The Exchanges

Q And so was that, was that your, your co-founder and your philosophy from day one, or have you found that the product rod, the product roadmap has evolved over the last couple of years?

A I would say we've stuck to nearly 70 to 80% of the roadmap we had envisioned for the next couple of years, and the 20% has been a lot of learnings, a lot of counterintuitive insights that we've actually got working with our customers. Uh, for instance, we thought, uh, Portfolio monitoring or comparative monitoring, uh, real time use cases will not be very important, but it turns out that, uh, as more and more AI tools profilerate, uh, people are getting lazier and they would not come, want to come to a platform and generate reports. They just want the AI to know when you need that information and hit it in your inbox at the right time. So it's been those kinds of things that, which have been very fascinating, which we had never imagined or envisioned, uh, when we started the firm.

AI assessment note: “we've stuck to nearly 70 to 80% of the roadmap we had envisioned”

Q And how is the, the current version, uh, current market version evolved since, uh, since day one?

A Oh, a lot, a lot, uh, our very first, uh, product feature was a company diligence, right? Uh, we thought that was one of the most recurring, uh, High volume use case for a lot of different personas. So we started with that, keeping, uh, what would be the demand from the market, uh, which was a good start. But I think, uh, right now from being a one single feature product oriented company to now a platform, it's come a long way, right? So giving our users the ability to not just use a very cookie cutter kind of an output, but instead curate it, customize it to their unique needs. Uh, giving them a no-code, uh, interface for building their own agents, uh, which really unlocks a lot of value, right? Now, you could use the building blocks Advocate has developed beyond just company diligence to sector research, to market mapping, to portfolio monitoring, uh, to creating your deliverables, whether it's a SIM for investment bank, and so on. So a lot of those avenues have been opened up, and a lot of that engineering effort has gone, gone behind, uh, Pulling in, uh, you know, alternative data sets, especially for private markets where we specialize. Uh, so the, the platform and the whole product features come a long way. Uh, we've also had a lot of learnings, right? Like we realized that you cannot be a specialist in both public and private markets at the same time. You need to start w…

AI assessment note: “from being a one single feature product oriented company to now a platform”

Q I love it. That's great. And the fact that you knew that ahead of time. Brilliant. So capital's in the door, um, time to cast a budget. How do you, how do you think about, um, allocating capital between product development and sales and marketing?

A Well, uh, pretty much every day of my life is like, you know, grappling through this question, uh, through micro decisions on a day-to-day basis, whether it's hiring, whether it's investing on tools, and so on. Uh, we, out of a firm opinion, that At the journey we're in, in terms of AI, since things are moving so fast, you have new models coming up every couple of weeks, uh, the new products coming up, new business models emerging, uh, for you to differentiate yourself, there is no other way but to build an excellent product. And for that, we need to have a very good product velocity so that we keep on shipping great features, add value, have those feedback loops with our customers. So still a large portion of our spend budget goes into product. And I think that's definitely heavily skewed in favor of product because if the product is there, our sales reps can go there and sell because, and if you don't believe in the product, if it's not the best, then, uh, you know, you, you can, you can just get lost in all the clutter of hundreds of other startups that keep coming up every day. So that's been our philosophy. Will that change? Yes. I think, uh, right now we're at that journey where we still feel about six to eight months is where we can still go very aggressive only on product. We've been lucky to get a lot of inbound word of mouth referrals through, uh, Uh, for, for custome…

AI assessment note: “still a large portion of our spend budget goes into product”

Q Sid. Um, before we wrap up, uh, lessons learned. Um, you know, as you, as you reflect back on, on the last few decades, what are the, what are some of the key takeaways that, that have really stuck with you, and that if you had a chance to go back and, and share them with yourself when you were first starting your career, um, what would they be? Sure.

A I think one thing that particularly worked well for me, uh, if I reflect back was always trying to learn new things, uh, you know, not being very constrained or pigeonholed into one segment. So I'm a, I'm a mechanical engineer by training. Uh, and then even after my engineering, I, I learned coding just out of hobby, uh, took up a business analyst role in a, in a consulting firm as a first job. Uh, so I was all over the place, but I think wherever I went, whether it was consulting or cop dev, uh, I always tried to build solutions, use coding. And I think that curiosity has definitely helped me a lot. There's also that exposure element, right? So no matter where you start, right? I grew up in India, uh, and of course worked my way through to get like good business schools there. But you still will have a, you know, exposure gap with someone who's in the Bay Area versus, let's say, even in Kansas City, right? For example, how do you bridge that gap? And, you know, earlier it was a lot more tougher now because of proliferation of tools and internet and AI, of course, has become a lot more easier. But I think what I'm getting to is Your ability to learn, your ability to be adaptive, your curiosity, uh, your awareness in terms of bridging an exposure gap is super important.

AI assessment note: “one thing that particularly worked well for me... was always trying to learn new things”

Q Well, um, a bunch more questions asked, uh, the company and the product. Before we do, um, would love to know what your career progression looked like, uh, leading up to Vocalo, and, and what did you, what did you learn along the way that helped inform how you think about running the company today?

A Sure. I mean, um, yeah, I can, I can provide you a brief background, right, which will probably set the stage, um, So both of us co-founders actually have a very similar background, right? We both started our careers as management consultants, uh, and both of us then also crossed paths in the same firm in corporate development for Tata Group. Uh, so both of us have had consulting and investment experience. Uh, interestingly, we got a little tired of building PowerPoint decks and not really seeing execution of many of the strategies that we would propose. And, uh, that led to a lot of, like, discontent, right, with our, with our roles, and we both, in our own little ways, tried to, uh, pivot our careers toward building product or tech. Uh, I pursued a master's in AI. I became the head of product for an IoT platform for the federal government. My co-founder was heading natural language processing for a insure tech giant, uh, right, actually insure tech for AIG, which is one of the largest insurance companies out there.

AI assessment note: “We both started our careers as management consultants, uh, and both of us then also”

Q But we also know that, you know, there's, there's risk, uh, involved as well with investing in any asset class. And so, how do you guys think about risk? You know, it's one thing to streamline, um, workflows. It's another thing, as you point out, to ensure that you have a clear picture of an asset class, but I would imagine that this is core to, core to the offering.

A Absolutely. I think that's a, that's a important question because right now the whole conversation, the whole narrative is very much skewed in favor of what's good, what's interesting, how, how will this show a hockey stick kind of curve, right? Uh, but I think, uh, off late because of AI, a lot of people tend to just think about the implications of AI in a negative way, in that form of risk, but just in forms of form of doing due diligence. Uh, where private equity funds or even strategic sponsors are looking to invest in companies, risk is one of the most important aspects, right? Uh, the way we look at it is, uh, to provide our audience, to provide our end users with an all rounded view, uh, your risk signal could be coming in from just not the background or reference checks, but it could also come in from a, you know, seemingly innocuous comment made on social media, uh, by the founder, uh, It could come in from, uh, how the users are perceiving your platform, and those signals propagate over a period of time. One might find it in, uh, places like Reddit, or, or a software review website, like on a GTO or a Captera. Uh, so there are a lot of unconventional sources of risk, uh, which is what we are trying to solve for beyond the traditional ones. Uh, Where we pick up signals from noise and help our customers get like much more awareness from an all rounded view, whether it's…

AI assessment note: “there are a lot of unconventional sources of risk, uh, which is what we are trying to solve for”