The Exchanges

Q It must have been crazy, right? You just closed around an acquisition, which you passed on and then you got a term sheet and then term sheet closed in a month or how much time?

A Term sheet closed like literally, like we just took a week to sort of negotiate on something from the first conversation. Yeah. The first, I mean, when we got the term sheet from Seligman, it took us a week. I mean, they obviously wanted to, Us to sign them. I think we were kind of completely okay with most of the terms. We just had to make sure we are on the same page regarding a few things, nothing major. We closed it in a week and, uh, yeah, like, uh, that's, that's basically how quickly things moved. Right. So I went back to India after that, because we had to tell it to the team. Um, there are a lot of people who have been there with us early on. We want to make sure that with this one raise, they are also rewarded. Their goals are understood, et cetera. So we did that.

AI assessment note: “when we got the term sheet from Seligman, it took us a week.”

Q Yeah. What, what, what's the next that happened after that?

A We kept building. We got a small house in Bangalore. It was in Indiranagar by the metro station. Um, we got our first couple of employees from random sources. Like these were not friends. I, one strong hypothesis I have is sometimes not hiring from your comfort zone is better because otherwise you end up having like the same learnings basically. So I generally look for like people who I don't know, but are also ambitious and can teach me something. So we ended up hiring based on like Twitter and like LinkedIn, uh, folks, ex-founders basically who had tried to build their company shut down very unique profiles. Like the second employee I had hired, I never went to college. He was training model since he was like, uh, learned everything on his own. Right. Another guy had built a services company, scaled it to like a million or so, and then shut it down. Um, very unique profiles we hired for, and, and we were able to build like a good team initially, I think. Till this point of time, the VCs did not even know that we would be able to build a team, a product, nothing. They just took a bet on like this two hungry people and let's see what happens. Right. But I think as this started happening and we started getting interest and they saw us working, um, we got offers to, do you want to raise another round? You want to like extend, et cetera. Um, for us, uh, it was very clear that we d…

AI assessment note: “We kept building. We got a small house in Bangalore. It was in Indiranagar”

Q Can you explain in layman what is that?

A Uh, layman terms. So state space models, uh, the computation required to basically calculate the outputs. Uh, is something called subquadratic. What it means is, let's say you have a input sentence, which has like 10 words in it. Um, so a transformer would take 10 into 10. Um, so a square of it's a hundred, that's quadratic complexity, but a state space model would take like less than a hundred, basically. Um, that's like the simplicity of it. It makes a very big difference, uh, when it comes to larger, uh, values of input text. So for example, imagine you have 10,000. So if it is square, so it is 10,000 into 10,000, uh, which will be like a hundred million, basically. Versus if it is like, uh, less than that, so it could be a 100,000 or one million. Right?

AI assessment note: “state space models, uh, the computation required to basically calculate the outputs. Uh, is something called subquadratic”

Q So some of the largest enterprise accounts that you cracked today, how did you crack them apart from introductions? What, what did you do specifically to make sure, you know, you land and win in those accounts?

A So I'll talk about this US based telephony company. Right. Um, so, um, And I'll name it later after this podcast as well, but, uh, just, um, so, so we got an introduction to our investors. Um, and I was like, I had just raised the like seed. The money was not yet in the bank. I was kind of getting on a call. I was like, Oh, if this person does not like me, maybe the term sheet gets pulled. I don't know. Um, so I'm kind of like, uh, a little, uh, little nervous, but at the same time, I know my things, like, I know what I am building. I know the problems I can solve. So we have this amazing voice model that converts voice to text called pulse. It's a speech to text model runs in real time, um, 64 milliseconds latency, um, lot of other features like emotion detection, gender detection, bunch of other things, right? So it's like a hundred different parameters that you should know really well when you're going to do the sale, right? Get on a call with the CTO. The CTO is a engineer himself. You know, he is like a public market CTO, but he is still an engineer. He knows, you know, his technology, his teams in and out. So he grills me on like every single aspect of the product. In the first call and, and the VC who is like investing in me is actually on the call with me at this point of time. And both of us are getting grid together basically, but I don't take these things personally.…

AI assessment note: “he grills me on like every single aspect of the product”

Q But then if it's such an attractive market, why not Anthropic and OpenAI playing in this market actively as 11 Labs?

A I think there is a different focus. So some of our investors, uh, in like our pre-seed words from OpenAI, um, as I spoke to them, they are building a different kind of intelligence. And I think that's an important form of intelligence as well, which is the, uh, which is the large models that can basically take in huge amounts of context, process it, and then come up with something meaningful out of it. Um, think of it this way, like, um, when humans made aeroplanes, we tried to learn from birds. And we are doing much, much better there. We are actually flying long distances, much longer than how much faster than what a bird can fly, but we are still not like as agile or light as like a bird, right? So we have mimicked flying, but through a different form of instrument, very similarly, right now in AI, we are doing LLMs, which is a different form of intelligence than human intelligence. So, uh, they have something and they are playing the sort of let's make LLMs bigger and bigger and smarter. And that is a market as well.

AI assessment note: “I think there is a different focus... they are building a different kind of intelligence”

Q How many VCs have you spoken to back then for your pre-seed?

A Yeah, that's a very good question. So, um, we did not, we did not know how to do this VC startup. We thought like, I mean, you know, you just like go and ask for it and like, uh, we will get money. Uh, I had no clue, um, what it takes, what does a VC look for? Nothing like that. Um, so we would get random analysts coming and messaging us saying, Hey, you're building something because we would put like stealth startup in our LinkedIn. Right. So, uh, someone would come message. We must've spoken to every single VC at that point of time. Um, and then they would randomly come and reject us. I'm like, I'm not even like raising, like, what do you mean? Uh, but, but like someone would come say, Oh, you are too early. You should do this. You should do this. Like, dude, I'm not asking you like, I'm, I'm working on it. Right. So, so, so that would happen. And then we knew we had something when this text to speech model went out basically, because that like, we were getting like, literally like with, there was a two week window where Like my LinkedIn following like two X, three X or something like that. My like, like the LinkedIn DMS were completely filled. XDMs were completely filled. Emails were coming in. I was in customer calls back to back. What do you want? What do you want? What do you want? And then the product was just a HTML page with the model with one GPU running behind it.

AI assessment note: “We must've spoken to every single VC at that point of time.”