The Exchanges

Q What have been your learnings from them? What is it required to scale 50 to hundred X?

A I think few learnings are one, they need to solve a big problem. And big problem means a large market. And if the participants in that market are very diffused, Which is the case with most consumer, ah, businesses. Then organizing that market can deliver you a very big impact. So look at our scale ups, ah, like Daksh, it was participating in a hundred billion dollar outsourcing industry. Or make my trip, ah, twenty billion dollar travel industry growing very rapidly. Or you take big basket, three hundred billion dollar grocery industry. So I think one common theme is that it need to be a very large market, highly underpenetrated in terms of how it is organized and how digitally influenced, ah, it is. Two, I think the early mover advantage. Though many of these will not be winner take all markets, But they will certainly be winner take disproportionate value, uh, markets like make my trip is really stood out from the pack or big basket has half of the grocery market in the country, in spite of many other people being in the same business. So I think that is the second characteristics or red bus, which organized buses and one of, and was one of the earlier players. So large market, very diffused, uh, Early entry. Those are the market, uh, characteristics. Second, I think, is all about the founding team. And in founding team, I believe, there are, there is one, there are many ways…

AI assessment note: “I think few learnings are one, they need to solve a big problem.”

Q What are the market wins you had, where you had a thesis?

A Yeah. See, three buckets where we have, uh, done reasonably well. One bucket is the entire, uh, travel industry. Where the winning companies that came from our stable are, uh, make my trip, uh, red bus, then, uh, taxi for sure. And we have a very promising company in Ray, Ray Liatri. So I think given that there is so much of, uh, Information is symmetry in this market, and it is less physical and more information, ah, driven. We have had lot of success with, ah, travel as one vertical. The other vertical which has done well for us is, ah, vertical commerce. Because, ah, retail, as you know, is, ah, six hundred billion dollar industry out of two, two trillion plus GDP. And there are some very large markets like, ah, grocery, which we participate through big basket or, ah, furniture, where we have, ah, live space as an online Home design company. So, that I think through the sheer market size, and the nature of unorganized industry, has been our second strong area. And the third one is the next generation outsourcing, where we have companies like Ambar Research, which was acquired by Moody's, uh, United Lex, which was acquired by CVC, uh, the private equity group, and currently we are investors in Xtria, which is a data science, uh, startup serving pharma industry, uh, around the globe, and growing very Uh, rapidly. So there the theme is leveraging India's intellectual capital, w…

AI assessment note: “three buckets where we have, uh, done reasonably well.”

Q And typically, uh, when you invest, what kind of value you bring besides money?

A Sure. So I think one is of course, you know, the strategic thinking and the inputs. Um, our founders, we leave all our internal departments open for them, the know-how open for them to come and meet Anyone they want. They can meet the sales head of Naukri. They can meet the marketing head of InfoEdge. They can understand how we do PR. It's all open for them to know and understand how we go about things. Second is we help them a lot in hiding. So if they want to make any top level hires, typically they have me or Sanjeev or both of us interview the candidate and, you know, then share opinion, et cetera. And the third is, of course, the general strategic direction of the business. We have frequent Meetings for brainstorming, business reviews, and more than the formal board meeting regime, we replace it with informal reviews where everyone gets included.

AI assessment note: “we leave all our internal departments open for them, the know-how open”

Q Yeah. And let's talk about the recent investments. So what do you saw in shop Kirana that you made investment and again did a follow on round?

A So, um, it's a great team, right? Um, they are experienced guys who have come out of PNG, right? Managing the supply chain, managing brand partnerships, so super understanding of the space and how to service the small retailers who they cater to right now. Uh, what was very interesting is that they had been operating in only indoor when we first invested, and they had made the economics work. It's very tough to make the economics work in the grocery supply chain space because the margins As they stand today are thin, so what do you do to increase the margins, or reduce your costs, or keep them in line, right? They demonstrated that to us, and then they scaled up with the first money we gave them. They went into Bhopal and Jaipur, and just the growth there was phenomenal, and the economics were great. So overall, I think it's a super team that is going after a space that they fully understand. It's a very large market, right? The B to B grocery supply chain is huge. Uh, lots of brands in India which are still under penetrated or don't have access. You know, we hear of the HULs and the PNGs, but lots of regional local brands, right, which need distribution. Uh, Kirana stores need better servicing, right? They are very small for the large distributors or for the best prices of the world. So I think overall, just everything, the thesis just seemed to have come together for the team…

AI assessment note: “experienced guys who have come out of PNG... and they had made the economics work”

Q So how many pitches? Or how many companies to pitch to you in a span of a month or a year?

A So to give you a data point, we have met about, uh, or we have received 1500 plus, uh, plans over the last two years. So that would roughly translate to 60, 70, uh, per month. That number is gradually going up. In fact, you know, there are months we have seen 8100 companies come in. Uh, physically I would imagine we would probably engage with, uh, maybe about a third of them. So let's say 30, 40 companies every month we would spend some time in having conversations with. And then as you deep dive and you start evaluating, eventually I think our hit rate would be more like one in 71 in 80 kind of, uh, investment. So we have done 18. We have seen 1500. So that's a good way of defining it.

AI assessment note: “we have received 1500 plus, uh, plans over the last two years.”

Q What's your highest aspiration for your portfolio companies to go IPO or to be a profitable private business? What is that?

A I think the first aspiration is neither of the two is really, can you build long lasting brands? Ultimately, all of us, both from us and the entrepreneurs point of view are passionate about building brands. And we believe that if you can build a brand that can, uh, sustain itself for several decades, uh, even more than a few years, then that is really a dream achieved for all of us. Now, if that objective is met, then the journey of, uh, an investor who's along the ride with the company could be through an exit by a strategic or through an exit by a private equity buying you out, or even some cases through an IPO. But The bigger vision is that how can, you know, at Fireside, we be part of, say, the next 25 iconic brands that get built out of India.

AI assessment note: “I think the first aspiration is neither of the two is really, can you build”

Q And so what's your, uh, typical roadmap for these founders? At what stage they should be hands on with every, uh, department in the company and what state they should delegate and start focusing maybe on, you know, investor relationships or be the face of the company and outsource or delegate most of the functions.

A So see, like I said, we come in very early. So our point of entry is actually, uh, typically five crore of revenue pre series a round of investing. So certainly, you know, we see the entrepreneurs being very hands on and very involved in every aspect of business, all the way through series A, even up to series B. So our journey with them, uh, is really, you know, getting the founders to be more and more, uh, capable and more and more, three 60 in how they understand every aspect of business. But like I said earlier, one of the things that we are very conscious about is you start bringing in senior leadership into the organization. So that by the time you are at a 50 crore run rate, you have enough strength of people in your team who can do the day-to-day business, uh, on an ongoing basis. And the founders, I think they probably need to start then thinking about the vision of what next, what are the new areas of growth, what are the new ways of building the business further, rather than just running the day-to-day business.

AI assessment note: “hands on... all the way through series A... by the time you are at a 50 crore run rate”

Q So what was your first firm and subsequent journey in U.S.?

A So essentially, um, this is post-business school. Before business school, I had worked in consulting, so we let that go. In the investing space, during business school, I worked at a shop called Wingstring. It was based in Palo Alto, right across from business school, and I would go there after work. And spend my time from five p.m. or six p.m. all the way up to, whatever, midnight, one a.m., just looking at deals. And that was very exciting because I could, ah, look at an incubator-like setup, which is what it was. It was not a pure VC shop. And we looked at a few companies invested and backed seven of them, and I was part of a few of them. And, ah, that whole incubator business model was flawed. Although we made some really good investments, that did really well, but the cost structure of an incubator, including a 30,000 square foot facility, and four partners, only seventeen million dollars under management, was not sustainable. So obviously, we ceased to exist after one year, but of the seven investments we made, I'd been involved in three. I joined one of them as the, ah, number two to the CEO, doing everything he wanted because he was transitioning from Israel into US, and that company was action based. It sold process and product and meeting management software. Uh, developed in Israel into US tech startups, but totally different business model. The expectation was that …

AI assessment note: “In the investing space, during business school, I worked at a shop called Wingstring.”

Q Do you see platforms like ShareChat enabling more financial transactions like loans or insurance in future?

A Of course. I think it's, it's bound to happen. Um, I would see ShareChat as a top of the funnel play that every day millions of users come to ShareChat and each one of them finds a different use case to use ShareChat. Now let's say a trader comes to share chat, uh, to find the latest rates, uh, that are in the market. Now that trader at some point in time also needs working capital finance. And at the other end, there are these NPFCs, FinTech startups who are willing to lend, uh, to these users, but frankly can't go into tier three and four towns because the channel cost will be too high for them to serve these customers. Can I be the go between? Can ShedChat be the place where these NBFCs and this trader come together and a loan can get dispersed? So yeah, it, it is likely to happen. When will it happen? I don't know. It's part of our plan. Maybe at some point in time we'll do that. And yeah, that is another way to impact the lives of these tier three and four town users.

AI assessment note: “Of course. I think it's, it's bound to happen.”

Q So coming to a more personal side of Alok, what's your daily routine like?

A My daily routine for six days a week is Indifi. Uh, so I normally like to, uh, you know, run in the morning. Uh, so I start my day at six, get my run over. Uh, I hit my desk at eight, eight, 15, which is a couple of hours before the crowd starts to come in. Uh, that gives me some time to get stuff off my desk. Um, And then, ah, evening, you know, my average day would look like seven, seven 30, so, you know, that gives me a good 11, 12 hours at work. Ah, I'm a early sleeper, early riser. Ah, so that by and large takes care of my weekdays. Ah, Sundays I actually, ah, like to teach kids. So, I do recreational math classes. Ah, so all of my Sunday morning is taken up by that. This is a hobby I have. Ah, Sunday afternoon is family. So, Broadly, that is how my week cuts out.

AI assessment note: “I start my day at six, get my run over. Uh, I hit my desk”

Q How did you and Sanjay meet? And when did you both decided to partner to start Bloom?

A So Sanjay and me, uh, it's definitely thanks to Mumbai Angels. So though we are contemporaries, both, uh, engineers from the mid nineties, basically we didn't get to meet until Mumbai angels. We found ourselves on a few common cap tables. And then, uh, we were kind of introduced by folks saying, Hey, you both are thinking about doing something in early stage. Maybe you should talk. And that's how the dating began. And it probably took, uh, nine months of that, uh, various vetting by, you know, my potentially one of them, a family office that was going to anchor him, his, Dad and brother and their family offers. And, uh, that journey led to finally us saying yes in middle of 2010. And so, uh, and then we never, never looked back. Uh, so very complimentary skills, very complimentary networks, very complimentary people. And so I think that's what, that's what's made it work.

AI assessment note: “we didn't get to meet until Mumbai angels... saying yes in middle of 2010”

Q Which markets where you had a thesis didn't work out for you?

A I think where thesis areas were either weak or where we were acting on the impulse of Uh, a little bit more emotion and a little less objectivity or getting market sizing wrong is where we have failed a bit. Um, gaming would be an example, right? First fund, three bets. Nothing has scaled or worked. Um, I don't think in the Indian market was ready. That's what all the series AVCs said. Never bet on any of them. So it became self-fulfilling. Um, all these guys struggled. Uh, two of them are still alive, but barely. They make They break even, but they're all million dollar-ish businesses. Um, the other thing I would argue where we failed quite a bit was trying to do vertical product commerce. Got onto the cycle too late. That entire commerce engine works as a herd mentality. Either everybody's making bets or nobody's making bets. And then you would bet on something like sports or school supplies or, Purple is the only one who survived. And I think they're built incredibly frugally from that generation. But otherwise all our bets died, right? And so there it's very tricky because the business scales, runs out of money. If you can't get the next round, you're screwed. And we didn't anticipate how critical that will become. We just, I think there was a lot of assumptions that, hey, you show a certain set of metrics and series A comes. I think in the first cycle we were as naive as t…

AI assessment note: “gaming would be an example, right? First fund, three bets. Nothing has scaled”

Q What were the early failures in the business, and how did you learn from them?

A Three failures if you ask me. One is the fact that we didn't attach as much importance to location, ah, as we do now. So, you know, picking a spot which is easy to access, there is easy parking facilities, ah, that it's not a one-way system. Uh, parents can get there and get out of there easily is absolutely critical. So for all practical purposes, we are like a high street brand, a consumer brand, where people should find it easy to come and get out of. The second was the importance of marketing. You know, my assumption coming in is that a school is something that grows by word of mouth. People say good things and other people come to it. But it is a very hyperlocal business. If you're opening in a center, Letting people know, ah, that you're opening in the neighborhood is very critical. So pre-marketing and post-marketing was something we didn't pay attention to, but we've done a much better job of it now. And the third was, you know, how to attract and retain High quality talent. So, the dignity of labor piece that I was talking about, saying how do we systemically make sure that people come through this, and we understand that these are also working women, and their challenges, and how do we provide an environment where they feel like they're supported, um, and cared for.

AI assessment note: “Three failures if you ask me. One is the fact that we didn't attach”

Q Now coming on to more personal side of yours. What's your daily routine like?

A So my daily routine typically I get up by about five 30. So, uh, I, I do yoga and meditation for an hour and a half till about eight AM. So eight, eight to nine, uh, family time breakfast, uh, daily course. And then we kind of get going on the work. Mostly I schedule my internal activities in terms of where I need to spend time myself directly. Uh, I, I schedule that in the first half of the day. Second half is typically allotted to, uh, conversations with a lot of founders, my partners, uh, colleagues, uh, and then I keep my evening free to meet at least one or two entrepreneurs, or I, I try to average it out over a week by about seven to five to seven entrepreneurs a week. I try to meet in person, talk to them, even if I funding them or not in secondary, I would like to just keep meeting different entrepreneurs to learn from them.

AI assessment note: “So my daily routine typically I get up by about five 30.”

Q What, what did you like about that journey?

A I think number one, so two things, um, very deep expertise in pharma. Background, uh, one of the founders was earlier at McKinsey, had worked with pharma clients for almost 10 years. The other two, uh, have been, you know, kickass engineers from Amazon. Uh, but I think that combination of tech plus Pharma experience was very evident, and I would say we see teams with that pharma expertise, but oftentimes they're very stuck in their ideas. I think this team was just incredible at also their, you know, mental flexibility. So over that four years, I actually saw them pivoting and trying out three to four different ideas, um, and, you know, really breaking into large pharma. A lot of those conversations started happening. Uh, they hadn't landed anybody as a real customer as yet, but we could see that momentum building up. So I think a lot of it just broke down to that experience with pharma, that domain expertise, but also the shipping velocity and ability to move very fast, which is not very common with these vertical teams.

AI assessment note: “I think number one, so two things, um, very deep expertise in pharma.”

Q And how do you evaluate product market fit now? Has it changed from the SaaS wave?

A So evaluation of the product market fit hasn't changed. I do think that what has changed in the AI way is a wave is PMF is a lot more ephemeral. So even when, let's say we look at Portkey, they initially started with model routing, but they quickly expanded into observability, into, um, you know, guardrails management, into various other things, but they expanded significantly into FinOps. And managing budgeting, cost control, key distribution, things like that. Uh, and they had launched this MCP gateway because MCP was growing, agent gateway, because people wanted one place to govern all of their agents as well. So you could see how quickly the space is moving and the market leader has to be willing to move with it. Uh, the other part again was, um, I think the articulation of the founders on How they're going to build the business was also very clear. So, uh, for instance, most of the revenue came from their managed offering, but I think, uh, Rohit and Ayush were very clear that they actually want to open source even larger portions. Like if you look at a traditional business, you would say, okay, let's try to extract value. But these guys were like, we want to actually open source everything here. We believe the value will be captured more at that governance and security lab.

AI assessment note: “evaluation of the product market fit hasn't changed. I do think that what has changed”

Q Why do you say that if you can just share some data points?

A Yeah, because see, India today is the fastest growing large economy. So the maximum compounding effect is going to come because of two things. A, your scale, and second, at what rate you're growing that. So if you multiply the two, then in absolute terms, your compounding will happen maximum there, right? So this is there in case of India. So I gave you an example of, uh, the per capita GDP. So we're already at 3000 over about 20 years. I think there's a good chance that we should get to about 15,000 or so, and that is where we will get categorized as a, as a mid market, you know, affluent nation from a poor nation currently. Uh, so I think within this century, uh, which belongs to India, I think next 10 years could be India's best years because After that, also, India will keep compounding and keep growing, but I think your base effect will start catching up with you as it is catching up with China now. So I wanted, basically, awareness of that to come to every Indian, right, because especially in a country where the average age of every Indian is 29 years, right, we are still very young, and compared to China, that's about 38, 39 years. In case of Pakistan, it's about 23 years. So, 65% of the population is below 35 years of age. So, we are a very, very young country. If I go to public places, I hardly see people of my age group, right? It's all, it's a very, very young countr…

AI assessment note: “per capita GDP. So we're already at 3000 over about 20 years.”

Q Can you share top three decisions that you know?

A Yeah. So I'll give you the best ones. I think, you know, when we were building AI Companion, we were in a race. We wanted to beat Microsoft to market, and we did beat, we did beat Copilot to market by a couple of weeks. And about a month and a half before our launch, we, we, we had basically, we were at that time, most of the market that was adopting early AI solution, this is 20, 23, was using customer data to train models. Um, they were using a single model like OpenAI or Anthropic or Lama. Um, and they were, um, they had Very loose, I would say security and governance. So one day, about a month and a half before our launch, we had built with the idea that we were going to use customer data to train the model and that we were going to charge for AI companion. So imagine that we built everything for months and months at not sleeping. Teams are awake all night building. And about a month before the launch, Eric called a few couple of us into the office and said, I've made some decisions. And he said, what, what are the decisions? He said, number one, we're not going to charge for AI companion. He said, what? You know, we're going to monetize this. It's going to cost us money. We're going to be underwater margin-wise, you know, and he said, no. He said, all these things will commoditize. The model costs will commoditize. It's our job to make it affordable. Everybody in the world…

AI assessment note: “He said, number one, we're not going to charge for AI companion.”

Q How much time does it take to raise the first fund?

A Yeah, I mean, it took us probably nine months from start to finish. Um, you know, part of it is because educating LPs on this new category was hard, because we would talk to LPs about how we think pre-seed will be the new seed, and they would turn around and call their seed managers that they were already invested in, and the seed funds would be like, You know, pre-seed is a fad. It's gonna go away. We do pre-seed, whatever. It's, it's not real. So it was really hard to, to like convince investors that this new category will come together until they talk to founders. When they talk to founders, the founders will tell them, yeah, like seed funds told me I'm too early for them because I don't have a product in market because I don't have traction. So yeah, if somebody was willing to lead my round before that, I would work with them, right? So that, that took us some time to, to educate first the investors to be able to raise the fund. And then the founders as well, right? On, hey, look, if your first run of funding, you should call it pre-seed. You should talk to pre-seed funds, not seed funds, because you're going to be too early for these seed funds. And that took some time, and we started this pre-seed summit where we had founders of Pinterest, Instacart, DoorDash, you know, Cloudflare, Stitch Fix, Affirm, I can go on and on and on, where we had them talk about their pre-seed …

AI assessment note: “it took us probably nine months from start to finish.”

Q And how did you meet the founders of Gamma?

A The founders of Gamma we met through, um, another angel investor who used to work with the founders of Gamma at Optimizely. So the founders of Gamma came out of Optimize that this was a company that was in the A-B testing space, um, that was, you know, really did well, uh, at one point in time. And, uh, my friend, my business school classmate, uh, used to be, uh, colleagues with him at, at Optimizely. And these guys were, the Gamma founders were spinning out, starting, starting Gamma. And my, my friend said, Hey, look, you had, you had invested in Airtable, uh, which is a sort of an analogous company in the sense that Airtable built a prosumer horizontal tool, in their case, focusing on Excel and, like, sort of making a better version of Excel, if you may. And Gamma's initial thesis was, we can do the same thing for PowerPoint, right? PowerPoint is obviously used by millions of people, but hasn't changed much in decades. Um, but the world has changed a lot in terms of how we do work, right? We don't, like, sit on our PCs anymore. It's a lot over the cloud. It's on our phones. It's very collaborative, and we think there's a new, kind of, uh, version that, that should exist. Um, so I think that, that was probably why I got pulled into that, that, that opportunity, and, and we were very lucky to invest in the first round of Gamma. And the company, again, has changed a lot over, ov…

AI assessment note: “The founders of Gamma we met through, um, another angel investor”

Q And which was your first job after UCLA?

A So I joined a very early post-acquisition Siri team at Apple, and the team was about 30, 35 people, still the founders were there, and, um, I ended up, it was a long story to, to, to join that team, but I ended up working there for Uh, a good number of years, about five years where I worked on the early knowledge graphs that Siri was built on. And, uh, it was kind of in the era of early deep learning models were being experimented. It was still very expensive to run it in production. Uh, but I kind of saw the whole arc of how do you build knowledge graphs and language learning systems without these models and then saw models kind of become a thing.

AI assessment note: “I joined a very early post-acquisition Siri team at Apple”

Q And, uh, you know, uh, Luna one was your first model, uh, that you built. Why did you build a model in the first place?

A Yes. So our Luna story is fascinating. So for, for those who don't know Luna, Luna is a small language model, which is specifically designed to solve the evaluation problem in AI. So how do you know whether, you know, an output is good or bad, or an input has some PII or any security issues. So these are very specific, uh, uh, tasks which Luna is designed to solve for. So by default, the size of the Luna model is much smaller. It is orders of magnitude smaller than, say, a general LLM, which is out there. In fact, our latest Luna models, which are some of our largest, they are, they typically range from one to three billion parameters, which is minuscule compared to some of the larger foundational models out there. Luna came into the picture when we realized that LLMs as judges, which was the traditional way that people were using to evaluate, and that has a whole history of it. Um, they don't scale. They don't scale in production. They don't allow you to do full scale observability, which means intercepting every single input and output of, you know, these AI systems. They simply choke. They are very expensive. And they're highly unoptimized to solve for the latency and cost problem. So we took this problem and kind of came back to the drawing board saying that, hey, how do we distill all this intelligence and reasoning abilities of LLMs and bring it down to a much smaller par…

AI assessment note: “Luna came into the picture when we realized that LLMs as judges... don't scale”

Q And to improve that number, they, you know, focus on tier one colleges only, right?

A Correct. It, it works in, uh, two ways. Uh, a lot of the loans go in tier one colleges where there are almost zero NPAs and then a bunch of loans on tier two, tier three, where they cannot deny because education is also a sensitive, uh, category, politically sensitive category. It's a priority sector lending category. They have to fill up their priority sector, uh, targets also. Uh, there are significant NPAs in those particular segments. For us, the market is in the tier two, tier three segments, which are good enough for us to lend to. Highly underpenetrated right now, and sort of opportunities which are traditional, you know, banks would not be able to cater to, and, you know, we get better over time in underwriting these, uh, institutes and courses.

AI assessment note: “Correct. It, it works in, uh, two ways.”

Q But still the numbers are different, right? Like the, if we just talk about the loans that, uh, the kind of loans are granted for home, for vehicle versus the number of loans that are granted for education. There's huge gap.

A Yeah. So education is categorized as a PSL. Uh, priority sector lending, and, uh, there are targets for that, but overall, I agree with you. Say, for example, uh, housing category or a vehicle loan category, uh, 50%, uh, would be 50% penetrated in terms, as in, 50% of the spends would be financed with loans. Uh, so it's highly commoditized. Now, education is The penetration for education spends would be close to five percent, as in five percent of, uh, loans in overall education as spends, which is close to around a hundred billion dollars. A lot of people are changing that, uh, you know, some of our peers like Anavance and Credila have done some wonderful work in, uh, you know, study abroad segment where it's the same story, but, you know, students going abroad, uh, they've done that and they've proven that particular market too, that They've proven that thesis, which you sort of spoke about.

AI assessment note: “overall, I agree with you. Say, for example, housing category... 50%... education... five percent”

Q How many we would have in tier one? How many tier two?

A Um, tier one would be say close to around 70 or 80 institutes. Um, tier Two would be close to around 302 50 to 300 institutes broadly, and tier three would be, say, around thousand, 1500, somewhere around that, and tier four and five would be enormous sort of institutes. I'm not even going there. So we've had, uh, Experience of working directly on the ground with tier two and tier three institutes, and what we see there is that placements happen. Uh, you know, uh, students are motivated. These are good students. They do something with their lives. It's, it's not that tier two and tier three student pass outs are the ones who, uh, end up being unnamed, unemployable sort of youth. Uh, either they get placed through the Institute and typically we would say, you know, say close to around 50, 60% sort of placement happening, uh, through the Institute and, you know, Anywhere from a four lakh to six lakh sort of salary CTCs, which are good to begin with. And then these students figure out their own careers. Some of them move and get trained into say certification programs or some finishing schools before starting up, uh, you know, small time sort of jobs. Some of them get into sales. But if you look at these students say, you know, one year after having passed out, So, 80, 90% of them, 80 to 90% of them would be in a job and would be doing something or another, would be earning money.…

AI assessment note: “tier one would be say close to around 70 or 80 institutes.”

Q Only one in that category that's pivoted to a very successful and give a very successful outcome to all the stakeholders. So maybe you can tell us about the journey. Where did this entrepreneurial gene come from?

A Okay. Yes. Um, so I, I was born and brought up in, not even in Bhopal actually. There's a small town called Sagar in MD. Uh, I cannot even ascribe a tier to it. It's a tier three or tier four. So I was there till 16. And then came to Bhopal by happenstance. And that's when I got to know about IIT and everything else. So, um, got into IITK. But I think the entrepreneurial bug sort of comes from my mom because my mom, while she was in Sagar, she tried a bunch of things. We used to have a cow at our home. Um, she tried selling milk. She tried doing like a snacks business, a lot of things to sort of, um, supplement the income of the family. And now in hindsight, I can say those were not good markets. And so nothing succeeded. Sagar itself was like a very, Um, poor city in some ways, but seeing her in action kind of inspired me and sort of put that bug in me. So yeah, when I was in IITK, I, I, I was interested in startup from that time itself, when startups was not a thing. I think the only startup people knew of at that time was Flipkart in India. Um, So I was always interested in startups. I was, I kept in pace with, you know, what was happening in the world. Um, and after that, I joined ITC. I spent three years there, and the main reason for joining ITC versus some other options that I had was, um, it's a hands-on job. So it's technical, it's managerial, but also you're doing thi…

AI assessment note: “I think the entrepreneurial bug sort of comes from my mom”

Q Which, which would be the top 10 players in MarTech, if you had to name today?

A Top 10 players? Oh, I think the undisputed king of MarTech is, are always HubSpot and Adobe, right? They are the big ones. Uh, then I would say Salesforce is there. Then right now, I think the big sort of startups that are to watch for, Clay is definitely one, right? Absolutely crushing it. Um, there is, uh, uh, Unify GTM, which is a, you know, BDR outreach platform. There's HockeyStack, which is, again, a customer of Goldcatsby, their customers too. And, uh, it's in, in the attribution space, but they're expanding. So I think those three startups are, are something to watch for, for sure. Um, And then there are some existing SaaS incumbents like Sixth Sense, you know, it's, it's kind of become a must have in that stack. Um, Gong is another one, right, which is, which straddles the boundary of both sales and MarTech. So I think those are the key players.

AI assessment note: “I think the undisputed king of MarTech is, are always HubSpot and Adobe”

Q Um, what do you mean by that? Can you share examples?

A So, um, every knowledge worker at Turing, either in coding or any other domain, um, I want them to use AI to do the work first. So agents should create. Humans should steer. Humans should create prompts, skill files, give the agent access to the right tools, access to the right knowledge sources. Humans should create the scaffold. An AI should create. For example, if you're creating a board deck. Yeah. Don't, the human should not, the investor, let's say the, the, my head of investor relations, he shouldn't create the board deck. He should give the model access to the right sources of knowledge, access to the right tools, write a skills file to apply consistent formatting, um, give, give the right instructions on how to create the board deck. Uh, use Codex or Cowork or Gemini or Grog to build it. Um, and then when there are mistakes in it, prompt the model, um, to improve it. Sure. And then keep improving the skills file so that next time it's even faster. Basically, um, the agents create V-one of the work. Humans are verifying and iterating. Humans never create V-one. There's an interesting post from OpenAI on, um, how they used, how they used codecs to, uh, to, for one particular software project, which was agent first, human second. Um, humans were not allowed to write code directly. They can only, like, prompt agent.

AI assessment note: “For example, if you're creating a board deck.”

Q And, you know, before we dive into, deeply into the journey of Wendly, would love to start, how did you and your brother ended up starting a company together?

A So my previous company, Wendley, had exited and sold to Workday. We were celebrating that at one of my friend's house. He was hosting a dinner. This was our college roommate. Uh, so my brother came over as well. My brother ran data operations at GE Aviation. And one of the migrations was not doing too well. He came in and he was like, hey, this is, he looked as if he had not slept the entire weekend because he actually hadn't slept the entire weekend, right? Uh, and most people who have run data operations resonate with that experience where they know when things fail and go bad, things can go really bad really quick. And then the ability to have visibility and know all the different moving pieces, uh, was not that easily available. So, uh, one of the things he thought about was there should be tooling that can help and show the entire picture of what's connected to what, all the moving pieces that does observability, alerts, incidents, management, All of those things. And he was toying with the idea of should he build a product like that? Yeah. And given that I had just been on the entrepreneurial journey myself, I very strongly encouraged him to take leap of faith and get started and not just solve the problem for GE, but for every company around the world by making this a SaaS product and getting started on that journey. So He resigned, quit his job at GE, gave some thought,…

AI assessment note: “I very strongly encouraged him to take leap of faith and get started”

Q And do you sell to the CIO or the chief data officer?

A Uh, generally it's three different personas that are part of every buy cycle. And it varies in terms of who's the primary versus the secondary and who owns the budget center versus not. Uh, the three personas is office of the CIO CTO, right? Cause they own the platforms and supporting the platforms, which is, uh, they're the ones who pay for Snowflake or Databricks or whatever else. Second persona is the CDO, the chief data, now the new title is CDAIO, right? So chief data and AI officer. Uh, they are responsible for the quality of the outcomes and deliverables in the metrics, which is like, hey, my tabular report and my analytics stack, is it producing value? Uh, so that's the second persona that's involved in the equation almost always. Uh, and the third persona is generally someone who owns Operations and support. So that could be like an SRE team and generally that falls in the CIO or CTO org, but sometimes it sits outside where they've outsourced operations or they've given it to one of your offshore providers. Uh, so like the, the vendor management, because a lot of the offshore providers, they log into our tool to do the job because a lot of companies have outsourced that offshore that.

AI assessment note: “generally it's three different personas that are part of every buy cycle.”