Hey, good morning. Our next presenting company is Research Solutions, trades on the NASDAQ under the symbol RSSS. Company's primary product is a SaaS platform that helps researchers in the scientific world primarily, makes their efforts easier in terms of finding articles, working within articles within their organizations. Here today to start the presentation is Roy Olivier, company's CEO and Chairman. With him is the company CFO, Dave Kutil. Roy? Thanks, Dave. Appreciate it. [audio distortion] Everybody. I guess I love the hat. I used to work in Fort Worth, Texas, and we wore suits back in those days, and I remember when the CEO came down and said, "You guys can't wear cowboy boots with suits anymore." Everybody was very upset. I still have my boots, though. Anyway, what we do. As mentioned, we really are an end-to-end platform for research. A vast majority of our clients today are corporate clients. About 80%-odd of our revenue is corporate. The remaining revenue is derived from either government or primarily academic institutions, specifically university libraries. What we typically do on the corporate side is we provide the workflow tool that supports the 'R' in R&D. If an organization is doing research on EV battery technology or pretty much anything else, they will use a platform to search the world's corpus of scientific research, basically what is a peer-reviewed scientific journal, and then they will start to create their own product. All the workflow tools that happen in between is where we play. O ur installations are people like Bayer, BASF, L'Oréal. We're involved in multiple top-secret Navy stuff where they're doing research on specific types of warfare. Anybody that needs access to the world's scientific research. I even use it. I'm a big Formula 1 fan. If you guys are, you might have heard about, a few years ago, there was a thing called phase-change materials they thought one manufacturer was using to cool their wheels. You can research that in our platform. I learned all about something I'd not heard of the day before I heard the term phase-change materials. Our mission as a company is really to simplify the complexity around research. We've really been focusing on transitioning the business from a transactional business that sold the article to a SaaS software business. In the process of doing that, we've shown material top-line growth improvement, stickier revenue streams. Our revenue mix, as it's moved from transactional, relatively low gross margin, to SaaS software, which is 85%+ gross margin, has improved dramatically, which has had a big positive impact on profitability, cash flow, etc. What we do from a big-picture point of view is support four steps of research. How do I find research similar to whatever project I'm kicking off? How do I acquire that research? How do I manage it, share it in a copyright-compliant way? How do I create my own drug, my own peer-reviewed or article-to-be peer reviewed, along with all of the underlying analysis? Our discovery tool is Scite. It's sold as a subscription product. Our access tool, and that's the business we've been in for 20 years now, this is our 20th year, is both subscription and transactional in nature through the sale of the actual articles. Our manage tool is subscription-based. We do not really play very much in the create side, but we do have some products there that are subscription-based, and we use Scite as our subscription tool as well. Now, we do believe that AI is going to change the way that people access this content. A vast majority of the peer-reviewed scientific research out there today is behind a paywall, so ChatGPT can't find it. They don't have the rights to get behind that paywall, nor does any other LLM. In fact, I think the general consensus that's reflected in our current stock price is that we will be disrupted and killed by AI. We don't think we will be for a number of reasons, which I'll talk about here. Fu ndamental to our argument is whether or not you believe AI will have access to 100% of content behind a paywall. You have to form your own opinion as to the answer of that. My view is, that's not going to happen. When you look at the publisher industry in general, the top publishers average about $3 billion in revenue and 40% EBITDA margins on the sale of that peer-reviewed scientific content. They're not going to license that for a $10 million deal to an LLM, because that's an annual number that they sell through subscriptions. Everybody is looking for a method to provide you as a user a better answer by giving you more than just the abstract of the article, more than just the summary, but not so much that you don't need to buy the article. T hat's where Scite comes in. Scite, for the last five or six years, has taken all that content, extracted snippets of the content that allow us to give you a much better search result if you're searching bladder cancer, Havana syndrome, peanut butter, whatever it is, and we'll provide you a summary. But at the end of the day, you as a researcher will need to ultimately go read all of that research and acquire those documents, which you're either going to do through a subscription to the publisher, what's called a token package, which is prepaying for discounts, or individual documents, which is our transactional business. I'm frankly going to skip the rest of this deck because you can read it in your spare time. I'm going to talk about will AI kill us, and how are we thinking about the world as we look forward? Today, our products are SaaS platform products. They're accessed primarily in terms of number of users through somebody logging into Article Galaxy, buying an article, taking delivery, putting it in a personal folder, sharing it with their research team, or if they're in Scite, they're going into Scite, they're asking research-based questions and getting answers that spit out, "Here's the articles I used to give you this answer." All those articles have individual DOIs, which is a unique marker for each article, and then you go buy them using Article Galaxy. I think we all know, just like every one of our lives has been impacted by AI, that today people are starting their journey inside of ChatGPT or Claude or Copilot. [audio distortion] On the corporate side, our view as a third party, we don't really care who wins. Copilot's got a big install base and a lot of traction simply because they have Office 365, and it's part of that. Our strategy shifted about a year ago from focusing exclusively on platform development to how do we be where our customer is? They're going to be in these LLMs. We've released two products over the last six months, literally, I think four months. One is a product that connects Scite into Copilot, Claude, any of the LLMs. The other connects Article Galaxy into those products. The way it works today is where I used to go into Scite and say, "Tell me about Havana syndrome," and it would search the world's content and give me an answer. I now go in and I can go into my I did this on the plane just because I was bored. I used ChatGPT, I installed the Scite connector into ChatGPT out of the ChatGPT store. I installed the Article Galaxy connector into ChatGPT out of the ChatGPT store. Then I sat there on the plane, and I said, "Tell me about Havana syndrome." It uses the Scite corpus of material to get behind the paywall where we have access to do that or to search the snippets which we've extracted out of about 40% of the world's content, and it gives you a much better answer than a ChatGPT or Claude can do on their own because they don't have the rights to get behind those paywalls, and they don't have any snippets that they're pulling out of articles based on those rights. What ChatGPT does really well is it gave me a fantastic, fully formatted answer that I could have printed out and used in a university, turned it in. A t the end of that answer was all of the articles that it referenced to create that summary for me. M y next line was, "Go get me all of those articles, but I only want the free ones." At that point, it talks to Article Galaxy. It says, "Where do you have a subscription in place where you've already paid?" A ny article you get out of that subscription is free to you. What is in your corporate library that somebody else has already purchased? What are the reuse rights associated with that article? How many times can somebody else in the organization use it before I have to rebuy it? What is OA or free, and what is paid for? Five seconds later, ChatGPT tells me, "I got five articles. You already own two of them. I delivered the other three of them to you in a folder called this inside of Article Galaxy." That's how you do it in a copyright-compliant way, which corporate customers are obsessed with because copyright compliance and copyright litigation is big in the markets that we play in, which is primarily North America and Europe. W hat we've done is we've effectively taken a $1 million - $15 million subscription investment that somebody like a Bayer or a L'Oréal has made. We've taken an investment that they've made in building infrastructure tools internally that have existed for years to have a corporate library, to understand what rights they have in place for that corporate library, this is all stuff Article Galaxy does, and being able to get documents on demand that they don't have a subscription to. As these companies are implementing either one or more of these LLM tools within the corporate infrastructure, and some are saying we're only going to support one, some of our customers support all of them, then we allow them to connect to all the subscriptions they have and to a workflow that supports their research organization in a copyright-compliant way. T oday, we are selling those products. We've seen a few things. We've seen our days to sale expand because we're now being installed as part of the infrastructure of the organization, so we have much more lengthy IT security questionnaires and AI committees we've got to get in front of, but we're seeing a much larger average sale. Our average sale with Scite and AG historically has been around $11,000, $12,000. We've closed several high five figures and some six-figure deals around these MCPs just as the connectors, and we expect that trend to continue going forward. We even have some proposals out that are well into the six figures and one that's out into the seven figures. Because the pain of having a $15 million subscription investment and a multimillion-dollar internal corporate library and rights management and all the stuff we do on the AG side and not being able to connect that to an MCP is big, and there's a massive productivity hit across 3,000 scientific researchers if you're not able to do that. W e're first to market with that product. We have 1,000 corporate customers, and our view is, we think we'll continue to compete very well for that corporate workflow business through our AG site and MCP tools. We think it's going to be a long time before people can knock us off of that mountain, because most of those agreements are three-year, five-year agreements. Our average lifetime value of a customer, seven years. Top customers, it's over a decade, because once we're integrated into that workflow, it's pretty tough to get us out of there. My point in bringing all this up is I think we have a lot of interesting moats around our business. One is we're the only people out there that have access to all the world's content in one place. Think iTunes, back when it started, you could get any song or any album you wanted in one place. You didn't have to go to different stores. You didn't have to worry about one copyright holder versus a different copyright holder. You bought it, Apple took care of the payments. That's what we do in scientific research. Another moat is we have been building AI agreements for content for five years. Most of the LLMs, most of the other AI companies that pop up every week, they're starting now. We cover about 60% of the world's content with AI read rights or some type of rights related to AI usage. That's more than all the LLMs combined today. The problem is not the technology. If the technology was let alone to do its thing, we'd see massive rush toward using these ChatGPT-type tools, and it would very negatively hurt our business. A s has happened in the internet age several times, the technology guys are now meeting copyright lawyers. It's a whole different conversation to get those rights. Anyway, we feel like we're positioned pretty well. As I mentioned, we've got a lot of moats around the business in terms of content licenses, customer base, and really understanding that workflow in our primary customer type, which is the corporate workflow. I'll pick up on a few of these slides, but any questions so far? Because I know I've covered a lot. This slide really just lays out for you, I'll go back to slideshow mode, these silos that I'm talking about, where here you've got the corporate academic AI tools, ChatGPT, Claude, LLMs. Over on the far side, you've got we're making significant investments in holdings, library catalog, databases, institutional repositories, full-text search. In the middle is MCP, whether that's AI, Scite, or both, that allows you to connect those tools together. Another way to think about it is that the way that things used to work and the way things work now is changing. The way things work now is somebody is entering a prompt into ChatGPT or Claude or whatever they're using. By connecting the Scite plugin, you get access to content behind the paywall, as opposed to just the abstract of the article. It will also connect through our technology to other Scite databases. For example, we have all clinical trial data. We have FDA drug data. We have 12 different databases today that you can add into the search results, including patents. Y ou can have the search involve what patents have been granted or in what status related to your query. If you have internal repositories of data, as I've met with investors over 20 years, all you guys take notes? Take all those notes, throw them in a repository, connect the repository to our connectors, and when that user enters that prompt into ChatGPT, it is going to hit all three of these databases and give you a fully cited answer with DOI numbers or sources of where that answer came from. Then you simply can tell the LLM, "Go get me those articles." They will get them. They will deliver them. At that point, you do what you do next, which is read them, summarize them, extract tables out of them, all of which can be done within the LLM. T he LLM understands, through interfacing with Article Galaxy, whether or not you have the rights to do that. Because companies that have acquired content that they own, as opposed to subscribe to, over the last 20 years may have 300,000, 500,000 peer-reviewed scientific articles in their library. Most of those do not have any AI rights. You cannot put them in an LLM. You certainly cannot train an LLM with them. Y ou cannot technically, without violating copyright, ask it to summarize that article or to extract information out of the article to build a table. All of which is common in the research world. AG will report to you, "Yep, those five articles that you have in a folder that you want me to summarize or extract tables out of, you have got AI rights, two of them. You need rights for these three." In some cases, we can sell you the right there, and you just click, you pay us, and we will take care of paying the publisher, and then you can utilize it for your search. In other cases, we generate a lead to the publisher, telling the publisher, "This institution is trying to access material behind the paywall 40%, 45%, 50% of the time. Here is the journals they are hitting. Here is the contact information. You should call them and sell them an AI rights deal directly." Which then will work with our platform. As we look forward, we are positioning ourselves a little bit differently with the publishers from somebody that is just a document delivery provider to somebody who can actually generate leads for AI rights sales, or when we think about the smaller publishers that do not have sales teams, we will sell that within the platform. Benefits for researchers, these are just how we sell the product. We basically are saving a ton of time, and we are improving efficiency of the most expensive resource in many cases within the organization. Just like in our business, the most expensive resource is developers. Anything we can do to improve developer productivity is a big win for us. Research organizations are very similar in that approach. We have some of the largest customers in the world today. As I mentioned, 80 some odd percent of our business is corporate. The remainder is either university or government. I will add that we are installed in around 60 different vertical markets, but three vertical markets generate half of our revenue, and that is pharmaceutical, medical device, and biotech. W e are very wired in there. W e have installs in a lot of other businesses as well. Market opportunity. The TAM for the business is huge. You may know or I will just give you a quick explanation. We do have a B2B business, which is enterprise sales into university libraries or corporations. We have a B2C business, which is an individual subscriber such as yourself. You can log in, pay $20 a month, and subscribe to our product. The B2B business is about a $4 billion TAM. The B2C business is much larger, but it's much more finicky, right? Everybody's competing for those B2C customers because you can sign them up. They're easy to sign up. I t's a high churn, kind of lower sticky business. The B2B side is where we really think of strategic revenue, and we really focus exclusively on that side. There is a series of footnotes on this page, if you download the PDF from our website, that provides sourcing for where we came up with these numbers. Big picture, we only use what we call research-intensive organizations in our TAM. A university library and that university has never published scientific research, that doesn't count in our TAM, because in our view, they're not doing it. Same applies to corporations. Corporations that are not doing any 'R' and R&D, they're not in our TAM. If a company has a business strategy to acquire companies after they have created a new drug or created a new medical device, they're not doing any 'R', they're not in our TAM. Mentioned we do cover a lot of industries today. You can see down here at the bottom, life sciences, academic, business, marketing, etc. Business model-wise, today everything is one of two things. It's either a document, which is a transaction business, 24%, 25% gross margin, $27 million, $28 million business, generates a lot of cash flow that funds the software side of the business. Software side of the business has been where the growth is. It's about 43% of revenue today, 85% gross margin, and that's certainly the stickier side of the business. As we have grown platform over time, as reflected here on the chart, we've seen obviously the overall company gross margin improve, generating more EBITDA and cash flow. This business is very sticky. Our net renewal rates are around 100% in this business. When I say net renewal rates, that's basically gross renewal rate plus upsells. If we sell more seats or we move them to a pro version of the product. While 100% is, I think pretty good, not as good as we used to be, right? We've got work to do, I think, in the churn part of the business. I think we can get those net renewal rates back up to 105%, 110%, which helps our overall organic growth rate. Document delivery business is a bit of a lumpier business. It moves around a bit. People ask, why is the document delivery business shrinking the last couple of quarters? Before that, it had a five-year CAGR of about +3. The reason for the shrinkage is pretty straightforward. We've lost a couple of customers, and we're seeing a lot of downward pressure on budgets worldwide. U.S. academic is being hit pretty hard by some of the changes that the Trump administration has changed to what's happening with university budgets, specifically library budgets and how you can use research grant dollars. Corporate side, we're just seeing a general slowdown. If you follow pharma alone, has laid off 10,000+ employees in the last six, eight months. We are seeing downward pressure on that side of the business. The good news for us is that every one of those companies has almost unlimited budget tied to AI advancements. And so where we are being successful there is meetings with not only the people we traditionally deal with, VP of research, director-level research, or librarians, but if we get the AI people in the room, they are the ones that have the money to talk about bringing in MCP to connect these things together to improve productivity of 5,000 researchers. We talked about this a little bit. We do expect to see a low single-digit decline in transactional revenue as we think about the next year, but then we expect that business to go back to a slight decliner or slight grower as economy improves and as we make some changes internally that we think will drive more revenue out of that business. We talked about mode a couple times. Big one, upper right, longstanding relationships with 2,900 publishers. Being able to deliver the world's content on one platform, we have more extensive coverage there than any competitor. We are the only people today that have a full iTunes. Any scientific research you want, other than very, very obscure stuff that is primarily not in North America or Europe, you can get it through our platform in one click. Lower right, Scite had been signing AI rights for years before LLMs came along, and we have the AI rights to do a lot of interesting things to produce better search results for a user that ultimately will translate to more document delivery sales. Upper left, strong, loyal customers that have been with us for a long time. Our average lifetime value across all of our customers is 7+ years. Our lifetime value amongst large customers is more than a decade. In some cases, 18, 19 years. Lower left, we cover 85% of all published STM content in one click. And when we say one click, we literally mean one click. You get it. There is a segment of the business that we also serve, which is print source. These are journals that are so old they are not available in electronic form. We literally go to a library, we copy it, we clean it up, we deliver it to the person who ordered it, and we pay the publisher their copyright fee on it. We are one of the only people left in the world that does that. We charge a lot more for that, by the way. After the war started in Ukraine, we got a lot of orders from the Lomonosov Moscow State University about missile technology. We did not fulfill those orders. They got lost. I will turn it over to Dave, who will walk you through a little bit more about our financials. Dave? All right. I'll try to make this brief. I think the key financial storyline that I just want to highlight here is the mix shift, and Roy alluded to it a little bit. I think you can really see in these few slides exactly the impact and how important that's been to the business. For the sake of time, I'll go ahead to this one, because I think it really highlights the shift, and this has been ongoing for a while. Really, the inflection point was in FY 2023, where you could see we started to turn to profitable operations, generating cash flow, and that's continued to pick up, and it hasn't been perfectly linear. Y ou could see how durable the EBITDA that we've generated is and the cash flow that comes with it. The other point I wanted to highlight, and Roy also alluded to this, is the margins on the platform business are in the mid to high 80%. On the graph that we showed before, although the transactions business is moderating and there's some cyclical headwinds there, we still see the pickup in going from ARR in four years. Just to give you a feel for it, in four years, we went from ARR for the platform of $9 million up to now just around $22 million. A t 80% margin, that's where you're seeing essentially the hockey stick here up and to the right. We expect that to continue because the platform continues to create a larger and larger piece of the revenue profile. W e're 43% on the platform side right now. Last year it was 39%, so that'll continue to grow as we continue to execute the strategy. This is the result, obviously, and it's a snapshot of the balance sheet here. We have over $12 million in cash, no debt, and a line of credit that's untapped as well. Really the big takeaway here is we have a balance sheet that supports our strategy. We can invest in the business, we can look at strategic alternatives as well. The balance sheet, I will note we have made earn-out payments of about $7 million over the last five quarters. We have three remaining. W e are continuing to grow cash, even with those payments being made on a quarterly basis. That will be done by the end of this fiscal year, which for us is June 2027. I'll just close on this. These stats show what Roy had mentioned about our stock being down. A lot of the indicators of how we run the business in those charts that we've shown have really built the strong growth that we want to see. You look at some of the cash equivalent numbers, and on the liability side, it isn't quite as large as you'd think because we have about $7 million remaining in the earn-out payment as well, so well-positioned. EBITDA generating $6 million in the last 12 months. We've doubled, essentially, we expect to double our net income year-over-year. Those factors we often feel are not always reflected in the stock price. With that, I think we have five minutes for questions. Yeah. Any questions? Pretty quiet group. We have been very active in M&A since I got here. We have meetings weekly. We have probably looked at 400 targets. We have done two deals, plus a customer buy. I have kind of slowed down emphasis on that side of the business, as we want to fully understand what AI impact is going to have on the targets that we are looking at. I do not want to buy something that turns out to be melting ice cream. We have kind of slowed things down there. For me, what to do with cash has always been almost a financial model against a dividend approach or a buyback approach or investing in growth via M&A. I like investing in growth via M&A, especially if there is a cross-sell opportunity because they have a customer base that we can sell our products into, etc. There are none of those imminently that we are going to move on. Dave mentioned we got $12 million in cash when we released Q4. You can assume that is going to go up. We are having conversations internally about stock price sitting where we want it. What are we going to do with this cash? There are a few options. Other questions? All right, well, thanks for your time. Have a great day.