Good morning, everyone. Thank you again for being here. Always great seeing you, Chuck. This is the 20th installment of The Main Event. This week, we're expecting roughly 800 attendees and close to 1,500 meetings. Typically, Track 1, the first presentation, is always given to a company that has had a wonderful relationship with LD Micro over the years. These guys were featured in my morning blast. At one time, Inuvo was the largest company position that my family had. It lasted for several years, and it turned out to be very prosperous. The dynamic has changed over the last decade, but I think that 2027 is going to be very interesting, and I'm excited for what is ahead.
With that, it would be very kind for everyone to give a warm round of applause to Robert Buchner, the Chairman and CEO of Inuvo.
Hello, and good morning. I am Robert Buchner. I am the Chairman and CEO. I joined Inuvo as a board member a little over a year ago, was put in the CEO post in February. As the company transitions from a legacy search business model to an AI-powered audience modeling company, I have made a lot of changes in the company, including a new President and CFO, Derric Ciccone, and then our Chief Accounting Officer, Aleesha Parris. They join me today. I am going to get on with it right away. I am going to be talking about our advertising solution for programmatic media. It is a large language model. The key value proposition is our models are generated in the moment, not based on past behavior and data tracks from past purchase behavior. That is a core differentiation from thousands of other ad techs, frankly.
We are a vertical AI company built on our own large language model with a specific application for placing digital programmatic media. These are some vitals on the company. As of mid-year, $7.5 million in Q2 revenue, 15 patents pending. We have got about $50 million invested in this technology over the last decade or so. We have 50 employees and a market cap at that time of $18.7 million. This marketplace, think of like an electronic stock exchange. Billions of ads are delivered every single day, and it is a dynamic industry. As Chris pointed out, there is a lot of change in regulation. The agentic movement is clearly upon us at this point in time.
There's new entrants and legacy models that are in jeopardy, and I am pleased to report what drew me to the company is that this technology, the IntentKey, was prescient because it anticipated the privacy era that we are living upon. There are a couple of vectors that are disrupting the marketplace right now. The walled gardens of Meta and Google and Amazon continue to keep their ecosystems held closely. Amazon announced last week that they are shutting down their DSP and migrating to a unified ad platform that will be all autonomous. Apple announced last week with their update that they have blocked hundreds of ad techs who have historically tracked individuals around the internet or have cross-stitched other data which is in violation of the way that they have a relationship with their customers.
We anticipated that many years ago, and so we are free to identify that high-value customer from Apple. We are able to read those signals, and we are able to place ads on iOS devices. The zero-click search dynamic is an interesting one. We all know that we are not clicking blue bars and traveling across thousands of websites. We still go to websites on the open web, and when we do it, we do it with great intent, and we are able to see those signals. The buying infrastructure, of course, is under threat, too. It is a giant marketplace, $220 billion and growing.
I have got a couple of videos to give you an orientation to it. Basically, the IntentKey sees patterns forming based on content that people are reading in real time or consuming online video as well. Our algorithm scores it, we have 25 million concepts, and organizes that, and we place an ad at that moment in time. That gives us a tremendous advantage in the marketplace. Here's a video.
[Presentation].
That last point's really important because in the world of digital media, about 75% of all digital media investment fails to hit the target in which it was intended. There's a lot of bot traffic and fraud. What our signals are able to see are patterns based on consumption in the moment, and that delivers net new customers. Our proposition as we talk to our clients in which we serve is really top of funnel. People that are early in their journey, exploring, researching a car or switching a credit card or looking for a new healthcare plan. We see those signals based on those behaviors without collecting personal data. We see those signals 24 hours before our competitors can, and we're able to activate those media buys. That creates a pricing advantage in the supply and demand in the marketplace.
Again, we're privacy by design. We are a large language model. Actually, the underlying technology came out of UCLA learning lab. It was purchased, it was put into market a couple of years ago. It is language-based, and it works just like Claude or any of the other language-based models. Ours is quite different because it is surgically applied to the advertising domain. This is an example. I have a video here in a second that is going to show you a GLP-1 query that went into our large language model, and you will see what the platform delivers, the kind of insight that helps inform the buys.
[Presentation].
I did that model a couple of weeks ago before another conference. I keep it running, and I look at it, and sentiment continues to grow, where GLP-1s have all different kinds of health benefits, and all that news and activity goes beyond weight loss. If you look at the pharma companies, they just keep retargeting and retargeting those people that want to lose weight or have had a history of weight issues or prediabetes when there is a big part of the population that can be identified, targeted effectively through this technology that are out on the fringes or way up in the upper funnel. That is the reason I have chosen this one. Our large language model was developed for one application, and that is digital advertising.
Gemini, Claude, Perplexity, all the others have the wisdom of humanity. Those models get updated as they do a release, but they're not in the moment the way ours are in a brand context. Ours are updated every day, every minute. We scrape 110 billion pages of content. That content is scored, is built into the algorithm. Those concepts become models. Those models can be activated immediately. Performance is really the litmus test with the clients we serve. When we're in head-to-head competition with other ad techs, especially legacy ad techs that are based on prior purchase behavior, we outperform with a return on investment 66% higher on average. We serve a range of brands over the years. I'm going to tell you about some verticals that we're going after really hard, but our retention rate is up in the 90 percentile.
The IntentKey performs best in hyper-competitive categories where share gains are won and lost in the moment of discovery. Automotive, healthcare, workforce recruitment, we've got a number of tracks in that area, and fintech. Those are some of the changes that I brought when I joined the company. Rather than chasing down every brand opportunity, every agency, every holding company, we're really concentrating our go-to-market with enterprise salespeople that can get upstream in these competitive categories where we have good credentials, privacy matters, and speed to buy delivers a performance advantage. Why Inuvo? Why now? What's our investment case? It's a very dynamic, large growing marketplace. We're well-positioned to capitalize on this based on our IP. We're on a path to profitability. This R&D is paying dividends now.
This Apple news that broke last week, we're going hard after that because we're one of the few ad techs that can actually read signals from Apple phones, and that's the most desirable customer in America, and it's 50% of the U.S. population, and it over-indexes with young people. We're really going to go after that hard. We are a company in transition. Our core engineering team have been together for more than a decade now. Our go-to-market team, I've turned over 100% of our sales team. We're elevating the brand's profile. We brought in new executive leadership, and we're going to capitalize on all these tailwinds that are presented to us. Importantly, like so many other industries, agents are taking control and reducing the friction of human labor. There's a collapse in the overall plumbing of the programmatic media infrastructure.
The walled gardens are growing. We're an open internet technology. 60% of all of our time is spent online, yet it receives 40%-some of the advertising investment because Meta's done a great job in social. Amazon is building their ecosystem deeper based on commerce behavior and purchase behavior. Google, of course, dominates search, and that has changed radically in the last year because we all simply talk to a phone or put a prompt into one of the agents, and it delivers everything you're looking for without having to travel through websites. But when we do travel to websites, we go there with high intent, and that is where our technology delivers. A few changes on the leadership team. I've CEO'd a number of companies from the highest order of advertising and the big holding companies. I was drawn to the data opportunity here.
I recently brought in Derric Ciccone as President and CFO. Derric has worked for four of the largest holding companies and has had $1 billion of revenue underneath his purview. He knows everything digital media and has had product delivery underneath him. We're not just building a front office. We're building a front office with people that get their hands dirty with clients, with product, and our product road's really exciting. Amir has run our product for the last decade or so. Aleesha's been with us for over a decade as well. I want to point to Sanja Partalo. I brought her to the board in the second quarter. She works with Martin Sorrell, who is the architect of the modern-day holding company, built the largest holding company in the world, WPP.
He's done it all over again in the last six or seven years. She runs his venture fund and incubates ad tech. She knows where emergent technologies all over the world are impacting brands, and I'm honored to have brought her to the board. On time, maybe a little under time.
[audio distortion] During the data during IntentKey and how you're now charging customers, or you may be able to get that through the--
Right. There's demand, like any marketplace, there's sell side, buy side, right? The buy side is called a DSP, so that's our primary data source that we draw on. We're with FreeWheel and Index Exchange, so that informs most of our models. That's the data that we're scraping. Our concept graph is actually drawing on those signals in the moment, put them in motion, and then that model and the data that is aggregated is matched to an impression and bought immediately. I don't know if I've answered the question, but these data integrations are increasingly important, and as more media buyers around the country adopt agents, and through large language, because they all have their agents running on their desktop now. Ours is a portable technology, really enabled by what's called MCPs.
I don't know if you guys are familiar with that term, but we anticipated that. We've done the integration. Once a client's on board, they can have Claude or Gemini open on their desktop and actually just prompt the models like they're talking to a colleague across the hall, or input a media plan and the model will be generated, they can tune it, and then they can automate the buy. We manage many of the buys, but we're migrating to a self-serve platform, and the industry's just gravitating that way anyway, and that's what will give us real scale.
Can you talk about who you sell your data to? Is it FreeWheel? Can you talk about the unit economics, like how you make, what's your pricing model?
We subscribe, we have agreements with FreeWheel. They're not buying data from us. We use their data in their association with publishers to create the models. Derric, I don't know if you've got anything to add to that.
Who is using your data or your models, and how are they paying for it? What's the pricing model there?
We have agreements with almost every DSP in America, and those are prenegotiated terms. When we manage a buy, and actually structure and implement those buys, our margins are roughly 45%. When it is self-serve, when somebody is on a platform and they are generating their own buys, our margins are in the 90 percentile.
I am just curious, what do you think the major difference between your company [audio distortion] ?
Well, a lot of those are kind of vast omni-channel ad techs that are cross-stitching a lot of data through clean rooms, right? And they repackage them and build those models. There are still inefficiencies relative to what we do. I like to think of ours are really pure models because they are privacy compliant, number one, they are immediate and fresh. So who that customer was two weeks ago or two months ago is not necessarily who the marketer thinks they are, and that is kind of the dynamic that does not get talked about enough in the industry because the stakes are so high for the big walled gardens, to be honest with you. Go ahead.
Maybe you could speak a little bit to where you think the revenue inflection will be, where it starts going back up and what pace it can start going up, and when do you think the company goes cash flow positive?
Yeah. This is a 2027 story as we rotate away from our legacy search business. We're going all in on our audience modeling. The pipeline has never looked better. I've talked about that, this vertical go to market. Our elevated sales team is making a difference, and so we've got visibility into next year is quite promising. We're pursuing larger deals at test level because every new client tests first relative to other ad techs in their stack. Those tests had been historically small. We're coming in at much bigger test levels to expand because once we're on, we tend to be evergreen. We tend to be very sticky. This is a cash flow positive story, we hope, by the end of next year. Anyone else? Thank you for. We've got one more.
[audio distortion] Just one. All of your data analytics are drawn from other companies' intellectual property. Is that correct?
Yes, by and large. We have our own media mix modeling technology that draws on--
You are aggregating.
Yeah, we are aggregating to read the performance. We provide that for our clients as well.
Thank you.[audio distortion].
Yeah. The models that we create, I will just put a finer point on that, are custom and unique to us, drawing on all the data sources across the ecosystem. So when that model goes live, no one else has access to that model. They are custom-built. All right, guys. Happy Tuesday. Thanks for joining us early. Appreciate it.