fit.

Prukalpa Sankar: I mean, I think it shaped everything that we do today as a company, right? We talk about this a lot. We actually have this slide when we talk to customers; it’s a manifesto slide. It’s a very rare thing, I think, that people have, when they are pitching to customers, where our manifesto literally says, “We want to be the kind of company that we wished we could’ve partnered with as a data team ourselves.” And in some ways, I think that drives all our decisions. It drives our product decisions; it drives our business decisions. The way we think about pricing, the way we think about… For example, we are a very value-led, adoption-based pricing model because when we were a data team ourselves, that was the kind of partner we wanted, right? We wanted a partner that could actually tie themselves to the success that we had. And unfortunately, we found that in the market, most people were [charging] million-dollar licensing fees, and they were like, “It’s going to take 18 months of implementation.” And that was just not the kind of partner we wanted.

And I think everything we do… Very early on… It’s a lot easier for us to explain what we do today because, you know, the market has heated up, and Gartner has coined a term called ‘DataOps’ and all of those things. But when we started, it was really hard to tell people, “Here’s the problem. We think there’s a problem. We know it’s a problem. We want to solve it.” And people used to ask me back then, what is your competitive advantage? What is your real competitive advantage? And this was before we had a product or anything like that. I used to tell our team that, “Our biggest competitive advantage is empathy. We have been this data team. We have been that data leader. I have been that data leader that has gotten that call at eight in the morning, right?” And, I think that gave us empathy for the day-to-day struggle that data practitioners face and allowed us to build products with a lot of love for that target audience in some ways.

And I think even today, in a lot of ways; I think it still continues to be one of our biggest competitive advantages, as the space heats up and things like that, but the very fact that we built this tool internally for ourselves; and we lived this problem for years before we actually… You know, Atlan was never supposed to be a product to be sold to anybody. We just built it for ourselves. And I think that gives us empathy in a way that almost no one else can. You can’t recreate that in any other way.

Leveraging data to build personal conviction

Anandamoy Roychowdhary: You know, it is interesting because again, you can very easily run into this trap of saying, “Hey, because we’ve lived this problem for as long as we have, we actually know what the solution should look like.” And you can be right for a very long part of that, for a very long time just in the back of that. So, while I think it’s easy to back your empathy, what’s also been very striking of this journey that Prukalpa and Atlan and Varun are taking, has been the fact that they are also fairly data-driven in their approach around things like which features to build, which customers to go after, what’s the right profile for folks that can succeed. I think all that stuff plays out really well. I think Prukalpa, you should talk a little bit about how you use data in different aspects of company-building. But I think that you guys are amongst the best I’ve seen in terms of being very, very data-driven.

Prukalpa Sankar: Yeah, so, I think for us, it actually just came down to personal conviction. And I think that’s the thing; many people think about data as a way to convince other people, convince the VC, and I think for us, it was very much… We needed to build personal conviction. And I think that sort of comes from our roots because for us actually making a decision to take an internal product, when we were running a pretty successful company and saying, “We are going to completely change. Start from zero to one again.” It was actually a really big decision. So I mean, the time when we said, “Hey, is this a problem that we can solve for other data teams around the world?” The first question we had to ask ourselves was, “But is a problem that other data teams around the world face too?” And so, I remember even before we wrote a single line of code, we actually did about 150 interviews or something like that of practitioners.

So, we started with users because our biggest thesis going into this was that we felt like the buyers probably are not going to get it, but the users are going to get it. And we felt like our entire thesis was around the amazing user experience we wanted to build for the ‘humans of data’ as we call our users at Atlan, and so we basically ran these humans of data interviews. We interviewed 150 users around the world, right from the global big tech companies to relatively early-stage startups, open-ended interviews, just trying to understand, “What are the top three problems in your day?” And I think that gave us conviction because we constantly found that even with zero prompts… I mean, we didn’t know what we were doing back then, right? So, they didn’t even have a product or anything they could refer to, but we consistently found that actually one of the top three problems that data practitioners were talking about were problems that we faced ourselves as a team.

And so I think that gave us conviction, “Hey, this is a global problem, this is a top-of-mind problem, and this is a problem everybody, literally everybody around the world, faces.” I think from there, we actually doubled down and said, “Okay, now we want to figure out how to solve the problem. And so actually for a while, we just went back to building [the product]. We found one customer we wanted to go deep with. We thought that they met a lot of our criteria. And we just wanted to get the amazing user experience, natural user love; we felt that was what was really missing. So we just went back a little bit into that phase of just building and trying to figure out how to solve that problem. And, we started getting success there. We got to this point where I think they [the customer] went from 100 users to 500 users organically on the product in six months.

Atlan has a lot of natural sharing and collaboration kinds of elements, so we started being able to see the real value we were able to create. I think they shipped 100 more data projects in that quarter than they had in the previous quarter. So we saw some of that. And, that’s when we said, “Okay, now we have to figure out, market”. We did the user-problem fit, now let’s think about the market. And the unique thing about Atlan was that we’re a horizontal product. And that’s a blessing. It’s a great market. It’s a really huge opportunity. We can essentially, at some point, really dream about helping every company in the world. But it’s also a curse because you can basically sell it to everybody, right? You can sell it to large companies, small companies, every industry, financial services, healthcare, and big tech. Right? And so it was really important for us to figure out how do we focus and how do we build repeatability in that journey of product-market fit?

And the thing that I hated the most was that almost everything else in the world, other than this journey of zero to one product-market fit, is a relatively linear journey. You can control it, meaning that if you make the right decisions and you put in the right inputs, if you work hard enough, it’s likely that it will be proportional to your outputs. Product-market fit is not that. You could make all the right decisions, you could do all the right things, you could put in all the right effort, and you can still end up not getting to product-market fit. And so that bugged us a lot. Like, there has to be a better way to get it. It can’t just be, “It just happened.” And you hear these startup stories all the time, right? Like, “It just happened. We just built this thing, and it just worked. We suddenly got to adoption.” That’s, you know, great. It’s great for founders who just suddenly got to adoption, but it’s also kind of annoying because there’s no real path to get there. And so, we decided to be pretty data-driven in the way that we did customer discovery. So we literally… And this is small data; this is not actually big data.

And I think what people really miss out is that you can actually get a ton of insights from small data. I think this was right after we partnered with Surge, and COVID was just about to hit and all of that.. And, we went through this phase where we personally interviewed about 200-300 buyers globally. And we marked them into every segment we could. So basically by demography, by industry, by company size, by data persona, by technographics, what kind of tooling they’re using internally? And we tried to get signals from that – where were customers? Again, we took a very non-product driven approach. Our first call was just an interview. It was almost an experiment; we were just trying to get the truth. We didn’t want people to tell us just because they liked us that, “Oh, this is a great product.”

We really wanted to know, are they going to buy it? Where’s the biggest urgency? What’s going to be the quickest way for us to grow? Things like that. And I think that gave us almost a map, right? We were able to say, “Green, reds, yellows. This is where there’s maximum pain, and this is where there’s maximum speed or velocity that we can grow with.” That helped us again get conviction to say, “These are the segments we want to go after, and these are the segments we’re going to completely say no to.” In fact, it turned out to be something very different from what the market actually uses to define its industry segments. It was not industry, and it was not demographics. It was not all the questions that normally people use to figure out their segment… But that helped us get to our ICP (ideal customer profile) a lot faster and with a lot more conviction.

Driving impact through founder-market fit

Anandamoy Roychowdhary: It’s very interesting, actually Prukalpa, as you recount that journey, two things that point to how strongly data driven you are… One thing is almost no other company, or no other founder has ever complained about finding product-market fit, but the fact that you guys were constantly annoyed by the fact that that required just more serendipity and less process and execution, was very interesting.

But I think the second thing, let’s just talk about this a little bit, because this is something everyone struggles with, right. The decision that you made, during Surge, to actually cut down the number of features that you had, right? You basically went from supporting a few different backends to being like, “Hey, we are going [with] Snowflake only”. A very difficult decision for a company to make. Typically, people are terrified. They always feel like cutting down TAM (total addressable market) when they do that. And so very few companies are able to pull that off, and you did. So, talk us through that a little bit, and also, did that work for you guys? I know the answer to that, but I’d love to hear it from you.

Prukalpa Sankar: Yeah, in retrospect, it worked out, right? And it’s easy to look back at these decisions in retrospect and be like, “Yes, we made the right decision back then.” But it was actually a really hard decision to make. For example, our second customer was about to sign a contract, again, a great logo. They basically said, “Hey, we want you to support these ‘on-prem’ sources.” And it wasn’t a lot of work; it was probably going to be maybe two or three weeks of engineering effort for us.

The second contract – a really hard decision for a company to make. We were in the middle of COVID at that point. So, I think there, a few premises have helped us a lot. The first premise is the fundamental – what are you playing for? So, for example, in Varun and my case, and this comes down to, literally, the time we founded Atlan. It was a very simple decision. For us, it was either we play big or we shut down. I mean, we know that there are only two outcomes that we can get out of this journey. We did not want to build a company that grew linearly. Because it was a very big decision for us to take a company that was really successful and was growing very fast…

So, when we decided to do Atlan, we were driven by this idea of impact in some ways. At SocialCops, we actually drove a lot of impact. We built India’s national data platform. We were a company that could say that in five years, we had impacted the lives of at least a hundred million people, maybe a billion people. Very few companies can say that. And so, for us, we could maybe continue to do that. We could maybe in 10 years grow like a McKinsey, grow 2X to 3X year-on-year. But basically, power 10, 20 national data platforms around the world. So, for us, the decision came down to almost this amazing beauty of software.

It’s like this purist goal of software, where software is like one and only thing in the world that allows you to actually build something that everyone in the world uses. It’s a very rare thing in the world. So, Slack is a great example. Something I’m personally very driven by. If you saw when NASA sent up the Mars Rover, there’s this picture in the command centre where they are actually collaborating on Slack when that happens, and that’s beautiful. In five years, there’s a company that has like 600,000 organisations in the world that use that company. I would probably say that a part of our success at Atlan or before at SocialCops – Slack had something to do with it. And, you know, NASA is using it. I think that became the goal for us. So with that, when you know that that’s the goal, that’s what you’re playing for… The reason we did all of this was because we said, “Hey, data teams are going to become really mainstream in the org fabric.” We think that every amazing thing in the world in the next decade is going to be driven by a data team. We want to power those data teams. We want to be the icon on the desktop. And so then, if you want to play that game, you have to hit scale.

You can’t grow linearly. So, for us, we wanted to build something that every data team in the world uses, right? It was very clear to us that every amazing thing in the world, in the next decade; maybe a cure to cancer or a man on Mars, self-driving cars, they’ll all have an amazing data team behind them. And we wanted to be the icon on the desktop. That was our definition of impact. And so, if you want to be that, then you have to play for scale. You can’t play a linear game. You can’t say, “I’m gonna grow 2X year-on-year. So, it was really important for us to build for scale. And so, I think that premise helps a lot.

Having the conviction to choose long-term goals over short-term wins

Prukalpa Sankar: So, every time we have really hard questions where Varun and I talk about it, we are like, “Okay, it’s alright. It’s okay if we fail. It’s really okay. We’re only doing this to play for that outcome, and if we don’t hit that outcome, it’s also okay. We know we’ll fail, but at least we went after something that was really big, and we failed.” And so, I think being comfortable with the failure in some ways; in our head, saying that, “Even if we win the short game, we get a few more customers, we get a few more logos, it won’t help us win long-term and so it’s okay to make a long-term bet versus a short-term bet.” Just getting comfortable with it yourselves, I think that helped a lot. And I think from there…I don’t think that’s enough, then you have to get conviction, right?

At the end of the day, as a founder, you need conviction to make really hard decisions. And so, for us, we get convictions from signals in the market. We get conviction by talking to users. And so, I think that’s where that whole, you know, market customer discovery exercise, where you’re constantly talking to users, but you’re also not just talking to users and trying to get intuition from it, but you’re actually looking at data and saying, “Okay, I feel like we can make a relatively… We have a bet here that is based on some data. And so, maybe our data is wrong, or our premise is wrong, but at least at this point, we’re making the best decision that we can.”

In retrospect, it gave us that conviction to say, “Okay, no, we’re not going to go after just winning a few logos. We’re going to make a decision to just go cloud-first, cloud-native.” At that point, we said, “We’re not going to do Microsoft Azure. We’re just going to do AWS and Snowflake.” And that cut down our number of customers we could win short-term, in our pipeline. But it gave us a lot of focus to build products for that market. And then, once we launched, it gave us that growth that we needed in some ways.

Rallying your team towards your mission with clear, effective communication

Anandamoy Roychowdhary: I was fortunate… Really privileged actually to be a part of that journey. It was hard, right? To Prukalpa’s point.. I mean, for people listening, the team enters Surge with one customer that would’ve been a 100% increase in the customer base, right?

And so, while this sounds very easy in retrospect, it was an incredibly hard decision. I was struck by the fact that the team led by Prukalpa, was able to come to that relatively quickly. It wasn’t a snap decision, it was definitely a tough one, but it was made relatively quickly. And then the whole team just lined up behind that. I think that’s the next question for me, Prukalpa, which is, again, in startups, it’s the mission, it’s the call to action that’s really important, and that very few companies do this as well as you guys do, which is like falling behind a specific sort of thing that we’re going after. Talk to us a little bit about that, which is, how do you get your team to respond to your call of action?

Prukalpa Sankar: Sure, I mean, I think the one thing I also want to add on, with these hard decisions, I think it’s really important to have people around you who support you to make these hard calls. And so, I mean, I remember, back then, Roy, you were super helpful. To me, when I would go in saying,”We’re thinking about doing this”, you were like,”Yeah, sure. That’s the right decision. It makes sense long-term”, and I think that helped a lot in some ways. Just to help us make those difficult calls and not create doubt in some ways, like “Let’s play for the short term” and things like that. When it comes to call-to-action, I think clarity is important. And I think it’s really important to communicate that clarity to your teams in some ways. And, I think for us, we’ve tried to always be really intellectually honest with our teams. I think that helps. So, for example, in every town hall we do, every quarter, we start with this slide and it literally talks about the stages that a company goes through. So, you know, problem-solution fit, product-market fit, go-to-market fit, hundreds of customers, thousands of customers, exponential growth. And, we have this last thing, which is an icon on the desktop for all data teams in the world. And we’ve sort of mapped those phases. And so every time we go in to talk to the teams, we are like, “This is the phase we’re in, this is what we’ve learned, this is why we’re making this decision.” And I think that helps a lot.

So, for example, when we made this really big decision around COVID to say, “Hey, COVID’s happened”, and Varun and I started with the premise of what do we believe about the world? So this has happened. It’s changed the world for sure. What is this going to do? So what are the opportunities it’s going to create? What are the threats it’s going to create? For us, what are the strengths we have? What are the weaknesses we have? Here is what we should do. And I think we start every year now planning like that. So, you know, when we start every year, we are like, “This is the thesis.” We think this is… And it’s like these abstract things. Like, “We believe this is going to happen in the data world. We believe we are going to move to a multi-cloud environment.” We start with those theses in some ways, and we agree on the thesis, and that helps a lot. Because once you agree on the thesis, you could be wrong about your thesis, sure – but then, you know that you’re making a decision based on a thesis that you’re expecting the world to go into, and we share this with our teams.

Like, “This is what we think is happening in the world. This is why we’re choosing to focus on this and not this. And this is why we think this is what we should plan for in the next three, six, nine months. And I think that helps a lot. I think telling stories on how you get to the decision is as important as telling the story about what the future is going to be. And we’ve found that that’s been a helpful way to catalyse people towards the action.

Focus on problems that are most likely to make you fail long-term

Anandamoy Roychowdhary: So yeah, Prukalpa, coming back to the Snowflake decision, and it was a tough one, as you pointed out, given that you only had one customer at the time… Apart from the fact that yes, we spoke to a bunch of people, and we got comfort. Were there other signals that you used to figure out… Because again, the investment at your end was pretty significant, right? It wasn’t just that, “Oh, we will have one backend to support.” We essentially retooled a large part of the product to work very well in the Snowflake context. So talk to us a little bit about that as well, which is, once you made that high-level decision, how did you double down, and how did you get the troops to line up behind you on that?

Prukalpa Sankar: So, obviously, I think there’s also some of these market signals, right? If you’re making a bet on a technology, is the technology growing fast? And believe it or not, it wasn’t that easy to say that Snowflake’s growing that fast, just even two years ago, than it is today. And so I think there was some kind of market signalling on – is this a technology we can bet on for the future? And do we believe that this is going to grow? I think some of the overall market dynamics – is this big enough for us to go after and focus on? Some of them were definitely customer signals. So, I think customer signals like, “We will buy this product if it supports Snowflake”, is always a great thing to have, right? And I think we tried to sign some of that in advance.

So, in fact, even before we launched the newer version of our product with Snowflake in it, we actually had, I think, 14 POCs lined up. And so I think that again gives you more conviction that “Okay, this does make sense because there is demand.” I think some of that you just constantly execute. I think it’s also important to be okay to reverse your decisions. And so we made a high-level decision. We said, “Okay, we are re-architecting the product. We’re going after cloud-first, cloud-native. We think we should bet on Snowflake first.” But I think then we went in and tested that in the market, and we got signals to say, “Okay, it is making sense. There is growth here; there is a demand here. And you know, let’s go back, and it’s okay to focus these many months of engineering effort just on this.” So, I think that constant feedback loop is important.

I think what people miss out a lot is that you make a decision and then you stick to it, which is good in some cases. But I think it’s really important to continue to keep reinforcing and saying, are you making the right decision? So I think that paranoia of, are we making the right decision in some ways and just getting enough signals constantly to reinforce whether it was the right decision or sometimes if it was the wrong decision, you go back on that decision, and say, “We don’t want to do this anymore.” So, for example, we started with cloud-first, cloud-native, and, you know, Azure is a very strong part of cloud-first, cloud-native. There’s no reason to not support it. I think about it, and we built some of that. I think three months in, we realized, “Hey, we have just a lot more traction coming in, in the AWS first ecosystem. Let’s not create more engineering complexity by going multi-cloud. The reason for that was because we were like, “Hey, that’s a solvable engineering problem.”

So I think I read a tweet the other day. Which is – you have to, in the early days, solve the problems that are most likely to have… The hardest problems that are most likely to have long-term competing failure. Building a mighty cloud ecosystem is an engineering… It’s an engineering resource and time problem; it’s not something that will lead to company failure, versus, can you get product-market fit? Can you get adoption and end-users? Those are problems that are a lot more important to solve to actually get to that longer-term… That can actually derail a company. And so I think reducing complexity in the early days, to focus on the problems that are most likely to make you fail long-term is probably a good judgment call that we started making in the company.

Building a global SaaS brand out of India

Anandamoy Roychowdhary: You know, that’s very profound advice because, again, we see a lot of startups struggle with this one, which is, you know, people like building products, or they come from engineering backgrounds, they like building code. And so a lot of people default to building, and not enough folks think about it strategically, like you just articulated, which is, what’s likely to kill you versus what is likely an engineering and resource problem that can be solved later and using that as a framework to prioritise is a very interesting, I think, very profound framework for folks to take away.

I think just continuing on the way in of that Prukalpa, I think, what was again very interesting about Atlan is that we’ve seen very successful SaaS companies in India, but they typically tend to play in slightly better well-defined categories. In many ways, you are in a category creation exercise. You are also remote. You started in India, are based in India, all of that. Most of your current customer base is in North America, but you’ve also had to build a global and remote organisation. So just talk us through some of those challenges. And what are some of the lessons that you think other SaaS companies that are following your footsteps would benefit from hearing your experience?

Prukalpa Sankar: I think your early customers make a really, really profound impact on your roadmap. I think that it would have been easier for us to get… I mean, we had networks, relationships. I had personally sold into Asia for about eight years. I think it would have been a lot easier for us to get to our first 10, 20 customers, just off relationships. And funnily enough, I think for us, we actually didn’t want to get our first 10, 20 customers off relationships. It was actually really important for us that people buy the product. Again, this goes back to that intellectual truth thing, right? We could game it. I could. Honestly, I think most founders can game their way to US$2 – $5 million in revenue. You just find the right people, get the right connections, just be really passionate… Mostly, you can get there. That doesn’t work anymore because you have not actually built repeatability; you have not really built an engine; you have just gamed your way in some ways.

And so for us, it was really important that we go after the customers who can help us build to our vision. We were in a very early market, so it was important that our customers didn’t push us in the wrong direction from a roadmap perspective. And, I think it was like going back to that thesis – we built this company to be the icon on the desktop for every data team in the world. And so then, why would you play for a short term, “Let me get ten customers more easily in three months”. Three months is not… We don’t measure ourselves on three months. So, you know, the reality is data maturity in the US is far higher than it is in any other place in the world. We were building for the cutting edge of data teams. The cutting edge of those data teams existed in North America, mostly, and all the others who use Atlan today in the rest of the world. But, it was really important for us that we go and partner with them. And so, that meant we were building a global company from day zero, and that was very clear and apparent in what we did. And even today, we’re not the stereotypical company in our space.

Most companies in our space have come from FANG [companies], classic Silicon Valley roots, and they built an internal tool in one of those companies, and they decided they’re going to build a company out of it. That’s the classic stereotypical company in our space. And so we knew that we weren’t the classic stereotypical company. And when you know that you are not the classic stereotypical company, then you have to actually prove through your work that you can be the cutting edge. You can be the best, you can build the best user experience. So that translated into everything that we did.

For example, when we did our Atlan brand, the first time we did our Atlan brand, we spent a ton of time, energy, and effort in making that brand stand out. Like our day zero was when we built our website. What is the best SaaS website in the world? It was Stripe in our minds, and we said, “We want to get as good as Stripe”, and we didn’t get there. We’re still very far away from that. But I think it got us much further along than most companies our size and scale would have. And I think that translated into everything that we did. The product, the fact that quality needed to be at a certain bar for us to be able to do it. In some ways, we are breaking so many stereotypes of the kind of company, about the places that companies like ours can be built out of. That means everybody needs to grow 10X. Everybody needs to hit that level of quality bar in some ways. In fact, it’s harder for us than I think it is for the stereotypical companies in some ways because we have to break biases as well, along the way. I think just keeping that in mind, knowing that that’s the bar and that we have to work way harder. Unfortunately, that’s just the way the world works. Is it fair? Is it not fair? We can go into those conversations, but that’s just the way the world works.

If you want to win, you’ll do what it takes to go and prove and luckily, I think customers, for them, ultimately what matters is, what is the best product? What is the best team? Are they going to be able to solve that [for us]? And I think that sort of helped us drive that. For us, it was actually a very simple decision to be global from day zero. The challenge that comes from it is really upping the bar. So, I think, internally from a talent perspective; internally, just the benchmark of what is okay at Atlan… And sometimes it’s not even about what the customers expect or what the market expects. It’s – who are we as a company? We have these values; we have a value called ‘never be satisfied’, and it means that we’re going to spend those extra 30 minutes before a presentation and make the fonts perfect and that matters to us as a company. Maybe it doesn’t matter to many other companies, but to us, it matters as a company. And so, being able to hire and build for that culture has been really, really important to us in that journey.

Driving decisions with conviction

Anandamoy Roychowdhary: I can definitely vouch for the fact that Prukalpa and Atlan are always the best prepared in the room for any conversation. In any introduction that we have made or in any conversation, or even in interviews, I know Prukalpa asked me to shadow her in some interviews, and just the level of prep, the level of detail is so much ahead of companies of a similar size and scale that sometimes it becomes very clear why Atlan deserves to win this space.

Dewi Fabbri: Prukalpa, you have demonstrated a tremendous amount of grit and resilience in building Atlan. If you had to pick just one important piece of advice for an aspiring founder what would it be?

Prukalpa Sankar: ‘Build for yourself’ is my advice. It’s very easy to build for everybody else. What I mean by that is, in this market, I am trying to hit the next milestone. I’m trying to get the next fundraise. I am trying to impress this VC. I’m trying to impress this customer. I think what founders miss out is that if you want to build a company, you are dedicating the next decade of your life to building a company. There is a serious opportunity cost that comes with that. You might not realize it now because in the early days everything is exciting; but it’s not going to be, in that journey that you have to take. So, if you have to… At the end of the day, you have to have enough conviction to be able to take that journey. And, it’s different for different people. The reality is that it’s very easy to get caught up in the hype, “Oh, we want to be a unicorn.” But that’s not the right path for everybody, it’s not the right path for every founder as well – you could build a fantastic company without raising any venture money ever.

At the end of the day, you are the one who’s going and building this; you are dedicating your life to this. Why are you building for everybody else? Build for yourself. Get conviction in who you want to be. Get conviction in what will make you happy. 10-15 years out, what will make you feel happy? Whatever, however, you define happiness, right? That’s for you. And then work backwards from there and build a company for that. I think that is the one thing that founders use a lot. You go to Stanford for an MBA; your life is set after that. There’s very little chance that you will fail in a traditional path. In a startup, failure is a very real part of that journey. There’s a serious opportunity cost to that journey.

So if you’re doing it for anyone who’s not yourself and you’re not optimising for that, I think it’s going to get hard. Maybe it’s not going to get hard early on, but it’s going to get hard at some point.

Dewi Fabbri: Prukalpa, that was really insightful. Roy, based on your experience of working with the team at Atlan, what are the three things that they did that are key for aspiring founders to do if they are eager to build a global SaaS company?

Anandamoy Roychowdhary: So three things, the first one would be, just to borrow from what Prukalpa was saying – your conviction has to be very real, and it has to be based on some deep understanding that you have of the problem statement. I also agree with – right now, it’s very cool to do startups, so lots of folks are doing startups. There are certain kinds of startups that lend themselves well to building things according to your own taste. SaaS is rarely like that. So, always ask yourself the question, “What is my founder-market fit here?” And I think what the Atlan team has demonstrated is that founder-market fit is very important. And I would urge people to go after problems that they have spent a lot of time thinking about and have some deep insights into that space. I would say that’s one.

The second learning from Atlan that I wish everybody adopted was just sheer doggedness. I’ve never seen Prukalpa take a simple decision. For everything, she’s trying to find out everything she can. She’s trying to go as deep as she can into the ramifications and asking what other folks are doing – always benchmarking against what’s best out there. You need that kind of commitment to excellence and not accepting mediocrity in any shape or form in anything that you do. If you can keep that going for a while, that compounds very nicely over a period of time and can help you build a very interesting company.

The third thing is just bringing your people along. Again, people who don’t communicate well… There’s always this thing that, “Oh, VCs really expect you to pitch very well” and things of that nature. But the reality is that if you don’t pitch well, then the chances are that you will not pitch [to] your own people well either. Storytelling, the ability to explain to people why they’re on this journey, why they should do it, why this is the best use of their time, versus the 900 other things they could be doing, is a very critical aspect of company building. The flip side of it being very easy to start up, is that everybody does. You really have to keep your team together. You have to keep them excited. You have to keep them on the journey. Hopefully, it’s ten years, but we’ve seen companies take much longer as well, and that’s rewarding too. It’s not really a time journey as much as it is a journey of outcome and achievement. You want to climb the mountain and not worry about how long it took. The ability to take people along is a very critical thing, and Atlan does that really well. I wish for all of you out there to just learn from how they do it, and steal and adopt some of those best practices for yourself.

Dewi Fabbri: Roy, Prukalpa, thank you so much for sharing those profound takeaways for founders, and for a riveting chat on developing conviction as a founder and Atlan’s quest to build a global SaaS company.

I’m Dewi Fabbri, and you’ve been watching Moonshot. For more interesting startup stories, visit us on our website Sequoiacap.com or follow us on your favourite podcast channel.