Building AI Agents for Enterprise Operations

**Pablo Palafox** (0:00)
Voice was the unlog to many of the operations that are really needed to move the world, if we talk about supply chain. This is not a supply chain specific problem that we are solving, it's actually an enterprise coordination problem.

**Luis Paarup** (0:15)
The bigger problem in the coming years for voice here is really knowing when to talk and when not to talk. So it's understanding all these nuances in the work, more than making the latency faster or making the voices more realistic, which I don't think that's the limiting factor today.

**Olivia Moore** (0:29)
I feel like Happy Robot has always been at the forefront of kind of humanness. Do you want the customers to know they're talking to an AI? Where does that go?

**Pablo Palafox** (0:38)
I think it's super important that...

**SPEAKER_4** (0:40)
Most AI demos happen in controlled environments. The real challenge begins when AI has to operate inside large organizations, where information is fragmented across systems, teams, emails, phone calls and workflows that have evolved over years.
Logistics and supply chains have become an early proving ground for these systems. Success depends not just on model intelligence, but on coordination, context and the ability to execute work reliably in the real world. Anish Acharya and Olivia Moore speak with Pablo Palafox and Luis Paarup from Happy Robot about voice AI, enterprise agents and the challenges of deploying AI in operationally complex industries.

**Anish Acharya** (1:24)
Olivia and I are here with the two incredibly talented founders of Happy Robot, Pablo and Luis. Welcome guys.

**Pablo Palafox** (1:30)
Thank you guys. Super excited.

**Anish Acharya** (1:32)
Very excited to have you. We're over due to have this conversation. Well, look, we're here to talk about the company and the incredible journey that you've been on. I know when we first met you, there had been a lot of buzz amongst YC founders and other folks about how you guys were at the edge of the technology and then really getting a lot of pull from a go-to-market perspective. So maybe take us back in time to the little office that had four or five people on 20th Street and what the origins of the company and the product were.

**Pablo Palafox** (1:57)
100 percent.
So Luis and I met on our second day of college, just to set the scene, ever since we've been building stuff together. Our other co-founder, Javi, he happens to be my brother, so I've known him for a little while. We always wanted to build something together, right? So when we got into YC, we were looking for complex problems we could solve. Keep in mind that Luis and I had been literally building submarines for robotics competitions to find mannequins underwater. That is the sort of problems we were looking for.
So when we decided on solving for that complexity, we looked at what Javi was doing as a CFO of the largest olive oil distributor in the world. He was literally moving tons of olive oil across the ocean, and that was that complexity that drew us into logistics and supply chain. He literally had to hire interns to call drivers to see where they were, to see where the shipment was, because Walmart was asking him, where the hell is my shipment of olive oil? So that was the sort of problems that we wanted to tackle, and maybe you can talk about why we actually started with voice there.

**Luis Paarup** (2:59)
I guess we took it from a very tech-driven approach. Really the limiting factor back then was having an agent that could speak on the phone realistically. We were in conferences, Javi was traveling all around asking people, hey, if we were to create a voice agent that could pick up the phone and sell its loads and track these shipments, would you buy it? And he's like, dude, of course, this is a no-brainer. I just don't think you can do it. So it was more so like the idea market fit or product market fit made sense from the beginning. It was more so like, can we prove ourselves? We can build this technology. And, you know, LLMs were picking up, we're talking about late 2023, probably. LLMs were like decent enough, 11 of us were picking up with the text to speech and everything was kind of working together, but we had to build something that could actually connect all the dots and actually make something work.
That kind of shaped our company, where we really had technology and innovation as the core of our company and always pushing this frontier and solving problems like firsthand. So that's how we got started in the voice phase.