A Product Market Fit Show | Startup Podcast for Founders
00:09:22 · Surojit Chatterjee, Pablo Srugo
Or a lot of things fall in the fringes of employees don't even know which department they need to go to, they need to talk to, which application they need to touch, and where they need to go. All of that is automated. For Wipro, for example, they report from thousand people. They have been able to first, you know, reduce the time taken for any of these requests. These are not just questions, requests from five to seven days to like few seconds to minutes. Right? So that's a big deal for the company overall. And then from thousand people going to like 550 people at 45% reduction. So employees are happier, higher CSAT. At the same time, you are doing the same work with much less number of people. Of course, HR is always overburdened. It's not like HR didn't have any work to do. They were probably doing only 50% HR and 50% paper pushing. So all the paper pushing, all that is gone or most of it is gone, so you can actually do a lot more real HR work. And that's it's not just HR, that's kind of true for every function. [00:10:27] S1: That's what I was gonna ask though. Are these are your AI agents like, are you vertically focused on HR or employee related matters?
00:10:27 · Pablo Srugo, Surojit Chatterjee
[00:10:27] S1: That's what I was gonna ask though. Are these are your AI agents like, are you vertically focused on HR or employee related matters? [00:10:33] S0: So we are a horizontal platform that can create these AI employees to what we call agentically transform your business. We are reimagining working with these enterprises, how their future org structure will look like with AI employees and human employees kind of coming together. And it's not just replacing one human by one AI agent. It's reimagining the entire process. A lot of times it's a broken process. You don't know how to replicate that with AI. We go deep. So it's horizontal. You can potentially automate anything. We go deep in HR. We go quite deep in finance ops, like, you know, APR optimization, source to pay, go to cash. And we go deep in some of the customer support use cases, which is also for very scaled companies, like companies which has very large number of humans actually running customer support today. We kind of help them optimize their operations and moving off kind of mundane work off humans to AI employees. [00:11:36] S1: So now that we understand kind of, like, what it is that you do, maybe let's go back to the beginning. You started this in early twenty twenty three. Give us a little bit of context. Just like, what were you doing before and how did you how did you have this idea? What's the origin story?
00:11:48 · Surojit Chatterjee
[00:11:48] S0: Sure. I spent my entire career working for high-tech companies here in the Valley. Right? So fourteen years at Google, I built and led mobile ads for that entire time. Mobile ads became almost $100,000,000,000 plus business for Google, Now even bigger, I I suppose. So I had seen this this massive growth. And then I was at Coinbase running all of product. I was chief product officer at Coinbase and helping Coinbase rapidly expand users and revenue between '22, 2023, and help Coinbase go public in 2021. So my experience has been running businesses, building and scaling teams, And I've hired a lot of great people in my career all the time. The one thing that always comes in the way is you hire all these talented people and then you realize, oh, 60% of their time is just keeping the lights on. Doing the same thing over and over again. I'll go and talk to, like, my engineering VP or engineering counterparts at Google or client base, and they'll be like, yeah, don't look at 3,000 engineers. We have only 1,500 because rest is just just keeping the lights on. And true for every function, right, everywhere. And that's to me, this was the fundamental question. Can we hire, like, really talented people and make them even more effective, give them work or put them in front of work that is really innovative, creative, and not mundane. Work like filling out spreadsheets, go into this tool, do something. That's what a lot of work today is in the enterprise. Lot of updating things across the board, updating things for your managers, creating reports. That's probably 90% of the work today, which I think can be automated. And humans are capable of doing a lot more, a lot more innovative, creative, and value added work.
00:13:46 · Pablo Srugo
[00:13:46] S1: And then how did you decide? Like, that's kind the high level problem, which I, you know, completely agree with and it's been a problem for for a long time. Obviously, with AI, it all of a sudden became a possibility of solving a huge component of that. When do you realize that that that's possible, and and what are the some of the early steps that you take to see that there's a real opportunity here?
00:14:02 · Surojit Chatterjee
[00:14:02] S0: Look. In late twenty twenty two, ChadGPT come came out and there's a lot of talk about LLMs. I started playing with it. I was deciding to step out of Coinbase. That was early twenty three. I started playing with it myself. Right? And and one thing in my head was, can we actually use this technology as a platform, as almost like an operating system to create ability to dynamically plan, use tools, and use memory? I mean, those are the unique human capabilities. We use tools. We have memory that it means we learn from feedback from the environment, and we adapt automatically. Without that, this technology will not work because you will keep building the fixed software which has been the root cause of this problem, which is, you know, we have a lot of software, but they are fixed. Right? So they they don't change. You have to go and keep tweaking, keep doing things for them to kind of adapt to your business. So for me, the first step was I started tweaking, writing my code, some code myself, which I haven't done for a long time. To be honest, I was an executive running large businesses and so on. But my roots are, you know, as a builder, as an engineer, computer science, bachelor's and master's. And then I started I started reading a lot of research papers at that time that came out in early twenty three on how can we use these LLM frameworks, LLM systems to start dynamically planning and and taking actions. This was really the root of the whole agentic thinking. The word agent was not involved if you read the papers. Nobody was using that word. We called it AI employee because we I just really wanted to model it after how humans work. That was the genesis and got together with few other, you know, ex colleagues of mine from Google. My co founder is also ex Google, ten plus years there. And we got very excited about this idea, started building some rudimentary prototypes. We worked a lot with early days, some of the PhD students who were writing these papers, like, almost as volunteers. Some of them joined us back later.
00:16:12 · Pablo Srugo, Surojit Chatterjee
[00:16:12] S1: You're such a selfish person. I actually can't believe how selfish you are because, like, you've been listening to the show. You listened to this episode. You loved it. You've listened to a bunch of other episodes, and you haven't told anyone about it. You haven't told any of the many founder friends you have about it, think about how many founders have helped you out when you're building your startup. So don't be selfish. Tell your friends about this episode, tell them about the show, and help me help them. With these big ideas, on the one hand, they're obvious in the sense that if you can say to someone, hey. If I could create AI employee that costs 10% of what a normal employee does and works twenty four seven, would you buy it? Yes. Of course, I would. Today, people are like, maybe you could do that. You were starting, it was more far fetched. And so how do you go from that huge idea to something tangible? When you start prototyping, what is the first version that you build? What's the first thing you wanna test out? [00:17:01] S0: Yeah. Great question. See, the technology has moved and improved a lot in the last couple of years. But at that time, a lot of things did would not work. So you have to have a belief in where this technology will be in a few years and start building with that future. It's like, you know, early days when e commerce got built. People would have a lot of doubts. I can only say they'd probably buy some books. Will I ever buy, you know, dress online? [00:17:29] S1: Let alone a mattress. Yeah. Things have changed.