I'd now like to turn the floor over to today's host, Rich Howe, CEO at Inuvo Incorporated. Sir, the floor is yours.
All right. Good morning, everybody. My name is Richard Howe, and I am the Chairman and CEO of Inuvo, ticker symbol INUV, and we trade on the New York Stock Exchange. We are the only company in the world to have designed, developed, patented, and commercialized large language generative artificial intelligence specifically for advertising. What our technology allows our clients to do is three things. We help them determine why it is that the clients that they have are interested in their products, their services, or their brand. We then inform those customers of ours as to where, based on our artificial intelligence, they should be placing their ads so that they can find more clients like the ones who have bought their products in the past.
And then we accurately and predictively tell them what the return on ad spend is across the plethora of channels that our AI is helping them identify audiences within. As a company, we've now served over 100 clients over the years, and we've delivered billions of ads on behalf of those clients. Our revenue is roughly $80 million on a trailing 12-month basis. We're highly patented with respect to our technologies with 27 registered patents. Our primary offices are in Arkansas and in California. We've got a top three auto technology and retail client, and we're serving a market that is quite large. In the United States alone, it's a $200 billion marketplace. We are a public company, and so throughout this conversation, I may say things that are forward-looking, so please do treat them as such.
The cornerstone of our value proposition is a problem that has been evolving over the internet for probably now the better part of the last three or four years. What most people don't realize is that the commodity of online advertising is, in fact, the personal identification information of a consumer, meaning the thing that's actually being bought and sold is a consumer's information. You may know this as the cookie problem. The challenge for the industry associated with this issue is that the entire ecosystem of online advertising was designed to, in fact, identify and target people. And that's true across the analysis of the information that's used to try to figure out what the right audiences are, the execution, once you have figured out what those audiences are, which means really execution means targeting consumers, the measurement of the effectiveness of advertising.
If you can't track people around the internet, you can no longer figure out who is actually converting, and ultimately, this leads the collective of all this to performance. We believe that this problem is systemic, and it will impact literally hundreds of companies who have for a long time been dependent on this way of doing things, that spells opportunity, and that's why we built our AI. We had had a history in this older form of targeting, this targeting form that's based on identifying and extracting the information of consumers, and so we had a basis to redesign this around a paradigm that does not require the use of identifying people and then using their personal information, both of which are, as I said in the earlier slide, going away, both as a consequence of legislative changes, state laws, country laws, and technological changes.
I call that the plumbing of the internet, mostly driven by the companies that own the browsers because it's the browser that is actually identifying and sharing your information. So what we simply did is we re-engineered this marketplace around the latest and greatest and best technology. And right now, the most advanced form of artificial intelligence on the planet is artificial intelligence that goes by the name of large language generative artificial intelligence. And we effectively aligned that AI around these four problems, if you will, the need to not have to do this analysis based on consumer data, the ability to not have to execute based on knowing who someone is, the ability to figure out what the right optimization is of your media placements without having to track consumers, and ultimately, a technology that proves and provides a result that's better than anything that exists today.
Our opportunity is quite large. While this is an imperfect slide, it does give you some sense of the quantification of the size of the market in the United States alone. We all know search advertising dominated by Google is a large industry, and there's roughly about $100 billion of advertising spent against that, they and Microsoft and Yahoo and other people in search. Social is the second component, of course, dominated by now Facebook, but that's also a very large amount of advertising that gets spent in these channels. But it also encompasses TikTok and now X and other so-called social media platforms, roughly $80 billion. But there's another marketplace that goes by the name of programmatic advertising that's been around now probably for the better part of a decade. And this involves connected television and streaming audio and display ads and native ads, online video, and the like.
It is a significantly large industry. This is the area where the change from being able to use a consumer's identity and their data to not being able to use a consumer's identity and data is most prevalent. So that is the marketplace that we are actually targeting as a company. The cornerstone of our company's value proposition is, in fact, technology proprietarily owned by us. We do not use anybody else's artificial intelligence or technology, meaning we do not build on top of others' technology. The technology we have commercialized began its life in a machine learning lab at UCLA. We took the cornerstone of that technology, and we commercialized it specifically for the advertising use case. The simplest way to understand how this technology works is to use a construct of our brains that is pretty much well understood by neuroscientists at this point.
But if you think about the brain, you can think about it in terms of the reality that our brain is like a gigantic library, and the neurons in our brains are like the books in that library. Each of us has many tens of billions of these neurons, and we empower those neurons through all of the information that we've learned throughout our lives. The interesting thing about these neural connections in our brains is they actually get stronger or weaker as they're reinforced. So if you take a look at this slide, you see sort of two simple concepts, noise and sleep. And very much the connection, let's just say, between sleep and noise, meaning when I encounter sleep, is it really about noise, can be looked at in terms of a probability, say, 40%.
And you can traverse in the opposite direction, meaning you can sort of encounter noise and then ask yourself, "Well, what's the probability that it's actually about sleep?" And that may be a lesser or greater probability, say, 10%. These connections are always changing just based on what's happening, what we're consuming in terms of information. We built a machine that mimics this structure. And to train that machine, we had to consume the collective wisdom of humanity as represented by the internet, the hundreds of billions of pages of content on the internet where just about everything that has ever been known has been written about, discussed, talked about, interacted with. And that is the scale of this kind of technology. It's a massive, massive data science initiative that involves lots of computers and data centers and crawlers.
And then, of course, very, very sophisticated mathematics to interpret the information. But generally, the construct is as presented here. On the right, you see sort of maybe an easier way to understand the manifestation of this technology when we use it for clients. And there's a real case here. Bose had introduced some years ago a new product to the market, which they called the Sleepbuds, obviously designed to help people sleep better at night, an extension, if you will, of the pods that we all use now to listen to music and talk to people on our phones. The difference, of course, being the going after the sleep marketplace for this. Now, of course, our AI, as I said earlier, has read everything there is to know about everything.
Consequently, anything that's ever been written about Bose and anything that's ever been written about the Bose Sleepbuds has already been consumed by our AI, meaning in its mind's eye, it already has a sense of all of the reasons why consumers might be interested in this product simply as a result of having consumed all that information. One of the reasons why, out of the thousands of those reasons, was not surprisingly related to people who are struggling with sleep. Interestingly, when we did this project, we found also an attachment within the AI to short-nosed dogs, more specifically pugs and bulldogs. We were surprised by this outcome. However, our AI informed us of things we didn't realize, which is that these animals have breathing problems. In fact, they suffer from an illness called brachycephalic syndrome, which is what causes them to snort and breathe.
Effectively, they're loud. Consequently, what our AI was telling us was that there was an audience of consumers who were purchasing this product who were doing so because they had these animals in bed with them at night, and the animals were keeping them up. Now, this is something that would be impossible to know and obtain using the conventional consumer-oriented data methods for determining who is interested in products or services. It's a great example of just how powerful the technology we built actually is. What you see on the screen now is maybe another way of thinking about just how powerful this technology is.
If you think about the collective internet, what we're talking about here is many hundreds of billions of pages of content, many of which now, because of the way this ecosystem works, are making available on those pages spots that can be purchased, advertising spots that can be purchased. Now, in its totality, this is an unwieldy list and collection of sites that appear almost impossible to try to create structure out of. If you look at this screen alone right now with the finite number, the very short number of websites that I show in these circles, you're not quite sure what this is. There looks like there's an Edward Snowden piece, and there's a Theranos piece, and there's a COVID piece down in the corner, but they don't seem to have any rhyme or reason to them, meaning they seem unassociated one to the other.
Now, when you bring a product into bear, and let's say that that product was the Wall Street Journal, who obviously is looking to try to find people to subscribe to its publication, and then keep in mind what I said our AI is capable of doing instantly, which is knowing why it is that people might be interested in the Wall Street Journal, which it can do instantly, by the way. You start to see some connections here, so up in the upper left-hand corner, the Edward Snowden issue becomes more interesting. The Wall Street Journal was one of the first to publish. There's a lot of people who are still interested in that story, so our AI can determine all of the websites that are associated with that story and, as a result, buy media placements on those websites on behalf of our client.
In this case, that client would be the Wall Street Journal, and then you can see on the right-hand corner is the Theranos story. The Theranos story was obviously the biggest fraud company in Silicon Valley, the biggest private company that was based on a fraud. Our AI has read everything there is to know about Theranos, and so it knows that the Wall Street Journal was the company that broke that story.
And consequently, any and all media, whether that's movies or videos or content, which our AI has also read, the AI can identify and say, "Hey, anything associated with this story is likely a good place to place a Wall Street Journal subscription ad because the people consuming that content are 100% interested in that particular story." And the same would be true with the Me Too story and going back quite some time to Panama Papers and COVID-19. The point is the AI has this innate ability to take chaos of the internet and bring some order to it. And the result of its ability to do that is better targeting of audiences and ultimately a higher return on ad spend for our clients. Now, one of the other things this technology was designed to do is probably as important as identifying the audience itself.
And that is, there's so many new and evolving channels within advertising to choose from. And this is a big problem for CMOs, the ability to be able to test, to spend money against these various channels as part of an exercise to obviously find their audiences and then get the highest return from those audiences. It's hard to do that. And the way that this has been done historically has been done by tracking people. So you land on Facebook, and then you go and you buy the product. You attribute the value of that to the fact that you were able to track that person from Facebook to the purchase of the product. And now extend that to any channel you want: TikTok, Google, video, connected television, streaming, it doesn't matter.
You came from there, so that must be the place that was the driver of the conversion event. This mechanism is broken. If you can't track people around the internet, which is what's happening right now, then you cannot do that anymore. This required the development of technology that is more predictive. And this is effectively what we did. By only taking the spend within each of those channels and having a history of it, our AI can figure out with a high degree of probability which of the channels is producing the higher return on investment for our clients and consequently can inform them in ways that they can optimize their spend so as to maximize that return on advertising spend. We have two effective client types for our artificial intelligence.
The first are smaller agencies and companies that effectively do not want to run their own campaigns, meaning they don't want to staff up and have the challenge of training people to be competent in running advertising campaigns through any of the various platforms that are out there. In this particular case, we will run those campaigns for them, and we will use our own artificial intelligence as the decision-making process for figuring out where this ad should go, as I described earlier. We call those the managed service clients. We also have a self-service version. So for larger agencies and those companies that do want to build up a staff and a team internally, mostly they do that to try to reduce costs associated with using an agency to do that. For those clients, we want, of course, to empower those clients with the AI itself.
And we've designed a self-service version of our AI that plugs into whatever their campaign system of choice is. The self-service version of our technology has a much higher margin than the managed service component of our offering, albeit both have very strong margin profiles. At this point in our evolution, as I said earlier, with now $80 million in trailing 12-month revenue, and as we pursue our goal to get through the $100 million a year annual revenue mark, we have served hundreds of clients. And consequently, at this point, we have, for the most part, gone up against every form of advertising technology that exists. And I will say that there has yet to be a scenario where we have lost in head-to-head tests with any competitor.
And in fact, on average, we tend to exceed the goals that were given by our clients by as much as 67%. And I'll point out that when we are given a goal by our prospective clients, as we're pursuing them to be a client, the goal they give us is almost always the best performing service provider that they have, service provider and/or agency. So in many respects, when we give these kinds of numbers, what we're seeing here is really the capability of our technology above the best technology that exists on the planet. We've had a very strong and steady growth rate within our company. And if we go back to the second quarter of 2020, nearly almost five years now, what we have delivered is a compounded quarterly growth rate of 6.6%. On most recent quarter in Q3, we did $22.4 million.
I will point out that if you look at microcap companies in the $50-$100 million range, the 300 or 350 of them that exist in that range, that compounded quarterly growth rate is roughly 2.5%. So we are significantly ahead of the median of that $50-$100 million peer group of microcap companies. We've got roughly $2.6 million in cash at the end of our third quarter. We don't have any debt. And we have a $10 million borrowing facility. It's a receivables borrowing facility, which we use in large part to fund our working capital. We see a continued growth rate for our company as we look forward. And we're pretty excited about the possibilities in 2025 of breaking through this $100 million barrier.
And the reason why we're excited about that is because at roughly $25 million in quarterly revenue at our current margin and investment profile, we effectively break even at the operating cash level in the business. And of course, that's important for us. We've been chasing that. It's about the amount that's necessary for us to overcome the development costs and the support costs and the maintenance costs of the technology, our sales team, our account teams, and all the various other functions that are required to support clients with a business that's, for the most part, a highly technologically oriented business, our computer systems, our data centers, etc. With that said, I will again say our company is Inuvo. The ticker symbol is INUV. And thank you very much for listening. Natalia, maybe I'll turn it over to you for any questions.
Yes, thank you so much, Rich.
We actually have quite a few questions in the queue here. And if we don't get to yours, please feel free to contact us at inuv@crescendo-ir.com. And we'd be happy to follow up with you afterwards. Our first question is, how does your generative AI-driven advertising technology differentiate itself from competitors? And how do you ensure defensibility in a rapidly evolving market?
Yes. So I'll answer the second question first. So we defend against competitors by the 19 patents that we have issued and the six patents that are now registered. And we've got a few others that are in the queue. So we have effectively established a moat around using large language generative AI specifically for our use case, which effectively means if somebody is going to be going down this route, they're going to have to come through us to be able to use it. And if they don't want to do that, then they're going to have to back off to some other technology to solve this problem. And given we've been doing this now for probably 30-plus years, we don't think there's a better approach to this problem than the one we have. So we feel pretty confident.
We've got a very, very strong head start on anybody in this area. So what was the first question, Natalia? I'm sorry. I answered the competitor one, but I didn't answer it.
Yeah, of course. Yeah. How does your generative AI-driven advertising technology differentiate itself from competitors?
Yeah, that was what I asked, so the differentiation is based on the approach, and that's large language generative AI. That is the differentiation.
Great. Our next question is, Adjusted EBITDA loss has narrowed significantly. What specific initiatives or operational efficiencies are you targeting to achieve positive Adjusted EBITDA in the future?
Growth is what companies like ours tend to focus on at this point in our evolution. As I said earlier, at $100 million or $25 million a quarter, we break even on the Adjusted EBITDA line. Our margins look good, which means if we can break through $100 million, the business will start to generate and throw off cash. Our expenses do not go up commensurate with the growth. So obviously, maybe said a different way, we don't have to proportionally increase all of our resources and our costs every time we add a dollar of revenue. That will open up as we get bigger. So yes, we're focused on growth right now, one, because we know growth will lead to breaking even on the Adjusted EBITDA line.
And two, we're trying to do everything we can to get as much of the market share as we can while this problem that is within our industry begins to become a bigger problem for the advertising community.
Thank you. Next question. In the last earnings call, you mentioned there's a strong start to Q4 and projection of double-digit growth. Can you share more details on your expectations for the quarter, if possible? And also, are there any specific factors driving this confidence?
We are expecting Q4 to be bigger than Q3. Q4 within the advertising industry is often larger than the third quarter, in large part because of the Halloween to Thanksgiving to Christmas season. Maybe said a different way is advertisers know a big part of the purchasing of their products and services occurs in this quarter, and so consequently, we would expect Q4 to be larger than Q3.
Great. Thank you. Next question is, what are your current plans for geographic or vertical expansion? And are there any particular industries or regions where you see untapped potential?
The technology itself, the large language generative AI, can apply in any language. So we're not restricted in any ways to any geography. There's work to be done to adapt between languages, but it's not a significant amount of work. We have chosen at this point in our evolution and our size to focus on the North American market, mostly Canada and the United States, simply because there's so much spend here and the companies are so large. We think that's a better use of our time and energy to basically take a run at that market than it would be to spend time and energy chasing other markets around the world, which would just lengthen the time it takes us to start generating cash.
Got it. That's helpful. Our last question here is, the DOJ is pushing for Google to break off the Chrome browser after an antitrust case. How would this impact Inuvo?
So the browser, as I said in my earlier remarks, is where the rubber hits the road, if you will, on identifying consumers and then ultimately using their data. As I said, the commodity of the internet is effectively the purchase of people's data. Right now, by the way, Apple already prevents the sharing of information in its browsers with anything related to advertising. And Apple owns 55% of the mobile browsing market. So this is why this problem is becoming an increasingly large problem for people. If you can't track people, you can't target people. It means most of the companies that are out there that have been serving the advertising community with technology over the last three decades are basically ignoring Apple.
If Google was to spin off its Chrome browser, then presumably the entity that would buy it would not be as closely affiliated with Google as the browser is now. And my suspicion is that that would result in less sharing of information from the browser to anyone, which would be good for us.
Great. Well, thanks, Rich. It seems like there's no further questions at this time. I just want to let everyone know we also have a packed one-on-one scheduled for tomorrow for Inuvo. And for those who couldn't secure a one-on-one meeting, we'd be happy to follow up with you after the conference. With that, I'll just turn it back over to you, Rich, for any closing remarks.
All right. Thank you, Natalia, and thank everybody for paying attention to the presentation. We do have an exciting company with a bright future, with a giant problem for which we have the best solution, and consequently, we see good days ahead, so thank you again. Our company is Inuvo, and it trades on the New York Stock Exchange under the ticker symbol INUV.
Thank you for joining iAccess Alpha Buyside Best Ideas Winter Conference 2024. we hope to see you at our next virtual event, the iAccess Alpha Buyside Best Ideas Spring Conference on March 25th and 26th, 2025. Have a great evening.