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Chapter 1: What role does voice AI play in enterprise operations?
Voice was the unlock 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.
The bigger problem in the coming years for like voice AI 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.
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?
I think it's super important that 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 Parra from Happy Robot about voice AI, enterprise agents, and the challenges of deploying AI in operationally complex industries.
Olivia and I are here with the two incredibly talented founders of Happy Robot, Pablo and Luis. Welcome, guys. Thank you, guys. Super excited. Very excited to have you. We're overdue to have this conversation. Well, look, we're here to kind of 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 sort of 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.
100%. 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 Lisa 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.
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Chapter 2: How do AI agents address coordination challenges in large organizations?
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. LLMs was picking up with the text-to-speeds 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, no?
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.
Amazing. One of my favorite memories of working with you guys is actually when we first met outside a very crowded coffee shop and you called one of the live voice agents and it was seamless and it did an incredible job in a very non-ideal environment. I feel like a lot of people might know Happy Robot from your amazing demo videos of the voice agent.
And that's definitely not all the product is, but it's an important part of it. So maybe walk us through like why voice to start and then what voices may be unlocked for you more broadly.
Yeah. What Luis was saying is very important.
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Chapter 3: Why did logistics become a proving ground for AI agents?
Voice was the unlock to many of the operations that are really needed to move the world if we talk about supply chain. So when we were going to these conferences and people were like, no, we are going to build these things that talk on the phone, negotiating rates on shipments was actually a big one. So we actually fine-tuned LLMs back then. We fine-tuned LLMs.
Mistral and Lama to actually make those voice agents faster because otherwise using some GPT-4 at the time was like extremely slow and GPT-3.5 at the time was like terrible at reasoning and actually negotiating. So we had to do a lot of tricks behind the scenes, build our own
agent infrastructure, if you will, but also build our own voice agent capabilities so that we could innovate faster than competition. And that actually gave us a really good edge in logistics and transportation in the early beginnings. So we started working with these freight brokers, then we expanded to these freight forwarders, then ocean carriers, then trucking companies.
Chapter 4: What are the unique challenges of deploying AI in operationally complex industries?
And today we actually serve many of the largest companies in the space of supply chain. We were discussing before, nine of the top 10 freight brokers in the US, seven of the top 10 trucking companies, like some of the largest fleets that actually move our goods everywhere in the US, which is crazy.
Two of the largest ocean carriers, those big boats we see in the bay, that is the sort of customers that we needed to build for and where voice was the analog for many of the operations.
So it sounds like it wasn't just voice. It was also voice plus negotiation. So perhaps track and trace, which is customer support, and sales, which is sort of this negotiation, is where we started. And I think that forced us to build a deeper set of technology than we otherwise would have built. Maybe, Luis, take us on the technology journey a little bit.
FELIPE HOFFA- Yeah. So before I tackle that, I guess one of the things that we had very clear from the beginning when you're working on the frontier of technology is really what you have to reinvent versus what already exists, no? And I think people might take an approach where they just reinvent everything just for the sake of it.
Some people would just wrap around anything else and be like more of a go-to-market thing. We started tackling the limiting factor always. And again, back then, GPT, Paolo mentioned, 3.5 was relatively fast and not so good. So we had to find T on the LLM. Soon enough, we realized that prompting and all these good models came out. Prompting was good enough. Scratch that. Let's do that.
And always focusing on that limiting factor then. voices, like the background noises, like supply chain is extremely messy. You're talking with drivers in their trucks with the radio on and background music and noises and accents. So always focusing on those limiting factors.
On the negotiation part, something we got very often was how do you prevent the bot from hallucinating a rate or like max buy? He's like, dude, I'm building this thing and it's just hallucinating max buy and it doesn't know how to negotiate. How are you guys able to do that? And I think it's because You don't need to show the AI what it doesn't need to see.
And I think we're very opinionated about this from the beginning where we're building these proxy servers and actually exposing to the agent all the things they need to see. And actually max buy, the max amount of money the bot can actually see or actually negotiate is not even exposed to the bot. We were not exposing that.
We were doing external negotiation algorithms so that the bot would just ask for permission, literally the same way a human would, like, hey, let me ask my boss. And it was really just calling a tool and asking for permission to do more. And we would inject back the rate, no?
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Chapter 5: How does Happy Robot ensure human-like interactions with AI agents?
So one thing that we heard from one of the largest tracking companies recently was typically when we buy technology, we see where we can apply that. With you guys, we actually have a problem and we come to you guys with that problem because we know that as a platform that you've built, we can pretty much build any type of agent for our operations from sales to customer service.
back office support and operations and even collections. So some of the use cases that customers came to us were, hey, we have a huge collections problem. Can you build an agent to reach out to customers via email or voice and collect money? We're like, of course.
We talked about these use cases with one of our largest supply chain companies and customers where we need to call customers to recover duties on parcels. And today we're running campaigns of 20,000 to 50,000 daily outreach to customers, collecting duties on parcels that otherwise they would not get if they don't pay the duty on the parcel. So that's sort of...
surprises, if you will, we've gotten from customers. Like, yeah, I also need to recruit drivers. Can you do that? And we obviously can build an agent that not only just recruits drivers, actually connects to the operation so that now they know they can service a truck with a customer earlier because now they have a driver to move that.
So there's all sorts of interesting connections between the functions. Maybe I'll give you another example. we built an agent to reach out to maintenance shops to see where a truck or when a truck was ready. You could just leave that agent in a silo and just have an agent that is practically reaching out to those repair shops to see when the truck is ready.
Well, it turns out that the sooner you know when the truck is ready, the sooner you can put it in the market to sell it as capacity for your customers to actually move things. So that was a very interesting realization of how sales in this case, and maintenance were tightly connected. So that is the context that we talk about.
There has to be an underlying context sharing across the different functions in a business so that the whole business optimizes for a global maxima, if you will, or a global minima, depending on what optimization problem you're trying to run, versus just minimizing the probably in one function, if that makes sense.
And then maybe can you talk about, like, how do you discover these workflows? Who discovers them? Who builds them? How do they get built? I mean, maybe, Luis, talk a bit about that.
Yeah. So we're very forward deployed. So we were early on understood that really to solve the customer's pain point, we had to build software that adapts to their operations and not the other way around, which is like the old era before AI was you build something and ask people to like run their business however you think they should be run. But we think it should be the other way around.
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Chapter 6: What is the significance of context in AI-driven enterprise workflows?
And from there, the system is going to start learning and getting all these contacts and sharing it across functions and across channels.
I feel like if I was trying to train someone to do my job, the contacts that they need does not live in Salesforce or any traditional software system. It probably would live in needing transcripts and emails and casual conversations and even things that software can't capture, hasn't captured.
I know you guys have this concept of the pyramid of complexity and how starting with some of these primitives allows you to get into more and more complex work over time. Maybe we could walk through some examples of the type of work that happy robot agents can do.
So the pyramid of work, as we define it, is essentially the easy, repeatable, low-hanging fruits type of work at the bottom. Think about an easy B2B sales call, an easy customer service type of operation, some payment collection type of work. Kind of the highly repeatable, easily automatable type of work.
One thing that we've already talked about here is how those actually interconnect, which is very important. You might have these disconnected or siloed functions today in a company, but very important to keep in mind that those are actually very connected.
And going back to the pyramid of work, what you have at the top is the deep, complex work that is highly strategic, that is almost the information that the CEO of that company needs to make decisions. So
when we think about the work that we're doing with our customers, we might start at some, we might start somewhere in the bottom of the pyramid, but very fast, we're going up the pyramid by combining those agents from sales and customer service and collections, combining the context, as Luis was saying, so that you build on top of every layer, so that every decision you make is based off of more context across the board.
When you're talking to that customer that has a complaint, you might want to remember that you already upsold them last month. And sometimes human agents might not even remember that.
When you're talking to a driver that had an issue at his delivery two weeks ago, you might want to remember that from the operations team, because maybe now you're more lenient with the rate that you are giving them. Those things are highly interconnected. And you need to build on top of them so that you grow into the strategic type of decisions.
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