The Exchanges

Q So, so how has the fund grown over a period of time? You mentioned the first fund was 125 crores. How, how big is the current fund? And how many companies do you fund from, from every fund?

A So first fund was 125, uh, crores. We funded nine companies out of it. And, uh, we wrote down two of them. Uh, we have, uh, we launched a second fund, which, uh, in, in 2016, this was a 300 crore fund. Where we funded, uh, about 19 companies. And now we are on a third fund where, uh, which we are raising, uh, you know, amounting to 500 crores. We've done a first close already, and we are looking at a second close by end of December. So our, our thesis has remained steadfast. We'll continue to focus on enterprise tech. And in the third fund, we anticipate, uh, creating a portfolio of about 20 to 22 companies. Uh, in the previous funds, you know, because of the fund size and all, we actually also did seed seed plus investments. In the third fund, given that the corpus is a little larger, we would do more of pre-series A and series A, while, you know, we will take very few selective bets on seed seed plus companies. Very few. That's how our portfolio construct is going to be.

AI assessment note: “second fund... this was a 300 crore fund... third fund... amounting to 500 crores.”

Q I would love to know about your journey. How did you land up in BC and what led you to founding of Xfinity?

A Interesting question. So, you know, as you said, I was always in, uh, in financial services for a long time. And then we founded a small boutique real estate fund called Azure. And, uh, I mean, the common thread between all of them was that, uh, I was investing, you know, so we were sort of, uh, I was managing corporate finance and structured finance in, in the Tata and Birla, uh, NBFCs. And, uh, uh, in Azure, obviously we were investing in, in residential real estate primarily. So investing has been, you know, sort of central to, to what I've done so far. And, and, uh, and then I had a stint in Infosys where, you know, I worked primarily on the corporate strategy and consulting side, worked with the then leadership team of Infosys, you know, built some good relationships and, uh, somewhere around, you know, it was an interesting phase because As you, as you know, the nineties and the 2000 have been really, uh, fertile hunting ground for the Indian IT companies. But I think somewhere around late 2000 with the advent of cloud, particularly it was a game changer because it kind of, I would say democratized entrepreneurship and a lot of startups started gaining traction. 20 13 was a time when, you know, we were just discussing with Mohandas Pai and Bala, both, you know, ex CFOs and Um, members of the board at Infosys. And we said that, look, we should really look at the VC space. …

AI assessment note: “we said that, look, we should really look at the VC space.”

Q validation, uh, you know, except a handful of companies and they were not so big back then, you know, through our free charge, which are the stories, which are going IPO now they were in 20, 13, 20, 15, you know, growing like 10 to a hundred million ARR journey on that journey. So, so what made you convinced that this is the right story to build a large fund?

A You know, uh, one is that we actually, uh, both Ashwini and Anand have been really stellar founders. I think what we liked about them is the fact that how they were looking at the application of AI, you know, so AI per se, a lot of, a lot of funds do it. And every second company that you meet is, as you know, Says that we are doing some AI, but I think the way they were envisaging AI and the kind of use cases that they were talking about, uh, really was, uh, was something that we bought into. And, uh, uh, we thought that they understood the, the big picture. Uh, you know, Anand himself is a true blue, you could call AI professional, uh, and the way he approached the whole product, building it ground up. Uh, the, the vision, of course, the product wasn't there at that time, but the kind of vision he had for, for the product and, uh, the way Ashwini looked at the market and said that, look, you know, these are the problem spaces that exist and we would like to solve. Uh, so that excited us actually. And, uh, and, and then we decided to sort of back them.

AI assessment note: “what we liked about them is the fact that how they were looking at”

Q And Shailish, you know, how do you feel, you know, because in, in case of consumer companies, the fund cycle, the consumer companies tend to either go very big or, or they come to, to home very quickly. The span of three, four years, right? Whether it was, in case of enterprise company, how do you imagine, you know, exits for Xfinity will be like?

A I think there are, uh, primarily Siddharth, I would look at it from, uh, two, two major sources. One is the fact that, uh, uh, you know, uh, most of these companies would be found valuable by strategics. So a lot of the exits that we see are, uh, you know, from, from the, from larger companies who want to buy and, you know, fill in a particular gap that they have in their portfolio. Uh, the second, uh, exit is, could be that, uh, given the liquidity and the surfeit of liquidity that we see now, there are a lot of secondary funds which have, uh, which are quite active. And secondary funds are essentially looking to buy out existing investors at some point of time and get into it. And then of course, scale the company further. So that is another route that we see. Uh, recently we have talked about, you know, we heard a lot of things about SPAC and other things.

AI assessment note: “I would look at it from, uh, two, two major sources.”

Q So, so tell us about your journey in Xfinity. It's been almost seven years, I believe. What were the key major milestones in the last seven years?

A Uh, so I would, I would reckon that milestones are yet to come, but, uh, you know, I think the first thing that I would, I would really like was when we actually founded the fund and we closed our first fund. It was a small fund of 125 crores or, uh, you know, twenty million us at the then prevailing exchange rates, but I think it validated our, our story. So we felt very good about it, that we went, And talk to people and say that, Hey, look, you know, this is the space that we are looking to, to work on because bear in mind at that point of time, you know, there were not too many. I mean, I think Xfinity was probably the first, you know, true blue focused, uh, B to B tech fund, you know, so there were many, many funds, which did B to B investments or enterprise tech investments, but it formed a small portion of their overall corpus. The, the focus was largely on the consumer tech. But we were here saying that, look, you know, we'll focus only on enterprise tech. And therefore when the validation happened, it, it, it was very nice. But I think over the period of time, you know, it has been a very gratifying experience because We funded companies at very early stage. Some of them were at stealth in stealth mode, working out of garage, you know, literally. Um, uh, and, uh, you know, from there, yes. So, uh, you know, those companies have, have become mature. Some of them have be…

AI assessment note: “when we actually founded the fund and we closed our first fund.”

Q And how do you advise? Because see, if I'm an entrepreneur today, a first time entrepreneur with, you know, less enterprise connects, I am between, you know, the same age as Nishith started Locus. And how, how do I go about approaching enterprises to validate my problem? That, you know, this is a problem they are facing and should I build for it?

A You know, you have to read a lot. So, and when I say read a lot, you know, you could read a lot in terms of annual reports of companies. You could read a lot on analyst reports and, and things like that, because that helps you form a belief because let's say enterprises, you know, and when I look at enterprises, when I mean large enterprises, some of them are publicly listed for small businesses. Also, there are several reports that come out. So today in your space, uh, you know, and, uh, when actually I'm a, I'm a pretty, uh, old professional. So during our time, there was no internet to begin with. So the reading was also confined to, you know, what is available or what is available in the library in terms of physical reading material. But now I think you have access to a lot of material, whether analyst reports, whether annual reports, you know, Gartner reports, et cetera, and really figure out what are the, what are the, uh, you know, Uh, connect the dots and really figure out what is it that is, uh, that is a hot button to press and then zero down on, on that. And then I'm sure, you know, today with LinkedIn and others, uh, other things, you could easily reach out to people, figure out, you know, what is happening, uh, uh, where are the pain points? What are the challenges businesses are facing? So I think that forms a, so there could be one, a primary research as well as …

AI assessment note: “today with LinkedIn and others... you could easily reach out to people”

Q which are above ten million in your portfolio are more engaged, for example, other, and, and these company would potentially, you know, uh, IPO in us because, uh, US has been one of the key markets for them. And I believe once they hit 50 mil ARR. So, so what, what do you think differentiates your top four portfolio companies from the companies which you funded at the same time?

A I think, you know, two, three things. One is that their, uh, Their agility and nimbleness, you know, first, because it is not that every time an enterprise tech company goes in the market with a certain hypothesis about its product and, and the proposition that it works. But I think what is critical is that are the founders having an open mind to come back and to the drawing board and then rethink about their product, rethink about their proposition. I think this is something that, that both of these companies or a couple of other companies that we have did do continuously. I think that is one thing. And the second thing, what I fundamentally believe is, is that execution is, is the key. So you can have a great product. You can have, you can do a lot of research. You can have a bunch of patents and all of that, but how do you execute? You know, how do you really acquire customers? How do you manage your operations? How do you deliver your product? How do you implement it? You know, how profitable you are, how do you manage costs? Uh, and that is something that, uh, you know, these companies do exceedingly well, uh, which is, which could be a learning for others because You know, execution is one thing that you might have a great product and, and if you execute badly, uh, you know, it doesn't really, it is actually a crime, you know, in that sense. But if you, if you execute wel…

AI assessment note: “One is that their, uh, Their agility and nimbleness”

Q Can you take an example, for example, one of your key portfolio companies, either Madstreet Den or Moe Engage or any of the other, other top two, uh, how, how they approach this, what, what you are mentioning about, about reading reports and What, what are the key behavior you observed from the founder?

A So, you know, first of all, I, let me, let me again, give you example of locus, right? I think, uh, and why I give you that example, because when you funded them, as I said, they didn't have much revenue to talk about and what they were doing at that point of time was last mile delivery. And, uh, you know, which was, and, you know, probably there was a word called hyperlocal, very prevalent at that particular point of time. And that hyperlocal was A problem, you know, the last mile they were trying to solve. And, uh, you know, so when we actually, the board and we can't claim credit as a couple of others, other of my friends also on the board, and we all actually sat down with Nishet and we said that, look, okay, last mile delivery is something that you're doing, but why don't you look at the problem in its entirety? You know, and, and we had a lot of experience ourselves, you know, looking at and having worked with enterprises and all and said that, look, you know, look at everything from the first mile to the last mile. You know, how do you move a particular piece from point A to point B and really figure out, you know, uh, what is it that you can do to optimize that movement? And that actually, you know, and Nishit was quick to sort of grasp and we said that, look, let us not bother too much about, about revenue traction. If whatever we are selling, we continue to sell, but …

AI assessment note: “Nishit was quick to sort of grasp and we said that, look”