Smallest AI

“Indian languages for me was like just an operational problem to solve getting data sets, et cetera. But it was not a scientific enough problem that helps us answer what's like the next level of AI and that how can small models be the future, et cetera.”

“I put the first 101 50 K into smallest of my own. And we basically got like some GPUs and we started like training models.”

“We asked them to reduce the check size. We said, we can't take one at six. It's like too much dilution for us. So we, I think we ended up at like some 500 or six or something.”

“11 Labs was completely into offline dubbing and things like that. They did not have a real time model, right? So we were one of the first people to build a real time text-to-speech that ran under 200 milliseconds.”

“And we go right to the level of The NVIDIA architecture to even write our own kernels. So we are not scared of touching any part of the stack.”

“We raised another 900 K extension at a higher valuation. And we had like 1.8 of which most of it was in the bank.”

“There are probably 20 companies in India who can give us that kind of a deal size and they would spend an incredible amount of time and effort.”

“Right now, the only other player who's focused on real time is Cartesia.”

“There is a telephony company that has all of their like meetings internal meetings, getting like recorded in the US using our voice models.”

“We closed the series A, we could call it a pre-series A, but like we closed with the Seligman Ventures.”

“But they are heavily dependent on state space models and we don't train state space models.”

“For example service now, for example, Right. We are working with them.”

“Like for example, 11 has a lot of focus on content creation and like dubbing. And if you want to build like an Instagram video or like a AI movie 11 would be a great player. We don't focus on content at all. We are focused on the real time space.”

“Research gives you ROI in like six months. So you can't like hire someone right now and then expect, if you have goals for 2026, like in September, you have to get those people right now.”

“There is a financial services company based in India, which is doing large scale debt collections using us, like, you know, every day we are like at a 100,000 calls plus using that.”

“Upsparks was like, not much like a hundred K if I am not wrong. Better was another two 50 or so. The total round was three 50 K and then couple of angels of 400 or something like that. This is at like a three million post money sort of around, right?”

“Having said that, I still think the capital we have right now is enough to build a hundred million ARR company.”

“It's between one to two million ARR.”

“It is converted to a seven figure multi-year multimillion deal. But we will announce it formally sometime like in April or so once we sort of clear a few things, but yeah, it is that like all of those things are in paper right now.”

“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 64 milliseconds latency lot of other features like emotion detection, gender detection, bunch of other things, right?”

“We are trying to build like a predictive view of voice and like conversations that we can have so that you can guess what the answer should be rather than How LLMs typically operate.”

“We got in between like an acquisition offer for like one 50 plus million dollars.”

“AWS is an active reseller of smallest by the way.”

“So we had a text to speech model which basically you could use to give natural realistic voice to the voice agent in under a hundred milliseconds. So initially it was at 200 milliseconds. It came down to a hundred milliseconds by the time our seed round was th…”

“I think the markets will diverge at some point of time. Like so for example, 11 is sort of doubling down on the content space but the real time space, like mimicking how humans do conversations is, it's like a very different problem than mimicking how movies a…”

“We are literally creating the like list of top 50 dream customers that we have and going aggressively through like a top down motion, a very targeted motion of how could we, you know, get them through a Palantir style approach.”

“So some of our investors in like our pre-seed words from OpenAI as I spoke to them, they are building a different kind of intelligence.”

“We recommend it because for us it's a hundred percent margins, right? If that happens.”

“From the time he was like, quote unquote, ready to raise to writing the, to giving him a term sheet. I mean, it was maybe a week, couple of weeks, something like that.”