Hello and thank you for joining us for iAccess Alpha Buys ide Best Ideas Spring Conference 2024. iAccess Alpha hosts virtual investor conferences where the presenting companies are recommended by a network of investors who they believe have a history of generating alpha. Today you will be hearing presentations from nine companies who have been selected to attend. iAccess Alpha will be hosting four virtual investor conferences a year. Their next conference will be the iAccess Alpha's Buys ide Best Ideas Summer Conference 2024, held on June 25 and 26, 2024. Today's first presenting company will be Inuvo Incorporated. If you would like to ask a question during the webcast, you may do so at any point during the presentation by clicking on the Ask Question button on the left of your screen. Type your question into the box and hit the Send button to submit your questions.
I'd now like to turn the floor over to today's host, Richard Howe, CEO of Inuvo Incorporated. Richard, the floor is yours.
Thank you, operator, and thank you, everybody, for joining us today. Yes, my name is Richard Howe. I'm the CEO. Our company is Inuvo. It's traded on the New York Stock Exchange under the ticker symbol INUV. We are the first company on the planet to apply large language generative artificial intelligence to the problem facing the internet. That problem is effectively that audiences are no longer able to be targeted by identifying a consumer and tracking them around the internet. We're not new to this game. It's important to understand that because, obviously, there's a euphoria within the world right now about investments in artificial intelligence. We've been doing this now for at least seven years on the large language front. In fact, our technology was first developed in a machine learning lab at UCLA.
We've got 90 people, and we've got two main offices, one in San Jose and one in Little Rock. We had a pretty good year last year with $74 million in revenues. But the real interesting part of the year was with the back end of the year where revenue was up 32% year- over- year, which sets us up for a pretty good year this year where we're expecting to actually get to free cash flow positive. We've got a number of executives on our team who have built multi-billion dollar businesses in the past, notable machine learning and AI and analytics businesses like Fair Isaac and Acxiom and HNC Software, which invented credit card fraud detection. So we really believe we're sitting on a disruptive technology here where the technology and the company could be on par with these other companies that we've developed.
I may say things that are forward-looking, so please treat them as such throughout this presentation. Our company effectively is solving two of the biggest problems facing the internet right now. I think most of you know this, but the internet is funded from advertising, so it's not surprising that the biggest problems facing the internet are also facing advertisers, and they're related to consumer privacy. Effectively, what's happening is all of the mechanisms to be able to track consumers are being eliminated. When you can't track consumers, you effectively can't find audiences, and you can't target those audiences. The entire internet was designed around doing that. So when this model changes, which it's in the throes of changing right now, literally hundreds of companies worth many hundreds of billions of dollars are going to be impacted by this change. We knew this.
We knew it years ago. That's why we started developing this technology, which we have patented. It's important to realize that a lot of technology is derivative, and some technology is innovative. This is technology we own, we developed, and we have patented. The two areas that we've designed this AI to solve are the two biggest problems associated with this transformation. One is finding audiences, and the other one is attributing the value of the media spend to whatever audience that you're targeting. The industry that we serve, which is in turmoil, is a large industry, $168 billion. To date, as companies who we serve, we are fortunate to have a top three auto, a top three retail, and a top three technology company. By top three, I mean top three in the world as our clients.
And that has been accelerating for us as the problems facing the internet become more prevalent. In totality, we have in excess of 100 agencies and brands we serve, and we've got four large platform clients that we serve. Effectively, what we do is we place ads, and we placed 11 billion of those ads in 2023. So we have the kind of scale necessary to grow this business. There's a lot of things to invest in. And so I like to basically make the case for why Inuvo is a good investment. And of course, first and foremost, if you're an investor, the hottest investment category on the planet right now is artificial intelligence. Unfortunately, for investors, it's hard to figure out the differences between one AI type and another AI type.
So I'll point you to probably the most exciting AI on the planet, which you're aware of, and that's the OpenAI, ChatGPT, and Gemini Bard. These are the two largest purveyors of large language generative AI. We have technology that is effectively on par with them, although we commercialize that technology for a limited use case. And that was the one I've described earlier, finding audiences, targeting audiences. And so we are the first company in the world to apply this kind of technology for this particular use case. That makes us a pretty attractive investment in the category of AI. We've got a catalyst in our industry.
Having built a number of successful companies now in the past, it's one thing to have the best technology, and you can be successful with the best technology, but it certainly helps if there's some market catalyst forcing your buyers to actually have to change. And we have that with our there's legislative reasons and technological reasons for basically wanting to or needing to have to change right now. Large industry. Our reputation has been growing. We've had a lot of mentions in the press, not surprisingly, as the D-Day for the changes within the internet approaches. We're starting to get more attention. We had good momentum heading into this year, and we've got a number of great clients as the foundation of the company right now. As I said earlier, the biggest issue facing the internet is that tracking consumers is no longer possible. Consumers themselves have demanded this.
They don't like to be tracked. They don't want you using their data. That's important because, as I said earlier, the entire construct for the internet was designed around doing exactly that. When this starts to go away, which is happening right now, audiences become more difficult, obviously, to locate, reach, and target. This produces a lower return on ad spend for advertisers. That's a big issue given all that money funds the internet. You've got governments applying changes as it relates to this area with legislation. There's now 13 states in the U.S. that have consumer privacy-related legislation and 17 with bills. You've got entire continents like Europe with GDPR who have put legislation in place.
Then you've got the largest technology companies on the planet that are basically preventing tracking, Apple being the most notable of them who have pretty much led the charge here. People don't realize, but Apple is now 55% of the mobile browser market share. Effectively now, you cannot target people on an Apple Safari browser, which means most of the advertising technology companies on the planet are basically just ignoring that inventory. Google started phasing out the cookie tracking in 2024 to begin this year, and they have a plan to basically be cookie-free by the end of this year. So the writing's on the wall here, and I think our company is at the right place at the right time. The next two or three years should be a very interesting ride for our company. Instability is the key, and our industry is considerably unstable right now.
The value of the media that our advertisers are placing is starting to decline because the tracking of the consumer is going away. We knew this problem was coming, and we designed two forms of artificial intelligence to solve the two biggest problems: one, identifying audiences and targeting those audiences, and two, being able to help CMOs figure out, of all the channels they're using across the internet, which ones are actually adding more value than the other in terms of the overall mix of their media spend. A very difficult problem. In fact, both are exceptionally difficult problems and required us to rethink this problem entirely. If you look at this slide, you see the consumer in the middle of it. That's effectively the problem.
All of the other companies on the planet and all of the other technologies that are being used to do ad targeting and media attribution today are based entirely on figuring out who this person in the middle of this slide is and who every person that is targeted and reached is. And that fundamentally is the part of the internet that is now being changed and is no longer available. The solution to the audience targeting problem was large language-based artificial intelligence. This is effectively technology that tries to, in many ways, mimic the way we humans experience the world. We do experience our world through our language, the way we see things, the way we think about things. They're all based on the language.
So if you were wondering what is large language model AI and why is it so important, it's because we humans experience the world through our language. The best way to replicate the way that we think and the way we make decisions is to, in fact, mimic that. The way you have to do that, the easiest way to understand it is you have to build a machine that can consume the collective wisdom of humanity, which is represented in the internet. That's effectively what we've done. The same thing OpenAI has done, and the same thing Google Bard has done. You effectively read everything that's available about everything, and you create a machine that can associate the words one to another, the pages one to another, the context of things relative to words or pages.
It's an exceptionally difficult problem, but it's one that we were well-suited to solve simply because of our backgrounds. Effectively, the change in the way this technology works versus the way it has been done for 100 years is, rather than trying to figure out who someone is, the AI actually tries to figure out all the reasons why audiences are actually interested in any product, service, or brand. If you look at the slide that I'm showing now, we'll take a simple use case for this and look at The Wall Street Journal, which I'm sure everybody listening here knows. So The Wall Street Journal is obviously in the business of news. There are hundreds, if not thousands, of reasons why any consumer might be interested in The Wall Street Journal. Of those thousands, one of them is the Theranos case.
The reason why that's important to the Wall Street Journal and is connected to the Wall Street Journal is because they broke that case. Now, what our AI actually is capable of doing is, for every single product, service, or brand in the world, our AI understands all the reasons why consumers are interested in those products and services. So in this Wall Street Journal simple case, one of those reasons would be Theranos. Now, the AI is way smarter than just knowing Theranos. It also knows all of the associations with Theranos and, ergo, then the associations with the Wall Street Journal. So, for example, it knows that Elizabeth Holmes is attached to Theranos, and Sunny Balwani is attached to Theranos and the Edison machine, and George Shultz and the DeVos family who made an investment, and Tyler Shultz, everything.
It knows everything that there is to know about this. They are all highly associated with the Wall Street Journal. If you could peer inside our AI, it knows this along with the other thousands of reasons. The way that this is enacted, if you will, on the internet is probably best understood by looking at the slide that you see here now. This is literally a machine finding audiences for any product, brand, or service in the world in a way that has never been possible before and, frankly, performs better than any other technology that has been previously built. We've had enough use points now to have been able to do that. But what I said earlier, effectively, is what happens. If you look at this slide, you see a bunch of sort of these circles.
These are meant to be web pages on the internet. I want you to recognize that there are literally hundreds of billions of these pages that, on any given day, are offering a media spot to be purchased, sort of a sea, if you will, of web pages, which makes the problem even more difficult. But what our technology can do is, by figuring out all the reasons why people are interested in the journal, which it does automatically, it then also has the ability to associate that with all of the web pages that are available on the internet. So if you look at this Wall Street Journal and we go in the upper right-hand corner, you see what we talked about earlier. There's a pocket of websites, literally hundreds or maybe even thousands of them, that are all about the Theranos fraud.
Our AI would associate those as a sort of an audience, one audience. But also, in terms of the Wall Street Journal, it would identify that the Me Too movement was a big draw for the Wall Street Journal. And it would associate all those websites as well as the Panama Papers or COVID or the Snowden case. It would effectively link them very much in the way you're seeing here on this slide. And as I said, this has never been done before in the past, and it works remarkably well. At the detailed level, meaning in any given transaction where our AI is actually placing a media for one of our clients, it kind of works like this.
In the old way of doing things, we would figure out who are all the people who might be interested in the Wall Street Journal, track them, and then when they land on a website, we would know it's them, and we would put an ad in front of them. We call that who-based marketing. You're effectively trying to match who might be interested with the journal with who is actually in front of the screen. Because you can no longer identify consumers nor track them, you can't do that anymore. So we changed that paradigm, and now we do it based on why using this large language AI. So on the left, you see the same construct. This is the Wall Street Journal. Our AI knows Theranos is associated with it. It knows all of these characters that are part of that story are also associated with it.
On the right, you see a random web page, one of the hundreds of billions that are out there offering a media spot. This one happens to be about George Shultz, the former Secretary of State. The site itself is more of a bio site, which walks through his storied career. There's nothing on this site that has anything to do with Theranos. But our AI would know immediately that the only reason why someone's in front of the screen is because they're likely interested in the Theranos fraud because it knows that George Shultz is highly correlated and linked to Theranos, along with his linkage to his grandson, who is the one who actually broke the case to The Wall Street Journal and all the other associations.
And so it would conclude, "Hey, the only reason someone's in front of the screen right now, even though I have no idea who this person is and nor do I want to know who they are, is because they're interested in the Theranos case." It would then say, "Hey, we have an advertiser client who is the Wall Street Journal, and we know that the Theranos case is important to them." It would say, "We should put the ad on here because this person's likely to subscribe to this Journal subscription." Never been done before in the history of advertising. The second problem we solved with our AI is equally as big a problem.
As the inability to track consumers around the internet continues to decline and go away, so does the ability to actually have a one-to-one correlation between, say, I showed a video to you on YouTube, and then you came to my website and you actually bought my product. That one-to-one connection is now broken because you can't track consumers around the internet. So you need a different way to be able to figure out how to attribute the value of any media dollar spent across the internet. We solved that problem as well with our AI. We did it with machine learning that actually predicts it. I'm not aware of any other company right now that actually has this capability either embedded in their technological offering. You can think of this as the CMO dashboard.
It allows the CMOs who deploy our technology to be able to control the knobs, if you will, associated with their spend across the plethora of channels that are available to them. Very exciting technology. The technology itself is incredible. Everything you see on the screen right now, which I won't go into, has been generated by AI. What I want you to understand is this is not technology that's looking up information in some structured database, which is conventionally the way most non-AI technologies work. They are effectively looking up something and then redeploying that. That's not what this is. Everything you see here and everything our AI does is generated. Our AI was trained to be able to know these things and deploy and present them to clients.
That includes telling them all of the various segments, if you will, of reasons behind why the audiences are interested. But it tells them what these consumers are generally made of, their income, their gender, their identity. All of the traditional demographics are being generated by the AI. It will tell them which geographies in the U.S. are most likely to be responsive to their products or services. It will tell them all the television programs that are aligned with those audiences, all the podcasts that are aligned with those audiences. It's literally an amazing technology. Now, just to look at our company itself, we've been on a pretty steady growth rate here since 2020. So if you look at the company since the second quarter of 2020, you would note that we have a 7.5% compounded quarterly growth rate.
Our balance sheet is not bad with $4.5 million of cash at the end of 2023. We don't have any debt. We have an unused $5 million borrowing facility, which we can tap into should we need additional capital. Our capital structure is probably one of the cleanest you're going to come across in microcap land. We effectively have pretty much straight common the whole way. There's a de minimis amount of options and warrants in the company. We've got a pretty good list of companies that we served historically. So I won't go through them here, but you can see how they cut across a number of different industries in travel and tech and retail and CPG and auto and the like. As I said, probably from the get-go, this technology can be looked at as the next evolution, if you will, in advertising targeting.
We have gone up now against pretty much every company that has an existing solution. We have handsomely beat them. In many cases, it's been well into the double digits, and it's been as high as 67%. That's the existing conventional targeting and tracking of consumer tech that's going away. So when that technology finally goes away in totality, the bar between our technology and what others are going to use is only going to get higher. Again, if you want to get in touch with us, the company is called Inuvo. We're traded on the New York Stock Exchange. Ticker symbol INUV. Ticker symbol INUV. Ticker symbol INUV. So I will pause now for questions.
Thanks, Rich. First question is, what impact will the election have on your business this year? And when is the earliest you would expect to see some contribution?
We have not had a lot of business in the political spectrum. We recognize that there's a lot of money to be had there. But there's also some risks with a company like ours that wants to be the standard for the way audiences are discovered and targeted if we go into politics. Most notably, if you end up serving one or the other of the political parties, you effectively offend half the companies on the planet. So we have done some political campaigns, but they're very small ones. So we've not really entered this area in a big way.
Thank you. Our next question is, you spoke about two new high-margin products that you rolled out in 2023. Can you walk through the opportunities and what the margins are and how fast you expect them to gain traction?
Yes. The first one is a self-serve version. We call it a self-serve version of the AI I just demonstrated and showed to you. And by self-serve, there's a lot of companies out there who would prefer to be running their own campaigns and have them not having us run the campaigns. We do have a managed service capability at Inuvo. We recognize this. So to get distribution for our AI, we effectively had to plug that AI into a bunch of these campaign systems. And that's effectively what we did. So now customers can access our AI, use it to target the audiences and track them, but run those campaigns themselves. This is at least a greater than 80% margin product for us. It's a lesser revenue product than our managed services, but it's a much higher margin product.
The second new product we launched was with one of our platform clients. In this particular implementation, without getting into the gory details of it, we're using our AI and some other capabilities we have within the company's technology to serve a large internet company that we have not named, and I won't do so now. In this model, we're effectively placing ads in a new way with this large internet company. We started launching this actual product in the fourth quarter of 2023, and it's already generating revenue for us, I believe, roughly around $50,000-$60,000. That's almost entirely margin. There's really no cost associated with that. We think that could be a lot bigger. Those were the two products that we launched.
Thanks. Our next question, is there any urgency by advertisers to figure out their ad spend plans before Google turns off their third-party cookies?
The answer is yes. For the ones who recognize that this problem is inevitable and that it's as big a problem as it is, we're starting to see an acceleration related to those CMOs looking for solutions to that problem. I will say this, though. The industry that we serve is mature. Consequently, there are literally hundreds of companies that serve in that industry who right now are at risk, and I would say dramatic risk, systemic risks, if you will, simply because all of their technology was designed around tracking people. Of course, we have the benefit of not having to have done that and have a technology that's kind of designed, if you will, for the next generation as opposed to the previous generation. The consequence of that is, of course, those companies are trying to retain and hold on to their customers.
In some cases, they've been doing business with their customers for many, many years. Those customers trust them. Of course, it's not surprising that they're telling them they've got the problem solved when, in fact, they do not. I expect we will see an acceleration of demand for our products and services over the next few years as those companies realize that their incumbent providers do not, in fact, have a viable solution.
Thank you. Next question is, can you comment on the large new client you announced, how big they could be, and are you recognizing any business from them now?
So that was the second answer to the question about the two new products. That is actually, no. No, there's two answers to that question. One of them was the self-serve version with the large internet company. And the other one is actually, yes, a large top-three retailer in the world. And we are serving that customer right now. We launched in the first quarter with them. There's a lot of room for growth and expansion within this particular retailer. And yes, we're pretty excited about it. We have had a tendency to not name our clients, so I'll put that on the table right now. Our industry is exceptionally competitive, and they listen to me, and they listen to my messages and my conference calls.
So as a result, we don't feel like we want to enable our competitors with any inside information about who we're doing business with. But we're pretty excited about that one customer that I did name, a large retailer.
Thank you. We're going to have to be wrapping up soon because we're running out of time. Our last question is, what percentage are your top five customers? How big are the two biggest percent? And how did that change in the second-half acceleration of revenues in Wall Street?
I wish my CFO was yeah. I don't remember what the actual distribution of our largest clients is, but it's certainly in our 10-K. So I would point you to go look at that because we do put that in our K. What was the second part of the question? Since I don't know what the answer is to the first one exactly.
Yeah. How big?
I'm sorry. Was that it?
Yeah. How big are the two biggest clients and percentage-wise? And how did that change in the second half of the year and acceleration of revenues? What are the impacts?
Yeah. Our largest client is a platform client. Actually, this particular client, you can go look in our documentation and know who it is. It's Google. We've had them as a client for at least a decade. We have a contract with them that renews every few years. It's been a great relationship for us for a lot of reasons. We are exceptionally excited about what we're doing with them, which I won't go into the details of. But there is a strategic initiative that is ongoing with them to which we're a party to. We see considerable potential growth over the next few years in this initiative.
Thank you, Rich. We run out of time, so I'll turn it over to you for any closing remarks.
Thank you very much, Natalia. Thank you, everybody, for being interested in Inuvo.
Thank you very much. That concludes Inuvo's presentation. You may now disconnect. Please consult the conference agenda for the next presenting company.