Hello, everybody. My name is Henry Dubina, and I'm the CEO of Telescope Innovations. We're a Vancouver-based company that was a spin-out from University of British Columbia. The Founder, Jason Hein, runs one of the largest chemistry and chemical engineering groups in Canada, and he's a world-renowned scientist in the area of process chemistry, which is our focus. We're on the Canadian, the U.S., and German stock exchange. You can see the symbols up there. What we do is we use artificial intelligence and machine learning and combine that with robotics and other automation and sensor technology to fully automate a chemistry lab to develop new chemicals. That's what we do. Our secret sauce is a combination of the artificial intelligence that we use, the machine learning algorithms, combined with a lot of subject matter expertise. We're a small company.
We have about 40 employees, and 30 of the employees are either PhD chemists, chemical engineers, or software engineers. That's really the secret sauce, is wrapping that knowledge base around the automation with the artificial intelligence algorithms that we develop or we use. Our scientific advisor, who Jason got his PhD with, is Barry Sharpless. He's the only two-time winner of a Nobel Prize in chemistry that's living, and there were six in all time. So we have very strong technical roots. On top of that, Jason, with me, put together a commercial team that was based on a company called Mettler-Toledo AutoChem, which is the absolute market leader in this space. AutoChem, over 20 years, from essentially 2000 to 2020, we took that business from $15 million- $200 million a year with an EBITDA of 35% that drove a very large market cap.
We took that management team, commercial team, and combined it with the technical team in place, and that's result in a very strong leadership team. On the right is what we are today, and where we started was with contract research. From his lab, we emerged by providing contract research services to the pharmaceutical and the clean energy space, especially the lithium battery space, where we developed a lot of process understanding. Then we had to develop tools to be able to conduct this research. From those tools, we combined that with our services, and we solved many important projects for pharmaceutical and energy companies. While developing those, we either developed new intellectual property, we developed new platforms that we either kept as trade secrets or we commercialized as products, which is the second bucket.
More recently, we've really been investing in SDL, self-driving laboratories, which are laboratories that run 24/7 with minimal or no human interaction whatsoever. Everything's done with automation and with the software. Today, we're doing about $10 million. Today meaning in our 2026 fiscal year. We expect we'll grow this business in the next few years to about $50 million. On the left side of the chart is our vision. The people who can implement these self-driving labs are the biggest, the largest, most profitable pharmaceutical companies in the world. Each SDL is about $2 million, plus you have to have a technical staff that can adopt it and make it run. So there's not so many companies that are going to be able to afford those type of platforms.
The top 50 biopharmaceutical companies in the world, probably the largest high-value chemicals companies in the world as well. There is another market, which is we use our SDL and our facilities and our people to provide subscription services to smaller and midsize pharmaceutical companies and CROs, because they are not going to have the wherewithal to be able to implement this technology. But to stay competitive, they are going to need to have the functionality, to stay competitive with companies in the West as well as Chinese and Indian pharmaceutical companies. In addition, we will develop our own intellectual property based on this platform in process chemistry for the actual basic building blocks which today typically come out of China and India alone.
From a standpoint of insourcing back to Europe and the U.S. of the building blocks, this type of platform is an opportunity for us either directly as a CRO or through a partner where we would license the technology to a contract research organization. We believe that opportunity is in the hundreds of millions of CAD. I talked about the secret sauce being our capabilities in physical AI, plus our technical team. Now we use that technology and those platforms to participate in three very attractive markets: biopharmaceutical, high-value chemicals such as agrichemicals, could be semiconductor chemicals, as well as clean energy space. We have a lot of technology that we have developed from our platform for lithium battery industry of today, lithium carbonate, the purification step, as well as the next generation of solid state batteries, which is lithium sulfide.
We have a lot of patents in this area that we plan to license in the future. What is physical AI? Physical AI allows software to design the experiment that you want to run to get the desired outcome you have for your process. By the process, I mean, typically when you discover a new drug substance or new chemical, you are making 1 to 10 milligrams. What is the big deal? The big deal is figuring out which chemical binds to the cancer-causing agent, if we use cancer, to the disease and binds up that enzyme so it no longer has the effect in your body. So it cures or at least stops the disease. But making it 1 mg or 10 mg and making it 2,000 kg is a whole another world.
If something is highly energetic in the laboratory at 10 milliliters and it blows up, it blows up in the fume hood. But if you are doing 2,000 kg, you know what happens if you have an energy release? The plant blows up. You just think back about the news over the last 10 years in China about all these chemical accidents. That is because the processes were not adequately characterized, so they did not build their plants well enough to get rid of all that heat, so the chemistry did not explode. Those are just some of the little things. We have automated robotics, we have analytical technology. By analytical technology, think it is a chemical GPS. It tells you how your reaction is proceeding, the steps you are taking. Are you getting to the desired end products at a high yield with a very little impurity?
Impurity is very important because a pharmaceutical cannot pass FDA approval with any impurity above 0.1%. Not a lot, 0.1%. That is one of the things we are trying to engineer into this chemical process or biological process to ensure that the impurities are very low. On top of that, you cannot release a batch in your plant unless it is below 0.1%. So it is not just the design, it is the actual final formulation of the product. We combine all that automation with artificial intelligence and machine learning to produce data and produce product to move it further down the workflow. Many of you know, or all of you know that to make one drug today, it typically costs a pharmaceutical company $2 billion and about 13 years. That is including from discovery all the way through release to manufacturing.
What we do is in this one area called process development and scale-up, which takes it from discovery to the plant. Typically, that costs $700 million, and it takes three to three and a half years for a company to do that. By the use of our SDL technology, we can significantly lower the cost by a factor of 10 to 20. But more importantly, we can decrease the time maybe by just a year. But even if it is six months, if you consider decreasing the development time so you can release it, get it through clinical trials one year sooner, you extend your patent life by one year. If you did that, think about the new Eli Lilly drug, this new GLP-1 combination inhibitor. That is supposed to be the first trillion-dollar drug ever. So one year on that type of drug, the economic drivers are very compelling.
This is going to happen. There is no question about it. It is going to happen in every part of most industries, this type of R&D automation. There are other things like robots don't make mistakes, humans do. So when you are adding stuff, you are supposed to add some 10 mg in the lab. Well, I had a few drinks last night, and so with my spatula, I am going to add 12 mg instead. That error and the propagation of that error ends up by giving bad data. Another thing is this is 24/7. I don't have to go to my son's soccer game. I don't have to go. I don't have to miss an event or miss something because I am in a meeting. So physical AI really helps increase the productivity of the entire workflow.
Just very quickly, the way it works is we plan an experiment, software does that, then we dose it with the robot and some special things that we make. Then we react it. We put all the stuff together in a specific order at a specific temperature, stirring in a specific way with the right catalyst, and then you have to analyze your results. So that is what happens every time. Every pharmaceutical process has between 10 and 13 of these steps individually. So there are 13 steps where they have to go through each one of these, and today the chemist does it typically by hand. Here, the robots do it all. So that is how we get this incredible increase in productivity.
We started this SDL project when Pfizer came to us because some work we had done with them in the past, and they said, "Telescope, I'm going to give you a bunch of money. We'd like you to develop a self-driving lab for our laboratories." That was in 2004, and they gave us millions of dollars to do this. They took no ownership position. They just wanted the technology and they wanted to participate in the development.
That was in 2024. We commercialized that product first quarter 2026, and we actually sold and installed successfully three systems. Each system is about $1.5 million plus services on top of that. We've sold one and we've sold multiple now to Pfizer, two actually. Then we were selected by the Korea Pharmaceutical and Bio-Pharma Manufacturers Association, KPBMA. They have a training and a feasibility site in Seoul, and they selected us as their SDL.
Now all those Korean pharmaceutical companies are learning on that technology, and then they'll hopefully adopt it within their own infrastructure. Then finally, we've already installed one with an order of a second one to a major European pharmaceutical company that you would all know, but I'm not at liberty to discuss who they are. It's going very well. Started R&D 2024, commercialized the product in 2026. Next year instead of three, my suspicion is we'll sell 6- 10 additional platforms. This is an example of-- This is not the SDL, but the technology inside of here supports the SDL, and that's called DI, DirectInject-LC. If you go back to what I talked about impurity, the ubiquitous golden standard to analyze any pharmaceutical is with the liquid chromatography.
It does the quantitation of the impurity and the active ingredient, and that is the gold standard. We've developed some technology that allows you to do that in real time. Usually, what you'd have to do is you'd walk up to the reactor, pull a sample, walk it down to a lab, and get the results in an hour or five hours or the next day. This all automatically, and we've commercialized that product, and we actually sell it through the Mettler Toledo organization that I used to be running, that I talked about earlier in the presentation. We're making very good progress with this product as a standalone as well as an integrated part of the SDL. As I mentioned, we distribute this by Mettler Toledo, and one of the reasons we do that is they have 100 people dedicated to this market space.
For us to build that up would be so expensive, it's just not feasible at this part in our development. Okay, another example of how our technology is being adopted is by other partners. I talked about Mettler Toledo. This is a company called Asahi Glass. It's a Japanese company, and their products are used all around the world. What they've done is they've taken the fluidics from that DirectInject-LC product that I just showed you, and they gave us a development contract and signed an OEM agreement to build it into their new product that will be released next year. This will be another channel for us, on a recurring basis, to be selling our technology at a lower level with not a lot of R&D expense because we've already developed it in our DILC product.
I see more of these type of partnerships developing. These are our customers and many of you know probably most of them or some of them. The critical thing here is we're a little $10 million company. However, we punch way above our weight class. The reason for that is Jason Hein's global reputation. Oh, by the way, a lot of these people I've known for 20 years. So we have direct relationships with the C-suites of these companies that allow us to have strategic discussions about where they're going with their R&D efforts. It's really helped us develop our business. Okay. An example of where I mentioned earlier that we do contract research, while we're doing the contract research, we're working with clients, but we'd also develop our own intellectual property.
Three examples of this in the clean energy space is we have a patent for lithium carbonate purification called ReCRFT, which is the crystallization step. So what happens is you get lithium, and it's usually either in rocks or it's in a brine. From that, it's full of all sorts of other things. You have to end up with lithium, which is 99.997% pure to make it into the cathode to go into the battery. We've developed technology that significantly shortens the development time due to this crystallization process that we've developed and we've patented, and we're trying to get people to license. As you know, the lithium industry has been not so fantastic the last two years, so their appetite for investment hasn't been great, but that's changing. That's changing rapidly.
As a matter of fact, the guy who's supposed to make this presentation you might have seen in your book is Jeff Sherman. He's down in Arkansas right now because Arkansas and the U.S. government has a huge project going on for lithium purification that we're participating in. So that's who he is today. We've also developed patents for lithium sulfide. You can get lithium sulfide today, but to make it's a chemical reaction that's 600 degrees Celsius. So the costs and the safety concerns about that are very high. We have a patent where you can do that at room temperature to have the same yield. So we're in the process now of working with companies to see if they will license our technology. Then finally, we're also in reagent free, battery recycling. So we have a very strong management team and board of directors.
Neil here is our latest person, and he's got extensive experience in the instrumentation world. Started three companies himself. Vaso worked for me at Toledo and ran one of the business units. Jason, I've talked a lot about already. I mentioned Barry, and then we have Andy Robinson and Robert Mintak, who were part of the founding members of the company and our biggest investors. They were the founders of Standard Lithium. So that's where our tie into Standard Lithium is. I'll skip that. This is our financial performance since we started. We have no debt. We've been doing it all through operations except for in the end of 2025 and 2026, we did a fundraise. We raised about $ 6 million, and we used that in 2026 for investor marketing and to build up our team so we'd stay ahead of the SDL curve. That investment now we've digested.
We're doing about $ 10 million this year. Next year, I expect we'll do $14 million-$ 15 million, and we'll be better than break even. We'll be cash break even. Pretty confident of that. Our cap table. We have about 90 million shares outstanding. Earlier today, we were at about $ 0.32 a share. So we have about a small $ 21 million market cap. The directors and management team, we own about 18%. Why us? We really are a physical AI company. A lot of people say they are, but they have no product. We really are. Second, the top companies have really validated us and our company. We have solid multiple revenue streams. We have a very strong leadership team, and we look at the whole energy and minerals market as really significant upside. Thank you. Any questions in the last minute? Yes.
What percentage of the next year's revenue do you expect to come from?
About, Including the AGI, that will be about 35% of our revenue stream,
and the rest is SDLs.
It'll be, let's say, $5 million-$ 6 million DI, DirectInject-LC. It'll be $2 million contract revenue, and the remainder will be SDL.
What do you think of your competitors, and what do you do differently from them?
What we do different than anybody is there are plenty of robot companies out there, lots and lots of them, including in our space. But what we have is we really have this technical team, really world expert technical team that integrates into our platform. Their brains are in our platform, and we test it just as the customer is going to use it.
Chemistry is very nasty, so you think it's going to work, but after it works one or two times, it breaks because the chemistry eats everything. I think that's the biggest reason. The other set of competitors are also opportunities for us, strategic opportunities. AI companies, Anthropic, Lila, Google, they need data, high-quality data, because garbage in is garbage out. We provide high-quality data every time. Both metadata, visual data, as well as analytical and sensor data. Thanks for your interest. I'm around.