Everyone, let's welcome Jeff Sherman with Telescope Innovations. Sorry, I'm a little bit late. My last appointment was running over, and they came and got me and told me, "You're supposed to be speaking right now." I apologize. I'll get it done for you in an appropriate amount of time. I think I have a copy of this for anybody that wants to take away. I have them in my booth that you can take a copy of the presentation as well, that you can make notes on or take home with you. Yeah, we're Telescope Innovations. I'm Jeff Sherman. We're driving innovation through intelligent automation. What does that mean? Essentially, after the disclaimer that you've seen in every one of these presentations, we develop physical artificial intelligence for people that do high-value specialty chemistry, so l ike the pharmaceutical industry, high-value specialty chemicals that are used for reagents for different things, critical minerals, crop protection products, et cetera. What does this mean? Essentially, we're focused on the development of the chemical process. Once they have a hit that they want to manufacture, they start to develop a process, and they need to compress the time to develop that process. We build tools for them that they can buy, that they will develop the chemical process very quickly for them. In this case, it's autonomous process optimization. We use a design of experiments model that will tell us which experiments to do. A robot will actually make this happen, create stock solutions, measure powders, put them together in a reactor, control the reactor, monitor them with an analyzer in situ, what's happening, give that information back to an AI model, and it will tell them which conditions or which to change and optimize this process autonomously. What is exactly physical AI? You think about it as a self-driving car, but in this case, we're doing self-driving laboratories. So really, it's intelligence that embodies these autonomous robotic systems that actually can sense something, decide what to do, and then act on that in the physical world. It's really the biggest trend right now. It's happening in big pharma. They're repatriating a lot of their development of chemical processes and R&D back to North America after COVID and some of the geopolitical issues in the world. They recognize that they didn't have the control that they needed. Now, doing that, they just can't number up with more people and be effective in doing this, so they have to leapfrog with technology, and this is one of the ways they're doing it, by building self-driving labs and putting more automation into their R&D. Part of the driver for this is with the FDA. Having more data-rich experiments helps with getting new products approved. It helps them feed their models, whether they're doing kinetics or thermodynamics or if they're doing a digital twin of a factory. They need data to feed their model to understand what's going on. This is the physical tool that actually runs the chemistry, gets the output of telling them the concentration of everything in the reaction vessel, the heat that's generated, all that information gets fed back into the model. They also get 24/7 operation, which adds a lot. If you have a single chemist doing work, they're only working 40 hours a day or 40 hours a week. They get output in the morning, and they don't know what happened overnight, generally. If you take a sample, take it down to the lab and analyze it, it might take days to get the answer. It might take a couple hours, but this will all happen automatically, and it'll all be in their eLab notebook when they come in in the morning. The other interesting thing is that traditional labs, if you compare to an office building, when you have fume hoods that send a lot of your air-conditioned air out the roof and you bring makeup air back in, it costs about CAD 1.5 million a year for a standard building compared to an office building to operate. They get 40 times the energy efficiency by using self-driving labs and not using all these big footprint traditional fume hoods to run their experimentation. They get additional speed in amount of work that they get done for their reactions as well. So really, they're getting a more efficient R&D, and this is in one of the next slides I'll show you. Reducing the time to market for a multibillion-dollar drug is enormous for them because they only have a limited patent life. If you can shorten that process by a year to three years, it's huge for them. Excuse me. I've been talking all day now. I'm losing my voice. Now, the other side benefit is reducing costs spent to get one process to market, which helps them on their capital costs or R&D costs, et cetera. But the main driver is reducing that time to market to sell product while it's under patent. If you look at the conventional way to develop product, there's drug discovery. Once you get a hit, we work in this space. How do we develop this and commercialize it so that they can then monetize and get a return on the capital they've invested, get the product to the patient while their patent is still in place before the generics would start to make a copy and sell that in the market at a cheaper price? What we're doing is shortening this. This might cost between CAD 900 million and CAD 1 billion. This might reduce it by, your cost by a couple hundred million. But what's really important for them is getting that two to three years of extra life of selling that product while it's under patent. They're very interested in shortening and compressing that. Interestingly, they're also able to now start to address some markets for some orphan drugs that serve a smaller group of people that they wouldn't touch before because they would never get the return on investment and get the profit. They're starting to work towards personalized medicine and smaller market products that they would only traditionally play as a blockbuster type of play. To back this up, the companies are spending an enormous amount of money in automation in labs, whether it's chemical industry, pharma industry, or now clean technologies. They're spending a fortune in automation to make this work. Our business does a few things. We've had 30 years of experience of working with all these players in big pharma and big chemistry, in different lives in our careers. Now, with this business, we're leveraging that to do contract research for them or build custom workflows for these partners like Pfizer, Takeda, Evonik in Germany, Standard Lithium, are companies that need our help to help develop their chemistry. On the other hand, we also use it to develop our own intellectual property that we can license to others. We've also made products out of these tools, and I'll show them in just a second. That's the primary driver for the business is selling these products to customers. The good news is we've also leveraged our relationship with METTLER TOLEDO, so we can reach 35 countries to do global distribution and sales and support of these products. As a startup, it's more of a challenge to do that if you have to build your own distribution across the globe. The first product was paid for by the Enabling Technologies Consortium, which is 18 of the top 20 pharma companies, said, "We want this product. Nobody's building it for us. The big partners won't do it. Will you build this product for us and we'll buy it?" So now, 15 of the top 20s have adopted this. They paid for the R&D to build it. Basically, this takes a sample. You put this probe directly inside of a reactor, and you take snapshots by pulling a sample, sending it to an analyzer, controlling the analyzer, and giving the data back. I have a GPS now inside my chemical reactor that tells me starting materials, products, any side products, how much remaining reagent I have, et cetera, in real-time. They pay CAD 100,000 for this product, and it's about 70% gross margin from the end-user price. The good news is it gives us a nice recurring revenue stream. We can build it into our self-driving laboratories, and it's a nice platform technology that we can build onto other analyzers. It's good for the distribution partner, METTLER TOLEDO. They're a CAD 4 billion company. They're in 35 countries. They have a local marketing, local salespeople, local service, and we've worked with them in the past. It gives them recurring revenue because they don't really get a good portfolio refresh. It takes them forever to develop new product, and so they're relying on us as a development product partner. We can put product into their sales channel and get better return on investment for them. Now, where the best part of the future for us really is coming is in these self-driving laboratories. Pfizer paid us to develop this in a non-dilutive way to build this product. We want this, we want our industry to have this, and we want these modules to be available on this tool. As a part of that, they've now purchased two on top of their development agreement, and they're now deploying that in their chemical development space. We have regular meetings with them about trying to optimize it and change it. This is sort of taking off. We've now sold one to another top five in Europe. These products go between CAD 500,000 and CAD 2 million each. One of our scientific advisors has two Nobel Prizes in chemistry, one of five people in history to ever get two Nobel Prizes in science. He really says this physical AI really is only as good as the chemistry it's based on. Our team, their chemistry is superlative. It's been exciting for him to participate. I won't read the whole thing, but our founder helped unlock his second Nobel Prize when he worked with him, and he's been a great advisor for us. Here he is. He went to Korea with our founder, Jason, and some of our team members doing the installation in Korea for the Korea Pharmaceutical and Bio-Pharma Manufacturers Association, which has 300 members. They're building this AI-focused R&D center in Seoul, and they deployed a unit there. And this is, you know, you say, "Is this real or not real?" This is, one of the systems actually working, where we buy some components, like the robot itself. No point in reinventing that. Universal Robots makes a great product, w e just incorporate that into a workflow that we need. In this case, it's uncapping a vial. It's going to be a reactor. You make stock solutions. There's three different arms working here, putting samples in, either dispensing liquids, weighing powders, putting something into a reactor and capping it, starting a reaction. This is powder dispensing on a balance, that's a CAD 90,000 balance. It's amazing. We build this flow into the autonomous workflow. The next device, actually, you can watch the material that they put in there dissolve. They're putting it in there's a camera that watches the solids dissolve to make sure that it dissolves completely and confirms what's going on. A row of five reactors that are all running sequentially, interleaved [audio distortion]. You can run five reactions simultaneously, different dosing rates, different temperature ramps, and we're capturing all the information in real time to push back into their eLab notebook in different models for doing thermodynamics, kinetics, et cetera. This is real. That's at a customer site and it's taking off. What are they doing with it? There's a variety of things. Without getting too technical, there's high throughput screening just to see if the end point of a reaction has happened or if it's still continuing. Did it get stuck? We work mostly in this development space where they're doing route scouting and narrowing, where you're trying to use a self-driving lab with automated lab reactors and analyzers so that you can see that you've got a better yield and a faster time. You know if you get into these sets of conditions that the reaction will fail and be dangerous or whatever, you're not going to get what you want. They do this very quickly, and then we work on refinement of the process on the other end as well. If anybody wants to go into big detail about that, I'll help you. We did this for our own intellectual property to develop lithium chemistry. Essentially, we took a variety of different materials from different sources across North America that had different levels of purity, and we can make battery-grade lithium out of it every time. To do this, a traditional process would take over 4,000 experiments to do it correctly, to map the design space. We did it in 100 steps, and the automated self-driving lab did this for us. The cool thing is that it reduces the need of cleaning up their feedstock so that they can cut out about 15% of what it costs to build a plant. The plant they're building now is CAD 1 billion in Southwest Arkansas, and it saves about 30%-50% of their OpEx on the reagents they would use. We are licensing that technology actively to companies that are starting to build these types of plants in North America. We've also got one for solid-state batteries for lithium sulfide as a reagent, and we're working on battery recycling as well. It's kind of a cherry on the top. We haven't licensed this technology yet, but it would be a nice thing on the investment. Just to get quickly, because I only have a few minutes left, w e're working with world-class partners, funding agencies, and contract research clients. Sometimes you want to bet on the horse. In this case, I bet on the jockeys as well. We have a world-class team with two Nobel Prize winners, two Nobel Prizes, and this guy unlocked one of the Nobel Prizes. These people ran these kinds of companies and have had very successful exits from other businesses and create value for the customers and for the investors. One of them was a company I was a part of that was, w e were six to eight people at the beginning, i t currently is functioning at a billion-dollar market cap within METTLER TOLEDO. Not all of them are that big, but that was a good example. Between the science and the value creation, we have a good history. Our third-quarter results were just released yesterday, and we're 80% up year-over-year through three quarters. The trajectory is right. If you look at the trailing 12 months, it's about 110%. We expect to finish between CAD 9 million and CAD 9.4 million this year. Balance sheet. 80 million shares are outstanding, 18% held by directors and officers. Well, when I printed this, it was CAD 0.80. It's about CAD 0.89 today. In U.S. dollars, that puts us at a market cap of about $50 million. Of course, I think it's worth CAD 100 million, but that's me. Hopefully we're starting to spend more energy on investor-facing marketing and making sure that people are aware of this opportunity. There are no warrants. Then we have some options for employees of the company of about 7.7 million shares with people that are board members, executives, and some of the team. We raised CAD 6.5 million last year in October. That should hold us for now. With the other income that's coming in, we don't expect to have to raise money anytime soon. We have other grants and other things that are coming in that help fund that, not just in our product sales, but for instance, for raising money for doing the lithium sulfide work. Why would you bet on this company? First of all, it's physical AI leadership. This is a self-driving lab. It's the number one trend right now in big pharma and in the industry to develop manufacturing processes. We have blue-chip validation through the big players like Pfizer, through METTLER TOLEDO, and 15 of the top 20 pharma have bought products from us or worked with us in a development way. We have different types of revenue streams from CRO work, product sales, and hopefully in the future, licensing. We have this world-class team I talked about from a scientific perspective in value creation, and there's a nice cherry on top with the clean energy upside of being able to do licensing. Hopefully, I made it in time. I have one minute so I can take some questions from you, and I'm happy to share the presentation with you. I have them back in the booth. I hustled over here. I didn't bring 20 of them with me, but I'm in 629. There's still a few meetings left open. I'm happy to, any questions you might have, I'll answer them. On the lithium, is that like a refining solution? It's purification, yeah. Yeah. They do DLE first, and they isolate the material, but then they make a concentrate out of that, of lithium chloride. Then, we do the purification, is that piece of the process. Most people had half of the sheet, and their old way of doing it was not very efficient, and it's called a bicarbonate loop. But we just do a crystallization technique that we learned in the pharma industry and optimized it for lithium chemistry, and so we can get the purified lithium carbonate. I'm off. I can talk to you just after. There's another person coming in right behind me. Thank you so much. Thank you. Tried to stay on time.