Building AI Agents for Enterprise Operations

Anish Acharya and Olivia Moore speak with Pablo Palafox and Luis Paarup about the challenges of deploying AI agents in operationally complex industries. The conversation covers the evolution of voice AI, enterprise workflows, and why logistics became an early proving ground for agent-based systems. They discuss context, coordination, and execution inside large organizations, as well as the role of forward-deployed engineering, enterprise deployment, and what it takes to move AI from experimentation into production.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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.

• 0:15Speaker 01

  The bigger problem in the coming years for 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 a limiting factor today.

• 0:29Olivia Moore?

  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?

• 0:38Speaker 00

  I think it's super important that.

• 0:40Erik Torenberg?

  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 Chahry and Olivia Moore speak with Pablo Palafox and Luis Parag from Happy Robot about Voice AI, enterprise agents, and the challenges of deploying AI in operationally complex industries.

• 1:24Anish Acharya?

  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 walk the origins of the company and the product war. Hundred percent.

• 1:59Speaker 00

  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, Xavi, 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 Y C, 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 Xavi was doing as 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.

• 2:43Speaker 00

  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.

• 2:59Speaker 01

  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. Like, we were in conferences, Xavi was like traveling all around, like asking people, hey, if we were to create a voice agent that could pick up the phone and sell these loads and track these shipments, would you buy? And it'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, L L M's were picking up, we're talking about late 2023, probably. L L M's were like decent enough. Eleven that was 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, no?

• 3:42Speaker 01

  That kind of shaped our company where we really had technology and innovation as the core of our company and always pushing this frontier, you know, and solving problems like firsthand. So that's how we got started in the voice space.

• 3:53Olivia Moore?

  Amazing. One of my favorite memories of working with you guys is actually when we first met, outside of a very crowded coffee shop, and you called one of the live voice agents. 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 voice has maybe unlocked for you more broadly?

• 4:23Speaker 00

  Yeah. What Luis was saying is very important. 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 L L Ms back then, like we fine tuned Mistral and Lama to actually make those voice agents faster. Because otherwise, using some G P T four at the time was like extremely slow. And G P T three point five 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.

• 5:06Speaker 00

  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 tracking companies. 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 U S, seven of the top 10 tracking companies, like some of the largest fleets that actually move our goods everywhere in the U S, which is crazy. Two of the largest ocean carriers, those big boats we see in the bay. That is sort of customers that we needed to build for and where voice was the unlock for many of the operations.

• 5:48Anish Acharya?

  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.

• 6:04Speaker 00

  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 global market thing. We started like tackling the limiting factor always. And again, back then, G P T Paulo mentioned, like three point five was relatively fast and not so good. So we had to.

• 6:33Speaker 01

  Find you on the other day. 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 raid? 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.

• 7:12Speaker 01

  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 only 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 raid, no? So those sort of things, instead of like just putting in the context, we're not having the L L M just freestyle it. We do it in a more deterministic approach. So it's always that mix of probabilistic plus deterministic where you need to let everything to the AI, no?

• 7:52Speaker 00

  It's building for the real world. The real world is messy. Those things are gonna happen where someone tries to jailbreak the agent and get that max amount of money that they can get. But we needed to build those guardrails very early on so that we could actually go to the likes of C H Robinson or Uber Freight and all of these big players that would only trust us if we actually were building real technology. That was pretty clear for us. We knew that we didn't wanna focus on the long tail of logistics and transportation because it's a very tricky space, but we knew we needed to serve the enterprise in transportation and supply chain and logistics. So that was very clear. That that shaped the type of products that we had to build, the type of primitives that we had to build.

• 8:33Olivia Moore?

  It's so interesting because Happy Robot was very early to both voice AI and enterprise agents more broadly, which is great. And also, it's like the ground has been shifting under our feet because the models themselves are kind of changing and evolving so rapidly. To your point about fine tuning versus prompting versus kind of what to do next, maybe we can talk through a few of the use cases you have where it's very clear that a smarter model by itself doesn't just do it and like why you need to buy a platform.

• 9:05Speaker 00

  I can bring up the Kuninagel use case. We recently announced our partnership with the marquee freight forwarder, great partners. I was having a personal lunch with their head of air. Shout out to our friend Imve at Kuninagel at his house in Smin. And what I learned from their operations is that this is not a simple customer service type of create a ticket in Zendesk and you're done, or you reply based on a knowledge base. Customer support for these real economy industries like logistics, transportation, freight forwarders, broader supply chain, even other industries like the telco space or the utility space. It's not as easy as just replying based off of a knowledge base again. There's a lot that happens afterwards

• 9:52Speaker 00

  that really has to get done to provide that update to the customer. So example, freight forwarding, Kuninagel, they are serving customers, very large customers, I cannot name who, but imagine that you are a big customer of Kuninagel and you ask, hey, where is my shipment?

• 10:09Speaker 00

  What happens now is an agent has to turn around and go find it. That go find it is very complex. You need that coordination. You need basically an orchestration agent that is, okay, this is an air shipment, so obviously relates to airlines. Who is the airline on this shipment? Okay, let me go to the airline's website. So we have browsing agents that go and scrape the website of the airline. Oh, bummer, it's not there. There's no update. Damn, I need to go send in an email. Okay, I'm sending an email to the airline. Two hours later, no reply. Okay, I need to reason that if they don't reply now, I'm gonna miss my SLA with my customer. So now I need to call them. I'm gonna keep calling them until someone picks up at the airline and tell me where the hell is my shipment. So that is the sort of coordination that we need

• 10:53Speaker 00

  to make happen for transportation and logistics, really. And how?

• 11:01Speaker 00

  And that has shaped the type of product that we had to build on an A V.

• 11:05Speaker 01

  Yeah, no, I subscribe everything. I guess another example on the negotiation, which is always like how we started and all these demos when pre-viral. And I guess one point of how raw intelligence really wasn't enough is when you're negotiating, for example, loads and there's like 10 carriers or 10 buyers calling at the same time. You cannot have all those agents like doing work independently, which is what happens to a certain degree with like humans. They're on a floor and sure, they shout to each other and they're like, hey, this is a very hot load. Please negotiate harder. I have someone interested, you know? All this information is really not in the model. So what we started doing is when you have inbound calls for the same load,

• 11:47Speaker 01

  you can start like sharing context across them like, hey, I have someone, they're very interested. Please push harder, like this is a hot load. Please also all this information sharing is literally what you put in the context window at any point in time. Like general intelligence or the raw intelligence doesn't really know if someone else is calling on that load. So it's all that about like, what do you know about the business? What do you know about the negotiation strategies? Maybe you know that pushing harder on this load because it's like cross-border is going to be better or whatnot. Like that's not general intelligence. That's very specific and different enterprises operate differently. Like you cannot just build an agent, fine tune it and have it work at any type of company. All those nuances is outside of the model and it's that context layer that we're trying to create, no?

• 12:28Speaker 01

  And that actually like, we can talk about how actually doing the work and executing the work is what gets you that. It's like learning by experience is you do something and you learn and you explore that space of the context layer so that you can keep learning, no?

• 12:43Anish Acharya?

  Really interesting. So you talked about two different things there, Pablo. You just talked about a very cross-functional workflow. Luis, you talked about the complexity of really mastering sales, you know? You guys started as sales and support. And so what are some of the other surprises that you've had, having started with more complexity? I think that's some of your competitors.

• 13:02Speaker 00

  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 through 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

• 13:48Speaker 00

  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 sort of surprises, if you will, we've gotten from customers. Now, like, yeah, I also need to recruit drivers. Can you do that? We obviously can build an agent that not only just recruits drivers, actually connects to the operations 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.

• 14:33Speaker 00

  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 proactively reaching out to those repair shops to see where the truck, 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 these cases 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

• 15:18Speaker 00

  a global maxima, if you will, or a global minima, depending on what optimization problem you're trying to run versus just minimizing the problem in one function, if that makes sense.

• 15:28Anish Acharya?

  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.

• 15:36Speaker 01

  Yeah.

• 15:38Speaker 01

  We're very forward deployed. So we very 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. So from the very beginning, we started like

• 16:03Speaker 01

  hiring and building this forward deployed motion with the FDEs, forward deployed engineers, like everyone is talking about them now. But I think it's about like really being customer obsessed and really focusing on like the value add and their problem and really sitting down with them and going to their offices and learning what they need. So sure, there's a lot of like synergies in the industry and what you learn from a customer might be relevant to another, but very soon we realized that there's not a one size fits all, even within inbound carrier sales, even like within this workflow in the enterprise, maybe like there's like a long tail where this might apply, but enterprises operate very, very differently. And that's why we built a platform that is flexible enough to adapt to anyone's operations.

• 16:47Speaker 01

  And it's because we were trying to like plug and play what we built somehow with a customer to another one, it didn't really work. Like they want something different, they want to change the procedure, they want to call these tools, they want to escalate whenever the carrier is not vetted and someone else wants to do automated. So we really had to build a almost like horizontal technology because of the variety of all the nuances in this industry, you know? And that's how we created a platform that is not optimized for like specific tasks, but more so optimized for like doing work. So our primitives are around workflows and data and integrations and you know, SOPs, prompts, you don't see like particular tasks being modeled because that's almost too opinionated. And customers don't want like opinion, like their vendors forming opinions about how

• 17:32Speaker 01

  to run their operations, like they've been running it for a long time. They don't know more about their business than I do. I just come with the technology and I just want to come to like solve their problems.

• 17:41Anish Acharya?

  Yeah, absolutely.

• 17:42Olivia Moore?

  I feel like the forward deployed motion has been crucial for AI application layer businesses. And also, it's prompted a lot of questions about what are margins? What is like a service versus a product? Kind of where is the long term alpha and moat? Maybe walk us through how you think about productizing the work that your FDEs do, which I think is kind of a unique strength, Happy Robot. Where does the forward deployed motion start and end? Do you do custom work for one customer? Would love to understand how that works.

• 18:14Speaker 00

  Maybe let's start with in the beginning.

• 18:16Olivia Moore?

  Yeah.

• 18:17Speaker 00

  I was the first forward deployment engineer without knowing it, I guess.

• 18:20Olivia Moore?

  Yeah.

• 18:21Speaker 00

  Which is pretty much what any founder would do, you know? You just go to your customers, spend a week there and just chase down the people that are actually doing the thing that you want to help them automate, right? So I did that and I would be like pinging this guy like, dude, you need to build this thing because it's going to make my life a lot easier. And it's actually going to be replicable across customers because I've seen it. So please build it. And he would be like, really? Do I need to build that? So there was that good tension between kind of that forward deployed motion and the product team. So we kept going with these like separate worlds for a little bit where I would be like leading the FDE team and the deployment strategists that we realized at some point we actually needed. That was a bit of a realization, a bit of a parenthesis here. We started just with forward deployed engineers and then they're like,

• 19:06Speaker 00

  the customers like, wait, you have these people building, but like who is managing? And like, I guess that's like the deployment strategist to some degree. So the deployment strategist is a figure that scopes the problem so that the forward deployment engineer can spend more time on building. Although now what we see is that the right FDE or deployment strategist, they have to be very cross-functional. Close parenthesis on the type of profile. So what ended up happening is We were too disconnected from like the forward deployment world and the product team. So we realized that that needed to be part of Luis's world. So that the FDE team would actually be an extension of product, which is what they should always be. It's an extension of product so that we can implement product faster.

• 19:51Speaker 00

  We can gather the feedback faster from the customers and hence capture that context faster than anyone else. So it's a bit of these iterative loop that we let Luis really realize we needed.

• 20:04Speaker 01

  Yeah, I'll add to that. I mean, if we go like first principles, what are we doing? We're deploying agents across different functions and channels in the enterprise. So our product is built for the deployment of an agent. Like, we really understand the deployment lifecycle because we work very closely with our customers. And we are actually deploying these agents. And something cool that happens is that you have, to a certain degree, your user in your house. Because we're building for the FDE for the most part. And it's not entirely true. Some, of course, some enterprises, they really appreciate having a platform. And we can talk about that later, about how interesting the mix of coming with a platform they can also use. And an FDE that they can trust is actually something very rare, and they mentioned that.

• 20:49Speaker 01

  But I guess to the point of like the deployment lifecycle, we really understand what it takes to like deploy an agent. There's a scoping phase, there's a building, there's testing, there's monitoring, there's like a self-learning loop. So I guess the point is where every feature we're building in the product is optimized for that deployment lifecycle. And the only way to know if that works is being very close to the deployment. And FDEs are doing these deployments, or they're getting feedback from the other team. So actually, more than the FDE is being very close to the product. I think it's more so like our product is a combination of a platform and a forward deployment motion. And it would really not exist. And there's like this conversation about like services and stuff. The difference is that the forward deployment engineer are like catalysts or accelerators to value.

• 21:33Speaker 01

  But what we're leaving in the customer are like agents running. There's a platform. Once the FDEs have done their work, they leave a working thing. So it's almost like you spend that time, you deploy a thing. But you're not delivering like an output that the FDE has done. You're literally delivering the agent working on a platform. So it's a very different distinction of pure services versus like a forward deployment implementation plus the platform running the value forever, hopefully.

• 22:02Olivia Moore?

  Awesome. I feel like another thing that has been a topic of discussion is kind of, what is the value of systems of record in the AI era? Does every application company need to become one of those? And I know you at Happy Robot have a view on kind of systems of record versus maybe systems of action or systems of execution. So would love to talk through kind of your view on that topic.

• 22:27Speaker 00

  Maybe I'll start quickly. We see ourselves as that layer of execution, really. Like, that's where the magic happens. You have to start doing the work to capture that context. So it's very important that we start with executing work, with getting the thing done, implementing one agent, implementing the second agent, connecting them through that context layer. But the context layer happens after you're actually doing the work. There, more than ever, the importance is on the execution layer. So for us, and Louis can comment on that more, that data piece is a very important piece, but it's, it happens after the agent. So what we've built is twin. Twin for us is really that data layer will reconnect systems of record of the customer,

• 23:08Speaker 00

  your CRM, your ERP, your transportation management system, whatever it is, your snowflake instance, and where agents can also populate their own or restore their own context. It's almost Happy Robot native data points. So we've basically created these, these data layer that holds both customer records and Happy Robot agent-created records, if you will.

• 23:35Speaker 01

  Yeah, I think there's an interesting tension in how much, how much time you need to spend ahead of deploying the agents and cleaning the data versus just deploying the agents and cleaning the data through doing the execution. And I think it's a mix. I think what we realize is these agents are creating a lot of information that really hasn't been captured before, and it really doesn't fit in any of these systems. It's more like high-dimensional, semantic, almost like memory intelligence. So I guess. The point is, many enterprises, I guess, are waiting to clean their data sources so that they can power this workforce of agents. And I think by doing the work and by actually having agents execute the work, you're gonna clean the data as you go.

• 24:20Speaker 01

  Because humans are great, of course, but they have a lot of limitations. Like, they cannot be in the same place, in two places at the same time. They drop a lot of threads. They're not very diligent and putting the data in the right system. Like, sometimes you forget, sometimes you write it down. So actually, you can clean all your data sources and then you can still run with humans and it's actually gonna probably get dirty very, very soon. The good thing about AI is it's very diligent where it puts data, no? So it's through the process of executing work, you're gonna progressively start cleaning all your data sources because you're gonna get visibility into all these things, no? So not only are you connecting the data, like the systems of record, like rows and columns and different entities, it's more so creating relationships across them.

• 25:01Speaker 01

  So, again, the shipment in the TMS is just a record. Like, that's really not the IPs. How, that, that record might exist in many different enterprises. Like, it's latitude, longitude, rate, whatever it is. Like, that really doesn't mean anything. What means something is how an enterprise is gonna, what, what the enterprise is gonna do with that. Like, once it gets into the system, how their processes are built, how their humans are gonna deal with that. So all that is really not in the system. It's more so like in people's brains, a lot of these contexts, like tribal knowledge, the operator's hole. And to a certain degree, they, it's not, it's super fragmented, no? So actually, by doing the work, we're gonna learn, learn a lot about this more conversational record or intelligence, but also we're gonna start like cleaning the, the end systems

• 25:46Speaker 01

  of record just by doing the work very consistently, no?

• 25:49Anish Acharya?

  You know, Luis, that's such an interesting topic because it's sort of like, my intuition, my naive intuition is that information about execution is maybe ephemeral or the value that decays over time. But I think what you're describing is how the value actually compounds over time and maybe that actually enriches the information in the system of record. Which one is true and why?

• 26:08Speaker 01

  Yeah, I mean, I think you're. So what you're doing by doing the execution, as I said, is one, creating a better understanding about the relationships of all these different entities. So you're starting to connect the TMS, the CRM, the ERP, the Snowflake, the Notion page you have, the docs, everything is so disconnected, you're gonna start connecting it, but you're also gonna start enriching the relationships of how to deal with those particular records, no? So I guess the compounding comes from like two angles. One is having clear or cleaner data sources, like literally the data points is gonna make everyone's life easier, but also understanding how to relate those different entities across the business, no? So I think it compounds from multiple angles.

• 26:49Anish Acharya?

  And then how much are you, initially, I imagine you're capturing the way work is done on day zero, but over time, you're changing the way that work is done. What is that interaction like?

• 26:58Speaker 01

  Yeah, and I think, if you think about it from a context perspective, the FDI's are really just seeding this state graph. Like, if you try to model the business as a world model, or a model of the business, you need to seed it somehow, like you can't just put the agent to work from day zero. But then there's a point where there's a flywheel, where like the second and third and fifth deployment takes less time. But I think the FDI's are the ones like going to the business and starting to seed all this context layer, and actually leaving it there for like learning and the second and the third one, no? So there's always this call star problem, and we talk about like fine tuning SLMs in the future, like reinforcement learning and all that stuff. I think that really doesn't make sense if you didn't have the basics, and you didn't

• 27:43Speaker 01

  have the first and second agent in production. And that's why FDI's are so important to like actually start this flywheel. Like, they would go there, interview the operators, get all the specs, and actually put those first agents to work. And from there, the system is gonna start learning and getting all this context and sharing it across functions and across channels, no?

• 28:02Olivia Moore?

  I feel like if I was trying to train someone to do my job, the context that they need does not live in Salesforce or any traditional software system. It probably would live in meeting 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 Habbi robot agents can do.

• 28:35Speaker 01

  Yeah, touch on.

• 28:36Speaker 00

  So the the pyramid of of work as we define it is essentially

• 28:42Speaker 00

  the easy, repeatable, low hanging fruits type of work at the bottom. Think about an easy B two B sales call, an easy customer service type of operation, some payment collection type of work, kind of the the highly repeatable, easily automatable type of type of work. One thing that we've already talked about here is how those actually interconnect, which is very important. Like you, you might have like 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

• 29:23Speaker 00

  work that is highly strategic, that is almost the information that the CEO of that company needs to make decisions. So when when we think about the work that we that we're doing with our customers, we might start at some.

• 29:40Speaker 00

  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 you 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 we are 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.

• 30:23Speaker 00

  Those things are highly interconnected and you need to build on top of them so that you grow into the the strategic type of decisions.

• 30:31Speaker 01

  Yeah, and I would add. That the real, my opinion, the real economic leverage and value for the enterprises really lives at the top of the pyramid. Like, those are the decisions that are less volume. Like, if you think about it, at the base, you have much more volume. At the top, you have fewer decisions that are actually going to drive the outcomes of the enterprises. And we keep talking and hearing about, like, outcome-based pricing or consumption-based pricing and whatnot. I think, really, if you reach the top of the pyramid, it's where you really make decisions that drive the revenue of the company. But you cannot start at the top. Like, those decisions are highly contextualized. The same way you can probably not be the CEO of a company if you don't understand anything what's happening below. So, actually, the only way to get to the top and make those decisions is by actually

• 31:15Speaker 01

  capturing all the context underneath. And that's where everyone is getting stuck at. Like, everyone is focusing at that base. It makes sense. It's already to a certain point being commoditized. Like, those are simpler tasks, and people keep talking about, like, you know, the AGI and general models being able to, like, automate that work, maybe. But the point is, if you get stuck at a corner of that base, you're never going to climb that pyramid of complexity. Because in order to, like, climb, you need to actually capture a context across channels and across functions. We've mentioned this a lot of, a bunch of times now. When I was explaining the example of negotiation, I was talking about, like, phone calls. But what if you get an email from another carrier actually putting an option? Like, all of a sudden, what if the voice agents don't know that there's an email coming through for the same load?

• 32:00Speaker 01

  Like, it's the same information. Doesn't matter the channel, no? And also, what you learn from that carrier is the same, like, the same customer you have or the same carrier you have when you're tracking a load or doing all these things, no? So, if you focus on, like, automating this part of the base, that one corner for everyone, you're probably not going to be able to, like, climb this pyramid of complexity, no? So, it's about creating a unified understanding of the business, in order to, like, start climbing that pyramid of complexity and going to, like, the deeper, complex decisions that actually drive economic value for the enterprises.

• 32:31Anish Acharya?

  Really interesting. Maybe you guys talk about how that opportunity has set you up to be pulled into other markets. But now we're starting to see pull in financial services, utilities, telecommunications. So why is the work that we've done in supply chain applicable to these other markets?

• 32:46Speaker 00

  With DHL, we've deployed over 40 agents across 80 countries, agents that are sharing context across regions and functions. What I realized, what the team realized when working with DHL and many others like Kuninagel or CMA CGM, second largest ocean carrier in the world, was wow, this is not a supply chain specific problem that we are solving. It's actually an enterprise coordination problem. When we think about ourselves as a startup, we're like 120 people. You know, we might have like some like miscommunications here and there, but really we don't have a coordination problem in the company. You can easily reach out to the people involved and you just ask questions.

• 33:31Speaker 00

  That doesn't happen in a company as big as DHL or FedEx or Deutsche Telekom or T-Mobile or Telefonica, these massive enterprises that have hundreds of thousands of people just coordinating work. We recently started working with one of the largest utility companies in Latimer in Europe. They have over 10 million customers, dozens of thousands of employees across the world. How on earth are they gonna know real time how to best serve their customers when they themselves don't even have the tools to interconnect quickly and to share context across them quickly? So what we realized is we were not really solving for a supply chain problem. We were solving for the coordination problem of the enterprise.

• 34:16Speaker 00

  Think about a utility receiving a customer call with someone complaining about a leaky boiler. First of all, you should already know that that customer already had the problem 10 days ago. That's for sure. Second of all, you should also know that the technician you sent was not the right technician. So now in this second attempt to fix that boiler, you need to send the right technician and the technician that is best suited for that particular boiler type. So that is now on the operations side potentially, or you could frame that as an operation type of problem, you know? Versus when I started with the customer calling in, that's more of a customer service type of problem, right? Again, to the point of how these functions are interconnected. But what happens after that technician is being dispatched to the to the customer's house?

• 35:01Speaker 00

  Well, now you have an additional layer of coordination between a customer and the technician. And the company that is lending the trucks to send that technician. That is that coordination problem that we saw in these industries in the real economy. Operationally complex businesses like utilities, oil and gas, telcos. So we're now seeing this pool from the market. We're already working with in POCs with three of the largest telcos in the world. We're being pulled into home and auto insurance because the sort of coordination problem of dispatching a tow truck to help you when you when your car breaks down is very similar to when a trucking company has a broken truck. That sort of problems are repeatable across the real economy, if you will.

• 35:46Speaker 00

  When there's this coordination problem across customers, partners and your own employees.

• 35:51Olivia Moore?

  I think this market too has, you know, broad-based voice-first customer support agents. There's the models themselves in voice trying to move into being agentic. And then there's more verticalized solutions that can move more horizontally. How do you think about like, what is a happy robot-shaped problem? And where does that expand into over time versus what are problems that are maybe less interesting for you to tackle longer term?

• 36:19Speaker 01

  Yeah, I would say highly communicational, like, and actually more than communication, like, interface of work to like, interface to the external world, meaning also like browsing a website to like, retrieve the ETA of a shipment is some sort of like, interaction with the outside world. Voice to a certain point is a soft API, as we were talking about, same as an email is a soft API or a website is a soft API, like when you're extending information between systems. Of course, an API programmatically makes more sense, but sometimes that doesn't really, it's not the case. So however, we can help move the flow of information between systems via voice, email, browsing a website or whatever it.

• 37:03Speaker 01

  Takes. And also when there's this high complexity in the, when, when the decisions are like contextualized and it's not like the SOPs are not super clear, no? I think that's the bigger point where sometimes the enterprise doesn't really know themselves, like people don't know what they know. You can ask them what they're doing and it's like, well, I'm doing this, but they really don't know the specificity of what they're doing. So it's actually through doing this execution of work that we're learning a lot about how these companies operate, no? So when the SOPs are not clear and it's like super communication driven, I think that's where we shine.

• 37:39Anish Acharya?

  Really cool. Luis, I want to actually pick your brain a little bit about the voice models themselves. Many of the other, you know, companies that we may overlap with rely on Eleven Labs, which is a fabulous technology where of course investors in eleven. You guys have done a bunch of your own model work. Why? What are the kind of trade offs of, you know, a vertical model versus a horizontal model? Maybe take us through a bit of that.

• 38:00Speaker 01

  Yeah, my eleven is great. We, we actually used them for a long time and they're, they're, they're great, of course. I guess to the point before, I was always focusing on like the limiting factor and seeing what do we need to do to solve the current problems of, of the market? I guess we started very soon realizing how there was a problem in like turn taking detection, like end of turn is probably the biggest problem in voice AI. And we realized that very early on because everyone was focusing on like making the latency lower and making the voices more realistic. And that's fine, but I don't really think that's the bottleneck right now to, to deployment of these agents, not even the intelligence, like model capability is high enough, like we're using models in certain uses that were released like two years ago.

• 38:45Speaker 01

  Like sure, like everyone is like pushing the frontier and increasing context windows and making more reasoning.

• 38:51Anish Acharya?

  PhDs doing customer support now.

• 38:53Speaker 01

  Exactly. Like everyone is waiting for someone to release like a 10 trillion token context window to like do whatever. Like we were using models from one year and a half ago to call drivers and ask if they're gonna make it on time. You don't need PhD level intelligence for that. I guess the point is as we make models faster, we realize how important the conversation handling and the flow of the conversation is. Like if you think about it, the faster the models get.

• 39:19Speaker 01

  The more you're gonna interrupt and the harder it's gonna be to like have a normal conversation. And actually, if you think about it, the bigger problem in the coming years for like voice AI is really knowing when to talk and when not to talk. And sometimes you need to speak fast, sometimes you need to wait because the other person has not done talking. Sometimes you might need to like stop and think. And that's something that the models are not today very good at, like really stopping and knowing when a question is hard and when they need to like probably trigger a reasoning thread that is more async and just think about it and say something like, um, and really be thinking, not something you put in the prompt because it's cool, but just literally have them think, no? So it's all about understanding the conversation. When, when is it my time to talk and why, what should I say, no?

• 40:02Speaker 01

  So I, we invest a lot in this end of turn interruption handling, filler detections, background noises. Like if, if my mom is speaking at the back of the car, uh, the bot doesn't need to know or interrupt, no? So it's understanding all these nuances in, in the work more than making the latency faster, which is of course we can be improved or making the voices more realistic, which again, I don't think that's a limiting factor today.

• 40:28Olivia Moore?

  Yeah, it's interesting. It feels like we're at the point where the models are so good that as they get better, especially with voice, it actually takes us further away from humanness in some cases, like the latency is too fast. Or, um, the interruption handling is too sensitive. Like if someone says a filler word, you don't necessarily want the model to react. You want it to keep talking. I feel like Happy Robot has always been at the forefront of kind of humanness. How do you think about how that shapes product development? How do you think about, um, where, what that looks like five years from now? Do you want the customers, the end customer to know they're talking to an AI? Do you want it to feel like a perfectly human experience? Where does that go?

• 41:15Speaker 00

  I think it's super important that.

• 41:19Speaker 00

  The experience remains as human as possible, even if you say that it's an AI. We're now live with hundreds of thousands of end customers or end users talking to our agents, not only via email or chatbot or website, whatever it is, but mostly through voice. Like voice is one of our more, like one of our primary channels. And one thing we saw is even if you say it's an AI, even if you disclose at the beginning, "Hey, Mr. Driver, I'm an AI agent. I'm calling you because I need to know where you are." At the beginning, they might be like, "What do you just say?" But then very, very soon, they forget. And they forget in a good way

• 42:02Speaker 00

  because they are now just having a normal conversation with a system that is smart enough to not make their life or their day even harder than it was already before. So I think the conversationalness, the human-like capabilities are very important to make technology work. So for us, the product is shaped around that experience. Some people were telling us at the beginning like, "No, you don't need these agents to sound superhuman. Why are you investing so much on the text to speech? Why do you care if the agent just mispronounces a load number, a shipment number?" It's like, "What do you mean? That's the whole point. You want the experience to be as good as possible." So it's very important that we continue building

• 42:47Speaker 00

  towards a really human-like experience. Again, voice is obviously a primary channel for us. But even across the board, like everything should feel human. Everything should feel just a very natural exchange of information as we were discussing before. We're just trying to build an AI workforce that is almost colleagues to the employees in these companies so that they almost collaborate together. That is very important to the DNA that we're building in Haber. It almost goes with the name, if you will. Like there's that, there's, there's that.

• 43:23Speaker 00

  Human-like sense in the product we build for our customers.

• 43:28Olivia Moore?

  Yeah.

• 43:28Anish Acharya?

  Well, Pablo, to build on that, it also strikes me that you make the employees, the human employees of many of your customers also more human. Insofar as, you know, I think it was Keely was telling me a story about DHL and Home Depot and the folks that had previously spent all week on a phone trying to just schedule deliveries with Home Depot were now taking folks out for dinner and building deeper relationships. Maybe talk a bit about what is the future of sort of humans and agents working together in these enterprises?

• 43:57Speaker 00

  It's a bright future. It's a very cool feature because a lot of the work that we're helping our customers automate is work that no one really wants to do. Think about collecting payments from customers. Would you really want to be calling your customer to be like, hey, like, you know, like this invoice is past due, man, like, are you gonna pay?

• 44:16Anish Acharya?

  Yeah.

• 44:16Speaker 00

  Who wants to be doing that, right? Who wants to be calling a list of doorman accounts to see who would want to ship with us? Or who would want to be picking up a call from an angry customer that whose delivery was late or whose technician broke the boiler? Or whose technician didn't fix the router? That is the sort of problems that agents can help your human teams alleviate so that, again, your humans can actually take that steak dinner with your customer and work on building up the relationship, not on fixed and the operational problems. That's the problem space we're looking at. The operational complexity that these businesses have that no one really wants to do,

• 45:01Speaker 00

  but that has to get done.

• 45:02Olivia Moore?

  Thanks so much for joining us today, guys. We know you are very busy serving a lot of very happy customers and there's so many more exciting things to come for Happy Robot.

• 45:11Speaker 00

  Thank you so much.

• 45:11Erik Torenberg?

  Thank you for supporting us all the way.

• 45:15Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.

• 46:00Erik Torenberg?

  For more details, including a link to our investments, please see a16z.com/disclosures.