Hello, everyone, and thank you for joining us for the 28th Annual H.C. Wainwright Global Investment Conference. My name is Joseph Brusca, and I'm an equity research associate at H.C. Wainwright. I'm pleased to introduce George Achilleos, Chief Executive Officer of NetraMark Holdings.
Thank you, Joseph. Always a pleasure to present the NetraMark story. Hopefully, it's an enticing opportunity for all of your viewers. At a very high level, I want to introduce NetraMark as it's an AI company, for clarity, that's very pointed and focused at the pharmaceutical industry in particular. We categorize it as precision AI that identifies patients that are driving clinical trial successes or failures. If you can imagine, when you have a clinical trial, it runs through the various phases, preclinical phase I, phase II, phase III, and into commercialization. As you're doing that, there's data that gets unlocked at each phase. The main purpose of our technology is to harness the value of what's in that data. I'll take you through what that looks like during this presentation.
We're a TSX-listed company under the symbol AIAI. We trade on the OTCQB under AINMF in the U.S. Let's get started. I always like to start with the problem. We want to make sure everybody's aligned from the perspective of, is there a clear problem that's being solved? What I've discovered within this industry is there isn't really a whole lot of debate with respect to the problem. When it comes to clinical trials, there's a couple key metrics. When you're looking at the FDA overall approval rate, from molecule inception all the way through to commercialization, you're looking at a less than 12% success rate. So a very challenging industry to discover a drug and take it all the way through to commercialization.
Even more interesting, I find, is that if you look at the later phases, once you get through phase II and now you're into phase III, the clinical trial failure rates at that late phase of a trial is up to 65%, depending on the therapeutical area. Imagine you've spent a bunch of time, money, and effort discovering a drug, getting it through phase II. You have a successful phase II, then you get into phase III, and then you fail. It's probably the worst time to fail, given the amount of capital that's been put at risk that potentially could go to zero. What we believe here at NetraMark is the data is held, and within the data is where the answer actually is.
We find the traditional methods that are exploring that data are not particularly good at finding a combination of variables that could potentially be causing some of these trials to fail. When we look at the problem, I think we are clear on that. Clinical trials fail at a very high rate. When we look at the solution, I want to run you through a little process here that we follow at NetraMark using NetraAI, which is the name that we use to identify our core technology. As I mentioned, what we do is we take the sponsor, the pharmaceutical company's data, phase I, II, or III clinical trial data sets. We organize that data so that we can insert it into NetraAI. We analyze that data. We identify a combination of two to four key variables, which we call a subgroup, that the machine has identified.
Then we take that identified subgroup, which we think has an impact on drug effect, placebo issues, or adverse events. So drug response, placebo issues, and adverse events. We use that information of those variables to tweak the architectural documents, I like to call them, which are the protocol or the statistical analysis plan, and to prospectively, before you, let's say, start phase III, insert that discovered subgroup into those architectural documents to essentially set up your trial from a design perspective so that it has an increased chance of success getting through the subsequent phase. I would like to highlight that this technology is purpose-built for the small, noisy data sets that are derived out of clinical trials. People always ask, can you do this with a large language model? The answer is no. This technology is purpose-built to analyze variables that are captured during clinical trials.
Okay? As we continue, I would like to also use a case study so that we can demonstrate how does this technology work within a real clinical trial setting. If you look to the left, you will see this is all based on an assortment of data that we were able to acquire from a drug that was focused at model derived at depression. What we did within that was we looked at this trial on the left, and if you can see here in phase II, that company that had this data in treatment-resistant depression had a very successful phase II study. If you look at the red line, you are looking at the placebo effect. If you look at the blue line, you are looking at treatment.
You can see here exactly what you want if you are a pharmaceutical company is a huge separation between placebo and treatment. They had a very successful phase II trial, lined up 3 phase III studies, all of which failed, and there was no separation between the placebo and the treatment. On the left, you see what typically happens often in trials. Then on the right, I would like to transition to NetraMark, where essentially we captured that data. We were given that data from the pharmaceutical company to analyze. What we were able to show is with the model-derived subgroups, with the variables that we discovered, we were still able to demonstrate and validate in that phase II data that there was a lot of separation between placebo and treatment, which is great. This is exactly what you want to see.
The question then becomes, okay, then when we applied that model-derived subgroup to the phase III data, were you still able to derive separation between placebo and drug? What you can see here is that that is the case. Imagine if this company had actually used our analysis to better design the phase III study, there would've been a much higher likelihood of success of those phase III studies, thus protecting all of that invested capital that went into phase III. This is essentially what we do, and we do it for all therapeutic areas with a focus on CNS, central nervous system-related disease, and oncology. Those are our sweet spots, and we often take phase II data or phase III data and analyze those to find better insights with respect to the drug and how it works.
Now, the next part as we transition to, great, how is this being commercialized, is really powered by what I call these three pillars. If you want to commercialize a new technology within the pharmaceutical industry, it's a very difficult process. Probably one of the main reasons is that you have a regulator hovering over top. In order to penetrate the defense mechanisms, if you will, that these pharmaceutical companies have, because they have risks that they want to manage, you need these three essentially pillars. The first one that you're going to get asked is, what kind of institutional collaborations do you have? The second question you're going to have is, what does the FDA think of this technology? The third question you're going to have is, has this been peer-reviewed?
Because of course, as a company and as a CEO of a company, we're going to speak highly of the technology. What do third parties think of the technology and the capabilities of the technology? If we look at the first one from a NetraMark perspective, and we evaluate institutional collaborations, we've got four really powerful collaborations that we're quite proud of. First is with the NIMH, which is one of the 27 institutes of the NIH, which is one of the largest biological research institutions in the world. We're contracted with them to analyze some of their ketamine data, et c, to start to find if there were interesting insights within that that would potentially affect drug response. The second collaboration was with the Mayo Clinic, and that really is around oncology. The third is with CAMH, which is the Centre for Addiction and Mental Health.
That's really around schizophrenia and depression. Then FMP in Europe. FMP had a landmark trial called the ROME trial, which was peer-reviewed and published in Nature, and it's one of the largest oncology studies to have occurred. We also have a collaboration with FMP in Europe. I think we can check the box with respect to institutional collaborations. The second box is FDA engagement. What does the regulator think of the NetraMark technology? We worked quite diligently for almost a year to apply and get accepted into what we call a CPIM, a Critical Path Innovation Meeting. Those meetings are all published on the FDA's website. There are not very many parties that get through this application process.
In November of 2025, quite recently, we were able to have this closed-door meeting with the FDA, where we were able to present the technology and the novel nature of the technology that we thought would distinguish the NetraAI technology from traditional AI, ML methods, traditional statistical methods, etc , that typically are used to analyze data once a phase of a trial occurs. The result of that was we had an FDA review press release that we put out, which headlined with NetraMark meets a major FDA milestone, where the FDA really supported or highlighted to us that the best way for NetraMark moving forward after going this was twofold. Number one was to identify subpopulations that could be prospectively versus retrospectively, prospectively inserted into a statistical analysis plan to identify a subgroup that would show positive efficacy with respect to the drug.
The other thing that we also achieved was what we call an MIDD pathway, the Model-Informed Drug Development pathway. If you even look on the FDA's site, in order to qualify for the MIDD pathway, you cannot be a traditional AI, ML method or Bayesian statistical method, etc. So it was a clear distinction that we received after that meeting that says, yes, your technology is novel, distinct, and there's a pathway forward with respect to the use of the technology within the clinical trial process. We are quite proud of that. The third really comes from an output from some of these institutional collaborations. So in November, December of last year, we had a peer-reviewed paper that was published in npj Digital Medicine, which is part of the Nature Portfolio, and that was a result of the work that we did with the NIMH.
It was co-published with the executive director of the NIMH. So we are quite proud of that. We have other papers from a peer-reviewed publication perspective in Frontiers in Pharmacology and others spanning psychiatry, oncology, and CNS, which we think are giving validity to the technology from a third-party perspective. Now, the other part is from a competitive moat perspective. I also get the question often, which is, who are your core competitors? As I suggested from the FDA CPIM meeting that we had in December of last year, realistically, this is quite a paradigm shift for the industry. It is very difficult to, and this happens often, it is very difficult to say, how does this compare to neural networks? How does this compare to XGBoost? Or how does this compare to the myriad of AI and ML approaches that are used?
It is really nothing that you can reduce back to something that is known. It is actually a novel approach to analyzing clinical trial data. So the parts that we think are the competitive moat are, first, we do not know of other companies that are competitors or in our space that have had Critical Path Innovation Meetings or closed-door meetings with the FDA, where the FDA has had commentary with respect to the technology as it moves forward. So we think that is part of our competitive moat. As I said, getting that meeting was over a one-year process, so this is not easy to go through that process. Secondly, the technology itself, as I said, this is a unique mathematical paradigm that was developed over seven years by Dr. Joseph Geraci, who has his PhD in mathematics. So this is purpose-built for clinical trials.
It's not a souped-up large language model, etc. This is a mathematical paradigm that was specifically meant for analyzing clinical trial data, and then the scientific validation. All of these things to collectively, I think, create a competitive moat around the NetraAI technology that would take years for others to permeate if they were able to build a competitive type of technology. As we continue down the path of commercialization, the next question really becomes, what's the addressable market? For right now, and excuse me for the Canadian dollar analogies, but we're a Canadian company, and so the average analysis for us takes approximately four to six weeks. We charge around CAD 300,000 for that four to six-week project. It's actually now moved up a little higher, closer to CAD 400,000.
The way we came up with our addressable market is we looked at globally the total number of phase II and phase III trials because that's where we apply our technology, and there's over CAD 34,000 of those combined. We took that, we multiplied it by the CAD 300,000 that we charge, and so you can see it creates about a CAD 10 billion addressable market. The really interesting part about this, though, is as we continue to do analyses and gain traction, we're able to gain leverage from a pricing power perspective. If you imagine, for every increment of CAD 300,000 that we charge for this project, if we went from CAD 300,000 to CAD 600,000, we would essentially, using this model, double our addressable market from CAD 10 billion to CAD 20 billion. The addressable market even has scale from that perspective.
As we, again, continue to go down the commercialization pathway, this company has a dual path strategy that it's focused on. One is going direct itself to the sponsors/pharmaceutical companies, and the other path is to use channel partners, a contract research organization, etc. I'll talk about that in a second. But first, from a direct-to-sponsor perspective, that involves us going to conferences and doing all of the lead generation activities that any technology company would do when it's targeted at, let's say, the pharmaceutical industry and really focused on analyzing phase II and phase III data. As I said, it's CAD 250,000 to CAD 350,000 per six-week engagement. There's over 50 active opportunities in our pipeline some of which are farther down the process of getting to contract signature.
We have 10 repeat contracts with our Beachhead client, which is a NASDAQ-listed biopharmaceutical company, where we started with one contract, we expanded to four, and then we expanded to 10. We continue down the direct-to-sponsor path to continue to build our pipeline and generate leads and opportunities using our own sales activation process. The other part is channel partners, which are important because in our industry approximately 70% of trials are run by contract research organizations, essentially outsourcing the running of the trial to a CRO. Last year in 2025, we announced a relationship with Worldwide Clinical Trials where we executed a master service agreement. We spent the balance of 2025 going through quality assurance, and then we went into the process in 2026 here of now activating the commercialization and getting embedded into their bid process.
Worldwide Clinical Trials operates in about 70 countries around the world. It was acquired by Kohlberg approximately two years ago. They are quite a prolific CRO. We are very proud of that relationship, and we think there is a massive opportunity for us to expand our footprint just by getting within the Worldwide Clinical Trials ecosystem and all of the bids and opportunities that they win with pharmaceutical companies. Those are our two paths to market, if you will. The other thing that I am very proud of is the leadership team. My background is technology. Started my career at IBM. I have been in various technology opportunities for over 30 years. I am very proud of the team that is supporting this. If you want to penetrate this industry, you certainly need a lot of expertise from within the pharmaceutical industry.
Josh Spiegel, our President, he was deeply involved with a company called VeraSci, which was sold to WCG Clinical a few years back, and that was in the pharmaceutical industry. Josh transitioned from that to President of NetraMark. I mentioned Dr. Joseph Geraci, who is our CTO, CSO, and was founder of the technology. He essentially, as I mentioned, has his PhD in mathematics, and he is the brains behind the mathematical paradigm. On the bottom level here is a very important layer for me. I look at Dr. Luca Pani. He is the former Director-General of the Italian Medicines Agency. He has also served on many senior European Medicines Agency committees. He is our Chief Innovation and Regulatory Officer.
Our Fractional Chief Medical Officer is Dr. Panteli Theocharous. He is also a C-suite executive, actually, also as he is Fractional CMO with NetraMark, he also has a very senior executive position within Worldwide Clinical Trials. Dr. Angelico Carta is our Chief Strategy Officer who was the co-founder and President of Worldwide Clinical Trials. He is now an advisor to them as has transitioned to our Chief Strategy Officer. So a really reputable, great leadership team.
Just the last couple of slides to share. It is a pretty clean share structure that we are quite proud of. We have about 94 million shares outstanding, a little over 100 million fully diluted. Very few warrants outstanding, most of which are held by directors, officers, or insiders. They have an exercise price of CAD 1.35 and expire in January of 2028. There is a lot of inside ownership alignment. Almost 22 million shares fully diluted that are held by directors, officers, or insiders.
The final slide, just from an investment highlight perspective, I think I outlined a very clear problem. Clinical trials fail at a very high rate. We have a technology that we have built that essentially identifies subpopulations that have an impact on drug response, placebo issues, and adverse events. It has been FDA-reviewed. It has got a very high margin profile, essentially a 95% gross margin profile. Every contract that we get in that we turn into revenue is essentially straight to the bottom line.
We have a peer-reviewed validation, as I mentioned. We are working our way now to be deeply embedded into CRO workflows. A pretty large addressable market, high insider ownership. From a capital perspective, we did transition to be listed on the TSX in early 2026 and had to go through a capital raise to identify 24-month cash requirement for them. With that, I would like to turn it back over to the team at H.C. Wainwright. Joseph, thank you very much for having me, and hopefully your listeners will enjoy this presentation.
Thank you, George. Thank you everyone for joining us. We look forward to seeing you at the conference in September.