Pablo Palafox:
0:05
It's something that you don't maybe fully know about. It scares you, right? I have to be clear. Like, ai is just statistics. It's no magic. It's just a bunch of statistical predictions. Like it's just predicting what is the most probable next word that I should be saying next. So AI is kind of dumb if you think about it. Obviously I'm just making it feel very stupid. But it's super smart. But it's smart because it's in a lot of data. But under the hood it's just statistics, right? So nothing to worry about unless we use it the wrong way.
Blythe Brumleve:
0:53
Welcome into another episode of Everything is Logistics, a podcast for the thinkers in freight. We are proudly presented by SPI Logistics and I'm your host, Blythe Brumleve. We have another great topic centered around AI and logistics tech for you. Today. We are talking to Pablo. He is the CEO over at Happyr obot and this is a conversation I've been wanting to have for a while. So, Pablo, welcome in. Finally to the show.
Pablo Palafox:
1:18
Thank you so much. I'm super excited to talk about everything logistics and AI.
Blythe Brumleve:
1:22
Heck yeah. So before we dive into what you're doing at Happyr obot, I'm curious as to what your background is. Did you know about logistics? Did you work in it beforehand, before you started working in it now? Or how did your journey end up in the logistics industry?
Pablo Palafox:
1:41
For sure. My background is actually in industrial engineering, so I did touch upon some logistics. During my bachelor's in Spain. I studied this center grad with my buddy and CTO now Luis one of my co-founders and basically I then focused on computer vision and computer science, in a sense like AI. I did my PhD in munich, um, before that I was streaming master's as well in munich, together with louis as well. So it's so. It's been basically a lot about like ai and computer science when it wasn't even a concept like I was actually doing some ai.
Pablo Palafox:
2:15
When it was more like deep learning, we referred to it as like, like people who actually said ai was like this is not ai right now, like people are actually saying this is AI, which is kind of debatable. But the correlation with logistics is more through Javi, my brother and co-founder. He was on the shipper side during his time as CFO of an olive oil company. He was a CFO of North America. They were shipping olive oil across the US and North America, right, and he was basically dealing with the whole. Hey, let's across the US and North America, right, and he was basically, you know, dealing with the whole. Hey, let's call the driver. Oh, they're waiting on, they're in detention, they're just something went wrong with the warehouse, like that kind of came through Javi.
Blythe Brumleve:
2:56
Oh, that's super interesting. An olive oil company, because that's all the rage on, you know, some of my social media feeds right now is what kind of olive oil to buy that is actually like the real olive oil and not. You know some you know industrial, americanized version of olive oil quote, unquote olive oil. So that's super interesting. I'm curious for your PhD. You said you got it in Munich and so when you're studying there and you're studying computer science and AI, you know deep learning, machine learning, I imagine all of those things. I'm curious what your your thesis was on.
Pablo Palafox:
3:32
Well, first of all, don't get Javi started on olive oil. He'll tell you everything about it, so don't even ask him about what bottle you should buy. I guess I'm glad he's not here, although I would love for him to be here to actually now drill you down Now we're going to have a follow-up.
Pablo Palafox:
3:47
Yes, let's do that. Just olive oil. But yet my thesis I actually dropped out of the PhD officially. I never fully graduated. I can go down that path and tell you about how messed up the research world is in terms of just a battle for papers and just getting more papers and research.
Pablo Palafox:
4:05
I mean, it's just, uh, I love my professors. They were great, I really appreciate everything I learned from from them. But the whole research arena is just meant for you like to produce papers and that wasn't really for for, I guess, entrepreneurial mindsets like I guess, mine, like I really wanted something else, I did. I did an internship at facebook facebook at the time, time, meta now, I guess and I realized that, hey, this is not for me, like this whole research arena, even if it's like through academia or like through industry sorry, like through some Meta, google, whatever still like just producing papers. So that wasn't for me. And then again, my thesis, to your point, there, it was more related to like 3D avatars, so like deep learning applied to like just reconstructing people dancing in front of a camera, just like kind of for that virtual clothing. You know, like Meta is now actually diving deeper into that as well.
Blythe Brumleve:
4:55
So that was, that was my thesis, yeah, yeah it almost sounds naturally yeah, I was gonna say it almost sounds like the Web3 that when it was at its its peak and I think meta tried to to jump in to that arena too and making the, the meta, themed bodies for meetings, I guess exactly.
Pablo Palafox:
5:11
Yeah, it'll come. It'll come like um, being able to like just see yourself with different clothings and just try things on, like that virtual try on.
Blythe Brumleve:
5:19
I think it's very powerful, but it's very hard what makes it hard is, I imagine, all the different body shapes and sizes, things like that.
Pablo Palafox:
5:30
Reconstructing your body in real time, being able to actually and accurately capture your movements and your positions and your positioning. Clothing, virtual clothing on your body in a realistic way is hard and obviously if you're like trying something, you want to feel good, so it shouldn't like look messed up, like the image that you see of yourself is like if it's funny looking, it's like I don't like that clothes, so obviously you're not going to deploy anything that's making you look weird, right?
Blythe Brumleve:
6:01
Yeah, and then the fashion brands probably don't want to invest in it because their clothes maybe don't look the best. But I'm sure you know, as you said, it's, it's, it's coming. It's just not exactly, you know, quite there yet. So so how did you go from you know, sort of discovering that you're not, you don't really like the atmosphere of the, you know, generating research papers, which is probably the way they get funding? How do you decide? Okay, I'm not, that's not my path anymore. I'm going to go this other path. Did you start up a company over in Europe or did you move to the States and then choose to, you know, start up a company here. What did that process look like?
Pablo Palafox:
6:39
Great point Javi, my brother and co-founder. He had already been living in the US for a for a while and he's always been trying to like distill into me, like you should do computer science, like you should do startups, you should like. Just, he was kind of waiting for me to like graduate and these kind of stuff like do something together, right. And at some point, once I'm like done with the PhD stuff, I bring my buddy, luis as well, and Javi, and we're like, hey, let's do something together. And we started ideating. Back when I was still in Munich, we started ideating and building some stuff around computer vision at the time. At some point we get into Y Combinator in 2023, like last summer, 2023.
Pablo Palafox:
7:14
Y Combinator is this very prestigious accelerator based in San Francisco which is funded by companies like Airbnb, dropbox, flexport. And we got into YC. We actually started ideating about the concept of LLMs and conversational AI and this is kind of how we got into building these voice bots for logistics, kind of the connection with Javi saying, hey, guys, I've seen a lot of issues with very transactional phone calls in logistics during my time as a CFO of a CPG company and we were like, hey, we like AI and voice AI stuff. Let's do something in the middle right. So that's kind of how it all came together.
Blythe Brumleve:
7:56
So how? I guess because YC is just something that you know. If you follow any kind of like all-in podcast or you know any of the or any of that circle of social media influencers, you hear about YC a lot. So I'm curious as to what did that process look like to decide okay, I want to start a company, but we want to go through YC.
Pablo Palafox:
8:22
For some reason, we always knew that we were going to apply to YC and actually to Europe and before, like, how like did we build a company right off the bat? We actually didn't, but at some point we did incorporate and the place where we actually incorporated was the US, even though we were kind of partly in Germany, partly in the US. Right Like Javi was here, luis and I were in Germany. The reason we did that is because it's so simple to just build a company in the US, right Like Javi was here, luis and I were in Germany. The reason we did that is because it's so simple to just build a company in the US versus Europe.
Pablo Palafox:
8:48
There's so many hurdles and issues and regulations in Europe Spain, germany, pretty much, all the same. Probably Spain is the worst place to build a company anyways, but even in Germany it's a lot of procedures. Language is also a barrier, of course, like you're having to sign documents in German. So it was clear from the get-go that we were just going to try to get to the US. Obviously, visas were an issue at the time. Now we finally got our visas and we're like being able to like be here and build a company and just focus on that Right.
Pablo Palafox:
9:14
So really appreciative of the US in terms of how easy it is to build companies and, to your point before, like that's kind of what we did, which is incorporated us as a c-corp delaware. Two days we have a company and uh, then when we got into yc, which they actually actually um, kind of forced you like they don't accept you if you don't have a c-corp delaware. So like, um, you actually have to have that. So once we we thought that we would apply to yc and we get in and we're like, great, we just might, we might as well, just move to the U S right. So we did that in in summer, like last June, oh wow, and yeah, it was fast, like it was kind of like in May. We got accepted June. We're like here and we just had to like bring our lives here. I actually asked my wife to like just find a job in San Francisco and try to get some, and she actually found an amazing job as a school teacher.
Pablo Palafox:
10:05
As a learning specialist here in San Francisco. It's like so easy to just like come here and like do stuff and build cool products. So yeah, that was kind of a journey. We can talk more about it.
Blythe Brumleve:
10:15
but yeah, absolutely. I'm just curious as to you know, is it just a fund, are they? I say just, but are they a funding partner and also kind of like an educational camp, you know, collaboration group, like what does that look like once you become accepted?
Pablo Palafox:
10:32
Yeah, they fund you with half a million dollars. That's kind of the ticket they give now to startups. But that's kind of just one small part of the whole package, if you will. The biggest and more interesting kind of learnings and kind of benefits you get from YC is definitely the network being able to just like look up online, like, okay, who's the CEO of Flexport and what's his email? Oh, okay, ryan, okay, let me get his email, let me just email him and he might actually respond because he knows that, hey, this is coming from another YC company.
Pablo Palafox:
11:15
So there's that level of trust and that level of trusting each other, being good to each other in that community. That makes it very special, right, the fact that you can just move across the network, benefit from others, getting help from others, helping others as well yourself, like that's actually something that I try to do and Luis and Javi, as co-founders of the YC company, we try to do ourselves Like how can we help other people just applying to YC? I just had a couple of friends from the past, from Germany. They're going to apply tomorrow to Y Combinators next summer. They're like, hey, can you give us some tips here, can you refer us? And of course, that's something that you can do. If you know people that are good and building cool stuff in the startup space, in startup land and you want to have them in your network, that's one thing that you can give back. You can just refer them back to that organization in a sense.
Blythe Brumleve:
12:06
That's super cool. What other tips would you give a business that's interested in joining like a YC? I?
Pablo Palafox:
12:12
would say being extremely focused on the customer and on building quick. So the motto of YC is build something people want. So that already reflects and talks to the fact that, hey, you have to be super mindful of the customer. Like, who are you building for? Why is it useful? Why are you doing this?
Pablo Palafox:
12:35
Like it's just not building a cool product, which is, granted, something that we all do, like we always, especially engineers we always like to first build, build and then talk to users. It should be the other way around, right, that's something that, um, we've been trying very hard to like avoid, uh, especially in these in the logistics space. Right, like, when we came into this space, I didn't know that much about it. How do you? But at least I, we didn't and it's been just like being very open-minded, very, very humble in terms of, hey, you don't understand anything about the industry. You better learn it and you better get close to people that teach you about it. So that's something we've been trying to do and that kind of talks about how YC or what YC is looking for, like people that are actually listening to their customer, listening to their users, talking to them constantly, really being really serving your customer. That's kind of the key thing that you should keep in mind if you want to apply to YC.
Blythe Brumleve:
13:32
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Blythe Brumleve:
13:59
But I would much rather you hear it directly from SPI's freight agents themselves. And what better way to do that than by listening to the experienced freight agents tell their stories behind the how and the why they joined SPI? Hit the freight agent link in our show notes to listen to these conversations or, if you're ready to make the jump, visit spi3plcom. And I imagine that, with Flexport already being somewhat of their funding coming from YC, I imagine that you know that group is already familiar with logistics, so there's no, you know, sort of convincing them of the value of the investment, which we've seen other you know VCs follow suit since you know the last couple of years, so it's interesting that they were maybe early to the game in that regard. Is that a safe assumption?
Pablo Palafox:
14:48
I would say so. They don't have a thesis like YC doesn't have a thesis in terms of industries. They're pretty much industry agnostic. They would just look at whether what you're building is useful for someone and if it's a big enough market, obviously. But logistics is huge, right, like there's so many problems, so many inefficiencies that we can tackle, so, yeah, 100%. They're not biased towards any particular like trend or whatever.
Pablo Palafox:
15:14
Obviously, now AI it's not real. I would not even say it's a trend. I mean, it's something happening. It's a technology the same way that mobile had its time right. So it's a technology the same way that mobile had its time right. So they are very focused on AI right now. They are kind of getting a lot of AI-focused companies in their new batches. But it's simply because I was listening to one podcast from YC the other day. It's simply because they just happen to fund people, smart people that are building stuff with AI, and that's kind of their thesis. People that are building stuff with AI and that's kind of their thesis. Like their thesis is I will fund people that are smart, have energy and want to build cool stuff and, depending on the time of like, on the decade, I guess they will be using a different technology, right Like now, it's AI.
Blythe Brumleve:
15:58
And so you go through this whole process, you move your family over from Spain or Munich, as you said, over to San Francisco and you get accepted into YC. What do those first few steps look like? Where are you, I guess, investing that money that you get?
Pablo Palafox:
16:19
YC's founder and not president anymore. But YC's kind of creator, paul Graham, told us in one chat at the end of YC's match sit on the fucking money, just like be a cockroach, love that. And this is something that he's already he's also like posted about on many of his posts in the kind of startup community programs like these big figure, and he's very conscious of how money's just a tool to get you to somewhere. But if it's not a blocker to just hire someone, don't hire them. Like don't just spend the money because you have it right. Just sit on it until you know that there's a path to like become, uh, to do something useful. Like keep understanding your users. They can go and actually like talk to your users, understand what they need. So that's actually what we did. Like we just kind of sit on the money. We've spent like five percent of the money that we got from yc and other investors when we raised our pre-seed, so we mostly have everything in our bank account. Obviously, you're now getting revenue from customers, so that's also going to. That's something VCs never actually tell you, unlike YC. Like VCs always will want you to like get more money. Like get more money, get more money. It's like, obviously they want that because they're just going to get more ownership right, which is, hey, it's's something it's fair, like it's their job right. But what you want as a company is just to be, um, mindful. Or if you're one way, obviously, and you want to have enough money to like, just survive and until profitability, but also, um, know that you're going to get revenue and this is something that yc also tells us a lot about like, hey, it's not that you're just going to have these amount of money like I don't know two million, or just that, that's it. Like you're you're not going to have these amount of money like I don't know 2 million, or just that's it. Like you're not going to get anything in. Of course, you are Like, if you do something interesting for some people and users, you're going to get revenue, right. So that's going to offset part of the cost, right.
Pablo Palafox:
18:14
So, yeah, what we did to your question before, what we did is just continue building, talking to people. We actually did a pivot after YC. We, we actually did a pivot after YC. We didn't apply to YC with this right side idea. It was a bit different, which is actually something that we can talk about as well. Like people applying to YC, I would say last batch, or our batch, 50% of the companies just pivoted during YC, which is crazy. Like they just applied with one idea and they just pivoted. So what we did is just like sit on the money, keep talking to users, and right now we're on the path to actually building something very cool. We are now seeing that someone actually wants what we're building, unlike what we were doing before, and now we might just spend a bit more on like new hires, more compute and this kind of thing.
Blythe Brumleve:
18:56
So it's really like the approach is is that it's almost more calculated now versus kind of what the VC environment was for the most of the last 10 years, where it's almost more calculated now versus kind of what the VC environment was for the most of the last 10 years, where it's just like fund everything and who cares? I mean obviously they, people. People care if they make money on it or not, but the money was sort of free flowing. This sounds like you know, very like conscious shift, where we're going to give you the money but we want you to spend it wisely, sit on it, as you said, and then talk to customers and then choose to make your investments after you reiterate on the product 100%, 100%.
Pablo Palafox:
19:34
Yeah, I think there's been that mindset towards profitability, um, and towards revenue, which I think is is very valid. Because how do you validate if someone wants what you are building, well, they pay you. If they're not paying, like, what's the validation? Right, this is very cool, but I'm not gonna pay. No, like you, you need to get validation through, like people paying for your stuff, right. So that's kind of something that actually wise has always been very, very um persistent about. I guess you know their videos, like if you go 10 years ago, 10 years back, you'll see that in their videos they'll like just be talking about hey, build something people want, build them like, make them pay for, make them pay for it, like they're using it, so they better pay, right. So, yeah, I think there's that mind shift towards building things that you can actually get paid for.
Blythe Brumleve:
20:34
And so, speaking of, you know, getting paid for a product, you know, obviously you're CEO of Happyr obot. You saw this need in the market and along with your co-founders as well. And so how did what did that process look like? You know, are you talking to customers every day? Or, you know, are you starting off with a couple of customers and then kind of proving it out and building it out from there? What did those sort of early like I guess first six months look like?
Pablo Palafox:
21:01
Even before going there, I guess I'll give a primary in what we do. So, basically, Happyrobot, we build voice AI assistants, so like voice bots for logistics. So we're trying basically to automate all of those transactional phone calls that no one is building relationship through. So anything like a payment status update, be it like a factoring company calling in a broker hey, any advancements, any deductions on this law, whatever. Those things are very transactional. I always hear about relationships and logistics. That's very important. Who's building relationships there? No one, right. So all those transactional phone calls, we want to help companies be more efficient on those. So we're serving freight brokers. We're working already with some of the top 50 US freight brokers, including some like Circle Logistics. We're also serving carriers automate their dispatch teams, giving load updates to brokers as well. So we have this cool platform that can just basically allows you to just like build visually how your assistant will operate, kind of like a phone system, like a phone tree, but with AI. So this is kind of what HappierVote does, like building these voice AI assistants.
Pablo Palafox:
22:15
Now to your point about how often we talk to users. Well, we talk to them every day. We have Slack channels with them. If they don't have Slack I just give. They don't have slack, we just I just give them my phone number and we just talk through through texting or like email. So I try to like be super on top of our customers, nice, and I think that's kind of what I want to distill into my company. Uh, moving forward like customer centric right, like just be mindful of hey, we have someone in production, it better work and we need to make sure that it's working all the time. 99.9% of the time it should be working and the assistant is doing its job and the customer is loving the product right. So far we've been lucky enough to not have any churn or people just leaving us.
Blythe Brumleve:
22:55
Heck. Yeah, I mean knock on wood for that statement right there, because that's definitely something to be proud of is not having any of that churn so far. And I think you actually have a demo that you're able to show us and I think that that will help the audience sort of understand, because I've been shown the demo before at a conference and I was pretty much blown away that that wasn't a real person that you're speaking to, because it sounds very much like you're calling into a real sort of brokerage floor. Very much like you're calling into a real, you know, sort of brokerage floor. You can hear the action going on in the background and you can have a what feels like a good, real conversation, but it happens to be a bot, and so I think you have a demo that you can show us, right.
Pablo Palafox:
23:35
Yes, a hundred percent, Let me. Let me just call here. So I'm going to be demonstrating how our kind of inbound carrier sales rep sounds like. It's a pretty simple flow. It's just imagine you're going to be kind of the carrier asking for loads and we're just going to be getting our assistant, in this case Kate so it's going to give me some load, information about some loads. So let's see how that goes. Circle how?
Pablo Palafox:
24:17
can I help? Hey, how's it going? I was looking at your Dallas to Kansas City. I was looking at your Dallas to. Kansas City. Sure, can I get your MC number? Yeah, it's 843-818.
Speaker 3:
24:38
Let me check that MC real quick. Okay, let me transfer you.
Pablo Palafox:
24:44
Okay, me transfer you. Okay, thank you. So here, what we saw is the MC validation was right and we just immediately want to transfer to someone, to the actual carrier sales rep, so that they can check on that for you. In this case, the AI saw that the load was not there, so we were like okay, let me transfer you immediately, immediately, so that you can talk to a carrier self-rep. It's like this validation of the EMC is already very powerful because you're only letting people work with the carriers they want to work with, which is already like a huge ROI for many brokers out there.
Blythe Brumleve:
25:18
So you're cutting down on those phone calls of just, you know, not necessarily like spammers or scammers or anything, but just unneeded phone calls for what your your currently, what your workload demands for that day. Um, so they she was actually transferring you to a real person or to another you know, sort of, I guess, robot.
Pablo Palafox:
25:37
In this case to a real person, because that was kind of the flow that that we, that we intended, but um, intended, but the potential to transfer to another bot could be there as well, like if someone wants to like get a load update, actually, let's say for a dispatch team, that load update, we will be transferring to another AI that can like give that load update, kind of having the router AI assistant first kind of routing to the right department and then within each department you can think about having like different AIs doing different jobs, like one for accounting, one for load updates, one for capacity requests, kind of that flow. And for brokers, the same right.
Blythe Brumleve:
26:15
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Blythe Brumleve:
26:50
For more info, go to tai-softwarecom backslash battle stations, and we also have a link for you in the show notes to sign up for a demo. Yeah, so I just pulled up your website because it kind of breaks this down really easily, where it talks about those different calls and those different workflows that you can work through. And what I'm wondering is, I guess, are these like maybe like pre-built filters or pre-built workflows that you kind of have already established based on those conversations with your customers and then from there you can kind of customize it? Is that a safe assumption?
Pablo Palafox:
27:27
Exactly, we have predefined flows already for for brokers there, for instance, like those outbound check calls. We actually have a uh, some LinkedIn posts coming up soon where we'll like demonstrate how to like, uh do outbound calls like drivers for in-transit check calls oh, interesting Also even for pre-trip check calls. And the AI will actually look at an Excel sheet, if that's kind of what the customer has, because typically connecting to the TMS has more nuance and people working on MercuryGate or McLeod is always trickier to connect to first. But we typically start with a simple Excel sheet and we just go from there. We give that to the AI and the AI can just go through all the numbers, all the loads in a list on an Excel sheet and just make an outbound call to that.
Pablo Palafox:
28:12
And yeah, then the same with a dispatch agent at a broker sorry at a carrier. Just basically routing calls to the right department is the one that we always start with carriers. They just want to have a very personalized approach to like their customer service. Like, instead of having like a press one, press two, press three, which is annoying as hell, uh, you'll just have like a like a nice human, quote-unquote voice that uh, you can just talk to and you say, hey, can you kind of talk to james, and the ai AI will know that James maybe is in the dispatch department, so she'll transfer you to that department.
Blythe Brumleve:
28:48
That's super interesting. Yeah, I used to have to do that in my old job, working at a freight brokerage, because people would typically hit zero and I was executive assistant and so any calls that went unanswered it would just immediately route to me and I would have to do this. So I immediately am already drawn to this. But I am curious. So you have these workflows set up and then you can kind of customize them from there. So what does, I guess? What does using the happy robot look like? Is it a Chrome extension? Is it, you know, an integration into your TMS? What does that? What does sitting? What does a dispatcher like sitting down at the computer? Are they actually like clicking buttons? Or is this mainly like an automated system that after onboarding you don't really have to worry about?
Pablo Palafox:
29:34
Pretty much.
Pablo Palafox:
29:35
Pretty much, once you have set up the assistant, we have a dashboard where you can see all the calls that's happened during the last days and you can just see okay, like this one call, we actually even show like tax. Like, okay, this one call was transferred to someone in sales and like, we have a tax for like transfer to sales. And then you have that visibility. We're working even like on like dashboards to like just show you like what are the most common intents from people right, like, oh, maybe 80% of the people are just calling about load updates. We'd better build an AI to like give those load updates, which was actually the flow we've gone through with our carriers. Like they initially said hey, I don't know what's going on with my phone system, I just want to have visibility into it. So what we do is, like we integrate into their existing phone system like, be it 8x8, iopad, vonage they just transfer the calls to our AIs who sit on top of a phone number. We buy that phone number for the customer. So it's kind of that middleman that sits in between your existing phone system, very easy to integrate. And what we do is first build this router assistant who understands the intent in natural language from the user. Hey, I want to talk to Lizette in dispatch and you'll just know that, oh, liz works at dispatch. So I better transfer them to dispatch without knowing what extension Lizette works at or whatever right, and then we'll have the visibility into what is the intent from people calling in and then we can build custom bots for different pieces of the flow.
Pablo Palafox:
31:19
As you saw there in the website, we have already very established workflows for load updates. Pretty much the caller will say, hey, I'm looking for an update on load one, three, four, five, whatever. Sure, let me look into that. So we'll connect to the customer's TMS, we'll read that load number, we'll look it up on the TMS, we'll see what's the status of that load and just display that to the AI so that the AI can actually just verbalize that in a conversational manner, right? So it's that connection to the systems integrating to the customer's existing systems. That is very powerful, because obviously, if you can actually look into what's going on in your TMS and provide actual information about the current status of a load, what's the usefulness of all these? Right, that's a very big piece of our product, like integrating to your existing systems.
Blythe Brumleve:
32:07
And so you really start off with integrating into the existing systems and then almost start maybe the data collection, like phone routing, first to learn the company and then from there I would imagine you would be able to add on additional functionality for that. You know, I guess sort of you know, kate, from the the robot to handle. Is that the way it works?
Pablo Palafox:
32:32
pretty much, pretty much. I can actually do a another another demo here with with uh one of our other customers, um moneyransport. If I call Monit here, they're a carrier. We'll see that flow there actually. Hey, this is Kate with Monitransport. Hey, I was trying to get a load update.
Speaker 3:
33:04
Sure, let me check on that for you. One sec, Okay. What is the load number?
Pablo Palafox:
33:17
Yeah, the load number-7-3. All right, so that's 1-7-5-4-7-3.
Speaker 3:
33:38
Yes, that is right. Sure One sec. It seems there might be a mix-up. Just to double check, the load number you gave me was 1-7-5-4-7-3, right.
Pablo Palafox:
33:49
You know what? I just made it up, so thank you. If you can just transfer me to dispatch, that'd be great. Okay, let me transfer you to dispatch One sec. Okay, let me transfer you to dispatch One sec. It's transferring to the actual dispatch department at the moment. So basically what you saw, there is me making up a number, obviously.
Blythe Brumleve:
34:19
So it's actually not going to provide information to people just making up numbers, which is a big piece of it. That's a good thing. No hallucinating, I guess.
Pablo Palafox:
34:24
Exactly. The word hallucination is one that comes up a lot with AI and LLMs. That's why we've built the systems we call them proxy servers, kind of sitting between the AI and the customer's TMS, and systems that kind of cleans up the data and will only return information. If you've been in the case of sort of co-logistics, for instance, if you've been validated as the right MC number, as a valid carrier, in the case of monotransport from the carrier side of things, we'll just validate if the load number actually exists, right, and we'll only provide a limited amount of information, right, we don't want to expose anything that the customer in this case mon money transfer doesn't want to expose to other people calling in. Right, like you don't want to give the exact coordinates from Samsara which we actually connect to, right.
Blythe Brumleve:
35:09
Oh true, yeah, that's a very good point, because I think there was a British Airways or British Airlines, I think. They had an AI chatbot on the front end of their site and gave an inaccurate answer to someone that was chatting in trying to get a reservation changed and it said it would be covered and then it wasn't covered and then so the guy ended up suing because they said your chatbot told me this and he won the suit. That was a really interesting just situation of how important I guess the reinforcement learning is and the feedback circle, the feedback loop, and I guess how are you, once you onboard a new customer, how are you maybe protecting the intelligence of the robot and so they're only sharing the information you want them to share, instead of I don't know like how much revenue you made off of that load or something like that? How do you protect, I guess, your customers' information from maybe nefarious actors or accidental actors?
Pablo Palafox:
36:14
That's probably the biggest thing that I focus on these days, because the AI backend like the voice generation, the transcription, everything is set up. Now the LLM part the AI part is the one that I focus on most these days with the team, and that part of not disclosing information we don't want to disclose about the load or the payment or whatnot that's kind of a very important piece, and how we do that is basically building this proxy server that I mentioned before, like kind of like an intermediate layer that cleans up information before the AI sees it. So we don't even let the AI see stuff that we don't want it to see, right? So any customer worrying about oh, if I'm giving you access to my TMS, it's going to just expose everything, right? No, we only have access to what you want us to have access to, right?
Pablo Palafox:
37:11
Do you not want to expose the max buy rate of a load? Obviously you don't want to expose that. You just have the load board rate. You want to get the best offer for your load. So you're not going to be telling or you don't want the AI to be disclosing what's the max buy rate for that load. So obviously we're not going to be exposing that to the AI. She's just going to be able to see a certain amount of information and understand whether the offer is good or bad. And if you're seeing how this proxy server we have kind of serves as a, as a firewall to the ai, oh, that's super interesting.
Blythe Brumleve:
37:47
Okay, because you almost have to protect the ai from itself of being too aware. So that that's. That's a super interesting approach. I'm curious if what you know, the fine tuning and the reinforcement learning, is it mostly the same for each customer or is it different for each customer?
Pablo Palafox:
38:09
The flow is the same. Obviously the content of the conversations differs, but we have a platform now, like a part of the product is data collection. So, like data set collection, every conversation I have with the AI, we can create a training sample out of it and say, hey, this conversation didn't go as planned. I need to, like I said very nicely, reinforce it back into the AI, like retrain, like fine tune again. You can use that sample. That didn't go great, you can tweak it. You can just like refine the conversation. Obviously you want to refine what the assistant said, what the AI said, and you're going to be using that sample as training material for the AI.
Pablo Palafox:
38:56
So, the same way that OpenAI, mixtrail, anthropiq, all these players train their models and fine tunetune them for specific chat use cases, we go a step further and fine-tune it to our enterprise customers for their specific, very nuanced and detailed use case Like, oh, is it an inbound carrier sales rep? Then we'll gather data and training material for that use case and train a particular assistant just for that use case. And it's much more efficient, obviously, because you're not using a very large LLM, that is just generalistic and you're just prompting it. You're actually fine-tuning the assistant to be good only at that. Obviously, it's not going to be able to help as a psychiatrist. It's not going to help you understand physics. It's not going to help you, like, understand physics. It's not going to help you do math.
Blythe Brumleve:
39:47
It's only going to be good at sailing loads, which is arguably, I think you know, the biggest use case for AI is really those specialized approaches where it's not going to be. You know, to your point, a lot of the large you know, chachibt, claude, things like that they're going to. From what I understand, you're the expert here, but from what I understand they're going to reach a level of where they're all kind of working with the same information, but it's your data that's going to separate the usefulness of being able to take action or use it in a positive way that's going to benefit you. Is that an accurate statement?
Pablo Palafox:
40:25
That's a perfect assumption. Yes, that's totally right. That's what we've been seeing now with our use cases. There's been some use cases that we've been able to tackle just with an off-the-shelf GPT model, be it 3.5 or 4, whatever version. Unfortunately, anthropic doesn't have function calling, but maybe soon. And I say function calling, which is a key term here, because that's kind of allowing AI to take action. So function calling is a critical term, but I'll keep that parenthesis for later.
Pablo Palafox:
40:59
To your point about how we are fine-tuning for specific use cases uh, yeah, totally right, the more you can specify your use case, uh, and have an ai just tackle that, the more efficient in terms of even costs.
Pablo Palafox:
41:15
It's going to be um more more it's going to be lower cost because you're going to be able to like find in an open source lm on that specific use case. It's like that's going to be cheaper than just like bluntly using a gpt4 for like this particular easy use case, quote unquote. So the more specialized, the cheaper and the faster. Also, gpt4 is known for being incredibly slow. So, like for real-time use cases like these phone calls, if you use GPT-4, it's going to be very, very painfully slow. Here you want latencies of one second, 1.5 seconds, two second stops. Obviously, if you're looking for information, you're allowing the AI to take a bit more time. It's actually nice when the AI is looking for the MC and it take a bit more time. It's actually nice when, like, the AI is looking for the MC and it takes a bit more time, because it just makes it feel more natural.
Blythe Brumleve:
42:07
Oh, that's interesting because you almost have to. You have to program that in there where it was. You know, I guess how much time does it typically take someone to manually check this? And then maybe there's a medium between you know the I guess the robot doing it in a second versus the human manual. Maybe you do it in 45 seconds versus a second, so it feels a little bit more natural. I guess that's your thought process and your approach.
Pablo Palafox:
42:33
It is. We just want to make it sound and feel like a very natural experience. If you say you're MC and half a second later you have the verification, you don't even realize. Like sorry, I was off the phone, I thought you were just going to check it, I was just preparing my coffee here. Like that's kind of what we want to allow the colors feel like. We just want to make it feel like a very natural experience, right? So in that sense, yeah, anything that we can replicate from the human side, we try to do that.
Blythe Brumleve:
43:04
How do you, I guess, from like a philosophy standpoint, how do you preach the values of like happy robot or just AI in general, to a lot of like the doomers out there, because there's a lot of them and they're very loud.
Pablo Palafox:
43:20
Yeah, and I understand that. I mean it's something that you don't maybe fully know about. It scares you, right? I have to be clear. Like AI is just statistics. It's no magic, it's just a bunch of statistical predictions. Like it's just predicting what is the most probable next word that I should be like saying next. So like AI is kind of dumb if you think about it. Obviously, I'm just like making it feel like very stupid, kind of dumb if you think about it. Obviously, I'm just like making it feel like very stupid. But I mean it's super smart. But it's smart because we it's in, it's in a lot of um, a lot of data, but under the hood it's just statistics, right. So nothing to worry about unless we use it.
Pablo Palafox:
44:04
You use it the wrong way. It's kind of like a knife. You can always kill people with a knife, true, very true. You can also cut tomatoes and prepare awesome meals, right? So everything in life, it's just a tool. It just depends on how we use it. I don't think we should ban AI for it being AI. It has its uses. We just need to know how to leverage them the right way.
Pablo Palafox:
44:31
And in logistics, we've been hearing a lot of those naysayers, especially smaller brokers. Maybe in some of the LinkedIn posts that I did like say, hey, this is never going to work. We need more humans, we need more warmth, we need more conversations, and I totally get that. But when it comes to to really transactional phone calls that some of the largest players have, it's actually great to be able to offload your team off of those transactional calls and just get them to actually build those real connections. Who wants to give payments out of Zedbates? So like a factoring company who wants to just say sorry, we can't work with you, you don't have enough months, it's like. Who wants to give payments out of Zed dates? So like a factoring company who wants to just say sorry, we can't work with you, you don't have enough months. Like your carrier, you know. Like who wants to like invite, who is saying no to someone over the phone. It's painful but if an AI does that, it's just business right and unfortunately that in this case on the carrier sales use case, that in this case on the carrier sales use case, kind of diving deeper into that if a carrier kind of work with a circle or whomever they just kind of work with them, maybe they have some some um, penalties that they got and and they don't have enough, they don't have enough months that they've been working for, so like circle would just immediately invalidate them. Why would you have a person saying that to to the carrier? You just have an AI gracefully and very politely saying, sorry, we can't work with you guys. If yes, yeah, let me transfer you.
Pablo Palafox:
45:57
So it's kind of again like going back to how you use it right. How you use AI, it just depends on the people using it right. There's going to be people that use it for bad stuff. I mean, I've already seen, especially with like voice cloning, which I can actually do another quick demo now with how I talk to myself. I clone my voice so I'm able to like talk to myself. Now People are going to use that to like just do bad scamming stuff. I mean that's already been happening. Do we now stop any AI initiatives because some people, some bad people, are doing that? I would say no. I would say we go for, we go after those bad people, bad players, and we try to reprimand them right and yeah, I, I think that that's, you know, it's very much the mentality that we've seen with any kind of technological advancement.
Blythe Brumleve:
46:46
You know there are people, you know, that were very upset when you know Henry Ford gave them a car instead of horses. When you know farmers got access to a tractor instead of, you know, using human labor. You know the role of, like, a social media manager didn't really exist, you know, 10, 15 years ago. But as new tech enters our society and enters our, you know, workflow, then it's just one of those things that you can adopt it where it makes the most sense for you. But jobs evolve, jobs change and, to your point, the, the transactional calls is it something that you know drives people to want to go to work in the morning? They want to build relationships, they want to make money.
Blythe Brumleve:
47:27
They, you know, and I would, I would hope that a lot of people want to do it in an ethical way, and I think I'm a firm believer of, you know, using these types of tools, like happy robot, to help companies grow in a way that makes the most sense for them. I mean, AI adoption, for my own company has helped tremendously, where we're able to publish more content than than some of the biggest companies in the world, and at a frequency that you know not many big teams can match. But we're small, we're agile and you know we, we have our processes mostly dialed in Um, but then from there you can really utilize your team to focus on the things that that needs the human touch. So you know having those, those conversations, I think you know routing, you know calls if it's an escalated manner or matter to you know somebody that can is a, you know, real human. I I think is probably the best use of their time 100.
Pablo Palafox:
48:23
That's exactly what I, what I try to um explain when, when people have concerns about AI, it's just giving people more time back. Like give your team more time back to actually do the actual sales calls, to actually build the connections with the carriers you actually care about, like those type of conversations are the one you want to have your team working on 100% Anything that is out there of that realm.
Pablo Palafox:
48:51
Yeah, you can just use AI or just let's call it like tools. I mean, it's just tools Like AI is just another tool. So aren't we using email like before AI quote unquote before AI actually became like known as AI in the common space? Before we had AI, we had email automation in other flavors, so is that also bad?
Blythe Brumleve:
49:17
Spell check, google search, autofill, all of these different things are a form of artificial intelligence, but for whatever reason, people have chosen this to be, I guess, their tool of fear. They try to restrict people from advancing in technological ways and I'm on the tech side of things, especially from the mindset of I don't know if you've heard of the people I'm one of them that is very polite to chat to BT, just in case it turns on us and they'll remember who the polite ones are.
Pablo Palafox:
49:50
That's so funny. I heard that and I I tend not to do that myself. I, I I'm now more like a super direct, like, do that, do these? I sometimes like, like, am I doing something wrong here? Like, will, will these come back to me at some point?
Blythe Brumleve:
50:06
That's definitely a funny approach to it is, you know, saying please and saying thank you, just in case Just in case.
Pablo Palafox:
50:14
No, this is. This is fascinating, I mean. I think also to the point about, like people maybe losing jobs or whatever. I've never heard no one yet I still have to hear someone tell me oh, I'm going to fire people because of these. They just put them to other tasks because they want to grow. I mean, companies want to grow and you're not going to just get rid of the good people. Obviously you want good professionals and employees to like stay with you but do more value-added tasks. So I've yet to see someone saying I'm going to just fire people because of, like, this school AI thing automating some of the tech calls or payment size updates or whatever. They just put them on other, more value added tasks. That's kind of my learning and my reading from from these.
Blythe Brumleve:
50:57
So what do you have? A maybe I imagine you probably have like a product roadmap. First of all, how difficult is it to have a product roadmap when so much changes in the world of AI from like day to day?
Pablo Palafox:
51:14
We just almost don't have it Like. We have it like flexibly on our minds, like we just keep talking about it all the time. But it's crazy, I mean every day you have something new and you kind of you kind of keep it up, keep up with it. During my time at the PhD, you already felt like in 2021 or so, you already felt that like there would be like a new paper every week. Now it's almost like every day there's something new and it's like well, I mean great, because like we're getting better tools, even for ourselves. Like the better LLMs we have from other providers, the easier the prompting and the fine tuning is for our specific use cases. So that's great. But it's still like to mentally just take some time off. You're just constantly thinking about what's new and how to improve it.
Pablo Palafox:
51:59
But yeah, I mean our roadmap right now is just building a very easy-to-use platform for any logistics player to just build different use cases on their own. Even, obviously, when the use cases are complex, we come in with our engineering team and solutions engineers and we like tweak the use cases and fine-tune those use cases with real-life data that we gather with them. But for like easier use cases, like you know, like that router AI assistant, that's kind of plug and play Customer comes in, I want to automate my phone tree, I want to do some load updates. Those packages are kind of built pre-built. So it's kind of that level of self-service that we want to have in the platform more and more so anyone can just build cool quote-unquote easy AIs. And then for the real hard use cases, currently today the level of fine-tuning you need is still there.
Pablo Palafox:
52:59
It's not as easy as just prompting, the same way that you prompt chat GPT when you're talking to it. Prompting gets you so far. It gets you only so far. So typically we need to sit down with big enterprise customers like Circle, starting to work with other from the top 50 US brokers defining their nuanced use cases. Then again they typically look the same all the time it's just carrier sales, it's just payments, status updates. The only difference is the connection to their existing systems, right, and TMSs but we're working on that connecting to other players in the industry to like, have, like this environment that these kind of yeah, this environment of like tools and logistics that we can just connect to and like serve anyone through any tool and TMS.
Blythe Brumleve:
53:48
So what does sort of the ideal customer look like for Happy Robot? Is it at the enterprise level right now, mid-market? What is your ideal customer that your solution is built for right now?
Pablo Palafox:
54:02
We're very comfortable in the enterprise arena Just having resources available from the company bringing their own engineers. They know how to operate their internal systems.
Pablo Palafox:
54:18
The bottleneck we've seen so far with more mid-market folks I guess, would be they rely on other TMS systems and some TMSs they take longer to let you integrate to them. So as a newcomer, we're still battling that in a sense. Can we connect to MercuryGate Today? Take some time. Mcleod take some time.
Pablo Palafox:
54:41
So some customers that already have their own TMSs or even have TMSs that are more API friendly, that's always kind of the better customer for us. We always overcome that through different approaches. Like there's also web scraping or like app scraping, so you can have like a bot. We've had some good success with an app scraper that would just look at the TMS and give us like the information every 30 minutes. It would just upload it, versus having to wait for the TMS to give us access to their APIs, which typically they don't love for some reason, which I don't understand. As an engineer myself, I'm like why we have open APIs now that people can just use. Like we also have that product.
Pablo Palafox:
55:31
So to your question as well, like we do shine with enterprises, we also have our developer kind of tier, if you will, where other startups in logistics are actually building on top of we have some startups building products for factoring companies. So they'll build a bot that, in the name of a factoring company, calls in a broker and says, hey, calling about these load number is this associated to? Like carrier ACME transport? Yes, okay, any advancements? No Okay, all good, thank you Bye. So we have some startups doing that with our APIs. That's why I'm sad to hear that other players in the industry are almost close in the door in front of you so that you can not even integrate to them. But hey, I think things are getting better. Some of the new TMSs are already open, api friendly, and that's that's great to hear.
Blythe Brumleve:
56:31
Yeah, definitely, especially for a supporter of this show Thai software, thai TMS. I know we just had a conversation with them last week about how they they prioritize integrations and having that API capability and just it feels like there's been a shift over the last like three to five years within TMS platforms that they realize that they need to be a little bit more open. I know we have taken a lot of brute strength to get here, a lot of convincing. Some of them are a little bit slower than others to adopt the new technology and to each their own, but it's the advancements when it comes to how much more productive that you can be throughout the day if your platforms can talk to each other is just astronomical and it really does allow you the ability to put people in their best position, where they're going to succeed internally and then ultimately, hopefully the company succeeds, customers succeed. It's a full loop circle. Hopefully the company succeeds, customers succeed. You know it's a full loop circle.
Pablo Palafox:
57:34
All right, pablo, is there anything that you feel is important to mention that we haven't already talked about? I would say don't fear AI. It's just going to be a tool. Learn how to use it, learn where it can be helpful.
Pablo Palafox:
57:46
I've seen a lot of people being very successful on AI for email as well. We don't do email. We actually decided explicitly not to do any email stuff. So if anyone wants to start with email, I have a good number of friends that I can refer them to doing some cool AI automation through email. Ai is going to be there, so you'd better learn how to use it and acknowledge it. Yes, so if you do want to implement, like if you're a broker out there, a carrier out there looking to automate some of those transactional phone calls, they can reach out to me at founders at happyrobot I guess that's a good email founders at happyrobotai. They can just reach out to us there. Just visit happy robotai and uh, yeah, we'll follow us on linkedin.
Pablo Palafox:
58:35
We do have a fair amount of good posts that I got some attention. Uh, good and bad. I should say uh, you're gonna see a lot of people just bashing us and all they're saying, like, this is cool, but hey, that's kind of uh, what every new technology suffers from at the beginning. I think so excited to be working on these and thank you for having us, by the way absolutely, um, you know, I guess, to the haters comment, you know you're not doing.
Blythe Brumleve:
59:01
You're doing something right if you have, you know, a few haters out there that are, you know, skeptical, but obviously you have paying customers and you have a you know a bunch of people around you that believe in you and believe in your mission. You're proving it because you know you have actual customers that are paying for your product. So I think it's really cool to see, and so kudos to you and the team. Where can folks, I guess, check out the Happy Robot website? We'll put that link in the comments. They can get signed up for a demo and hopefully it's a good fit for you guys.
Pablo Palafox:
59:30
Exactly, yeah, happy to do that Awesome. Thank you so much, pablo. Thank you so much.
Blythe Brumleve:
59:40
I hope you enjoyed this episode of Everything is Logistics, a podcast for the thinkers in freight, telling the stories behind how your favorite stuff and people get from point A to B. Subscribe to the show, sign up for our newsletter and follow our socials over at everythingislogisticscom. And in addition to the podcast, I also wanted to let y'all know about another company I operate, and that's Digital Dispatch, where we help you build a better website. Now, a lot of the times, we hand this task of building a new website or refreshing a current one off to a coworker's child, a neighbor down the street or a stranger around the world, where you probably spend more time explaining the freight industry than it takes to actually build the dang website.
Blythe Brumleve:
1:00:21
Well, that doesn't happen at Digital Dispatch. We've been building online since 2009, but we're also early adopters of AI, automation and other website tactics that help your company to be a central place to pull in all of your social media posts, recruit new employees and give potential customers a glimpse into how you operate your business. Our new website builds start as low as $1,500, along with ongoing website management, maintenance and updates starting at $90 a month, plus some bonus, freight marketing and sales content similar to what you hear on the podcast. You can watch a quick explainer video over on digitaldispatchio. Just check out the pricing page once you arrive and you can see how we can build your digital ecosystem on a strong foundation. Until then, I hope you enjoyed this episode. I'll see you all real soon and go Jags.
