Inuvo, Inc. LD Micro 20th Annual Main Event Conference
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Turn this on. Okay. Good morning, everyone. Thank you again. Oh, let's see. We're live. Awesome. 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.
Thank you, sure. Of course.
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 are 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. Every day, people read articles, watch videos, and engage with content across the open web.
That activity creates signals about the topics and ideas attracting attention. IntentKey analyzes those signals without identifying or tracking individual people. First, it identifies the concepts appearing across the open web. Then, it looks for relationships and patterns among those concepts. Those patterns are used to build a custom model based on an advertiser's goal, such as finding people who may be interested in buying a vehicle, choosing a health plan, or planning a vacation. The model represents the ideas and interests that are most relevant to that goal. Next, IntentKey matches the model with available media. As opportunities to show an ad become available across websites, video, connected television, and other digital media, IntentKey evaluates each one in real time.
When the media and surrounding signals align with the advertiser's model, IntentKey can place the ad. This puts advertising in front of people whose current interests are relevant without needing to know their identity or follow them around the internet. Signals become patterns. Patterns become a model. The model is matched with media, and the right advertising reaches the right audience. That's IntentKey. 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 machine 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.
This model was recently created for a GLP-1 brand, generated using simple natural language. Find people exploring GLP-1s and Ozempic. Here is the audience output generated in seconds after analyzing over 100 billion pages of content. First, let us look at audience size and sentiment over the past 30 days. This graph indicates a daily audience of roughly 2.3 million people currently in the discovery phase of their open web research. Notice that sentiment has risen in recent weeks. This shift is likely driven by recent news coverage and social media conversations around off-label uses, touting benefits beyond weight loss and diabetes management, such as addiction and nicotine treatment, as well as cardiovascular protection. Next, this heat map across the U.S. provides guidance on pockets of high engagement.
IntentKey pulls signal intelligence down to the zip code level, pinpointing exactly where the highest propensity of interest in GLP-1s exists at the moment the model is deployed. Looking at the demographic data from this model, the audience over-indexes toward women with children, skews middle-aged and affluent, and shows particularly high intent among Hispanic populations. Finally, let us explore the concept graph, where many of our patents reside. These concept clusters define the exact subject matter being consumed across the open web. For example, when you click on weight loss, you can map the entire intent path, from general browsing of articles and videos to high-engagement subject matter at the bottom of the marketing funnel. These models can be generated on the fly and tailored instantly to support evolving brand agendas.
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.
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