AI Minds #051 | Pablo Palafox, Co-Founder and CEO at HappyRobot

About this episode

Pablo Palafox, Co-Founder and CEO at HappyRobot. HappyRobot automates communication across channels with AI workers that integrates with your systems, manage conversations, & log data.

Listen to the episode on Spotify, Apple Podcast, Podcast addicts, Castbox. You can also watch this episode on YouTube.

In this episode of AI Minds, Pablo Palafox, CEO and co-founder of HappyRobot, joins us to discuss the innovations and future of AI in logistics.

Pablo, who has transitioned from academia in Munich to leading a cutting-edge AI logistics company in San Francisco, shares his journey from his days as a Ph.D. student and an intern at Meta to co-founding HappyRobot.

Pablo highlights the challenges and breakthroughs in implementing AI-driven communication systems within the logistics industry, providing valuable insights into how AI is transforming freight brokering and operational workflows. He dives deep into how intelligent workflow automation is revolutionizing business communications, and the potential it holds to reshape logistics operations.

He emphasizes the importance of integrating Voice AI into day-to-day operations for increased efficiency. He also explores how AI can enhance decision-making in the logistics sector by streamlining communication and optimizing workflows.

This episode offers a comprehensive look at the powerful role AI plays in modernizing logistics and the future impact of AI-powered automation in streamlining operations and improving communication across industries.

Fun Fact: Pablo did his Master's and started a Ph.D. in Munich, Germany, working with notable figures in computer vision. However, he shifted from a predominantly academic path to entrepreneurship and eventually ended up co-founding HappyRobot.

Show Notes:

00:00 "AI Minds with HappyRobot CEO"

04:58 Munich to Logistics Startup Journey

06:13 Over reliance on Niche Expertise

09:12 Surviving Startup Challenges Together

14:00 Freight Broker Communication Challenges

16:04 AI Enhancing Freight Broker Efficiency

20:18 Freight Load Digital Marketplace

25:23 Automated Rate Management in Logistics

27:28 AI Layered Checks Prevent Vulnerabilities

29:36 Exciting Developments Ahead

More Quotes from Pablo:

Transcript:

Demetrios:

Welcome back to the AI Minds podcast. This is a podcast where we explore the companies of tomorrow being built AI first. I'm your host, Demetrios. And this episode, like every other episode, is brought to you by Deepgram. The number one speech to text and text to speech API on the Internet today. Trusted by the world's top enterprises, conversational AI leaders and startups. Some of them you may have heard of like Spotify, Twilio, NASA and Citibank. This episode is a very special episode because we are joined by Pablo, the CEO and co founder of HappyRobot.

Demetrios:

How you doing, dude?

Pablo Palafox:

Doing great, man. Thanks for having me.

Demetrios:

Well, I know that you are from where I. The country that I used to live in. You grew up in Spain and then you migrated over to the country that I now live in Germany. You spent some time in Munich. What were you doing there?

Pablo Palafox:

Yes, so in Munich I was actually doing my Master's when there 2017 and then ended up doing my PhD there with Angela Dye and Matthias Meisner. To great minds on the computer vision realm. So that's. That's how I ended up there. And I spent there like five years, man. It was a long time.

Demetrios:

So the German culture, I like to make jokes that most people do the opposite of what we did. They go from Germany to Spain, but we went from Spain to Germany. And that is a little bit of a pattern. Interrupt your. You glossed over something real fast that I want to mention. Your teachers, they just had a bit of success, right?

Pablo Palafox:

Yeah. Matthias Meisner is one of the co founders of Synthesia and I think they just raised a little rand. Massive 180 million. Nothing, nothing fancy.

Demetrios:

What are they going to do with all that money? That is amazing. So congrats to them. And that's cool that you got to hang out and learn from Matthias and then you decided to work in big tech for a little bit.

Pablo Palafox:

During the Ph.D. i actually did a bit of an internship with Meta. It was Covid time, so it was remote. It was interesting. I should have come to Sausalito actually. I was working with the virtual humans team. Great folks. And I was doing like some.

Pablo Palafox:

Basically my PhD was around virtual avatars, if you will, 3D reconstruction. And I should have come here to SAS Aledo, but I ended up doing an internship from a little village next to Munich. So that was interesting.

Demetrios:

Yeah, that I can imagine. That's like me living in this little village next to Frankfurt and I joke that there are more cows in my village than Humans and most people don't know what AI is here, so that's fair.

Pablo Palafox:

That's probably. I can see that.

Demetrios:

Yeah. It keeps your feet on the ground, that's for sure.

Pablo Palafox:

It's quiet. I mean, it's just like a different lifestyle. I remember when we actually. When I actually dropped out of the PhD and start thinking about ideas with my two co founders now. We were in this little village and it was great on one side. it's like you can think, pause and, do stuff quiet for time to time. But at the same time, who do I interact with if I want to, exchange ideas? Yesterday I just went to, a dinner with a bunch of other founders. Great.

Pablo Palafox:

You exchanged ideas. Ideas to see what's going on in the world. So pros and cons, man.

Demetrios:

Yeah. Because now you're in San Francisco and so eventually you made that jump. What was it? What was the catalyst that made you get over there?

Pablo Palafox:

So we basically were desperate to get into yc, as every founder is. Let's say that we were in that motion. We need to get into yc. How do we get there?

Demetrios:

It's the only path forward.

Pablo Palafox:

The only path. Looks like it's if you don't get into ic, there's no way.

Demetrios:

The idea isn't worth it. You don't have the validation. Yeah, they let just about anybody in these days, so. No, I'm joking. I'm joking with you there.

Pablo Palafox:

I mean, it is really worth applying and, trying to get in because the. It changes your life. I mean, it changed hours for sure. But anyhow, we are talking about 2022, summer 2022. We're right out of, me dropping out and, Luis quit. Luis is my CTO and my co founder quitting his job in hpe. And then Javi also, already almost on the verge of, Javi is my brother and co founder on the verge of, quitting as well.

Pablo Palafox:

Javi was actually in the US already. But we can talk about that later because it ties into, what we're building today and how we ended up in logistics. But Luis and I in Munich, we actually. I actually brought Luis to Munich to work with me in person. So, I bring him to this little village next to Munich and we start ideating stuff, talking remotely with Javi every day. And at some point he's hey, we should, learn about startups. I don't know how we even ended up knowing about yc. Because in Europe it's hard to know about startups as early as you learn here.

Pablo Palafox:

Like kids, 16 year old here, they're already thinking about applying to YC. It's crazy. When I was 16, I was I like playing soccer and I study a lot and my mom is happy because I study a lot and I get my degrees. Europe is a lot about degrees, by the way. It's crazy. But anyhow, we go summer 2022, we're okay, let's apply to this thing, let's see what happens. And we got rejected, which was great because we kept pushing in. And then 2023 we apply again and we finally get in for the summer 23 batch.

Pablo Palafox:

We can talk a bit about what the idea was. Doesn't.

Demetrios:

Yeah, what was the idea? The original idea. How much did it change?

Pablo Palafox:

It changed a lot. So since I was doing my PhD in computer vision, we were oh well, Pablo knows this thing, so let's capitalize on that. Wrong idea, wrong decision. Because we kind of indexed too much on what we thought we were good at. Sometimes it's good, sometimes it actually can backfire a little bit because we stayed too long in that space, in that realm. We even just didn't look at GPT when it came out. I think the GPT3 was that time around we didn't even look at it. It was like these people looking at LLMs.

Pablo Palafox:

What is that? Like language. What is that language? Models. We're like super focused on our computer vision thing. We were basically building a synthetic data generation platform so you could train better computer vision models. And that was our initial idea. We go three, four months without talking to almost anybody. Just we had like one customer that we thought it was a customer. They were not paying, so usually they were not a customer.

Pablo Palafox:

And we're great. Like we're building for this space it's awesome. And then, fast forward to being rejected from ic. We kind of semi pivot as well to like a computer vision auto labeling tool. Doesn't matter too much. And then we do get into YC with that idea with this auto labeling tool, almost like a scale AI competitor, if you will. And then we had a bit of revenue. So when we start YC, we had I don't know, 70k of ARR.

Pablo Palafox:

We we're in a great spot, this is great. And then we go into the YC retreat and everyone's we're pivoting okay. Oh, we're pivoting. Interesting. Should we be pivoting? are we doing something wrong? And I wish we had because we spent the whole batch just trying to push harder on our original idea. And since we had customers, it was hard to say bye bye. But eventually we did.

Pablo Palafox:

And maybe I'll stop here before diving into how we transition to the other into what is hyperbole today. But it also. Fun story.

Demetrios:

How did Javi convince you? What did he say about the transportation area?

Pablo Palafox:

So it was a bit more of an exploration and it was. I mean, it was actually pivot hell for us after yc, I think I've told this story somewhere else, so I. I'll go again and just expose ourselves. We pivoted on demo day.

Pablo Palafox:

So, demo day.

Demetrios:

Oh, wow.

Pablo Palafox:

I see i, these days where, you know, you present to, the world, to investors and stuff, and that day. Or we tell to our partner, Diana. Diana who? She's amazing. Diana this doesn't feel right where we're pivoting.

Demetrios:

Wow.

Pablo Palafox:

And from that day, we have like a month or so of Pivot Hill, just going around in circles, pulling our hairs and just trying to throw spaghetti at the wall and see which one sticks. It was hard, but it was also interesting. We definitely saw that we would stick together forever as a founder team, as a founding team. Javi, Lisa and I, it was this is hard, but we know that we're going to make. We're going to go through. We're going to get through this process. And we did. Eventually we were okay, let's build something we enjoy building.

Pablo Palafox:

Luis wanted to build on a voice space. I wanted to do something with LLMs, because we hadn't really touched any LLMs by that time. It was our computer vision thing. Great. We didn't even know what an LLM properly was. I mean, I obviously knew what Transformers were. Back in 2017, I had actually worked with Transformers for Computer Vision. But, we started kind of combining those together.

Pablo Palafox:

So we started building agents. Probably one of the first. the first phase of agents. I think it was. I think Vocode. I don't know if you remember vocode. I'm not sure if they're still around. They were probably one of the first folks doing agents, and I think that sparked a lot of ideas for folks like us to build on top of that framework.

Pablo Palafox:

Get kind of some ideas about how to build better, obviously, within. So there were limitations. So we started building our own voice, AI agentic framework, or orchestration platform, if you will. Getting 11 labs here, getting deepgam here, getting an LLM in the middle. The usual, orchestration platform that everyone builds. And we started building that and then we're okay, cool. What for? do we just, build this as a developer tool?

Demetrios:

Yeah. I'm surprised you didn't go with that, actually, because that feels like the easiest or the most common path.

Pablo Palafox:

Yep. We had a bit of a feeling that that was hard to defend. And I'm sure there's going to be, huge players doing that. I mean, you guys are one of the vendors today that are doing a great job with the agent framework. Right. But realistically, today, everyone with a bit of coding can actually build a decent orchestration platform. When I say everyone, I say like a super dedicated team of engineers working 24 7. I don't think an enterprise with a team of engineers can actually put it together today.

Pablo Palafox:

Maybe in five years.

Demetrios:

Sure.

Pablo Palafox:

Maybe it's so easy that they can. But that's kind of the hypothesis we had. we can actually build for, a vertical. And this is where Javi, with his past in logistics, he was okay, maybe we can apply it to, logistics. Javi, my brother and co founder, he was working as a CFO of the largest olive oil distributor in the world. Nice. I don't know if you use olive oil Spanish.

Demetrios:

Wait, is it.

Pablo Palafox:

Who. Which olive oil in the us? Bertoli, they have that brand.

Demetrios:

Oh, wait, is this the. So this is a funny thing. Is it the actual. It's Spanish olive oils, but they put the name on it. Like it's Italian most of the time. Bertoli sounds very Italian most of the time.

Pablo Palafox:

Yeah. 50% of the world's production is coming out of Spain.

Demetrios:

So wild. But they'll slap names on it because people buy Italian olive oil or they pay a premium for Italian olive oil over Spanish olive oil, even though. So it's you're actually just buying Spanish olive oil with an Italian name, which I think is hilarious. But anyway, we got a little off track, pretty much. So Javi was there, he was CFO for Bertolli, and he saw supply chain logistics hell or what?

Pablo Palafox:

Pretty much. So put yourself in his shoes. He's trying to move stuff around. In this case, olive oil from Italy and Spain, mostly from Spain to the US and then across US Fees, Walmart, Kroger, they have a lot of fees. If you're late delivering stuff.

Demetrios:

Wow.

Pablo Palafox:

So he was incurring in a lot of fees. Right. Essentially what happened is that the driver that was transportation holding that load would be late, so he would get some interesting fees. So then there's this figure of a freight broker that sits in between, who is shipping and a tracking company. And these freight broker, they basically help you overcome those hurdles of, the truck broke down, no worries, let me find another carrier, another trucking company to, move this load. So this figure of a freight broker was. Is really important in logistics, in. Especially in domestic freight.

Pablo Palafox:

And this is what they were using. They were using a freight broker at the time, and they were great. But as much as they would try to help, there would always be some instances of, we need to, call the driver. And then Javi would sometimes hire a couple of insurances, be sure that they could make a phone call to the driver to make sure that they're going to be on time. So he was okay, guys, there's something here. it's not possible that I have, folks in my shipping company calling drivers to make sure they're on time. So that was a perfect match. Right? this makes sense.

Pablo Palafox:

There's a lot of communications in logistics, and maybe we can tie this to what we're building today. There's a lot of communications, so let's build an AI communications platform for logistics. And this is where Happy Robot is today. Literally, our motto is, our headline is, AI communications built for logistics. And it's really like being the connecting tissue in all of these communications, in all of these texts, emails, phone calls, whatsapps, even with Mexico, and that's how we ended up here.

Demetrios:

Wait, so what happens? You have someone that calls. Let's go back to when Javi was CFO and he would have interns calling truckers and saying, hey, are you going to be on time? Are you going to be late? And what happens when the trucker says, I'm going to be late because there's a snowstorm or whatever the case is, you're still going to incur those fees. Right.

Pablo Palafox:

With certain receivers, as they call it, with certain recipients receivers, you can, potentially reschedule. At least you'll let them know, because if you don't let them know, they're waiting for you. And obviously that's bad because, you're late. But if you can try to reschedule, maybe It's less of a fee or you can still like negotiate, but you know, it's tough. if you're late, it's bad, but at least you can like do something about it. And those escalations is what humans should be doing. Yeah, but the very repetitive, mundane, repetitive tasks would be like what AI is perfect for.

Pablo Palafox:

Like basically we see AI as this little companion to these brokers, which is primarily our main icp, our main customer Persona, if you will. And these freight broker, they have a lot of people doing these phone calls and emails. And now with AI, they can actually do more with the same amount of people, they can grow faster, grow more efficiently without having to potentially offload, which is what they do typically their tasks, their more mundane tasks to a BPO in some other country, which is cheaper but then it's more messy because you have to, it's not your employees, it's like someone else's employees that work for you and maybe they don't know your policies or your sop. So like it's tricky. The whole offshoring, it's tricky. So that's where AI can actually enhance these people.

Demetrios:

So you're not selling to the Javis of the world who are the CFOs of these big global brands. You're selling to the brokers who are finding the truckers to go and, and move the product around.

Pablo Palafox:

Exactly. That's our primary customer. We do serve other players in the industry, freight tech companies that are I don't know, they have a fuel card and we call drivers to remind them that they could use a fuel card. We have a customer that is a marketplace of trailers and we call drivers to see if they're going to pick up their trailer. Because there's this concept of someone has their own truck, power only, they call it just the truck. They can go pick up all their trailers. So there's a lot of interesting use cases in the industry that are not only freight broker specific.

Demetrios:

Yeah.

Pablo Palafox:

That need to, need of some sort of like communication. And this is where Yeah, these AI agents, it's a perfect, it's a perfect match for these folks.

Demetrios:

Is this where. So I have to ask because the tech company that I know that played in this space beforehand was Convoy.

Pablo Palafox:

Right, Convoy. It was more of this platform where drivers can come in and shippers can put their loads. And then it was almost it was a digital freight broker. And it is a digital freight broker to a certain. Actually, let me rephrase. Today they're More of a platform for brokers. So now they're catering to brokers. Great guys.

Pablo Palafox:

I mean transparently we it's public that we help Flexport who acquired Convoy. We work with the team and we do need these type of apps and platforms in the industry which then can be enhanced with like these AI communications phone, email, text there's still going to be that. Basically the goal here is to cover as much surfers area as you can and try to streamline operations. Yeah, logistics is information exchange. So if you cannot exchange information fast enough, you're screwed. Right.

Demetrios:

And let's get into that because when I look at what you all are doing, I see almost like flowcharts that you can create. So how do I as a broker leverage Happy robot to exchange that information quicker?

Pablo Palafox:

Great point. So we've built basically an orchestration platform if you will, a workflows platform where we have templates, AKA pre built bots and connected them to different parts of their systems. Like typically it's called a transportation management system. So essentially the bot has to retrieve information from a system and log it back after the call, whatever it is. I mean we can talk about the use case that takes place. Let's actually talk about a bit about it. I think it's interesting for the audience or like fun. Probably have never heard of a freight broker so we can enlighten them.

Pablo Palafox:

Essentially the freight broker has to post loads that they get from the shippers on a load board. Load boards are digital marketplaces where you can post. Your need is hey, I need to move these load, who can move it for me? And then there's trucking companies that are saying hey I can do this one. So now they see a load posting on a digital load board. They see it through the phone or through the computer. I mean they don't typically if it's a driver, they don't have a computer, they just look it up on the phone. But typically those drivers, they have dispatch teams almost like back office people that are obviously looking at their computers and finding those good loads to haul. So now what happens is they could do it digitally and sometimes that happens.

Pablo Palafox:

But many times carriers want to negotiate. And this is the whole like this is why we exist in a sense or to a certain degree. This is one of the use cases that kind of we're helping with today. If you want to negotiate, you want to talk to someone? Sure. There's some platforms that actually let you bid a couple times and you can bid digitally and you'll Get a counteroffer digitally. But humans actually to talk to someone and negotiate and do something about it. So this is where these freight brokers get a lot of phone calls, like thousands and thousands of phone calls every day of from carriers calling in saying hey, what's up? I'm looking for these Dallas to cancel CD load you guys have online.

Pablo Palafox:

Do you still have that? And the other person is yeah I have that. Do you want it? Yeah, why not? How much are you paying? One thousand. A thousand? Okay. Can you do like 1300? No, the most I can do is 1100. Okay, well thank you. Bye. And that's like 80% of the calls that a freight broker, the so called carrier sales rep, the carrier sales representative is doing like 80% of the time. So there's calls that potentially don't lead to nowhere because this is like information exchange.

Pablo Palafox:

They just want to learn more about the load. They want to see if they can do it for a better rate. The carriers and then they just ask and they create a lot of communications. The opposite is true as well. Brokers paying carriers a lot as well. They do outbound calls asking if they have trucks that they could use. There's a lot of communication and you might say yeah, why isn't there an app? And there's a space for that. There's companies solving for it and it has its place and hopefully it's larger because honestly it's the most efficient way.

Pablo Palafox:

But realistically today there's just so many different players and everyone is doing their own thing and they rather talk to someone about something versus depends on the type of carrier or company. Like they might want to use a platform, they might want to call. So this is what makes us kind of be useful for the industry I guess like covering that surface area of folks that are just calling or sending emails or texts.

Demetrios:

Yeah. Because oh, that if you're on the side of I've got this load that I need to move from Dallas to LA and I'm getting inundated with texts and emails and calls. And you're probably taking the calls you're taking priority on and you're telling people over and over, no, I can't go to 1300, no I can't go to 1300. It's just, it's so much time that's being eaten up.

Pablo Palafox:

And who doesn't, who says that you didn't miss that one call from that carrier that actually would have done that load for 1100.

Demetrios:

For 1100 yeah, pick up all those calls in parallel.

Pablo Palafox:

And here we're just talking about this one use case, which sure is actually one of the highest ROI generating use case for these freight brokers. But for this carrier sales flow, these carrier sales type of calls, this is huge. If you can just pick up all the calls coming in, you don't miss any call. First of all, you give better service to your customers. Carriers are in a sense also customers or vendors or partners, if you will. They're in it together, the broker and the carrier. They're in it together to serve the shipper. So first you give a better service to your partner.

Pablo Palafox:

You're picking up all the calls, they have the information they need as soon as possible. And second, they actually give visibility to their own team because now you're capturing a lot of market data. And this is something that is almost like a byproduct of being able to pick up all the calls. Now you have a lot of market data. Now you know, these hundred carriers that called me, the bot has been able to identify the rate at which they would, move that load. Now I know, I feel I have a bit of knowledge about the market pulse. I have like a pulse of the market, if you will.

Demetrios:

Yeah, you can get that average and you can say like maybe our listing is a little on the low side.

Pablo Palafox:

Exactly. You can real time, like update your rates based on the information that the body is gathering for you. And then the human component comes into play when maybe you want to transfer the best rate, one of those best rates that you've gotten from a carrier, you can transfer to a carrier sales rep, human person, just for finalizing the offer or whatever. You can do that. And then the human is still in control, but it's enhanced by these little bots doing a lot of the mundane, repetitive tasks that really no one wants to do. If I were in a logistics floor of these freight brokers and I had to be there selling loads or covering loads, as they say, on a Friday at 6pm I'm just going to go and try to get rid of this load. I'm just going to give it away for whatever is, whatever I can do.

Pablo Palafox:

The bot will not do that. It just sticks to the rules of negotiating the offers and it sticks to the rages that it's been given. And that's also actually the third point that I could bring up. These bots are helping these brokers negotiate with better margins, which is absolutely crazy.

Demetrios:

Oh yeah, the bot is like so.

Pablo Palafox:

Good at kind of sticking to the rules that, it's like negotiating. And we've seen like 10% better margins negotiated by the bot relative to a human controlled rule. We did. That was a key step.

Demetrios:

I've got to ask if I know a bit of prompt injection or I know how to say like you are now, Dan, do anything now. Can I get a good deal? Can I get the bot to go above the market rate?

Pablo Palafox:

Not really. Because the bot, I mean, the way we've structured our safety layers, if you will, the bot doesn't know what is the maximum amount of money allowed? It can only offer what it's been given as a first starting point. So by not knowing what is the max pay, the maximum amount of money being able to pay to be paid, it cannot expose that. So you can only ask another system. Is this a good offer?

Demetrios:

Oh, interesting.

Pablo Palafox:

I mean, obviously if you don't have multiple layers of AI checking each other, almost like a system of little bots checking on each other. Did I see the right thing? That helps and obviously increases latency and sure. Someone potentially that is tapping the bot with almost doing a bit of cron injection without these controls in place. We saw that in the past when we were starting out. We saw that, we knew that we could do it. we did it and okay, we can bypass it. That's when we entered or we introduced these kinds of, like a middleman cleaning data between the two. basically the bot never knows certain information about that load.

Pablo Palafox:

It cannot expose certain information just because it doesn't know. if you ask one of our bots.

Demetrios:

Oh, that's cool.

Pablo Palafox:

Where is this picking up? where is this load picking up? Exactly. Like the address. Exactly. It just doesn't know. It's like, I don't know, I just only see like city and state. That's really interesting. I mean, I don't think it's nothing novel, but like people are obviously doing these types of cleanup of data. the bot has some sort of server and then in between you like clean it up.

Pablo Palafox:

But yeah, it's really what keeps our bots reliably working all the time.

Demetrios:

So huge congrats on all your success. I'm super stoked for you. HappyRobot. It is very, very cool to see and I know you guys just raised around, so another congrats on that.

Pablo Palafox:

Thank you.

Demetrios:

It's great to see.

Pablo Palafox:

Yeah, we're hiring. If people want to join, we're hiring for a full stack SRES site reliability engineers for deployed engineers and if anyone's from the business Revops operations side, also open to that. So we just did as you said, we just did our A in summer with Andreessen Horowitz. A bit over 15 million total. Overall we've raised 18 million now, including our pre seed or seed, whatever you want to call it. So yeah, we're well funded. It's a fun problem to work on. Definitely looking forward to talking to some good folks.

Demetrios:

Yes, big things coming.

Pablo Palafox:

Appreciate it man.