All right, we will begin our next presentation. Let's please give a warm welcome to the CEO of Inuvo, Richard Howe. All right, thank you. Maybe I'll just start right away with, if you're interested in an investment in artificial intelligence, there's very few of those that are based on real AI. We are, as far as I can tell, the only company on the planet to have commercialized large language generative artificial intelligence. Our use case is advertising. Just so we're clear, this is technology. Grok, in the sense that these are technologies based on scanning and crawling what I call the collective wisdom of humanity, as represented by the internet, and then building a model. In our case, that model was specifically built to solve the advertising problems, which I'll describe to you in a second. I may say things that are forward-looking. Please treat them as such. First things first, why did we build this AI for the advertising use case? The answer for that is because this is one of the largest markets in the world. We at Inuvo actually focused on two components of that marketplace: the search advertising market in Spain and the U.S., and the programmatic market. That is about $80 billion. These are gigantic marketplaces where you can actually build a billion-dollar company because there is so much money being invested in it. For the next slides, including a demo that I have here, I am going to focus on the artificial intelligence, which is the key differentiator in our company. First, let's talk about the problem. The problem is these markets are changing. In fact, it is probably a once-in-generational change. They do tend to change every generation or so. Right now, the markets that we serve are in the midst of a quite significant change. The change is related to the use of your data. For the last 30 years, modern advertising has been based on the exchange of a single commodity. That commodity is, in fact, your data. People think it's advertising, but it isn't. We buy and sell identities and the data associated with those identities. That is the commodity. What's happening in these marketplaces is the ability to do that is slowly deteriorating. Apple began this change. It was a technological change that sort of was the catalyst. That was reinforced by legislative changes. Countries, states, all coming on board and saying, hey, it's not ok to track people around the internet anymore and use their information for ad targeting. In fact, easily half of all mobile media transactions today are ignored by the thousands. We have no such constraint with the technology I'm going to show you. That is, in large part, why we built the technology the way we did. This technology is not incremental. It's revolutionary. In much the same ways that the media industry went from doing what we know as contextual technology that became the behavioral technology, which is following you around the internet, the next evolution in this technology is to use artificial intelligence. We knew this seven years ago when we started building the LLM, before anybody was talking about LLMs as a technology. What we effectively wanted to do with this technology was take the complexity out of the process. It was becoming clear to us that being able to market effectively almost required a PhD, simply because the plumbing of the internet needed to be known, where to get the right data needed to be known, how to do analytics needed to be known. This was exceeding, for the most part, the abilities of people making decisions about what to do in advertising. We wanted to democratize that in some way and make it easy by providing a simple AI solution to all of those problems. We boiled the problems into two categories, which I'll talk about here in a second. They were really a category for measurement, meaning how do I actually measure effectively the performance of my campaigns across all the channels? If I can't track people through those channels anymore, how am I going to do that? The second was, if I can't track people around the internet anymore and follow them, then how am I going to discover audiences and target audiences? Those were the two problems that we trained our AI to be able to do. The first of these is the measurement problem. It is equally as important as the discovery of audiences and targeting audiences problem. There is a growing number of channels. Up until the onset of privacy, the method that was used to be able to figure out which of the channels you're using is contributing to the performance that you're getting was as a consequence of being able to track you. I know that you saw a display ad on some web page. From there, maybe you saw a connected television ad. You went to your social media page, and you were looking at something there. You ended up. It has proven itself to be exceptionally accurate. We are pretty pleased with this technology. Most CMOs, this can become their dashboard, if you want to look at it this way. How am I spending money? Where should I spend more money? Where should I spend less money? Turning the knobs, if you will, which is something they all want but rarely get. The second component is the audience discovery and the audience targeting component. Again here, remember what I said. We were building a new paradigm for our tech. We were saying, we do not want to know who the person is in front of a screen. We do not want to track this person around the internet. How do we solve this problem? The LLM we built was the answer to this problem. In simple terms, the way to think about it is represented on this slide. This example is a real one. It's one most of you, since you're all mostly investors, would know. I'm sure you all remember the Theranos fraud, pretty widely publicized, one of the biggest frauds in American history. The company that broke that, the news outlet that broke that story, was The Wall Street Journal. John Carreyrou was the reporter. In fact, the individual that was a whistleblower in that case was a guy by the name of Tyler Shultz, who is George Shultz's, former Secretary of State's, grandson. George Shultz was actually on the board of Theranos. Now, because our AI has read everything ever written about The Wall Street Journal, and it has read everything ever written about every product, service, brand that you can think of, it knows all of the reasons why people are interested in The Wall Street Journal. Now that I told you that The Wall Street Journal was the one that broke the Theranos case, it's not hard to imagine that one of the reasons why an audience might be interested in a Wall Street Journal subscription would be because of the Theranos case, which, by the way, our LLM told us for this particular case. You see that represented contextually here with connections between it. Because in our AI's mind's eye, these connections exist. They all have probabilities on them, which I haven't shown here. It has the probability between these connections. If you look at the Wall Street Journal product on the side, thinking the product there is the Wall Street Journal subscription, it's saying, hey, Theranos is highly correlated to The Wall Street Journal. It knows more than just that. It knows that the Theranos case involved Sunny Balwani and Elizabeth Holmes and the Edison machine and George Shultz. By the way, there's hundreds more connections just to Theranos. This, right? That's the product side. The right side of the model is the media side. There's two iPad screens there. The top one's a connected television. It's actually a movie. It's called The Dropout. The bottom one is just a web page on the internet. It's a biopic about George Shultz. I'll go to the bottom one for a second and tell you how the AI works here. That page is one of hundreds of billions of pages on the internet. It just happens to be a biography of George Shultz. Now, there is nothing on that website that indicates that George Shultz is affiliated with Theranos in any way. What our AI knows is it knows what the probability is for why someone's in front of the screen for that page. In this particular case, it would say, oh, George Shultz, there is a really high probability that the only reason someone came to this page was not because of the former Secretary of State's storied political career. It is because of his affiliation with Theranos. That is why the person's actually here. It would match that. It would say, oh, because this person's interested, who I don't know, and I don't care who it is, because this person's interested in Theranos, what customer do we have, our company, that also has a similar relationship with Theranos? The answer would be, oh, look, The Wall Street Journal's there. It has a high correlation with Theranos. It would say, we should put our ad here. The important thing to keep in mind here is no data whatsoever has been looked up, accessed to generate this information. It's truly artificial intelligence that didn't exist. It was trained, trained on the collective wisdom of humanity. It is generating this knowledge. This is a paradigm shift. In the modern way of doing things, you'd look up an ID, and you'd go in a database, and you'd find all the information about a person. We do not do any of that. We do not require any data in our modeling. What I would like to do now that I have sort of teed up this Theranos thing is I will show you just how easy it is within our platform to actually build a model like this. I have got a little two-minute, it is really a demo, right? Just hang with me here, and I will play it. What you're looking at here is the IntentKey platform interface. At this point, all a marketer needs to do is define their audience. For example, in this case, we're asking the AI to build an audience interested in the Theranos fraud. Based on that information, the AI generates a set of seed concepts. These are the foundational ideas it identifies as most relevant to the prompt. Marketers can refine these by adding or removing concepts before the platform finalizes the targeting model. Once satisfied, the platform builds a full contextual model. Here we see concepts like mail fraud, Elizabeth Holmes, Sunny Balwani, and George Shultz, all closely tied to the Theranos story. Each concept has an associated importance score. For instance, Elizabeth Holmes ranks 85 out of 100, which makes sense given her role as CEO and central figure in the fraud. Marketers can also explore how these concepts connect. Clicking on Elizabeth Holmes, for example, reveals strong ties to Sunny Balwani and wire fraud, reflecting their shared convictions. You'll also notice signals like George Shultz, a board member, and David Boies, Holmes' attorney. What makes this so powerful is how it aligns with real-world interest in the product, in this case, a Wall Street Journal subscription. The Wall Street Journal originally broke this story. Sure enough, John Carreyrou, the reporter who exposed the fraud, appears in the model. Beyond targeting, IntentKey provides rich demographic and geographic insights. You'll see the total addressable audience, what percentage is highly interested, how interest has trended over time, and overall sentiment. We also break down gender, age, education, and income. This audience skews older, more male, highly educated, and higher income. The map shows geographic concentration. Dark green areas are highly engaged. Dark red areas are not. Now, building an audience model is one thing. Execution is what really matters. Marketers need to be able to act on this immediately. With just a few keystrokes, they can select their campaign system of choice all in a matter of minutes. What you've just seen has never existed before. Not even the ad of it. Attendee verification, dial-in numbers, and Q&A features. Test of the day and time, and you are ready to go. Share the specially generating event link with audiences. Use our analytics to review who attended. If you would like a more individualized experience, you can even schedule one-on-one meetings with key stakeholders. Sequire Audience offers a complete solution for all. They are not actually trying to solve the problem of empty miles that we're setting out to solve. Basically, what Procter & Gamble, Pepsi, load from point A and delivering it to point B? Obviously, that's a huge part of the equation. That's obviously what we're doing. We're also spending a lot of focusing on where is that truck going the minute it's dropped off its load. The way we're doing that, which I'll get to that in the next slide, think about it as a giant network, right? The more shippers that we have on the network, the more transportation providers we have on the network, the better we're able to make that network more efficient. This is what we're doing that's completely different than the way that the traditional digital freight brokers are handling it. What we've been able to prove through some of the pilot programs that we've been doing so far, like I mentioned earlier, traditional model, only 65% utilization of a truck. That same truck running through the SemiCab platform is able to get 90% utilization. Another way of looking at that is instead of one out of every three miles it's empty, that same truck now is only one out of every 10 miles empty. Huge, huge, huge efficiency gains. We're able to show our shippers that we can cut their total spend by 10%-15%. We're able to cut empty miles by about 70%. At the same time, we're able to still improve on-time delivery. I want to show you guys just a visualization of how we're thinking about this and how our approach is different. These are screenshots that are coming directly out of our SemiCab transportation platform. In the top left-hand corner, you're seeing all of the trips that have been successfully completed through our platform. Again, think about it as a network. What we're doing is we're looking for high-volume bidirectional traffic that is very, very, very predictable. That's why we love working with these Fortune 500, these enterprise-level customers. I'll give you a good example. Let's say you've got Pepsi out of Texas. Let's say Pepsi is shipping a lot of product from Texas to Chicago or from Texas to New York. Typically, those trips, they're always one-way trips. They ship out. They're full. They come back. They're empty. What we're doing and what we're doing differently is that we're looking for corresponding customers. You can kind of see it here in the bottom left-hand corner where it's highlighted in yellow. Those are the lanes that we know we can be the most efficient. The reason we do that is because we're looking for customers that are operating in a bidirectional manner. My example earlier of Pepsi shipping one way, let's say from Texas to Chicago, we're looking for corresponding customers, could be Coca-Cola, that's shipping the opposite direction. They're looking for things shipping from Chicago or New York backwards. What our AI optimization tool is able to do is we're able to look at all of that load data in real time, 24/7, and it's constantly improving and making those routes more efficient so that when that truck driver drops off that load to Chicago, that truck knows exactly where it's going right after it drops off, and it's going to the next most efficient point to pick up. Differently, that's how the software platform works. You can see the same thing visualized on the right-hand side. This is now broken out into geographic density. The nice thing about that is it's showing us the lanes, the shipping lanes. If they're shipping on those lanes that are highlighted in bold blue, we know we can price that customer really, really aggressively because we know we've got so much traffic that's up. We're just keeping that truck running constantly, back and forth, back and forth. I should also point out, we are asset light. We're a technology company. We do not own any of these trucks at all. We don't want to own the trucks. We want to go very much like an Uber model where we're contracting, sorry, subcontracting those trucks out. We do not want to own them. I just want to further illustrate a good case study here that we did. We were working with a large and $80 billion CPG company last year. They came to us and they said, hey, if we were to run all of our freight through you guys, what would that amount to in savings per year? They were nice enough. They gave us about six months' worth of real actual shipping data. We ran it into our platform. We had our AI optimizing tool that analyzed all the data. Basic transportation. We ran it through the tool. It came back and it said, given this level of volume, we believe we could have optimized 77% of all of that traffic. It could have cut down almost 12 million miles of wasted transportation movement, which would have saved that particular customer about $30 million in just a six-month period. You are talking about almost a $60 million savings over an annualized period while still executing on-time delivery. Pickups and drop-offs on time. This customer is now an existing customer of ours. I just want to talk a little bit about the founders of the business. This is important. The two founders, Ajay Cooper, Vivek Sehgal, both career-long supply chain logistics developers. Both of them came out of big tech, coming out of places like Google, out of GT Nexus. This is key because we've retained both of them. They're both part of the team. We signed them both up to long-term employment contracts, where it's key to us that these two individuals are very highly motivated to stay with us and to continue to see the success and the growth of the business. This is because this software is not vaporware. This is not software that is still in development. This has already been built. It's been invested in. It's already been commercially deployed. We're already transacting with many, many, many large major customers. It's already out there deployed and transacting. We're doing millions of loads. The software is working. To point that out. Now, right now, SemiCab serves two main markets: the US market and the Indian market. When we acquired SemiCab late last summer, we acquired their US entity. We're now in the process of closing on their Indian subsidiary. The reason why we're so excited about India is we're seeing most of the growth, the early-stage growth opportunities, are all coming out of India right now. I'll explain the reason for that shortly. The Indian government has this quasi-sponsored program. It's called the NDFE, the National Digital Freight Exchange. What that is, it's essentially 35 major consumer product companies in India, all highlighted here on the screen. What the Indian government has done is it's basically gone to a lot of these companies and said, hey, there's a big problem in India right now: congestion, infrastructure. These are all priorities within the Indian government that they're trying to solve. They are working with these companies. They have appointed us, SemiCab, to be the exclusive vendor to work with all of these companies to try to solve some of these very, very large problems, particularly in India where you see just massive congestion. I mean, the average truck in India only moves around 3,000 mi a month. We have already seen that that same truck through our platform is going from 3,000 mi a month upwards of 6,000 mi a month just by being more optimized and continually moving. The carriers love it because their assets are always moving. The customers love it because they're getting cost savings. We love it because we're creating value. That is how we're generating our margin and our profit. That is it on the SemiCab side. I'm going to switch gears now. I'm going to go to something very, very different. I'm going to go to the Singing Machine. So Singing Machine is a 40-year brand. What we do is we design and distribute karaoke products to retailers like Walmart, Target, Amazon, Costco, Sam's Club. We also handle our international.