Hey everybody, it's Patrick Escolas with another Banyan Tire Tracks Podcast episode. I'm here with Matt Harding of Greenscreens, the CTO. Thank you very much for being here today, Matt.

Yes. Great to be here. Patrick.

Hey, so we've talked with Greenscreens before, I think twice now. I think you're lucky number three. So, we like talking to you, or you guys really like the abuse of having to listen to me. I'm not sure which it is.

Maybe a little of both.

It's definitely both. I always have a great time talking to anyone from your organization, and you guys come out with some awesome tools, especially as it regards to AI within logistics and shipping. So, before we get into Greenscreens and what you guys are up to now, I'd like to know about you and how you got into where you are now. I'm always curious, how'd you get into logistics? How'd you get into shipping? Where'd that come from? And how'd that journey look like?

Yes. Well, I'm of the generation that sort of finds itself in logistics. I don't think there were any supply chain education programs. You go back to the nineties, it was just kind of like, that was just one part of the org. So, it's changed a lot. I kind of got my start in manufacturing. I was an industrial engineer. Found myself up in Boston, working for the Princeton Transportation Consulting Group, which was one of the early companies, owned by Yossi Sheffi from MIT Center for Transportation Logistics. So, I got introduced to lots of people. George Abernethy, Tom Sanderson, Chris Caplis, lot of really big names on the shipper side, and started out as a procurement consultant running opti bids. So, it was the first kind of optimized bidding solution for the shipper market.

There's a lot. You have consultancy in there, a few points is that, because you weren't executing yourself. You were just telling someone what they should do. Or was it just because the position was different.

Yes. We built this really difficult-to-manage software, and they wanted to sell it to industry, but nobody could figure out how to use it. So, we were like, “Well, we know how to use it. So, you if you hire us, we'll apply it to your business.” It turns out, companies run bids every year. So, they didn't need a solution. Obviously, that space has evolved quite a bit as well.

So, I got to work with shippers, kind of understand how they look at networks. And then, as we started bringing all this bid data in, we had like, 20, 30 companies. One of them was Walmart, Quaker Oats, Nestle, really big shippers. So, we started playing with the data. I mean, this was before data science, before machine learning. 

I was going to say, you do at least have it in Excel form, or were you printing out those –

No. We had SAS, which was a statistical package, and we had databases and things like that. But we built regression models to sort of predict the price based on the goods. Then, as an eager young analyst, kind of industrial engineer, I went and built code, and I could measure a network before we put it out to bid to see if it would be over or under. It turned out it was highly predictive. The very early stages of rate benchmarking and –

That had to feel amazing the first time you did it and then it actually worked out. Because you're like, “This could be awesome, or I could have no idea what I'm talking about.”

Well, it's funny you say that, because I know this is a long path through my experience, this kind of interesting story. So, we had a customer that was the – the VP came in, we were looking at the bid results for the first time, and he brought an application that he picked up at McDonald's, and he said, “If we don't find savings in this bid, this is my next job application.” True story. They were no savings in the bid. So, we had this new tool where we could measure – it was just hot off the press. We went and measured their network and they were on average, was like 3% to 5% below the market as we could measure. But this is the first time we knew what a market was to have enough data to do something like that.

So, did you guys prep him for – do you guys, did you give him crap like, “Hey, do you want fries with that”, for the next time you talked to him?

No. We kind of reassured him that, “Hey, with all this market data, had we had this up and running, we probably could have given you some guidance before you ran the bid that you had market rates.”

I like that. So, it was pretty cool, yes.

Yes. So, that's where you're at, kind of doing the first bid. I know you were with Uber Freight for a minute. What did that look like?

Yes. So, so I went to Transplace after that period. I did TMS and then I went to Transplace, ran the consulting and engineering, and then went to Chainalytics and worked on their shipper product, their 3PL product. We had benchmarking in Europe and try to gin one up in Brazil. Unsuccessfully, we had a bulk. So, we built a lot of different models there. Then, I ended up at Transplace again, working with Frank McGuigan. They wanted to kind of bring data science to the business, improve their data and analytics. Then we got acquired by Uber.

After that acquisition, I was there for a couple years, running data and analytics for the shippers, and then I ran into Dawn Salvucci-Favier, and the rest was history. Been with Greenscreens now for about nine months, and absolutely love it. It's a really, just a great experience, great people.

Listening to you, it sounds like you have been along this journey and we'll get more into what I hope to hear from you, what you guys are doing now. But from all the way of manually trying to figure out what's going to happen to getting some of the automation in there and getting some better tools, and, like you said, some code. Then, now we're in this place where AI has hit and utilizing that to do some of the things that were very, I don't know, probably time and data-intensive, and manual task heavy.

So, sounds like you started when you had nothing, and as far as the tools, and now you're at a place where there's many tools, and that has to be – I mean, was there a point that you're looking back like, “Man, me 20 years ago would be kicking myself if I had access to this?”

Yes. Well, it's really interesting when you look across the last 20, 25 years, and you look at the cost of storage, you look at the network speeds, you look at the cost of compute. You look at all the tools that are out there in the market that facilitate scaled automated machine learning. Some of my prior roles, we would do one model every six months, and we'd have a small army of people in Bangalore, working through that process. We're creating and validating and choosing across dozens and dozens of models like every second. It's almost like we're cultivating models which are highly accurate, but because they're accurate, it's almost like they have this perishability, right? So, you do the model –

It's 100% accurate until we do the next model.

It’s good for a days, and then you're building the next one, right?

Yes.

So, to do that at scale, and to do that in a way that's very honest with your customer, because not only are they getting the use of that information, but then you're reporting on your accuracy, right? It's just really great to see. Not only that, I mean, it's still going. I mean, the engine is still churning. We're looking at GPUs and we're looking at even faster throughput and output. Even as we're sitting here talking about the last 25 years, the next 5 years, you could see just as much change as we saw in the last 25.

And speaking of that change in some of kind of the where we start and where we go, when we first started talking to Greenscreens and when Banyan began a partnership, it was strictly on the third-party logistics. Broker side for being able to get AI generative, whether it's your data or market data, to get truckload rates within the lanes. So, what are you guys doing now that's different from that? Because I got an inkling. I thought of what it is, and I hope, but I want to let you announce what's coming.

Yes. Well, we’re actively working with shippers, proof of concept, but it's built on a lot of proven tech that from my perspective, I’m looking across those 25 years and like what we were doing way back with the McDonald's story. The hardest problem to really solve in the industry, in my mind, was giving small brokers accurate rate predictions based on their network. You think about lot of the models of old required lots of data over long periods of time. There'd be some – it would take them time to catch up to the market. But the technology that we have for the brokers is doing the hardest thing right now. As you know, shippers much different business objectives in terms of keeping their network under control, keeping things stable, dealing with exceptions when they happen, but even trying to have controls around that. It's just a different model.

So, shippers generally have excellent data. They're fairly mature in terms of their solutions, of which Banyan is a big provider. The data quality is typically very high. Contract rates are fairly stable. We're going to, we're pivoting that technology on that side. Now, there is no visibility of the broker solution to the shipper, right? We don't share –

For good reason, I’m sure.

We don't share broker data between brokers, and we won't share broker data with shippers, and we'll never share shipper data with brokers. So, we have all of the guardrails there too.

I was just going to say, that's an important call out to any brokers list right now, and a bit of a awe to any of the shippers. They're like, “Oh, cool. I get to see what” –

No. There's no sell rate sharing to the shippers, that's for sure. We want efficiency and we want speed in the transaction. I think both sides with their own tools and their own sets of data can achieve that.

You made a point there whereas the shipper data is clean, within the comparison from the broker data, is there a much bigger avenue for dirty data or inaccurate within that, from the nature of the beast? Why is that a differentiator from an outside looking in?

Well, I think just from the standpoint of the pivot being less of a hurdle, I think that's kind of where I'm going there. I think, with the brokerage community, obviously, you've got very small brokers, 10 million or less, which use our solution. It can be a very basic kind of data profile. Then you have the billion plus, which we have a large number of those as well. So, the systems are all at different levels of evolution. Some are using the smaller TMSs, some are using the more legacy TMSs. We have to figure out how to serve all of those customers, given their different life progression in the technology space.

So, we have all of the means to codify and clean and develop account-level business rules and things like that to clean data. So, I would say that, the shippers, I think they just operate with a little bit more of an order to cash. There's a CFO at the end of that transaction. So, it just tends to be – I mean, I'm not saying that's not the case for the brokers, but it just tends to be a little less –

Convoluted, perhaps?

Yes. I don’t know how to describe it. But data quality is highly varied on one side. I think the other side has matured a little bit, at least, have less options for technology. They tend to settle into their technology. Whereas on the other side, there's a lot of new entrants and there's a lot of innovation. I think the Renaissance on the broker side has been quite amazing to see. Partly, one of the reasons why we've been able to be so successful over there is because of the tech and the partnerships and the things that we've been able to do to machine-to-machine connections. So, there's definitely a lot of investment Renaissance over there with some of those partners.

That's nice to see, because one of the common themes that I've seen, and I've only been in the logistics world for maybe two, three years. I've recently had children, so I don't know what time is anymore. But yes, I came from a pure tech space into logistics tech, and one of the things that blew my mind was how many people were still doing so much manually. Still picking up the phone, still dropping into the Excel sheet, or even writing it down on a notebook to keep track and Post-It notes. I think it's awesome that finally, some of these pieces that for so long had gone without any automation, any tech, any kind of, “Hey, do you want a tool that at least keep track of everything you're moving here?” Getting those things in place.

But back to where the shipper product that you guys are working on with the AI, where is that? Is that data coming from you guys? Where's that coming from? You got a pool of data?

Well, it’s going to be pay to play, just like it is with the broker side. So, the shipper, if they elect to use our service, they have to provide information. Then, we do all of the same processes to validate to clean, all onboarding and configuration that's needed on the business rules. We'll start off more heavily on the UX side, in terms of the interface. But shippers have a lot more places to put information. When I think of shippers, I think of, is it near the market? What's happening from just an industry perspective? I have supply chain design, and sort of engineering studies, or routing studies. Or maybe as a shipper, I want to know where my competitor’s facilities are, so I can model that to figure out their landed costs to be a little more competitive. So, the shippers have, I think, a lot more planning-ish types of applications where that information, especially when it's real time and relevant to the market, can be very impactful. 

I wouldn't have thought of that. That's a great call out. It's not to say that brokers or 3PLs are purely reactive, but by nature, they're reacting to the demands of whoever needs things moved. Whereas the shippers, they want to have that planned out well in advance, both just to have what they need to move and or go through if their manufacturer process, but then also to budget accordingly as well.

Yes. Imagine you're running a national network, and there's an acquisition being considered, and somebody says to you, “Hey, we got facilities in these locations. What's the transportation cost going to be?” You're not going to be able to call your carriers up and say, “Hey, I don't have any freight yet. But could you price this for me?”

Yes. Put a lot of time and effort into this, and I can't guarantee you'll ever get anything out of it. 

So, a lot of that planning, you're not going to get a lot of input from carriers if it's not real freight. It's going to be market-priced. Then you have cases too, where you've got pre-pay versus collect, or you have merchandisers on vendor inbound, or people that are quoting product and they need something to put on the invoice to make it look – to be close, right? So, there's just so many other places where that information is helpful in the supply chain, both from a commercial perspective, but also from like, a planning and engineering perspective as well.

Is that something just like the current tool, where there's going to be the market rate and then their specific data point? Or is it going to look a little different within that?

I would expect some differences, simply because booking a load, that's one exercise that you know is happening non-stop within these brokerages. They're bringing people in and you may have somebody who's somewhat inexperienced, but you can get them up to speed very quickly with something they can trust.

The shipper side, they're going to be wanting to know where am I relative to the market? And then maybe some insights as to what's causing that. We all know the director, VP level on the shipper side, working the carrier relationships, trying to make the case internally to the organization that, “Yes, well it affects us.” And they say, “Well, how much?” So, if you're able to give them what we call a target rate, which would be just training off of your network to say what the price would be, and then compare that to either industry-specific or general freight specific rate, those differences should start bleeding into the better picture of what those deltas are and why they exist.

The industry-specific is something I was not thinking about, because from the tool, and where the 3PLs, I kind of think that they – and sure, there's brokers that only touch certain industries, but generally it's more runs the gambit. Whereas with the shipper, yes, I can't compare. I'm moving food. I can't compare myself to someone that's moving bumpers, right? So, yes, that is – okay. You probably do have a little more nuance within the shipper tool that you're putting together right now. That'll be awesome to see.

What I've always liked within Greenscreens is A, you have the product standalone, but from a very biased standpoint, for someone that has a TMS or has system in use, with a partnership and integration, being able to put that into another system is really important, and without me spewing everything that I think from like a sales perspective, where do you think there are advantages with having it integrated into a system versus using the product standalone? Not to say that going to Greenscreens and getting the data isn't powerful by itself.

You're exactly right. I think, one of the aspects of this sort of technical Renaissance is that there's only so much screen space that people have, or the alt-tab, going through your applications can be really cumbersome, and that's part of the reason why some of it's so manual. People have to write something down here to have over there. So, we try to meet –

I'm thinking of my 54 tabs open right now. 

Anytime there's a screen share, there's like, two rows of tabs –

Got it. I don't show that screen. It's embarrassing. This is the clean one.

Yes. So, from a technical perspective, we can offer API, which is just direct, so we make all the calls to the data and that will be heavily invested in on the shipper side, right? So, we've got some work to do on the initial work to do the proof concepts and get alignment with our partners. Then, from there is okay, “Well, how do I get some of this over there?” We have two ways that we do that. We'll do that with just the basic APIs, with the endpoints, and the tokens, and all those good things. Then, we have what we call Java bundles, which are like applets that if you have a SAS solution, we can take over part of the screen with our own Java, sort of information. Then that allows us to keep a little bit of our brand that have a direct link into our system, right? So, there's some benefits there that it's kind of a portal into our UX if you want to go in and get the full experience. We can offer partial experience if you got the real estate in your app to do that.

And in the applet, do you have a little icon like Clippy from Microsoft telling you, “Hey, I think you want to move your shipment today.”

We do have a negotiations coach, so it’s tracking the holidays and road check, and all the different things that are happening that affect freight and as well as like a historical how to use common language to talk about how often a lane is moved at a certain rate, things like that. 

That brings you – so two things. A, I'm just going to call out. I think Microsoft missed an opportunity to bring Clippy back instead of their co-pilot for their AI. And B, take that as you will. Looking at the different lanes and some of the benefits of the tooling of itself from a broker, and this can be different with new people getting into it. From a broker side, it's your business to know what's happening in every lane. What the prices looked like. Whether or not you're moving in that lane all the time, you've got to know. From a shipper side, if I'm not moving in a lane, I probably don't know what's going on. So, if I have to get a new route, or if I get a new vendor, or I have a new distribution hub to get to, tell me about where this can be beneficial for that, because that's where I see a big difference in the power of AI for shippers versus that for a broker.

Yes. Well, it's kind of interesting, because one of the big moments for me working with broker data at Greenscreens is understanding that while there's variation in prices, obviously, you might have a Blue Light Special over here, huge discounts over there. Generally, the margins are much tamer and much better ranges than I thought. I thought during COVID, shippers were saying, “Oh, my route guide is not working, and I'm getting hit by these spot rates.” The reality is, is that through all of those inflationary periods where we look at the data, there's definitely inflation on the rate. But what's driving it is the buy side. It's the carriers that are pushing the rate up, and then the margins on top of that are staying relatively in control. That was just very eye book opening for me to see that. The problem is, is that, because the shippers were getting maybe – I saw some cases of 60% tender rejects on major, major shippers. It wasn't a percentage of margin that the shippers were suffering from. It was the asset providers not able to take the freight. As more volume went out there on a rising base, it felt extremely painful. But I don't see the – brokers are still competitive with each other. It’s just they had to buy at a higher rate.

So, I think, to tie that back to the shipper side, I never talked to as many CFOs at big shippers in my career as I did during the COVID experience, because all of the customers that I was helping on the shipper side, were saying, “I am under intense pressure. What's going on?” So, as you're portraying the markets both spot and contract, and you're able to characterize the relationship of those markets, it helps facilitate better decision-making.

I think that, obviously you don't want benchmarking to compress your margins, but I don't think that's the case. I think there's a there's an element of contract stable. Maybe spots got a premium, maybe spots a discount. But where you are riding that out, that's kind of where everyone's riding at. So, I think the transaction speed will improve. If you have a customer that doesn't know what a lane is, or anything about that, you might be able to get a better rate on that, or better margin. But over time, that's just going to get exposed anyway.

So, the key is to ride the market efficiently. I've seen cases too, where shippers had a very good deal with lots of freight. I mean, 20 points below the market on multiple millions of dollars of revenue for that carrier. When that carrier says, “I can't do this anymore, and we’re going to pull out.” You want to avoid those situations, because when they do, it vaporizes instantly. So, having a great deal, whether you're a broker or a shipper, is always a short-term outcome. It just got to –

Got to be a sustainable deal.

Yes. You want a sustainable outcome and all of this AI machine learning work is only going to continue. So, the key is being successful, knowing that the information is going to be coming more prevalent. 

Awesome pullback there. One of the many concerns, or at least the initial concern, with AI and anything was taking away human jobs. What I've thought within this space is that this is going to make it easier for your new humans in your organization to execute as if they were the person doing freight for 20 years for your organization. Now, A, what do you say to that AI is going to replace someone? And B, how is AI going to help? Because I wouldn't say that any company is alone in that the hiring process where everybody's looking for more. People are doing more with less people and the turnover is pretty high.

Yes. So, the way I see it playing out as it stands is that it's creating a consistent reference point for the knowledge of rates of which people can use to be successful. The concern is that AI is going to do everything, take everything away. Part of the reason why supply chain, like new technology entrance into supply chain, had a difficult time disrupting the industry. Disrupting the industry. It's like, “Okay.”

What a term.

“Hey, let's partner.” No. So, the disruption –

Got I AI and partner and disrupt, all in one –

The disruption is a great way to gin up investors, particularly those who don't know a lot about the industry. But the reason the disruption is so challenging in my mind, and I've seen this just working with people in data, is that really good people in supply chain haven't – their experiences have not been digitized. I'm not sure they successfully can. If you think about digitization, order to cash only covers the necessary things for payment, it doesn't cover what people truly know about a certain location on a Friday. Or this person started their job last week, you know what I mean? There's so much information in reality that the digital part of that is a very small subset.

So, we're going to push the limits on the kinds of information that we can pull, because the more you have, the more the machine learning can actually get more and more accurate –

That makes sense.

– and more specific. But at the end of the day, I use the metaphor of a like a high-precision scope. You're going to be able to see a target from 500 yards –

Whether or not you can hit that thing.

– by you don't know how to pull the trigger. If you're shaking up with that trigger pull, it doesn't matter how much of a scope you have. So, I see us as being that scope –

It's great analogy.

– in the decision support. But at the end of the day, everyone's hunting and gunning, and there's just different levels of competency out there that I don't think any digitization is going to take away. I just don't.

That is a great answer. Like I said, I love that analogy. For whatever reason, I know Barney Fife didn't have a scope, but I was thinking him behind one just shaken all day long. And I think there's – you touched on a few points here. So, as of now, Greenscreens within that AI world, it's been that rate generation whether from market, whether from your data, or even as you're talking about some industry. Where does AI go next within freight, shipping technology? Is it just going to continue at this rate generation? Or where do you see it? You've been playing in the space for a while now.

Yes. I see a lot of attempts at structuring unstructured communications. If you look at any 3PL or any big operation out there in supply chain, probably 50%, maybe more of the time is spent in email. That just speaks volumes to where the industry is at some level. I do think that the structuring of unstructured information and being intelligent about something that's coming across, automating the ability to just put that right into the place where the most efficient interaction is, I think that's an obvious opportunity.

The analytics portion, there's a lot of talk about, “Hey, just talk to your data and the minority report. You just speak and it gives you answers.” People are very suspicious of information if it's not completely vetted and produced by someone they trust.

I can't imagine yet, I'm going to bank my – you go back to the McDonald's like, if I get this wrong, this is my new job application. I can't see that individual entrusting something that's giving him data that is not thoroughly vetted. Because to my other point about not everything being in the data, there's all these exceptions and exclusions and things that can get messed up within the same system. So, executives really want to trust what's being fed to them. I know the AI is being perceived as, you're going to speak to a robot. It's going to give you facts. Everyone's going to be happy. I don't know if we're ready to trust it at that level yet. It's got a ways to go.

I did see a video of Dave Chappelle telling jokes about a topic that was fed to an AI, and it wasn't even Dave Chappelle, but the cadence, the mannerisms, and you could tell it was a fake, but –

It was pretty close?

It was close enough to know that five years from now, you're not going to be able to tell.

Oh, that's going to open up a whole new can of worms.

That's an afterhours discussion.

I was going to say, that's a whole another podcast. I appreciate that insight, and especially you bring up a point that I don't think I would have thought of by myself is that, sure, it's really easy to put data in front of somebody, but who put it together, who looked through it is going to be an emphasis on how much trust I'm going to put into that, whether I'm looking back at how we did or especially, as I'm looking forward, to structure my next budget or my next supply chain initiative.

Yes. I mean, the best analysts that I've ever worked with, if you ask them a question, go run this report, or this is what I want to see. If they're not asking you 50 questions to clarify what you even mean, you can't trust the answer.

That's my buy-in as a salesperson. If I had to say something, and they're like, “Yes, I get it.” That's not good. Someone's always going to have some question to follow up. That's a good point out there. So, I guess, as we're wrapping this up, like, Greenscreens on the broker side is out running and if you haven't touched it, and you work in that space, look up Greenscreens. If you're using Banyan, talk to us, and we'll get you the right person. But for the shipper side, when should we expect a rollout or a first look of what's coming?

We're in the process of doing all that. So, we'll be hitting the market. We are very deep in our roadmap and we've integrated input from our customers and working through that. I've been serving shippers for 25 years. Our CEO, Dawn Salvucci-Favier has been serving our shippers for almost the same amount of time. We're just super excited. We've been asked to go into this market. The timing wasn't right given the focus the broker side needed. So, we've got just a very strong base there and a lot of great customers that are doing very well. It just seems time to sort of break that off. It'll literally be like two separate types of functions. But yes, we're very excited about that and just keep your ears out. We have really great marketing team. Our brand is really strong and there'll be a lot more coming from us as we get close to the finish line here.

That's awesome, and briefly here, while you mention it, what else? Is there anything else on the roadmap? As you talked about, you're deep in the roadmap on the shipper side, and if you don't have it in hand, or you don't want to talk about it, that's fine. But what's the next step for Greenscreens?

Well, I mean, we're going deeper into the broker space. I don't want to give away too much marketing mojo. Last time I was on a podcast, it was like talking with somebody at a conference, and turned out to be I was sharing a little too much too early.

State secrets getting out there from Matt.

We’re going to let the barn door open. But I would just say that for the broker community, one of the things that's happening with the amount of information that we're collecting is the ability to make market-based and peer-based comparisons that don't – that take, that's all high level, so that you can't – you'll never be able to see, or you'll never be exposed in terms of your business. But think of things in terms of me versus the market, what types of decisions I'm making. We just launched our new product Ignite, which is about quotes and what's your win ratio. So, a lot of quote information. It doesn't get captured. It hits the floor.

Yes, because if you don't it, it gets thrown away, right?

This isn’t important. Well, it's usually important because it's actually speaking to the side of the business that isn't being productive. When you make that comparison in terms of where you are in terms of price, win ratios, by employee, by unit, by customer, there's a lot there to sort of look at the other side of that coin. That’s just released that I can talk about. But we'll be going deeper into helping different types of models that we're looking at, continue the modeling, interest in other areas and other modes. Like, I said the mere vs the market at different levels and just sort of a market level.

So, yes, just ongoing innovation. The world of data, as our data grows, our value increases, and then we've got to figure out ways to bring that to the market so that the brokers can be as successful as they need to be.

Oh, that's awesome. The Ignite product sounds awesome from a salesperson who has to meticulously go through why they've lost deals to figure out where to do it. That makes 100% sense to me, regardless of industry. As we wrap up, I know I said that before, and then I made you say a whole lot more, but I liked hearing it. I always leave a spot here for – this is a platform, whether or not people are watching or listening. What's your message to anyone out there, whether that's from you personally, Greenscreens or just from a tech space, anything? Here's your spot here. What do you got to say to the world or as I always joke, the 17 people that are watching right now?

So, there was a dust-up on LinkedIn a while back, and I just want to clarify myself.

Yes, all right.

Some of us have technology that we use every day, and it can be explained to us in layman's terms, but we don't have to know all the details. We just need to know that it's been validated and accurate. So, my only point and the the only clarity I want to throw out there, is that we're not trying to hide the complexity of what we do to our customers. In fact, we're quite open about the effect of it. But if you did want to go into the deep, dark corners of our machine learning, and our algorithms, and our approach. One, it's intellectual property, so we're not going to share that. Two, you're going to need about four years at some university and passing grade to be able to talk to the people that actually do this. It's not an offensive thing. We have all kinds of white papers. Machine learning for dummies, all that kind of stuff. I wouldn't say people are dummies, but like for –

It's okay. I only admit to be a dummy. It's not a problem.

But I would just like to say that we use technology all the time that we don't have huge command over, but we still trust it. Our number one mission is to be trusted and to be a partner and to be real. I mean, there's a lot of AI, ML marketing out there that we've seen over the years that I was a bit aggressive in the marketing, but we're legit, and we believe in what we do. Me, personally, Patrick, I can be more thrilled to be with this team. Just the greatest culture, extraordinarily hardworking, very legit, honest, hardworking people. So, that's my final message.

I like it. It started with the dust off to be cleared up. That's more exciting than some of these. But I really appreciate that. I'm happy you like your team and Greenscreens has already put out some great things, excited to see, what the shipper side looks in the near future, and take an opportunity here to say I love my team at Banyan as well. Because as you probably know, it's about who you work with that makes logging in each day and going through the tough times worth it and bearable. Yes. Happy you’ve got a team there.

We appreciate the partnership that we have with you, and we certainly look forward to strengthening that partnership. So, you guys have been great to work with.

Hey, that's awesome. Thanks to everybody listening and watching here. This is Matt Harding with Greenscreens.ai. It's been another fantastic episode of Banyan Tire Tracks Podcast. If you like what you see, like comment and subscribe and special call out here. Registration is now open for the Connect 24 Over the Road, road show. We're doing it not just in tropical downtown Cleveland, but we're also hitting Atlanta and Las Vegas, so see where you can get permission to go to and come see us in person.

Awesome.

Matt, thanks so much for the help today and for the information. Looking forward to talking with you and continuing to work from Banyan to Greenscreens and a very awesome partnership. 

Yes. Great time. Patrick. Thank you.

Thanks, Matt. Have a great one.