Prime Venture Partners Podcast

From $900 to ~$19B: How One Indian Entrepreneur Built 2 US Unicorn Giants

• Prime Venture Partners: Early Stage VC Fund

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đź’ˇ What does it take to build 2 multi-billion-dollar companies?

In this episode of the Prime Venture Partners Podcast, we sit down with Dheeraj Pandey, Co-Founder of Nutanix & DevRev, to unpack the real challenges, pivotal decisions, and insights behind building and scaling a successful enterprise tech company.

00:00 - Introduction
00:52 - Dheeraj’s Early Life
04:15 - Leaving IIT & Taking a Second Shot
07:40 - Studying Computer Science at IIT Kanpur
10:55 - Early Career: Oracle, UT Austin & Trilogy Software
14:20 - The Entrepreneurial Itch & Starting Nutanix
19:30 - Scaling Challenges & Near-Failure Moments
26:35 - The “Tightrope Mentality” for Founders 🎯
33:05 - Going Public: Nutanix IPO & Key Lessons
39:40 - AI’s Role in Enterprise Software 🤖
43:10 - The Shift to DevRev & His New Vision
46:05 - Advice for Founders Scaling SaaS from India
49:00 - Final Thoughts & Key Takeaways

🎙 What’s Inside This Episode?
🔥 Why he left IIT after 2.5 months and came back stronger
🔥 How he built Nutanix into a multi-billion-dollar company
🔥 The risks, near-failure moments, and biggest lessons he learned
🔥 Why AI is reshaping enterprise software & what’s next
🔥 His take on scaling a startup, hiring, and finding product-market fit
🔥 How DevRev is redefining enterprise software with an AI-first approach

📢 This is not just a startup success story—it’s a masterclass in resilience, innovation, and leadership.

With DevRev, Experience conversational AI at work. Startups can get $10K in credits (12 months free) to accelerate their AI-first journey. Apply now - https://devrev.ai/startups/apply

đź”” Subscribe & Turn on Notifications for more game-changing insights!

#StartupJourney #SuccessMindset #BuildSomethingGreat #TechFounder #Startups #Nutanix #DevRev #Entrepreneurship #TechInnovation #SaaS #AI #VentureCapital #Leadership #FounderMindset #EnterpriseTech #PrimeVenturePartnersPodcast

Speaker 1:

Once you're on a tightrope and crossing a valley, you can't turn back. Probably one of the rare people in the history of IoT is to have quit IoT after two and a half months. I was a risk-taking person from early on, so we picked a pretty contrarian use case and we went against the mob. In 2011, people had started to say that Windows is dead. We had three near-death experiences. You know AI doesn't care about departmental boundaries. If you have a Google workspace and if you have a GitHub, all you need is Debra. Enterprise software at the core is about reliability and availability and security. Extensibility Many of these itties in the enterprise is what you get paid for.

Speaker 3:

Welcome to the Prime Venture Partners podcast. I am delighted to have with us today Dheeraj Pandey. He is founder and CEO of DevRev, an exciting new AI startup, and then before that, of course, he's very famously known for co-founding Nutanix, which is now much more than a DecaCon, I guess. Welcome to the show, dheeraj.

Speaker 1:

Thank you, Amit.

Speaker 3:

Dheeraj, love to hear a little bit about your journey of sort of growing up in India and what led you to kind of eventually the road of entrepreneurship.

Speaker 1:

What led you to kind of, eventually, the road of entrepreneurship. Yeah, in fact, you know, I was in Patna, which is, you know, a capital city, one of the eastern states, bihar, as many of you might know, and I was there till I was 16, 17. And I went to a couple of schools in Patna One was Don Bosco and the other one was St Michael's and I just, you know, tried to be in top three, top four kind of class. And I used to be there, I used to enjoy math, and mostly math, I would say, more than anything else. And then, you know, I took the J in 92 for the first time and I hadn't really prepared at all for it.

Speaker 1:

This was the year that I graduated from high school and I got a rank of 1,420. And I really wanted to come to IIT, kanpur. So I'm like, okay, whatever branch I get there, I'll take it. I didn't want to go to any other IIT. So I went there and I got civil engineering and within a couple of months I realized that the probability of a brand change to computer science is way lower than if I were to take the JEE again and were to be in top 100 and then get into computer science. So I was probably one of the rare people in the history of IITs to have quit IIT after two and a half months of attending classes and I came back and I retook the exam next year and this time I was in top 100 and I was ranked 84. And I'm like, okay, this time I'm definitely going to get computer science, which I did back in the day Kanpur used to. You know, if you had to get computer science, you have to be in top 100. And so this is 93, started journey by 94,.

Speaker 1:

As you remember, india was going through this massive liberalization process. The economy was opening up and there was something about that new India that was opening up. And Manmohan Singh as a finance minister Bless his soul, he passed away, but he was such a kind of North Star for me. I'm like, wow, you know, bless his soul, he passed away, but uh, he was, uh, such a kind of north star for me. I'm like, wow, you know, oxford educated, economist and all this, and, and then, uh, and he's really transforming the country. Uh, so I really wanted to be an economist, I want to do a phd in economics, uh, even though I was doing computer science in undergrad.

Speaker 1:

And then I talked to my cousin who had just come back from the US and he explained to me very similar to what's happening right now with AI. Basically there's a big revolution underway with the browser and HTML and the World Wide Web and HTTP and everything else and he's like you got to really continue computer science. It's the best time to be in computer science. You know and really do well in computer science. So, lo and behold, you know I just kept enjoying computer science and a lot of math as well. You know, did really well. I used to love, love math. You know, back in the day, if not for computer science, probably would have done a lot of math and been a major in math. I was a good visual thinker and now obviously I've slowed down a lot in the last 30 years.

Speaker 1:

But in 96, I'm like OK, we have two forks in the road. I could go and join a job or I could just apply to grad school, and that's what I did. I ended up applying to a lot of grad schools. I did get a couple of jobs, but I figured I'll come to the US and really pursue a PhD in computer science. So I got a few offers from like UT Austin, Urbana-Champaign, usc, columbia, all these places, and I ended up choosing UT Austin because of the fellowship. So I landed in the US in 1997 in Austin, texas, and you know, after a couple of years I said I got to be in the industry right now. So I went on leave of absence and for a couple of years I was on leave of absence but never went back to finish my PhD, worked at Trilogy Software for about a year, year and a half, in Austin, texas, and then worked for quite a few system software companies building file servers and databases at Oracle and then distributed data warehouse and then in 2009,.

Speaker 3:

basically started Nutanix. Fantastic, very, very inspiring. And I was going to say a little bit tongue in cheek that leaving IIT in the first year with the 1400 rank to get back in, that was like your first experience of a pivot, a hard pivot.

Speaker 1:

It was. Indeed it was. It was. I think I was a risk taking person from early on. You know, I think and that was one of the first big risks they actually took and you know I always thought about the worst is not bad enough. Why wouldn't you? And, uh, the worst situation there was I had gotten admitted to since defense of physics and like, yeah, just gonna do physics and we'll figure out the rest from there next time, because I've done fairly well in my high school, you, you know, secondary exams and stuff.

Speaker 3:

Absolutely so. That leads me to sort of the fact that you worked at a lot of different companies, including Oracle, and on file system database etc. In the enterprise software arena. What led to the idea for Nutanix, number one and number two? Just this notion of you want to be an entrepreneur because once you're there, you're settled. You're doing your PhD yes, you went on a leave of absence or sabbatical or whatever. What made you think that you wanted to start a company back in 2009?

Speaker 1:

You know, there was always an itch and I think in 2000, I think 2009, february I had a really long discussion with you know, a strategic discussion about with my founders of the company that we were working at. There were three of us who actually knew each other for almost a decade or more and I think the core insight that we're trying to bring was that SQL and NoSQL are here to coexist and we were kind of pigeonholing ourselves into just SQL and that to only data scientists and that to only ad hoc querying and all that like on-demand analytics, versus what the world was doing with Hadoop, and the developers were getting into analytics and they were using Java and MapReduce jobs back in the day to really build a ton of the analytical pipeline. And we're like look, we're building a large-scale distributed system, so it should actually come together. We should not take sides on SQL versus NoSQL. But it wasn't to be. I think we couldn't convince the it should actually come together. We should not take sides on SQL versus NoSQL. But it wasn't to be. I think we couldn't convince the folks there that we should actually build a larger business, a larger company that really brought both of them together. So I think we just kind of spoke for six months and said, look, we've got to do something on our own, and we didn't even realize that this was the worst time to start a company. And we didn't even realize that this was the worst time to start a company Because the fire within it reminds me of the fire within was called Antaragini. For us in our IT days, the cultural festival was called Antaragini, but basically it was that Antaragini. We said, look, what's the worst that can happen? You go and find a job and you'll find a job in the Bay Area. It's the cornucopia in some sense, you know.

Speaker 1:

So data was kind of at the core of what I had built in. Both my co-founders, mohit and Ajit had you know, especially Mohit had actually been in this space for like 10 years and he and I bumped into each other at Zambil, which was my file server company that we were all together at, and so data was going to be the thing, distributed systems was going to be the thing. We're going to fight, not on the hardware vendor's turf but on our turf, and that turf would be pure software running on commodity hardware, because that's all I had done myself, you know, starting with Zambia was file servers and commodity hardware. You know Oracle had moved to commodity hardware with databases, oracle RAC, exadata, and then Astrodata was also distributed data warehouses and commodity hardware. So the idea was to do something with distributed systems, pure software. And so what's the killer application right now? And the killer app back then was VMware. Like, how do you run VMware workloads on a distributed architecture? None of us knew even how to spell virtualization, like even the V of virtualization, so we had to learn virtualization like to the core and then go build systems on top of it and make sure that we got the first use case right.

Speaker 1:

And in 2011, people had started to say that Windows is dead because Apple is everywhere. Who's going to care for Windows? And we took a pretty contrarian view that look, long live Windows. You know, long live Windows. And the way it would happen is through a digital Windows system, like you'll have to stream Windows from somewhere you know, from a data center, and Windows will then come together as clusters of systems that people are actually streaming, because in business world, people will still have a ton of Windows because of Office and everybody was like what are you talking about? This is never going to work, windows is dead. So we picked a pretty contrarian use case and we went against the mob. The mob was saying that there's no future for Windows, if anything.

Speaker 1:

Three years later people started saying things about SQL. The SQL is dead and here we are in our long-lived SQL. So basically we built the first use case around that and we went pretty deep to US Federal Very early on. We went to US Federal. So there was a kind of a duality there, like just being really good with both contrarian views. There was a contrarian view about going early to federal people like okay, no, startups go to us federal as one of the early segments, and it was all about great people. We hired some really good people who were miffed with vmware. They were vmware uh sort of folks and they came and built something and, you know, we started out being, you know, a really good partner for VMware. We used to run on top of VMware and obviously over time we started to get a lot of their talent and things became pretty testy. They became a frenemy, then they became an enemy, you know, and then we just had to build for all that.

Speaker 3:

Amazing, amazing journey. A bit of a question for you know entrepreneurs in India who are trying to build. Now, of course, we're going to talk a lot about you know AI and SaaS and all that, but like going back five, seven, 10 years ago. Why is it that a lot of enterprise software companies necessarily haven't come out of India? And I know there is a few right but, and what does it take in the early days, in the zero to one journey for an enterprise software company? There's, of course, the tech stack you have to build, where you probably have more conviction as engineers or whatever. We have good quality engineers. But then all there's also the customer validation, figuring out the initial kind of co-creators or the co-partners to build with, and so on. So maybe just a little bit of building a large scale, whether it's a now SaaS company, AI company or enterprise software company, in the zero to one phase.

Speaker 1:

If you have any thoughts and suggestions for our listeners, yeah, I think I would say that founders many of the founders in India are not the folks who actually have done infrastructure work and a lot of business software, but also enterprise software at large is a lot of infrastructure work. Like you know what we're doing at DevRev now there's so much of infrastructure work. Devrev now there's so much of infrastructure work. It's like you have to pay the dues for the object model, the event model, the security model, the SQL model, all that stuff I mean, and over time, obviously the AI model. All this stuff is infrastructure. It's systems engineering and the founders need to respect systems engineering, because enterprise software at the core is about reliability and availability and security, extensibility. Many of these itties in the enterprise is what you get paid for. So I think being very good with infrastructure is what I mean.

Speaker 1:

Think of Freshworks, for example. I think why did they not go above the lower end of the mid-market? It's a lot of SMB and, at most, the lower end of the mid-market Because they also built lots and lots and lots of apps, lots and lots and lots of products, but they didn't have a shared infrastructure. There was no platform underneath and the platform was not supposed to be scalable. I mean. When I say scalable, I don't mean millions of users, I mean extensibility by an enterprise. They could say look, I know it's your platform, but I can go and extend it, customize the heck out of it and so on. So those are the kind of things that I think the founders not the people that you hire afterwards only it's the founders who need to actually have the appreciation for what it means to build a platform company. A lot of enterprise software is platform.

Speaker 3:

Fantastic. And let's say you were to figure that out, or you have a co-founding team that is cross-border. How would you do the early customer validation in terms of, okay, is there really a need? I'm like a SaaS company or perhaps a consumer company or whatever, where it's a little bit easier to do so how would you figure out, like, okay, is this something that people are going to be willing to pay for, or what is it going to take to get to that level? And perhaps also, I was very curious to hear about this Maya principle that I read about you while I was researching this podcast.

Speaker 1:

Yeah, I think on the first question I mean, at Nutanix, the first 10, 15, 20 probes we did, they said, don't do it Because we were talking to the wrong audience. And that wrong audience was maybe enterprise and higher end of the enterprise, where things are pretty calcified. I mean we were trying to blur the lines between teams. We said, look, we don't need all these specialized teams. Now obviously, lo and behold, what AWS is doing is very similar, except that they had taken it out to a new location which was about streaming infrastructure and renting infrastructure. We were doing it on the same location, which is on-prem. So it probably was harder for us, because sometimes when you change locations, it's easier, because now you get the self-selected people who are like, yeah, I want to rent infrastructure, I want to bypass all the people who build infrastructure and go to somebody who's willing to be a vendor where I can stream stuff from you know and swipe a credit card, as opposed to wait for nine months to procure and plan and build and rack and stack and mount something. So, coming to early customer validation, I think it had to be brute force, like, look, we love distributed systems, we love this data problem and the only way to build a company in this is to pick the right application on top, which happened to be an operating system like VMware, and just go deep with it.

Speaker 1:

And I think the first four years was not first five years, I would say, were not easy on the product. You know it was. We had like three near-death experiences, you know. So what I tell people and I tell myself and everybody within the company is that once you're in a tightrope and crossing a valley on a tightrope, you can't turn back. There's no turning back. At best you can adjust and you can't lean too much to one or the other Because physically think about it the imagery of turning back on a tightrope is almost impossible. You will fall and a lot of people actually think of turning back.

Speaker 1:

Now we did have to actually do a lot of micro pivots along the way, a lot of micro pivots, uh. But you know you also get paid for seeing through things, maybe two, three quarters in advance, so it doesn't look like it's a hard pivot. Uh, I mean, even at devrev, I think you know, initially we thought we just start replacing a lot of apps, because what we've really built is what the market is coming towards. I mean satya just talked about this three, four weeks ago that a lot of app boundaries will blur, you don't need all these apps. And uh, I think we were the first ones almost four years ago said look, the app boundaries must blur because we built all these apps with these departmental boundaries and extremely calcified boundary to that, and departments are created to organize humans.

Speaker 3:

But agents don't care yeah.

Speaker 1:

Yeah, ai doesn't care about departmental boundaries. If anything, the more you give it, the more we call it a knowledge graph. The more connected interconnected the knowledge graph is, the more it can reason with folks you know. So I think you need to really have that level of conviction, not get flailed and, like you know, start to dilly-dally on the initial thing, but at the same time you need to be humble enough to know that you need to take two-degree turns sooner than what a hard pivot would actually be. I mean, so we started with oh, we can rip out Jira, we can rip out Zendesk, all this stuff, and we can rip out Service Cloud. Now in the mid-market, we're doing a lot of that. We're ripping out a lot of these things because you do need to start from, because you can't retrofit AI into Zendesk and Jira and you know, and Intercom and things like that. But in the large enterprise we said let's go and coexist and that was starting out with what we call AirDrop.

Speaker 1:

Airdrop is our data integration platform. We basically do two-way syncs with all legacy systems and it's a hard problem to do two-way syncs, a really hard problem. But it's also unlocked this thing about how our agents are not at the mercy of salesforce apis in real time, at runtime or service now apis or atlassian jira apis, because we just use our own data platform for everything and then in the background we sync it back to where it needs to go and so on. It also reduces a lot of SaaS licenses we don't need. Imagine for enterprise search, for example. How do you even make search work if every link that the search engine actually unearths you have to click on it and now you need to log on to another system. Now you're proliferating SaaS licenses, more so than shrinking and consolidating SaaS licenses.

Speaker 1:

So I think at the core of the early paths and early journey is about having that level of conviction. You know just the fierce resolve and, at the same time, humility to know that you will have to keep micropivoting. The use case is very important. I mean, one of the things that we did well at Nutanix was really picking the right use case, which was virtual desktops. And here we said look, let's start with support, because at the end of the day, you know we come in when there's enough complexity. Otherwise people can keep using Notion and Slack and just be fine building a company, but then it becomes really hard to change that culture over time, to really be AI native, for example, and to think of okay, can we stop disturbing people on Slack if we can ask an LLM of what happened and who works and what and all sorts of enterprise questions can get asked there. So we come in when people have paying customers and then be like, hey, you can actually get a lot of this support stuff. But even for those who are starting out early, I think they're like what does it mean for you to really get the best software development experience and product management thinking before you get to having paid customers?

Speaker 1:

I think at the core of PMF, I tell people that there's no end to the journey of PMF. You have a PMF problem at 1 million, at 10 million, at 50, at 100, at 250, at a billion. Because if you've not started to think about when you're at 100 million to say, okay, how will I even get to 250 million? And it can just be adding capacity to the company. You have to add capability and capacity and really think about capabilities as well as capacity together and they're orthogonal things. I mean capability is about partnerships, but capability is also about features and products and multi-product thinking and things like that, while capacity is about adding more salespeople or more channels or more regions and things of that nature. So there's no end to the journey of PMF, you know, and as long as people realize that, they'll probably be in good stead.

Speaker 3:

No, I love this notion of capability versus capacity. I think most people tend to think more linearly in terms of capacity. Like you said, right, more salespeople, more engineers, next version of the roadmap. But you always have to be kind of ready and open. Like you said, right, more sales people, more engineers, next version the roadmap. But you always have to be kind of ready and open and sensing what the opportunity is right. So, yeah, so I know you spoke about maya.

Speaker 1:

You talked about maya a little bit and I just want to basically, uh, you know, when e-tronics was building apple, the iphone was getting a lot of traction as well. So a lot of my design sort of thinking and what it means to actually make things simple and I'm still on the journey, I'm probably 10% of the way on what it means to really make things simpler and I struggle, you know. I just every day I think about have you made it simple enough and simpler, and so on. Uh, so there's a really good designer, um uh, who actually created some great brands, uh, in the U S, like uh Greyhound and you know early Coke brand and all this stuff. His name is Raymond Louie.

Speaker 1:

Uh, l O E WW-Y and he talked about this concept of most advanced yet acceptable. So how do you really cross the chasm with this M-A-Y-A principle of most advanced yet acceptable? And that's what a lot of startups actually have to go through, because once they go through the innovators, there's a chasm to cross to get to even early adopters and early majority. I mean, the innovators will like the most advanced stuff because they don't have the problem of legacy or brownfield. But the moment you go to people who have some money and some money, more money and even more money. You know you have to think about how to take the past into the future. You, you, can't ignore the past, and that's what Maya is about. It's about being most advanced, yet acceptable.

Speaker 3:

So, anyway, we started talking about DevRev already, but I wanted to talk about the transition for you both from getting Nutanix to an IPO, to a DecaCorn and more maybe then taking a step back. So how was that transition like at a personal level and then at an intellectual level to start yet another company, which is going to take a lot of time and energy, and of course, I know you're excited about it. But maybe just a little bit about that transition.

Speaker 1:

Yeah, yeah, you know, I think, uh and I tell this to myself, but I think it was pretty evident that, had it not been for the public cloud, we'd probably be bigger than vmware, because we had gone through the transition of subscription as a public company, uh. But then, you know, the business model changed. Uh, people wanted to actually rent more infrastructure and stream more infrastructure, but there was a pretty good path. We had to actually be a $100 billion business because we were doing data, that VMware was struggling with data and, if anything, the people who actually own VMware wanted to continue to do proprietary hardware the old legacy world of EMC and all that. But then things changed.

Speaker 1:

Public cloud happened in around 2016, when I was going for the IPO non-deal roadshow, and even the IPO roadshow, there was a lot of people who said, but what about that? And I think it had started to really hit my sort of stream of consciousness that we need to figure out how to change the business model of this company, otherwise we won't survive. And we started doing that starting in 2018 onwards, and it took us a couple of years to even get to the basics. But then in 2020, I'm like, okay, I can continue to be defensive about the public cloud and be ignorant about it, but the developer in me would not start a new company on-prem. There was this realization that more and more things, people want to actually stream and just be able to use lightweight stuff which was hosted in AWS or over time in GCP. So I'm like now's the time to really decouple my left brain from the right brain. There is an investor in me and then there is a creator, slash operator in me, and I need to look at them as two different things. And it's the hardest thing for founders to say, okay, you know what, right now I'm an investor and an operator in one company. What did it mean to be an investor in one and go and create slash operate the other? You know, and in many ways, you pick from the left and put it to the right. You know. So the things that you do in terms of, uh, diversifying and all that.

Speaker 1:

I'm like I have another 20 years. I turned 45 in 2020. I'm like I have another 20 years to give to the industry at least. And what are the problems that I'd like to work on? So Vinod and I were talking and he talked about GPT. Vinod was also the first investor in OpenAI and also our second investor, big institutional investor at Nutanix in 2011. And I'm like, wow, I need to really refresh myself. I used to be very good at math, but that was 20 years before that, so I started to really read on it and I'm like, okay, I'm passionate about business software, customer support.

Speaker 1:

We've done a really good job of business software at Nutanix, which is the reason why IT didn't own business software at Nutanix.

Speaker 1:

We kept it in a separate team and that's how we kept transitioning our business model, because it was an engineering problem, not an IT problem. We said we've got to really have a ton of developers actually go and do things around even things like configure, price quote and things of that nature that as we kept changing their business model, we need to keep changing, you know. So I figured you know we need to start thinking about really bringing a lot of these silos together. And uh, that's how the idea of Devra really came about that, look, we have tons of silos, you know, not just in customer support, but but product management, software development I mean, even sales is so siloed from the rest of product and support and engineering. So the idea of really bringing it all together, even though it was not as AI native of thinking, but now looks like the AI market is only bringing it towards us, you know. So it's been a good serendipity, you know.

Speaker 3:

Absolutely. I love what you said about the fact that departments and stuff are organized more for human beings and organizing labor, not software, let alone AI or work and departments do work with each other. So what is the greater vision for DevRev and how are you doing it this time around? What is new between how you built, perhaps, Nutanix I know different company, different era and what you're doing now perhaps?

Speaker 1:

One of the things was to really do this also for very small companies. We said what does it mean if people only have GitHub and only have Google Workspace, then what else do they need to really complete their business? Of course they'll have HR systems and payroll and all this other stuff, but how do they complete their business and really also start to be like a product manager? Because a lot of founders, I mean there's no MBA for product management. If you realize and it's one of the core things in creating anything is to really think about what truly matters and how do you really gain empathy for the user, the end user, which is so important. So I think the core of DevRev was customer and product are the two entities that most businesses have to understand and most businesses struggle. I mean, they all worship their work. You know which is tickets and issues and incidents and opportunities. This is all work management. You know, for every department there's a work management tool. Sometimes they call crm, sometimes they call software development tools, yet other times they call support software, but at the end of the day it's core work without really understanding is it? Do we understand the customer and do we understand the product? So we said, we're going to build a knowledge graph that are rooted in these two things the customer and the product, and everybody in the company needs to understand customers and products. So, rather than now being forced to get a CRM license to know about the customer, what if you brought customer in into the back office, the mid office, and same thing with product, rather than keep product as projects within the back office? How do you take product into the front office so that they are not just process people and account management people, but they're also really knowing what's coming out, what's high quality, what's low quality, what's usage, what's engagement? How do we provide feedback? So, really, customer and product became the two core pillars of this knowledge graph.

Speaker 1:

And then we said look, then you get users, sessions, people work, there's a lot of enterprise activity that you need to capture, but also unstructured data. So now you have a knowledge graph. How do you even use a knowledge graph? Like well, you need to at least think about search, which just never happened in the world of SaaS Analytics, because they punted it to IT say, hey, it solves analytics for you and brings all the data together across different departments and different software tools, and then workflows like well, again, that's punted to. Either in the SMB market it's Zapier, workato, pipe, dream or higher in the market, you need to go to service now because there's nobody who does workflows better than some of these guys. So everything was punted SaaS companies never solved for search, never solved for analytics, never solved for workflows. So we said any modern SaaS that we build has to have these three big pillars.

Speaker 1:

But then how do you build these engines without apps? We're like well, if you don't build our own apps, then it's like, you know, there's no Windows without Excel, powerpoint or Word. There's no iOS without music, email, phone and some of the native apps like Maps and so on. So we said we're going to build three very good apps and then also a chatbot. You know we call that an agent which actually sits on the customer's side. So we built these three apps and a chatbot. One was a support app, one was a build app. So support and build are cousins of each other. They know about what's happening in the customer side. How do you build software? How do you prioritize stuff? And then a grow app, which is really about CRM. So we have these three apps, but they're on one platform, so there's no struggle between like hey, are these silos again? And so on.

Speaker 1:

And then the way we actually go sell is through solutions. And so we have a solution for the mid-market which is around going and replacing Zendesk and Service Cloud. I mean, in fact, some of the large commerce companies in India. They've replaced like million-dollar displacements of Jira because they want something which is AI-native. But for the smallest of the small companies they're like here's the cluster. If you have a Google workspace and if you have a GitHub, all you need is DevRel. If you have a Google workspace and if you have a GitHub, all you need is DevRel. And you start with an integrated company rather than a completely siloed company.

Speaker 1:

And then for the high end of the enterprise, we are going with enterprise search. Let's go and solve, search for them and be very differentiated. So it's consumption-based pricing. You don't have to pay for shelfware that people are not using. We also reduced the number of SaaS licenses so in search you don't need to actually have as many Salesforce and ServiceNow and Jira and Zendesk licenses. And these are the basic three solutions we're doing. So enterprise search, going and replacing and modernizing customer support. And then for the startup, like a cluster of all three DevRev apps plus the chatbot comes together in one.

Speaker 3:

No, very, very, very exciting, innumerable questions. Maybe one that I will ask. This, being representative of startups, is how do startups work with you guys? Right, either as partners or building on your platform or whatever One? Is you selling to other clients and customers, and whether you're placing Jira or Zendesk or what have you, but are there ways in which startups can work with you guys?

Speaker 1:

Yeah, I mean, you know we have a again going back to the idea of extensibility and customization, we built a marketplace very early on in the journey of this company and the idea of marketplace was that, just like Windows, you remember, most things in Windows were done in the user space as an application, and this is 30 years ago. The architecture was microkernel-based. They got a lot of people from DEC, and DEC people were microkernel people and they had come up with Mark, the Mark kernel, and I think, if anything, linux copied a lot from that as well from Windows. But the idea that has kept going on ever since is that you don't shove everything inside the platform, you put things above the platform through a marketplace. People call it app store over time. So we have a really, really flourishing marketplace architecture. People go build all sorts of connectors we call you know AirDrop is actually a way to do connectors for data. Then we have workflows and we have analytics. So basically it's a great place for startups to actually go build all these plugins with us. We call them snap-ins and I think at some level, using the product itself will give them a ton of ideas. We have a freemium model. Gaurav is passionate about freemium, and we've done a very good job of saying don't worry, if you've not raised meaningful dollars, we'll actually do this with you, and that was the other thing that we did very differently this time that look the long tail of companies that are still not raised enough money. You know how do we help them. You know how do we build a community around them. You know, and how do we still give them support rather than leave them at the mercy of just, you know, being on their own. So we've done a lot to really build this PLG muscle and Gaurav, having spent 12 years at AWS, he's bringing a lot of that PLG muscle to us. So I think at the core, we also want to probably share notes with startups on how we're thinking about AI, because AI is actually quite a spectrum.

Speaker 1:

The more I dig into it, amit, the more I realize that it's actually quite a spectrum. And one end of the spectrum is people who just think that prompt engineering and foundation models is done, it's a done deal, that's all you need to do. But then quickly you realize that prompts get to become too big and it starts to confuse the heck out of foundation models because they don't understand attention with such a context, a large context, and then they're like hey, we need to do RAG. So now you need to do RAG, which is semantic search, and now you need to understand embeddings and vectors and vector databases and that's the kind of thing in the middle. But then RAG for everything is a harder problem. Like, you need to do RAG for not just documents but RAG for every workflow asset, which is every automation you build. You want to do search on those things. Similarly, you want to do RAG for analytics widgets. There's so many widgets that people build in the enterprise that need to be searchable. So you went from prompt engineering to RAG, to RAG for everything.

Speaker 1:

And then you realize that people are looking for reasoning. Like, hey, I want to reason, because now you're going from a lot of peripheral agentic work, which is what happened in customer support, front office, l1, l2 support. Now you're getting to L3, l4. And these are getting closer to the mid office and then back office. There you need to have reasoning.

Speaker 1:

You need to spring signals from five different sources.

Speaker 1:

Look at the history of the last three years of how we did things.

Speaker 1:

So then you need to do signals from five different sources. Look at the history of the last three years of how we did things. So then you need to do supervised fine-tuning, and I think one of the things I just heard recently, controversially, when Nandan was saying that India nobody needs to learn how to fine-tune a model and I saw Arvind talking about this from perplexity that he's so wrong and I agree. I think, going back to your question on enterprise software, and why would India not produce such companies in the future, it's because we're not going to be deep enough in AI, the new systems and systems engineering and building infrastructure is to go beyond prompt engineering to not just RAG, also to supervise fine tuning, small models running on the edge hosted by you and Kubernetes, maybe bedrock, but I think understanding that spectrum is something that we'd love to share with startups and have them really build deeper businesses, not just, you know, gpt wrappers, which is what a lot of prompt engineering is, but to build deeper businesses.

Speaker 3:

No, very, very, very exciting, very, very exciting thoughts. Just one more thing about just more broadly, beyond Evrev how do you think about the SaaS companies of yore and by yore I mean the last five, seven years, I don't mean 10, 15 years ago? How are you seeing their evolution into the AI world? Because obviously now they're the incumbents and they don't have the luxury of starting AI first. They have to adapt to AI. So how do you think about an existing SaaS company founder who's already three, four, five years in, has products, has customers and has maybe two, three, five, 10, 20 million ARR for them to adapt to the AI world?

Speaker 1:

So if you look like 10 years back, a lot of the modern PLG SaaS companies I mean some of them got acquired, like Slack got acquired, figma, almost so. Notion is trying to really figure out its place in the world of AI. Canva has been another PLG darling, but they will need to. I mean, maybe they need to find more time to embrace AI, because the DNA of those companies was probably more design and they haven't figured out AI because the DNA at the very top is not beyond design and we look at AI and design as kind of two sides of the same coin. In fact, ai and UI are kind of the yin and yang that you need. So I think that a lot of the modern companies that are building PLG era, they will find it hard to make any more AI than what ChatGPT already provides you. If you look at verbs like summarize or generate, based on some points and things like that. Now, maybe the win for them is that they keep it integrated and maybe the simplest and most powerful thing that they can do is like, hey, you don't need to buy a license for ChatGPT if I can provide that to you and maybe that's good enough. You don't need to do anything more than that, that they become chat GPT wrappers for the low end of the market and maybe low end of the mid market or something. But I think the last five, seven years, most companies that have been formed they didn't have enough of, I would say, systems chops, infrastructure chops, because AI is now an infrastructure problem. You know, anything that could have been GPT-wrapped has been, will probably get done and maybe those people will bring a lot of go-to-market skills on the other side and maybe that's one formula that I'm seeing succeed. It's okay, I'll actually pick a department or a function in a company and just go deep into building AI for them and that probably is a good I would say $100 million to $100 million story in terms of revenue, and then you just be very good at inside sales and the go-to-market machine and probably building some really good workflows. But I think where it starts to become a problem, because if you can't make a platform, you can't make half a billion, a billion dollars in revenue period, because to go upmarket if you only need to make a billion dollars, and how do you go upmarket if you don't have an extensible platform where the partners, the SIs, the Accentures, the Infosys and even the customers. Developers can actually build stuff on top of, and the most important stuff they'll build is two things workflows and analytics widgets. So I think the company's last five, seven years either they'll be very good like Gong was. Gong was very good in 2020, 2021 era and all like oh, oh fast to 300 million.

Speaker 1:

And the question is now what? You know, uh, basically there was so much churn, uh, because there's so much zarp money, that uh went away with interest rates being high, that the smb suffered. So I feel like going to the mid market in enterprise is probably the real challenge and opportunity for a lot of companies right now, because the interest rates are where they are, whether we like it or not. Until the wars end, until Ukraine normalizes, I think it's really hard. I mean Europe has to actually get back to normal. I mean Russia has to start to provide more things to Europe. Those things start to happen. Then interest rates come down. Until then, there's a really hard thing, especially non-digital startup companies. They're not coming back anytime soon. Startups in software are probably one good thing. That is still okay in the SMB, but everybody else has to really think about the mid-market and the enterprise. Rajat.

Speaker 3:

Mittal Fascinating, dheeraj. So, as we bring this to a wrap, you mentioned one very interesting thing about PMF at 1 million, 100 million, 250 million, a billion I'm simplifying a little bit, but there's also I'm just going to make up a new term like organization or founder market fit at each stage. So how do you keep evolving as the company is scaling, and whether it's you or your co-founding team or your leadership team and any thoughts, and obviously, since our audience is a lot more early stage entrepreneurs even, let's say, do the exercise at 1, 10, 50, 100, you know, like at the early stage of that ladder, and what are some kind of tips, suggestions, maybe even amber or yellow flags to watch out for so that you know you're yourself scaling with the company?

Speaker 1:

Yeah, I think at the core, it's about how do you really get to relate with people over time, and this includes not just investors and your board members, but also your executives. You hire some executives who are very good at what they do, and the only way you can retain them is if you know that they're good at things. Then you are, and building people relationships is probably another way to really build a great mid-market enterprise business too, because people buy from people in mid-market and enterprise. I think process is another piece that people have to start to respect, because process matters, efficiency matters, sustainable stuff matters. Growth hacks don't work when you're starting to really grow bigger. So a lot of founders who are product people, how they really begin to embrace people and process is probably the way to do this, cause then more people want to come and work for you, work with you.

Speaker 1:

Uh, and in this, in this whole journey, you need to really know what it means to let go of things that were very near and dear to you and also be very good at negotiation, because your word and your sort of what used to be the diktat is not going to be that you know sort of impactful anymore, because then you're only hiring doers, as opposed to people who are not just doers but thinkers. I think how you continue to hire thinkers and of course not every thinker will align with you. They'll say, let's do this and let's do that and this is not going to work. So it's such a fine balance between the things that brought you here and what percentage of that you continue to keep and what percentage you continue to go and evolve is basically at the core of this. So learning to negotiate, learning to actually really build those bridges with people you're hiring and with customers, respecting people and process, is the only way you can really scale a company.

Speaker 1:

I mean, because not everybody can be Elon Musk or Bill Gates, you know. I mean even Jeff Bezos. I mean I'm a big fan of Jeff and you know people like Steve Jobs. You know they said, look, and Jeff himself had gotten so good at framework thinking. You know he probably was very good at it from day one.

Speaker 1:

But this idea that, look, I need to bring framework so that now I can leave that behind in a meeting room so I don't need to be in every meeting. So frameworks also become a good way to scale the company, because then process thinking comes, scalable thinking comes. But someone like Steve realized that if he can't deal with people, you need to get a Tim Cook and you need to complement yourself with that kind of a person who deal with people and process. There was a lot of process in Apple dealing with China and manufacturing and Foxconn and all this stuff, and then knowing that the people within Apple had to respect that process to go and sit in China for six months every year if that's what it took to really build Apple in. So really at the core is people in process.

Speaker 3:

Yeah, I think people process and delegation. That's a wonderful place to end, and I remember at Google, there was Eric Schmidt, there was Sheryl Sandberg, there was Nikesh Arora. There was a whole bunch of people that were brought in because the founders had the creative mojo, but they were like all this other stuff I need. We need people to do this right. And Dheeraj fascinating conversation. You could go on and on, but I want to be respectful of your time. Thank you so much for being on the Prime Venture Partners podcast.

Speaker 1:

Yeah, it's a pleasure myself. Thank you again and hope your audience finds it's meaningful.

Speaker 3:

Thanks, Dheeraj.

Speaker 2:

Dear listeners, thank you for listening to this episode of the podcast. Subscribe now on your favorite podcast app for free and you'll be the first one to know when new episodes are available. Just search for Prime Venture Partners Podcast in Apple Podcast, spotify, castbox or however. You get your podcasts, then hit subscribe and if you have enjoyed the show, we would be really grateful if you leave us a review on Apple podcast. To read the full transcript, find the link in the show notes.