Welcome And Guest Introduction

SPEAKER_01 0:00

What's going on, everybody? It's your boy, Truckin' Ray, and we have you guys tap into another episode of the Dast Deliver Podcast where we talk about people moving the industry forward. Today we're going to guess with us is bringing tech, AI, and innovation into a space that doesn't always move fast but needs to. Ken McLeod from Laconic Tech is in the building. Ken, welcome to the show. Ken, thank you so much for joining us today and being on the show. I'm really glad to have you here. How are you doing?

SPEAKER_00 0:32

Awesome.

SPEAKER_01 0:33

You love? Yeah, doing really well. I'm looking forward to spring. Kind of kind of done with winter. Nice. Yeah, man. So uh I got a lot of listeners who want to know what you guys are doing over there. Laconic, uh uh tell us a little bit about how you got started. How do you jump into this space and you know where did it all take you?

From Engineer To AI Builder

SPEAKER_00 0:53

Yeah, so uh I'm an engineer by training. I spent most of my uh career working in factories and normal nine to five kind of jobs. Uh so obviously we're dealing a lot with logistics and moving parts uh back to suppliers and vendors and customers there. Uh I started side hustling doing custom software just before the pandemic, so like 2019, uh doing kind of back office automation back in the pre-AI days. And then when the AI stuff started exploding in like 2023, like that just very clearly became a superpower that we could add to these applications in order to make them way more flexible than the old school if-then stuff. So the the side hustling was doing pretty well. I started thinking about maybe I should make that my full-time gig. And meanwhile, at the day job, uh I had survived three rounds of layoffs, and then the fourth round of layoffs got me and kind of made the decision for me that it was time to take the side hustle full-time. And it's been the best thing that ever happened in my career. It's been uh nothing but sunny skies since then.

SPEAKER_01 2:04

That's amazing. I mean, a lot of times you you get a little fearful, but hey, you got a little nudge to say, hey, this is where the where you're yeah, awesome. Uh did you always know that you end up in tech or um was there anything that pulled you there?

SPEAKER_00 2:20

I'm a 90s kid and I've been a computer geek since back when uh you know we had the big wooden cabinet in the corner of the living room with the giant PC in it.

SPEAKER_01 2:29

Nice, nice, yeah. Yeah, I love technology as well. Um seemed like it was always something cutting edge coming out, and but uh yeah, AI is just taking everybody by storm. Um it's a hot topic to talk about. Um, and so you guys are in the right space right now. Uh, what was the first version that you built, or how was it kind of different from what it is today?

The Load Board Bidding Agent

SPEAKER_00 2:52

Yeah, so the the very first tool in the logistics space that I built out uh is for a company called Coolmore Logistics out of Memphis, Tennessee, their brokerage. And the tool there is uh basically watching load boards, looking for opportunities that like fit their envelope to bid on, right? So they have a couple of customers that post like semi-public load boards, like an invite-only load board sort of situation. And there's a lot of brokerages and carriers who have access to these loadboards. So, in particular, when like a juicy, profitable load comes out, it gets bid on fast and then it'll be off the board uh because you know the customers gotta have that load moved. So the first good-looking bid is probably gonna get accepted. So they had this problem where in order to be grabbing those, you'd have to have somebody like sit in there hammer and refresh on the load board. Uh, and then that person isn't really doing anything else because they're just hammering refresh on the load board. And even then, it's like really hard to get a person to do a decent amount of due diligence on every single load when you're looking at hundreds of loads a day. So, what we set up is an AI system to watch these load boards, and every time an opportunity comes across, it will uh make a few API calls to get stuff like truck availability, uh current diesel prices is obviously a big deal for everyone right now. Uh and then it goes through a series of rules that we've written up with them on what sort of loads kind of fit their envelope and are good loads for them. And then if that's a good load, it uses the information it got from calling out to the APIs to calculate a good price, and it goes ahead and places the bid in a matter of call it a minute by the time it's done with all that. So now without having to have somebody sitting there hammering the refresh button on the load board, uh, they just automatically, without a human having to do a thing, get bids in on all the profitable loads that fit their envelope well.

SPEAKER_01 4:57

Nice. So, what kind of companies benefit from the solutions?

SOPs Into Automation Without Replacing People

SPEAKER_00 5:01

Yeah, so it's really everybody who has got uh repetitive tasks like that, where you can write out uh well, I guess what you're doing with a computer, right? We're not driving a truck with this stuff. At least we're not, anyways. I know there's people working on that sort of thing. Uh anything where you could write out an SOP document on how we're doing this, right? So that's a perfect example of like that watching the load board. You know, you're gonna refresh the load board all day. When a new load comes up, you're gonna go look for available trucks, you're gonna check for historical uh data along that lane, like our what are our recent shipments that we've booked along that lane cost us. Uh, you're gonna go check current diesel prices in that area. Uh and then you're going to, so long as you're gonna check these three criteria, and if everything looks good, then you're gonna bid it according to this formula, right? You could imagine writing that out as like an SOP document and then handing that to an intern and sticking them in front of a computer and going like, okay, buddy, here's what you're doing all day, is following. So in any job where you can do that sort of thing, we can probably now build an AI agent in order to do that. And then we take the people and we move them on to more high value stuff, like the managing by exception, where like, oh no, we had a truck breakdown, we had a customer wasn't ready for the pickup, that screws up this whole schedule, and now we got to move around and shuffle all the dominoes. That's the sort of thing we probably don't want in AI handling in real time, but we free up all of this monotonous work so that the humans can go focus on the like actual emergency stuff and the building relationships, right? Like the sales, uh, both carrier and customer sales side of it, where relationships matter.

SPEAKER_01 6:52

I like that. Yeah, that's awesome. You're solving problems that people didn't even realize they probably had. So, what's one of the biggest mistakes companies make before they come to you?

The Biggest AI Strategy Mistake

SPEAKER_00 7:02

Oh, this one's definitely uh you've probably seen somebody do it, like walk into a room in one of these companies and say something like, uh, we need to be doing more AI, or we've got to be using AI. Where can we use AI? And like this is classic solution in search of a problem kind of thinking, right? It's like a hammer looking for a nail. Uh, you don't want to be thinking about it as where can we use AI, because you're almost certainly going to wind up picking something uh that isn't gonna show a strong ROI that's not actually solving a real problem. You're just gonna find something that like fits with your conception of AI. What you want to do instead is think about what are the real constraints of the business right now. So, like what's preventing us from hitting our goals? Whether that's a revenue goal or a profitability goal, or uh, we're gonna move this many loads this month, kind of goal, whatever it is. Think about okay, what's the bottleneck in this pipe that's stopping us from reaching that goal? And then once you identify what that is, now we can take this step back and look at it and go, we've got a bunch of tools available to us now that we didn't have six months ago because of how fast all this technology's moving. Can we attack that bottleneck with one of these tools in a new way that we weren't thinking about before?

SPEAKER_01 8:24

There you go. Nice. Yeah, that's great. Because uh yeah, you can waste a lot of money um trying to do things uh cheaper and then it ends up being more expensive. So um let's talk about AI. Um, it's the buzzword, everybody's talking about it. Uh, how does AI actually uh being used in trucking or logistics today uh that you feel is uh not just hype?

Real Logistics Uses Beyond Hype

SPEAKER_00 8:46

Yeah, so it's on workflow stuff like that first example we were talking about, where uh it's difficult to write that out in old school kind of if-then programming language. Uh because you have things like is you know, is this let's say you've got a particular city and you've got to know, is it near this metro area? You know, you've got some town in Texas, you want to know like would people consider this near Dallas? Let's say. Uh just knowing a straight like what we do in the old school programming days, where you'd take a distance from that uh that lat longitude for the city that you're looking at to the center of Dallas, and you go, okay, we're gonna call near 50 miles. So it's 53, so we'll say no, it's not near. However, like I picked Dallas specifically because I know it's got huge sprawl, right? Like 50 miles from the center of Dallas. Anyone who lives around there definitely still considers Dallas metro area because it just sprawls out forever. That's a great example of the sort of thing that's like hard to capture if we're doing old school programming, right? You're not gonna have this giant database of the radius from each city center as what counts as near that city. But that's exactly the sort of stuff that LLMs are great at. You give it, you know, you give it a location and say, is this near, would somebody consider this near Dallas or Dallas Metro? And now you can, and they can tell you yes or no, and then you can do flows based off that.

Smart Starting Points For Small Fleets

SPEAKER_01 10:21

Huh. All right, that's great. Um, so I mean you're making uh real world use um for a lot of people out there, so you can see that it actually works. Uh um doing AI right now, um, there's something I really wanted to get into. A lot of people are uh talking about AI to, you know, what what is the effectiveness of AI? You know, is that actually helping companies and some red flags that companies are wasting money or uh business owners are asking to for AI to do the hiring, but yet is it actually helping out in that area? Um, what are some simple starting points that small trucking companies uh can use AI for? Maybe um some data points that they could look at to say, hey, this is where AI is really being effective.

SPEAKER_00 11:09

Yeah, absolutely. So one of the things uh to be thinking about here, like one of the mistakes people make a lot is putting AI between themselves and their customers. Right. So here, and if it's if it's a transactional sort of thing, this might be okay. But if we're talking about more relationship building or sales, uh like it's not gonna be as good as a human is at building that relationship to make a sale right now. So that's a perfect example of where you might save money on getting rid of a sales guy and tell yourself that you're gonna have AI do it. But I'll guarantee if the sales guy was any good, he was gonna bring in a lot more business than the AI is for now, right? We're recording this in in uh early 2026, maybe a year from now, I'll be in these words. Uh but it's in the situation where there, like in a sales role, let's say, if the AI is even a little bit less effective than the sales guy, uh like this thing is not gonna rely. It's gonna do the exact opposite, and you're gonna wind up money in the hole. Uh so instead you want to try to find places where, you know, maybe like uh in that load board example, maybe we're dropping the ball right now, and there's a bunch of loads that we're not bidding that we could be bidding, and then we implement a system like this, and now all of a sudden we can be bidding them. Like even if the whole thing stopped working, we're still just falling back to where we are now, right? So it's thinking through the cost benefit uh kind of analysis of stuff like that. And then the other big piece is we don't want to be thinking about replacing people wholesale, like in that salesman example that I gave, because the tech isn't there yet. And like, boy, if you want your team to not support you, tell them you're replacing people with AI. Uh instead, what we want to do is think about individual workflows that people are doing. Like, you know, like uh like watching a load board or like finding a carrier for a load that we just got tendered, or uh finding a replacement truck for one that just went down, this kind of stuff. Identify those out as workflows, and then we can probably automate the heck out of those workflows, but that workflow wasn't all that person was doing, right? They had 20 other responsibilities too. So now we just took that off their plate and freed them up to do the more high value stuff. So we're given our existing team superpowers, we're not replacing humans with robots.

SPEAKER_01 13:42

Nice, nice. I can see the passion as well. Um, you know, you think about um these detectives out there working cases and things like that. Do we want a AI to be solving a case? Um, I think I listened to one true crime thing, and they talked about how the human instinct is faster than any computer. So if we can keep training our instincts to be faster, to pick up on those areas where the company needs to grow, where they need to be successful versus getting bogged down with a lot of uh processing. So I think you're on the right track. Sounds great to me. Um, using AI to make us better is going to be the key into um bringing a better product, a better um service for our for people to have and to provide.

Using AI For Data Analysis

SPEAKER_00 14:24

So yeah, another great example there I was thinking about when you brought up the detective example. I was thinking of like weeding through uh like reams and reams of documents, boxes of boxes of papers, like in an old school detective show. Uh another place where we can get a lot of value out of AI is in data analysis, right? So, like talking and logistics companies, just by the nature of what you do, build these huge volumes of data, right? We know about all the loads that we ran, the dates they were run on, the customers they were run for, uh, what the costs wound up being, what the profit was. We just, by virtue of operating, you wind up with a huge data set. And what most operations aren't large enough to have uh like a geek on the staff who can then dive into that data and ask things about like, hey, which kinds of runs are actually most profitable for us? You know, what kind of customers, what kind of loads are we moving, what kind of geographies are we going to to from? Uh, or or maybe it's other data about drivers or particular trucks, or you can think of all sorts of things you might want to ask. Uh and a small operation can't have a geek on hand to go jump into the data and answer that. But almost everybody now can wire up AI to those data sources, and now the AI can be your on-staff data geek to go answer those questions. So if you want to know, uh, you know, are runs like XYZ more profitable than runs like ABC, they can go dive into the data, uh, run some simple scripts, query the database a few times, and come back with the answer for you.

Token Costs And Controlling Spend

SPEAKER_01 16:26

Perfect. I like that. Um, man, you got to think about all the work you're putting into uh for your company. Um, what I'm sure there's struggles and challenges, some lessons that we could talk about. Maybe people can learn from your experience. Uh, what's been one of the hardest lessons you've learned uh building this and um scaling it out for you for yourself?

SPEAKER_00 16:47

Yeah, so I think that the one of like the most interesting technical challenges and one of the stuff that I and my team have to chew on the most is uh like everybody's used to thinking of AI as free, right? Because we all practically all first get exposed to it in one of these free subscriptions, or I should even say subscription, free membership, right? Uh where they're limiting your usage usually. And when you're doing business use cases over the API, there's like there's no free tier at all. You're paying right from the first uh token that you're passing through the system. And it it's like you're paying a handful of dollars per million tokens, right? And you can roughly think of a token like a word, like a word of input or a word of output. So at small scale where you're asking it to look at one particular job, uh one particular document, it's still pennies, it's not significant. But then you kind of between the free tier stuff that we're used to and the fact that any one job we ask it to do is gonna be a few pennies, you kind of get used to thinking of it as free, but then it can creep up on you really fast if you've got something that you're doing over and over and over again, right? If it's something that's running all the time, all day long, and you're pushing huge dreams of data through it, all of a sudden the pennies start becoming dimes and then dollars and they start mattering. Now, none of these get to like huge, you know, scary numbers for a big company, but big enough that it matters. And then we wind up having to go in and do a little more careful engineering about like what which of this data do we actually need to pass through the AI? You know, are we wasting a bunch of tokens on stuff that doesn't matter and doing things like can we go find a dumber, cheaper AI to act as a filter for our big brain, expensive AI, so that we're only sending stuff through the big brain that we actually need to, and we're kicking out all the kind of useless nonsense stuff.

Avoiding Lock In Across AI Models

SPEAKER_01 18:49

Man, so break that down for somebody. So you have uh a more of a mainframe AI. So for somebody that maybe is looking into expand or uh so you have a source that you go to. Um uh can you tell us a little bit more about that?

SPEAKER_00 19:03

Yeah, no, so we're this is another important bit. So we use the AIs from all the major providers uh that you've heard of, right? Like Google, enthropic, open AI, uh Grok. Uh, and we'll try really hard to not get locked in on any one of them. There you go. Because then you're locked in, like, which is never a good thing. And also, uh like these companies are all spending billions of dollars to compete with each other. So just because one of them is the best right now, that doesn't mean they're gonna be the best three months from now or three weeks from now when the next big release comes out, right? So all these systems are built to very much treat the AIs like Lego bricks so that as the new ones come out, we can plug the hot new AI into the system. And the other thing that lets us do is for particular jobs, certain AIs are better than others.

SPEAKER_01 19:59

Yeah, isn't that weird? I I try to explain that some people they're like, Well, I don't I like you know, I like Gronk, I like uh Chat GPT, um Gemini. And I'm like, Well, did you did you run it across all of them? Because sometimes you may you could get a different answer. Um is it true or is it we going down a conspiracy lane?

SPEAKER_00 20:17

No, no, no. Like they're definitely they're built different, they're like different, you think of it like different personalities or different people, and they each have certain things that they're better at. Like one of the clearest examples of this right now, with the way things sit in March 26, is anything that's got to do with visual, right? Like where we want to feed it. Uh maybe we want to feed it a picture of the cargo in the back of a van trailer and ask it some question, you know, is this loaded or empty? Or is this secured or insecured, right? You want to ask some kind of question like that, or you're actually feeding it the image and asking it a question about the image. Uh Gemini is hands down better than all the other ones at this kind of deal. I've got engineering customers where we're feeding Gemini images of engineering drawings, and it's doing a pretty darn good job of understanding the engineering drawings from looking at an image of it. And it's like it's just head and shoulders above the other ones right now. But there's other stuff like following long complex lists of instructions, like those SOPs we were talking about earlier, where like it's clearly a little behind, let's say uh quad, which is phonanthropic. So it it and then like I'd say probably the hottest image generation model right now to like make new AI pictures like everybody sees on uh social media, that's probably with open AI. So right there, we've got like three very common things that we're doing all the time in this world, and like the best the model that's best at it is different for each of them.

SPEAKER_01 21:52

Yeah, exactly. Um, if you get to use Gronk at all, yeah, yep.

SPEAKER_00 21:58

I've used Gronk quite a bit, actually. And in that uh the project that we started this talking about, that's been powered by Gronk.

SPEAKER_01 22:07

Wow. Yeah, because you're you're testing the limits of it. And that's kind of nice to hear that feedback. You're doing more volume than a normal person would do. And I just kind of um play around with it here and there. And um, yeah, sometimes it seems like Like I get in an argument with it sometimes. It wants to do what it wants to do. And I'm like, let me go to the other one here and we try that one.

SPEAKER_00 22:29

Yeah, I tell you, it's not so applicable to like the load board stuff we were talking about earlier. But I use grok a lot personally when I want to know some bit of information, right? Like something happened in the news or uh or there's some rumor about a football player or something, and you want to know is this trade gonna go down. The thing that grok is super good at because it's so integrated with Twitter or X now is it's both very capable of and very eager to go dig around social media and even YouTube and uh blogs and Reddit and go find like what the latest information floating around out there on the internet is. Those are capabilities that theoretically they all have, they can do that, but Grock is both really eager to do it and darn good at it.

The Next Five Years In Trucking AI

SPEAKER_01 23:18

Okay, that's what I was thinking. Yeah, because uh, you know, uh X doesn't really hold back on free speech, it just lets it go. It's got the fire holes open wide open there. So um man, you know, that's amazing. Um uh sharing that with us. Appreciate that. I know a lot of people can probably use that in their back office or just for themselves personally to advance, um, maybe in their career, what have you. So I appreciate you sharing that. Now, also, too, um, when we talk about the future trucking AI, where do you see AI helping or taking logistics in the next three to five years?

SPEAKER_00 23:56

Yeah, I think it's it's this kind of stuff we've been talking about about identifying workflows that like that we can write a good SOP for how to do this. So it's that kind of like non-emergency situation in the normal flow of things, uh, and then automating those so that then the humans are freed up to do what humans do best, like maintain relationships with other humans, handle like unusual emergency situations. So when you put all that together, it's gonna mean that uh companies can grow a lot bigger than they used to be able to on a given team size. So, like where uh you know doubling in size used to take doubling your team, now we're going to be able to grow uh without having to add so much head gap.

SPEAKER_01 24:45

Nice. Okay, yeah, and then they can swings and these unstable markets that we have going on. I mean, that is that is huge right now. I mean, it seems like you can't catch a break. Uh one month or one week, everything's changing. Um, if you you know you thought you knew what was going on today, give it just a couple hours, right?

SPEAKER_00 25:04

Not this past month or two, that's what I'm trying.

SPEAKER_01 25:07

Wow. Uh so what should owners and fleet uh carriers out there companies are preparing for, you know, they're trying to have a projection, they're trying to look out to the future, see what's going on. Is there anything coming up that people are not ready for?

SPEAKER_00 25:24

Yeah, I mean, I think that a lot of this stuff that we've been talking about here, I think most people in the sort of normal, like dirt under your fingernails business world are probably not ready for this kind of stuff that we've already been talking about, right? And not thinking thinking about all the stuff that's got to happen in the company as workflows. And then which of these workflows could we write out a standard operating procedure for and automate? Like, granted, like nothing I just said there is super cutting-edge Star Trek stuff. Uh, but like that's where all the leverage is in the next few years. And sure, there's lots of Silicon Valley people talking about it, but I don't think there's a lot of main street people talking that way yet.

SPEAKER_01 26:07

Yeah, I gotta kind of take some time to just hunker down and really go at those those processes to make sure they're smoother and more simplified and more automated, and then you'll be more agile.

What Laconic Tech Builds And Supports

SPEAKER_00 26:21

Uh and you're again like you're thinking about it more like you're a business consultant than like you're an AI guy, right? You're not looking for a place to do AI. Instead, you're you're laying out all of the processes and workflows that we're currently doing, and then you're finding the one that's the bottleneck and saying, okay, there, how can we attack that?

SPEAKER_01 26:42

All right. So tell us a little bit more about the company, maybe people that aren't aware or they're hearing us from the first time. Um, what is it that uh people can look to you for if they were to just uh hey, go to your website and uh look for a service right now, today?

SPEAKER_00 26:57

Yeah, absolutely. So we're a team of all American engineers. Uh everybody's getting a little bit of gray in their beard right now. And uh basically what we do is we partner with companies, uh most of the time, those kind of main street businesses with dirt under their fingernails, stuff like logistics, manufacturing, uh heavy equipment, these kind of companies uh in order to identify where the opportunities are to build stuff like this out and then actually build them out and implement and hold your hand through the follow-through process of actually getting the team using these tools and then adjusting as uh kind of the plan collides with the real world and we realize that we've got to uh to tweak the way we were doing things in order to get the output that we're wanting.

Turning Tribal Knowledge Into Rules

SPEAKER_01 27:46

I like that, you know, because if you don't like what your productivity is, you know, let's say proofs in the pudding. So if you don't like the recipe, you don't like the way it tastes, you gotta try to change it up. And I think engineers, man, the things that they come up with is beyond some things we can imagine. So thank you guys for doing that. Um, what's one of the most challenging things that your team has been able to accomplish that you you would say or want to share?

SPEAKER_00 28:16

Yeah, so I think that the a lot of times the most challenging I mentioned the the cost thing earlier, like when we've got something that's just taking a fire hose of data and winds up costing a lot more than we were expecting. But we already talked about that one. So the the next one comes in these situations where we just wind up with a huge list of rules that we're gonna follow, right? So uh you could imagine one of these things like bidding on a load board. This didn't happen in that case, but this sort of thing could easily happen uh where you just wind up with, you know, right now Sally's sitting in the front office and she just knows in her head all the stuff to bid on and not bid on when it comes up on the load board. So if we actually manage to get all that stuff out of Sally's head, you might wind up in a situation where there's 200 rules, and you know, and it's stuff like, oh, well, you never want to pick up from that dock on a Friday because then everybody's leaving work and then the truck can't get out and you can't make the delivery. So if we're ever picking up there, you got to tell them that you're picking up Monday morning, never Friday afternoon, and it's like, but that's just that one location, and there's 200 of these. Uh so just like you can imagine it's super hard for Sally to teach all that stuff to a new person who's gonna come take that job when we actually do get all those rules written down and we try to pass it to an AI, it's also pretty darn hard for the AI to hold all that stuff in its head and like be diligent about evaluating every single one of those. So there's a bunch of different ways we can uh we can skin that cat and attack it, like breaking them up into chunks, using different more advanced AI models, uh trying to reword the list of rules in order to try to make them more general. But it's tackling stuff like that is probably some of the biggest challenges that we run into.

What Ken Is Building Next

SPEAKER_01 30:06

Yeah, it makes sense. I mean, that's you hit the nail on the head there. That's uh sounds like trucking, all those different things. You know, driver goes through the wrong door. You're wasting time, you're wasting money. Oh, it's huge. Um, so man, this has been a powerful conversation. I think a lot of people can have some takeaways from there. Um, before we wrap it up, I always like to ask um, where are you headed next?

SPEAKER_00 30:29

Oh, I think it's growing this thing, right? So I've got I've already got a couple engineers underneath me, and we're kind of on a mission to take these real mainstream businesses and uh and drag them picking and screaming into the future.

SPEAKER_01 30:42

Nice, nice. I like the um, you know, we that's we got no choice. Um seems like that's where we're headed, that's where a lot of the money is going. Um we think about a lot of the advancements that these uh AI companies are gonna invest in, uh, this country. Uh if they keep that, if they keep those words and those contracts, I mean, that's gonna be a busy, busy time. I mean, just think of the influx of uh revenue that's gonna be coming through for a lot of these companies, that's gonna be insanity.

SPEAKER_00 31:11

It'll be an exciting few years, no matter what happens, that's for sure.

Where To Find Ken And Laconic

SPEAKER_01 31:15

Yeah, I know the world is very unstable with a lot of things, but um, if you go back into the past, I mean that that's instability was always there as well. So um if people keep focused on, you know, looking ahead, like said, keep them keep them focused on that. And you're gonna you're doing a great service for for a lot of people, a lot of companies out there to be successful. So thank you. Yeah, so yeah. And so where can people connect with you guys and learn more? Um, where would you direct them to go so that they can um not get lost out there and and go to the wrong place?

SPEAKER_00 31:47

Yeah, so the website is laconictech.com. That's l-a c o-n-i-c t-e-c-h dot com. And my email is Ken at laconictech.com. Uh, you can also find me on LinkedIn under Ken McLeod.

SPEAKER_01 32:02

Yeah, I was I was looking for it. I said, hey, there you are. I found chance got a lot of good content on there as well. So yeah, well, thank you for sharing that. And uh, so we appreciate you coming on the show, Ken. Uh, for anybody that's listening out there that has uh something they need to tackle, something they need to make sure that gets done, uh look for Laconic, man. They're they're doing big things out there. So we appreciate you coming on the show and talking about that. And if you you guys also enjoy that's delivered podcast, uh please sure to subscribe, share it with someone you know that can use this information. And uh, we hope that it also helps a lot of professionals out there to be successful, not just in trucking, but also in life. And so be sure to subscribe, share with somebody. And uh, I'm your go to guy, trucking Ray. That's what I like to do is to share these stories and successes of a lot of professionals out there. Uh so here's another episode of Last Delivery.