← The a16z Show
The a16z Show
Building AI Agents for Enterprise Operations
00:46:18
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.
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- [00:01]
Pablo Palafox
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.
- [00:16]
Luis Parrak
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 think that's a limiting factor today.
- [00:30]
Anisha Charya
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?
- [00:39]
Pablo Palafox
I think it's super important that most
- [00:41]
Podcast Host
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. Anisha Charya and Olivia Moore speak with Pablo Palafox and Luis Parrak from Happy Robot about Voice AI, enterprise agents, and the challenges of deploying AI in operationally complex industries.
- [01:25]
Olivia Moore
Olivia and I are here with the two incredibly talented founders of Happy Robot, Pablo and Luis.
- [01:30]
Pablo Palafox
Welcome, guys. Thank you guys. Super excited, very excited to have you.
- [01:33]
Olivia Moore
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 are 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%.
- [01:59]
Pablo Palafox
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 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 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.
- [03:00]
Luis Parrak
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. Javi 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, LLMs were picking up. We're talking about late 2023 probably. LLMs were like decent enough. 11th 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, 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 OS phase.
- [03:54]
Anisha Charya
Amazing. One of my favorite memories of working with you guys is actually when we first met outside a very crowded coffee sh 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 Voice has maybe unlocked for you more broadly.
- [04:23]
Pablo Palafox
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 LLMs back then, like we fine tuned Mistral and Llama to actually make those voice agents faster because Otherwise using some GPT4 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. 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 trucking companies, like some of the largest fleets that actually move our goods everywhere in the US which is crazy. Two of them are just 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.
- [05:48]
Olivia Moore
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.
- [06:05]
Luis Parrak
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, but not so good. So we had to fine tune the other. Then 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? It's like, dude, I'M building this thing and it's just hallucinating Max by. 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 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 just putting in the context window and having the LLM 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.
- [07:52]
Pablo Palafox
No, it's building for the real world. The real world is messy. Those things are going to 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 want to 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 shaped the type of products that we had to build, the type of primitives that we had to build.
- [08:33]
Anisha Charya
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 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.
- [09:05]
Pablo Palafox
I can bring up the Kuhnenagel use case. We recently announced our partnership with the Marquee like freight forwarder, great partners. I was having a personal lunch with their head of air. Shout out to our friend Imva at Kune Nagel at his house in. 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 that really has to get done to provide that update to the customer. So example freight forwarding, koonagl. They are serving customers, very large customers. I cannot name who, but imagine that you are a big customer of Kunenagel and you ask, hey, where is my shipment? What happens now is an agent has to turn around and, 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 going to miss my SLA with my customer. So now I need to call them. I'm going to 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 to make happen for. For. For transportation and logistics. Really? And how. And that has shaped the type of product that we had to build at an avatar.
- [11:05]
Luis Parrak
Yeah, no, I subscribe everything. I guess another example on the negotiation, which is always how we started and all these demos went pretty 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 in at the same time. You cannot have all those agents doing work independently, which is what happens to a certain degree with 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 hard. 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, 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. So 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 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. Is that context layer that we're trying to create. No. 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. You do something and you learn and you explore that space of the context layer so that you can keep learning.
- [12:44]
Olivia Moore
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 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, than some of your competitors?
- [13:02]
Pablo Palafox
So one thing that we heard from one of the largest trucking 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 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 like, hey, 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 operation so that now they know they can service a truck with a, 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 build 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 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's there, there has to be an underlying context sharing across the different functions in a business so that the whole business optimizes. 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, the problem in one function, if that makes sense.
- [15:28]
Olivia Moore
And then maybe can you talk about like where, 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.
- [15:36]
Luis Parrak
Yeah, so 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 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 hiring and building this forward Deployed motion with FDEs. Forward deployed engineers. Everyone is talking about them now, but I think it's about really being customer obsessed and really focusing on 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 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 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 build a platform that is flexible enough to adapt to anyone's operation 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 it automated. So we really had to build up almost like horizontal technology because of the variety of all the nuances in this industry. And that's how we create a platform that is not optimized for specific tasks, but more so optimized for doing work. So our primitives are around workflows and data and integrations and 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 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 come to like solve their problems, you know.
- [17:41]
Anisha Charya
Interesting. 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 FTEs do, which I think is kind of a unique strength of happy robot. Where, where does the forward deploy motion start and end? Do you do custom work for one customer? Would love to understand how that works.
- [18:14]
Pablo Palafox
Maybe let's start with in the beginning. Yeah, I was the first for deployment engineer without knowing it, I guess, which is pretty much what any founder would do. 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 these guys, like do it. 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 separate worlds for a little bit where I would be leading the FD team and the deployment strategies 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 the customer's like, wait, you have these people building? But like who is managing? I'm like, I guess that's like the deployment strategy to some degree. So the deployment strategy is a figure that scopes the problem so that the forward deployed engineer can spend more time on building. Although now what we see is that the right FD or deployment strategies, 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 four deployed world and the product team. So we realized that that needed to be part of Luis's world so that the FD 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, 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.
- [20:05]
Luis Parrak
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. 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 FDS for the most part. And it's not entirely true, 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 FD that they can trust is actually something very rare. And they mentioned that. But I guess to the point of like the deployment lifecycle, we really understand what it takes to deploy an agent. No, 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 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 if these are doing these deployments or they're getting feedback from the other team. So actually more than the FDS being very close to the product, I think it's more so like our product is a combination of a platform and a forward Deployed motion and it would really not exist. And there's like this conversation about like services and stuff. The difference is that the forward deployed engineer are like catalysts or accelerators to value. But what we're leaving in the customer are like agents running there's a platform. Once the FDs have done their work, they leave a working thing. So it's almost like you spend that time, you deploy the thing, but you're not delivering an output that the FD has done. You're literally delivering the agent working on a platform. So it's a very different distinction of pure services versus a forward deploy implementation plus the platform running the value forever, hopefully. Awesome.
- [22:03]
Anisha Charya
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 we'd love to talk through kind of your view on that topic.
- [22:28]
Pablo Palafox
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 Luis can comment on that more, that data piece is a very important piece, but it happens after the agents. So what we've built is twin, twin for us is really that data layer where we connect systems of record of the customer, your CRM, your erp, your transportation management system, whatever it is, your snowflake instance, and where agents can also populate their own or store their own context. It's almost Happy Robot native data points. So we've basically created these data layer that holds both customer records and Happy Robot agent created records, if you will.
- [23:35]
Luis Parrak
Yeah, I think there's an interesting tension in how much time you need to spend ahead of deploying the agents on clean 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 because 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 going to clean the data as you go. Because humans are great, of course, but they have a lot of limitations. They kind of 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 this right system, 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 going to probably get dirty very, very soon. The good thing about AI is it's very delicate where it puts data. So it's through the process of executing work, you're going to progressively start cleaning all your data sources because you're going to 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. So again, the shipment in the TMS is just a record. Like that's really not the ip. 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 going to. What the enterprise is going to do with that once it gets into the system, how their processes are built, how their humans are going to 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 is like tribal knowledge, the operators whole, and to a certain degree it's super fragmented. So actually by doing the work, we're going to learn a lot about this more conversational record or intelligence. But also we're going to start cleaning the end systems of record just by doing the work very consistently.
- [25:50]
Olivia Moore
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 of it decays over time. And 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?
- [26:09]
Luis Parrak
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 going to start connecting it, but you're also going to start enriching the relationships of how to deal with those particular records. So I guess the compounding comes from two angles. One is having clearer or cleaner data sources, like literally the data points is going to make everyone's life easier, but also understanding how to relate those different entities across the business. So I think it compounds from multiple angles.
- [26:49]
Olivia Moore
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?
- [26:59]
Luis Parrak
Yeah, and I think if you think about it from a context perspective, the FDS are really just seeding this state graph. Like if you, if you try to model the business as some world model or a model of the business, you need to seed it somehow. You can just put the agent to work from day zero. But then there's a point where there's a flywheel where the second and third and fifth deployment takes less time. But I think the FDS are the ones going to the business and starting to see all this context layer and actually leaving it there for learning and the second and third one. So there's always this cold start problem and we talk about fine tuning SLMs in the future, reinforcement learning and all that stuff. I think that really doesn't make sense if you don't have the basics and you don't have the first and second agent in production. And that's why FDs are so important to actually start this flywheel. 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 going to start learning and getting all these contexts and sharing it across functions and across channels.
- [28:02]
Anisha Charya
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 habirobot agents can do.
- [28:35]
Luis Parrak
Can I touch on?
- [28:36]
Pablo Palafox
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. Like 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 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 are 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 and 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. Those things are highly interconnected and you need to build on top of them so that you grow into the strategic type of decisions.
- [30:31]
Luis Parrak
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. Those are the decisions that are less volume, 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 outcome based pricing or consumption based pricing and whatnot. I think really if you reach the top of the pyramid is 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 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 AGI and general models being able to 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 climb, you need to actually capture context across channels and across functions. We've mentioned this a bunch of times now. When I was explaining the example of a negotiation, I was talking about phone calls. But what if you get an email from another carrier actually putting an option all of a sudden? What if the voice agents don't know that there's an email coming through for the same load? It's the same information, doesn't matter the channel. And also what you learn from that carrier is the same customer you have or the same carrier you have when you're tracking a load or doing all these things. So if you focus on automating this part of the base, that one corner for everyone, you're probably not going to be able to climb this pyramid of complexity. So it's about creating a unified understanding of the business, being able to in order to start climbing that pyramid of complexity and going to the deeper complex decisions that actually drive economic value for the enterprises.
- [32:31]
Olivia Moore
Really interesting. Maybe guys talk about how that opportunity has set you up to be pulled into other markets. 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?
- [32:47]
Pablo Palafox
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 Koonagel 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. We might have some 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. 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 Latam and Europe. They have over 10 million customers, dozens of thousands of employees across the world. How on earth are they going to 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. 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 operation side potentially. Or you could frame that as an operation type of problem 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 customer's house? 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 pull 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 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, when there's this coordination problem across customers, partners and your own employees.
- [35:52]
Anisha Charya
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?
- [36:19]
Luis Parrak
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 exchanging information between systems. Of course an API programmatically makes more sense, but sometimes that doesn't really is not the case. So. However, we can help move the flow of information between systems via voice, email, browsing a website or whatever it takes. And also when there's this high complexity, when the decisions are contextualized and the SOPs are not super clear, I think that's the bigger point where sometimes the enterprise doesn't really know themselves. 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. So when the SOPs are not clear and it's like super communication driven, I think that's where we shine.
- [37:40]
Olivia Moore
Really cool. Luis, I want to actually pick your brain a little bit about the voice models themselves because many of the other companies that we may overlap with rely on 11 labs, which is a fabulous technology. We're of course investors in 11. You guys have done a bunch of your own model work.
- [37:54]
Pablo Palafox
Why?
- [37:55]
Olivia Moore
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.
- [38:01]
Luis Parrak
Yeah, 11 is great. We actually used them for a long time and they're great. Of course, I guess to the point before, I was always focusing on the limiting factor and seeing what do we need to do to solve the current problems of the market. I guess we started very soon realizing how there was a problem in turn. Taking detection end of turn is probably the biggest problem in voice AI. And we realized that very early on because everyone was focusing on 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 the deployment of these agents. Not even the intelligence model capability is high enough. We're using models in certain use cases that were released two years ago. Sure, everyone is pushing the frontier and increasing context windows and making more reasoning.
- [38:51]
Olivia Moore
And PHG is doing customer support now.
- [38:53]
Luis Parrak
Exactly. Everyone is waiting for someone to release a 10 trillion talking context window like do whatever. Like we were using models from one year and a half ago to call drivers and ask if they're going to 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. If you think about it, the faster the models get, the more you're going to interrupt and the harder it's going to be to have a normal conversation. And actually, if you think about it, the bigger problem in the coming years for 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 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, and really be thinking, not something you put in the prompt because it's cool, but just literally have them think. So it's all about understanding the conversation. When is it my time to talk and what should I say? So we invest a lot in this end of turn interruption, handling, filler detections, background noises. If my mom is speaking at the back of the car, the bot doesn't need to know or interrupt. So it's understanding all these nuances in the work more than making the latency faster, which is of course can be improved, or making the voices more realistic, which again, I don't think that's a limiting factor today.
- [40:28]
Anisha Charya
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 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 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?
- [41:15]
Pablo Palafox
I think it's super important that 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 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 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? 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 towards a really human like experience. Again, voice is obviously a primary channel for us, but even across the board, 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 HAP Robot. It almost goes with the name, if you will. There's that human like sense in the product we build for our customers.
- [43:29]
Olivia Moore
Well, you know, 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. We're 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.
- [43:57]
Pablo Palafox
It's a bright feature, 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, this invoice is past due, man, are you going to pay? Who wants to be doing that? 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 fix then 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, but that has to get done.
- [45:03]
Anisha Charya
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.
- [45:11]
Luis Parrak
Thank you so much for supporting us all the way.
- [45:16]
Podcast Host
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 X16Z and subscribe to our substack@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. For more details, including a link to our investments, please see a16z.com disclosures Sam.