Good evening, everyone. Welcome to this investor briefing, where we're sharing a detailed update on Qualitas AI initiatives. I'm Andrew Schwartz, Group Managing Director and Co-Founder of Qualitas. Joining me today is Michael Kolo, Chief AI Transformational Officer, and Philip Dowman, Group Chief Financial Officer. Before we begin, I'd like to acknowledge the traditional custodians of the land from which I am presenting. I also acknowledge the traditional custodians of the lands from where you are participating today. We pay our respects to their elders, past and present. Today, I will open with an overview of the structural AI opportunity for Qualitas and why we believe we're uniquely positioned to capture it. Michael Kolo will then provide an update on our AI initiatives. Philip will close by quantifying the long-term shareholder value these AI initiatives are expected to generate. Finally, we'll open for questions. I'd like to open with why are we discussing AI right now, less than a week out from June 30? The simple answer is that we've been spending a lot of time developing our systems that we believe will lead to efficiency gains at Qualitas. We didn't want this just to be the back page of a year-end financial presentation. The topic is way too important, and it's deserving of a deep dive discussion. AI is fundamentally reshaping our industry. For Qualitas, it represents a transformational opportunity, and it's worthy of its own webinar. At our FY 2025 results, we announced the appointment of Michael Kolo as our Chief AI Transformational Officer. Over the past 10 months, Michael has led a number of AI initiatives across the firm. Let me start with the nature of our business. We undertake approximately 40 to 60 investments each year. We are a real estate-focused alternative asset manager with a deep network of repeat counterparties. While every investment is different, our assessment process is highly structured, and it's intensive. We apply established frameworks across data collection, underwriting, and credit assessment. The investment assessment process is highly labor-intensive. It requires significant time, significant resources to conduct rigorous due diligence of each and every opportunity. That combination of repeatable processes, large volumes of information, and detailed credit assessment makes our business particularly well-suited to AI augmentation. In response, we've developed a proprietary AI-enabled credit execution platform. It's designed to improve the efficiency and scalability across the investment process through generative AI. The platform delivers two core benefits. Firstly, faster and more efficient investment assessment. Secondly, a deeper analytical insight while still preserving the rigorous underwriting standards for which Qualitas is known. What sets us apart is our ability to implement this technology at pace. With approximately 140 professionals, we do not expect employee adoption to be a material challenge for us. Our team has already demonstrated strong AI engagement with more than 90% adoption of Claude and ChatGPT across the business. Of course, what we're discussing today goes well, well beyond Claude and ChatGPT. We believe our staff are flexible, keen to learn new ways to achieve their objectives. To be clear, we're not handing over decision-making to machines. That's not what we are saying. Ultimately, this is a business built on wisdom and careful human judgment. What we are discussing is how AI can make us a more efficient, increased throughput by assisting us with vast data collection and triangulation of information we undertake. Applying our human wisdom and our careful judgment is ultimately based on data analysis, and it's that data analysis is where we have focused our AI efforts. Interestingly, the conversation internally has shifted. It's no longer just about hiring more and more people to meet a growing demand of a business. It's about using AI to expand our capacity, enhance our productivity, and scale more efficiently. We continue to be nimble, and we continue to be adaptive. We have deep conviction in AI augmented analysis with decisions firmly human-led, as I previously said. Now, let me walk you through the specific objectives we're targeting with AI. This slide captures three areas where AI will deliver material benefits for Qualitas. As I said, our strategy goes beyond the adoption of tools like ChatGPT and Claude. We've built a proprietary AI platform purpose-built for our funds management business. It combines large language models, machine learning, and 18 years of Qualitas institutional knowledge, including more than 400 proprietary investment committee papers. That gives us a capability generalist tools just cannot replicate. The first advantage is the immediate efficiency it delivers. It allows us to decouple fund growth from head count by increasing the number of investments per employee without a proportional increase in cost. AI allows us to decouple fund growth from head count. The platform accelerates data extraction. Our underwriting, our credit paper preparation, and funds management workflows. Credit papers that once took days can now be generated in hours. Key documents, including contracts, development approvals, resale reports, can now be extracted, structured, and analyzed far more efficiently. This frees our investment professionals to focus on origination, critical analysis, conviction building, and risk judgment. Over time, this supports operating leverage while raising the depth and consistency of our due diligence. The second benefit is better underwriting, faster decisions without compromising our quality. We're embedding 18 years of Qualitas underwriting expertise into each new investment. Every investment can draw on pattern recognition from hundreds of prior transactions. That improves the consistency, it reduces subjectivity, and supports faster, higher conviction decisions. The platform pre-flags risks and validates assumptions. This allows the investment committee to focus on judgment, not data reconciliation, and it certainly reduces the iteration between investment committee and the investment team. The third benefit is that it provides AI-enabled intelligence. Every transaction, borrower interaction, market data point, and investment outcome feeds back into the platform. The more investments we screen, the stronger the platform becomes. That creates a compounding data advantage across origination, underwriting, and risk assessment. The asset is unique to Qualitas. It reflects our transaction history, our repeat counterparty network, and our institutional expertise. We see it as a defensible, competitive advantage. In summary, we're building a proprietary intelligent asset across transaction data, underwriting insights, borrower intelligence, and market information. Now, I'll hand over to Michael, who will introduce the platform we've developed. By way of background, Michael holds a PhD in finance from the London School of Economics, brings two decades of experience across BlackRock, Fidelity, and AXA. He founded AI Ventures, advised boards and super funds on machine learning, and now leads the AI agenda at Qualitas, Chief AI Transformational Officer. He's here to show you how we're putting AI to work. Dr. Michael Kolo, over to you. Thank you, Andrew, and thanks everyone for joining. Andrew just walked us through what an AI augmented underwriting means for us. Enhanced quality and speed together, a capability that keeps learning and compounds our data advantage over time. I want to do now is to take that scheme one step more concrete, and to show you why we chose to apply AI inside our due diligence and credit execution process specifically, and what we've actually built as well. Let me start with the workflow. When a new investment comes to us, we run an initial evaluation, draft a heads-up paper for the investment committee, and then move into a detailed due diligence process, including an ESG assessment across many different angles. Depending on the complexity of the investment, that can take months because each investment will be different and have many different unique angles as well. It's enormously data-heavy, first of all. A single investment might carry 160 documents, and sometimes significantly more, covering everything from the borrower, to the builder, to the transaction summary, to cashflow reports, to legal contracts and elements, and details of the actual building designs, and so on. There's a whole mountain of data there that our analysts have to understand and analyze. The investment team has to pull the right numbers out of valuation reports, contracts, and financials, know which ones matter, and confirm that they're correct, especially when different accounts disagree, or more importantly, there's nuance and context around different kind of numbers that could be used for the analysis. This is not just a data problem. It's a real domain expertise and judgment problem. That is actually quite common in financial services and certainly in financial statement analysis as well. The second part is once we have the data and we've extracted the context and the right kind of numbers from all the heap of different kinds of documents or form that the data comes to us, comes the second mountain, which is the analysis. Risk has to be assessed from every single angle. The borrower, on the same principles as any credit assessment, and the loan itself, perhaps, whether it's a building loan, a residual loan, a land loan, analyze the way our policy dictates and shaped by what 18 years in this market have taught us what to watch out for and what to think about. There's no template that you can simply roll out and tick boxes against here. This is a core investment intelligence, and it's really at the heart of Qualitas and how these deals are analyzed. Of course, the question becomes why start here with AI? Well, there's two main reasons. The first one is really around data and data extraction, and it's probably one that's kind of most commonly understood in the market, which is what generative AI and language models specifically do very well is that they read unstructured documents. They extract data from PDFs, images, contracts, and extract not only the information but again, the context around that piece of information surrounding it as well. In finance, context is often where the meaning lives. If you lose that, you lose much of what makes that number matter or indeed how that number comes into analysis as well. This is probably one of the main reasons why this form of AI is differentiated from more traditional technology or automation processes that we would have had in finance over decades, which is that it's really able to go into that unstructured database and pull out all of that relevant information. The second is reasoning. These systems don't just extract data, and increasingly what they're able to do very well is that they help and think and reason through the analysis itself, and they do it very transparently in a very traceable way as well. We can get a very clear audit trail running from a document to a data point to the analysis that we built upon it, and that keeps us accountable, and that keeps the human in the loop, and that keeps us a lot more comfortable in terms of now understanding why certain analysis is happening. I think it also helps us refine and improve the analysis as new information comes to light as well. For people working in this space, they will know that information is constantly moving and updating. Analysis needs to be revised as well. The systems that are handling that analysis need to be adaptive to that. Both of these have improved dramatically over the last couple of years. AI systems have become ever more capable as reasoning models, for example, have been rolled out more widely and have been matured, and then more and more capable models have arrived, not only in the commonly understood world of things like ChatGPT and Claude, but also in much of the open-source models as well that have become a lot more capable over time. We expect that trend to continue over the number of years, and I think our opportunity is to integrate these systems into our investment process now, but also knowing that they're already highly capable, but they will only get more and more capable over time and likely cheaper as well to execute as open-source models and alternatives come to market that are just as capable as some of the closed-form models that we have today as well. I 'm just going to jump now to let me show you what we've actually built. Let me give you some high-level numbers around this, and then we're going to jump into a little bit of an architecture here. Let's start with the investment committee paper. It's a substantial piece of documentation. It has to capture analysis done over mon ths by a team of experts. It's no surprise that a system augmenting that work, producing much of that base analysis, is very substantial too. Here's the scale of it represented in these eight numbers. First of all, we start with, there are nine assessment chapters. Again, this mirrors the structure of a typical investment committee paper for a certain type of loan. There are different kinds of loans, again, different levels of complexity that may all require more or less chapters, but I've taken that as the average. Behind them, we have about 33 different agents. The way to think about agents at Qualitas is a very deliberate analyst that is doing one type of task. Very task-specific. They don't have a general-purpose agent roaming around our systems, moving data around. Each one is responsible for a single well-defined piece of risk assessment. It's highly traceable, it's highly monitored, and it's really understood in terms of why it's doing it and how it's doing it. I'll show you that in a moment in a more graphic kind of way. On the documents, as mentioned before, at least 160 documents per investment. We run more than 370 verifications and checks across those documents, which are primarily checking that they are correctly signed, that the right individuals and entities are named. A lot of the kind of standard but very important ground-based of truth that we have to do in order to have trust and faith in the data that's coming in. It might be mundane, but it's exactly the kind of detail that has to be right and across a huge variety of documents. Once we've confirmed we're working with the right documents, we extract about 400 data points and metrics, many of which we calculate ourselves. This is going to be the foundational bedrock of a lot of the analysis that follows from here. That's a really important point. These 400 metrics are, some are regarding financial statement analysis, cash flow analysis. Some could be details of the buildings, some could be other types of sales contracts involved. There are a whole bunch of different kind of metrics and data points that we calculate. Not only do we then use AI to extract and make those calculations, we second and third check those numbers, often with another set of AI auditors on top of them. AI checking AI, rechecking these numbers again and again and again. Now, for a per son pulling out 400 metrics and rechecking them relentlessly, it's painstaking and time-consuming. For AI systems, it's actually quite straightforward, and it can be done within the process itself. We get a lot more faith and trust in the fact that those numbers are going to be correct and have the right form and structure and so on. From there, we move to 260 analysis steps, that number is really a measure of how sophisticated the Qualitas credit system is. This number is really derived from the internal workings of the company, spanning everything from sensitivity analysis to financial modeling, through the trust and corporate structure sitting across the transactions. Beneath these steps, it's a library of, in this case, more than 1,900 different risk questions or angles of risk that we can be thinking about for any given investment. Not all those questions are going to be relevant to every single inves tment. The more sophisticated the transaction is, the more potential angles to investigate it on. The systems come pre-armed with a battery of these 1,900. I expect this number to increase over time, because as Andrew mentioned already, a lot of these systems are adaptive and they will essentially be learning from us as we go through and do more deals and learning how to refine and as well as include new angles of risk as well. When these 33 agents go to work, they're carrying out enormous research efforts. They're gathering the data, they're building the analysis, they're answering those questions. They're synthesizing all the way back up into something that a human can actually see and more importantly, make judgments upon and understand. Added up, there's more than 200 pages of analysis in the bottom right corner, sometimes even more than that, for any single investment. It's a huge library of information that's collated. The system also has the ability to compress it down into the most relevant points. For example, for investment committees to make decisions, to ask questions, to interact with that information a lot more in a two-way. I want to be clear about what this is and what it isn't as a result. This is an investment analysis engine that leads up to a human decision maker, an expert who knows how to weigh the different kinds of risks through experience, through expertise, through time, through judgment. What the AI is doing is getting better and better at producing exactly the analysis that a person needs to make that call or to understand it, but also to engage and interact with that person to help their understanding. It is not designed and will not replace human judgments. Designing these systems isn't just a matter of working through a large checklist. I mean, what I've given you here is eight numbers that make the system feel very comprehensive and large. What I haven't really shown you here is how that interlinks with each other. Asking a set number, a large set of number of questions or doing a fixed number of analysis in automated ways, it resembles a template, and this is not a template. It's a deeply integrated system that draws information from right across the process. We wanted to show you visually of what that could look like, and we're going to do that now. Right. Give me a moment to explain what you're looking at here, because it can be a little bit overwhelming at the beginning. This is a simulation that helps us visualize the entirety of this investment platform. We have nine different pillars there that you see at the top there. Each one represents one element of risk in these chapters of the investment committee paper. What you're seeing here is the interrelationships of those and how they build up into those chapters and all the way up into the IC paper at the very, very top. Effectively, what you're seeing here is the, and I'll talk through in a minute an example, how this structure all comes together to go through from documents, to metrics, to analysis, to chapters, and then to the investment committee paper as well. We're going to take one pillar, which is the builder pillar in the middle there. That's basically everything in red. What you'll see is at the very bottom of that pillar will be the documents, in this case, that are being extracted. The next level up is going through the metrics that are being pulled out, then the analysis that is happening from there, then all the way up into that little red square, which is the summary of that analysis there. What's happening here is that little bit of animation that you see is the data flowing up the chain, I suppose, as we get more documents in, as the analysis is running in the background. Each one of these things is being executed by, in this case, one of the number of the 33 agents looking at the builder specifically. We've got the animation back, which is lovely. We've got the analysis steps going up to that level. From that level, you go up into the builder chapter as well. That would again, would position these nine pillars as quite separate entities. What I wanted to show you here is that not only are they separate entities, they're very interlinked. In fact, a lot of the interlinking that you see here in terms of the paths between them is really how the system is designed to use context and data from different elements as well, and to bring it together into a single coherent picture. In the case of a builder, for example, the information we gather about a particular builder that's undertaking a project may also be relevant to, for example, the way we think about stress testing the cash flow models. What you see there is the analysis chapter being linked to the builder chapter as well. It's quite, I suppose, a complex web of interrelationships. The whole point for us to show you this is to give you a sense that these systems and this new age of AI that allows us to do analysis for these more complex systems is not simply a templated to-do list of many, many different things, but it's really an interlinking type of cognitive system almost. This idea that every system is connected and is listening to the other parts as well. That's the platform. The real takeaway, I suppose, from all of this is that building it takes genuine care in the detail of how every single one of these points works with each other and within itself. The real discipline in how all of that work builds up to a single point of decision in the investment committee paper that represents faithfully our investment process. That brings my section to a close. I will now hand over to the Group Chief Financial Officer, Philip Dowman. Thank you, Michael, and good morning. Let me now translate what you have just seen into financial terms and explain what these AI capabilities mean for long-term shareholder value creation. We believe Qualitas is one of the first Australian private credit managers to develop a proprietary data-trained AI underwriting support platform with broader implementation plans through FY 2027. As you have seen, we are systematically building AI-driven operating leverage into the Qualitas platform. This is powered by two reinforcing drivers. Firstly, faster AI-accelerated investment decision-making, and secondly, the scalable application of intelligent automation across the broader Qualitas platform. Together, these translate directly into structurally improved margins as we scale some faster relative to increases in our cost base. By building a proprietary AI platform with a dedicated in-house AI team, rather than licensing generic off-the-shelf solutions, we are achieving a faster time to benefit realization. Over the long term, we expect these AI initiatives to enhance our funds management margins and support sustained compounding earnings growth. At our 2023 Investor Day, we set then a long-term funds management EBITDA margin target for the Australian business of over 50%. We have now achieved that target in FY 2024 and FY 2025. Supported by our AI transformation and broader operational efficiency initiatives, we are today upgrading that long-term target. We are now targeting a funds management EBITDA margin for the Australian business of over 60%. Our business is not only growing, it is also becoming more scalable and as a result, more profitable. That concludes the formal part of our presentation. Please feel free to submit any questions you have in the Q&A portal. Thank you. Thanks, Philip. The first question has come in. How does Qualitas ensure the accuracy and reliability of AI-generated assessments? Michael, over to you on that. I love that question. That's a great question, thank you. Probably one of the questions that we thought about first when we really thought about the system. The answer is through a lot of detail. A lot of detail that I probably wouldn't have time to go into this particular call. When data is recalled by our systems, we have a series of steps that ensure that we understand what document it's coming from, where in that document it's being brought in, and why. We have a lot of context around that single data point. We then make that transparent to the investment team, and then we have a number of different audits or agents that run over that to double-check and often triple-check whether the number is not only correct, but also contextually the right data point that we want to use for that analysis. Because oftentimes it's not necessarily about absolute correctness, it's about context, it's about awareness. Those agents then repeatedly get us to a point that we feel extremely comfortable in terms of the accuracy. The system runs slower, and it certainly runs in a more iterative way than otherwise it would be. We've really optimized primarily the system for accuracy and reliability over speed or over other things as well. We take a lot of care with that. Ultimately, all of these analyses and steps we make extremely transparent for the investment team because to Andrew's point previously, they build conviction in the deal and part of building that conviction is understanding the numbers and why the numbers are what they are. That transparency and that calculation is afforded to that team so that they can build that conviction and that reliability and also the transparency underneath it. It's a topic that we spent a lot of time thinking about, and it's a great question. Thanks, Michael. Just let me bring up the Q&A function. Second question, how are you protecting proprietary information with these implementations? Michael. Fantastic. Another great question. Thank you. All of the data that we use within models are constrained within our cloud environment. Everything is locally hosted in the same way that Microsoft's OneDrive or something else would host your files. All of the models that we call upon and that we utilize in this particular tool is contained within our cloud environment. Nothing leaves our cloud environment. Nothing is used to train models. Nothing is in any way leaving, especially for our investment process or any kind of private information that we have as a course of the deal gathering as well. Again, that was probably number two on our most important parts of our list when we first started thinking and building this system. Thanks, Michael. Next question. The last long-term margin target was met within one year. How do you think about the timing of your new long-term margin target? Thank you. We have said long term, for us, long term is between three and five years. This is a slower bird, therefore long term is within that five-year time horizon. Next question. Which underlying models are you using for your AI system? Will you be buying or renting GPUs to run your model? Michael. More specific questions. It's a really good question. At the moment, we are using the frontier models. I won't disclose here what they are, but there's only a few of them really to choose from. What we are certainly developing at the moment is, a lot of testing around new open source models, which I kind of hinted at during the presentation. That open source models are probably one or two generations behind the frontier models in terms of capability. We're already seeing that the frontier models, without even the Mistral of this world, can do a lot of the analysis that we have created, in part in the way that we have created the analysis tiers as well. What that allows us to do is probably within a year, 18 months or so, we'll be able to use a much wider assortment of models to do this kind of work. Not only the premier frontier models, which are the more expensive but the better models, but also a wide variety of other models as well. We don't require on-premise GPUs to run this. We rent them through the cloud. This is a Azure type of arrangement at this point. I think in terms of our calculations for the economics of that makes more sense than hosting them on-premise. Again, technology moves very fast, we are keeping an eye on it. Next question to Philip. What are the expected FY 2027 implementation costs, and how much will be expensed versus capitalized? That's a great question. The bulk of the implementation cost, the implementation cost of the new platform is relatively modest. We have a tendency to want to expense as much of that as we can, just through our operating P&L. There may be some opportunities to pick up some R&D credits along the way, which we're certainly investigating. At this stage, our expectations for FY 2027 is that the bulk of the costs will be expensed. Next question to Philip. What's been the P&L cost and realized benefit of the AI platform in the current financial year? Has it given a net benefit yet? I think the first thing to really point out is that this journey on AI was well known. Michael started early in the FY 2026 year. Our initial investment in AI capabilities is within our current budget and within our current guidance. In terms of benefit realization, that is really something that is not a feature of FY 2026. FY 2026 is more about the build. You're on mute now. Next question to Philip. Of the uplift to 60% EBITDA margin, there are a number of drivers, including scale, AI, deal size. How much of the margin lift is directly attributed to AI? That's actually a very difficult question to answer. Our business plan is predicated on continued growth, including investment in AI capabilities. What we have modeled is the scalability of the investment volume through moderation in headcount growth is certainly positive to our operating margin and why we were comfortable putting the greater than 60% operating margin into this presentation. We do not have a specific mix between AI and all those other levers which you pointed out. It is certainly an underpinning construct having the AI technology stand, augment, and improve the operating efficiency. We have not created a particular split within that target of 60%. Next question to Philip. Do you think it's achievable to get to over 60% funds management EBITDA margin? Yes. Same, next question. Maybe I'll start along that for years and provide a view here. It's not a CFO view, it's more a CEO view. I think that it's really going to come down to exactly how scalable the technology allows us to become. As we said earlier, at the moment, what's happening is we're having to put on more costs as the business grows. You can see that over a number of years, where you've got a revenue line that tracks up in a very healthy CAGR, but you've also got a cost line that is tracking up as well. Not exactly in a linear relationship, but in a near linear relationship. Hopefully, over time, you pick up a percent or 2% or 3% margin uplift through scale. I think what this technology does is it breaks that relationship. All of a sudden, we can have more revenue, more transaction flow, without having to keep putting on the same incremental level of overhead that we previously would have had to hire in order to meet the growing demands of the business. I think the answer to could we achieve more than 60%, which is a very fine question for somebody to ask, is really going to come down to the velocity and the volume that we can actually take on, knowing that a lot of the execution analysis is really being driven by this particular bit of technology. The other comment I would make is a comment about realism as well, which is, this has the potential to be highly efficient and highly accretive for Qualitas. I think the realism of it is that some of the fund investors themselves may well say, "Look, we want a share in the benefits of that." That's an unknown for Qualitas. I would argue that that would be a wonderful place for us to find ourselves as well. Because if you think about the competitive barrier that that actually builds for other firms to really have the fee competition that something like this would provide for Qualitas, that competitive barrier is very substantial. I think, I would answer it as absolutely yes, dependent on volumes and fund coming through. The realism of it is an offset by sharing the benefits around, not just with shareholders. I can see LPs wanting to get some of the LP investors, fund investors wanting to get some of the benefit of that. Equally, I think that's a massive competitive barrier for others who are looking to build scale with institutional investors. That's how I'd answer the question. Thank you. Back to you, Nina. What is the relative cost of the AI system versus having an associate running the process? Is it one-tenth or more or less? Michael, I wonder if in the first instance, if you should take that question. Sure. I'll take it not in a cost base, but in a kind of hours worked or capacity kind of way, then pass it to Philip or yourself to cover the rest. I think if a system is correctly configured, the primary advantage is in the data extraction and organization element. Very fiddly work that takes a very long time, takes days or weeks, takes hours with an AI system. I think where it becomes a little bit more blurred is the analysis component, where the system, if correctly configured, does amazing job at doing the analysis. Eventually, in order for the person to understand and own it, they have to spend the time to getting to know the numbers, possibly by talking to the AI system and by that kind of mechanism, learning and understanding and ultimately representing that conviction to an investment committee. My estimate is, I think the number that was thrown up of one-tenth is about right, a lot of it depends upon the time it takes for the analyst to then go back and really understand and absorb that information rather than simply information production, which is how people sort of think about it in a very basic way. I think, sorry, just maybe putting that financial lens on it. I think Michael's comments underscore the opportunity that we see from using AI to augment that analysis process. We do have the human in the loop, and we certainly do not want to back away from the fact that humans will be reviewing all of the materials and applying the wisdom of the team to the process. Whilst the actual process improvement at an AI analytical sense could be easily one-tenth, we'll then add back in some human overlay. We're certainly looking for sizable improvements in the effi iency of the process. One-tenth would only be one element, and then there's some cost to add back in. Next question to Andrew. Are you able to provide a sense of the expected number of investments per year? Currently, you mentioned it's 40-60 per year run rate. I think, look, it's not something I'd be prepared to put a number against because it becomes a very theoretical response. What I do think is that, and I'm guessing the person asking the question is really trying to get a sense of what does it mean by way of existing overhead as well, relative to future fund growth. I'd say the answer lies in not so much overhead savings today, but I do think that it enables us to grow quite substantially. This technology is really focused on one part of the business, which is the data collection analysis, triangulation, the writing of investment papers. I do think that this technology enables you to substantially increase the number of throughput that you can actually do. You've still got constraints around that. You've still got an investment committee that can only meet certain times of the year. You've got accounting functions, other fiduciary functions, it really becomes a question over time as to, well, then how do you take this technology, which is language models, pattern recognition, and apply it to other parts of the business, to really create that m ore throughput efficiency as well. I'd want to temper any response I gave to that by re ally highlighting, I think where the savings come from is the ability to take on m ore volume of transactions per year, which a lot of our shareholders understand is also a function of our average investment size. Be able to do that in a world of not necessarily having a, in a linear sense, keep increasing the number of people who are directly involved in those functions on a go-forward basis. Next question to Michael. How does the platform evolve as new deals are completed? Is there active feedback loops that improves the model's underwriting recommendation analysis over time? That's a great question. So much so that I would've asked myself the same question. Yes, absolutely. I think that's a really important part of it. Whenever you create a system like this, taking a step back for a moment, it's never a one-and-done kind of proposition. You create something, you roll it out to the business, the business starts to utilize it. They make adjustments to the way that the AI system reasons or what should be utilized in different forms of risk assessments. Over time, the investment team learns from the deals that they do and as does the system through the lens of the investment team as well. I would say that it's almost like a co-integrational kind of a interflowing process over time. Again, if you think forward and you think about the number of deals that we're going to be doing over the next five years, and the uniqueness of the system to adapt to those deals, to learn from those deals, to learn from the people doing those deals, more importantly. I think you really present something that is really unique to Qualitas and really compelling over time because it's been built up deal by deal, layer by layer in real-ti me. Thanks, Michael. To Philip. To what degree is the platform already built, and what costs will be incurred in FY 2027 for the implementation that has been highlighted? I think we've partially covered this question already. We have developed a platform that still has to be fully deployed and fully tested. We have AI resources as an internal OpEx cost built into our numbers for FY 2027. The predominant benefit from the platform will be, and I noticed one or two of the other questions talk about the, how do w e think about the operating margin, in a linear versus back-ended fashion. I think it's important to acknowledge that we ar e still building out our AI capability. It is very nascent. I think it is fair for the audience to think about our operating margin as not linear from 50%-60%, but with a higher acceleration as we get through into the back end of that three to five-year period. Hopefully, that answers the question. Thanks, Philip. The final question to Michael. What is your long-term vision for AI and Qualitas from here? Do you think competitors will be able to create a similar platform, or do you anticipate that this will be a competitive advantage against most of your peers? It's a great question again, and I think Andrew and I have talked about this at quite some length, so he might jump in and add some points as well. I think, in some sense, AI is accessible to us all. We all have ChatGPT or Claude, and different organizations have adopted at slightly different rates, but essentially, the common AI has been democratized. Therefore, you should expect that most organizations are using it for something around the edges. The question of whether you can use it in a deeper way to really get to the core of the value proposition that the organization produces requires an interesting symbiotic relationship between people that really understand technology and the people who really understand the investment problem. I think that symbiotic relationship is unique. I don't think it's well present across the markets. I think people are struggling to create that bridge between the two worlds, we have been struggling for decades. I've been in investment management for 20 years. I used to work at BlackRock and other places as a quant. I'm keenly familiar with this kind of separation between the technical and the investment kind of areas. It seemed to be a separation that's persistent over decades, I expect it to be persistent as well. My vision here is that if we can get this right, I have absolutely every confidence that we can, because of the way that Qualitas is structured, because of the way it's led, because of the problem it's trying to solve. Then we can maintain that competitive relati onship as we build and really ride upon the AI wave that is coming at us and will continue to come at us as the models become more sophisticated, more integrated. The fact that we can customize that to what we know to be the right way of doing risk assessment, that we have the conviction behind that, we have a human in the loop and human judgment in the loop importantly around that, I think is still an area that many other investment firms will struggle to emulate over time, even though that's the goal here. It's a complex problem, but I do envisage that the lead that we're building here I really am very ambitious and very positive about continuing to maintain and build upon as new AI systems come through and that symbiotic relationship to be the driver of that, essentially in our moat. I'll add to that as well. What I would say is, firstly, never underestimate your competitors. That's absolute rule number 1. I do think what goes in favor of Qualitas is the fact that, if you look at the local market, I think that there's quite a fair degree of investment that is required here. Put people's minds at ease. What Philip said is 100% right. That investment was budgeted for and within our guidance range. It is an investment to get to the point where we've got it to, not everybody has the scale of a business such as Qualitas that enables them to make that investment. If you look at the global peers, I think that's probably the better focus for the question. If you look at the global peers, I've no doubt they're working on similar technology. I think the advantage that Qualitas has, is the fact that we are only 140 people. Unlike some of the globals that can be 4,000 or 5,000 people, quite literally. We only have to convince 140 people. What I have found so far on Qualitas is this incredible eagerness and willingness and hunger by the staff to really adopt the technology th at we're creating and certainly has been rolled out to date. I love the fact that when we're communicating internally amongst each other, it's not about, "We need this person for this role." It's now much more about how do we create an AI agent for the role and get it on the agenda of the AI team to enable us to have that capability. I do think if you look at the biggest challenge to absorbing these technologies, it's really the rate at which employees adopt it. One of the things that I've been really excited about is the fact that our existing teams embrace it. They're not threatened by it. They're very embracing and wanting to take this on. I think, to whoever's asked the question, it really gives us a competitive advantage in the market over others by way of our scale and our ability to develop the technology. Also it just gives us an advantage because of our size. Hopefully that suits well for us. Thanks, Nina. Thanks, Andrew. There are quite a few more questions in the portal. Given in the interest of time, we'll just get back to the remaining questions offline. This now concludes the presentation and the Q&A session. I'll hand it back to Andrew to conclude the session today. We appreciate everyone's time. For us, we're excited to have really been able to have the opportunity to talk about what we're doing in the area of AI. As Nina said, to the extent you've got any other questions, feel free to reach out to anyone on the Qualitas team, and we'll be sure to answer your questions. I wish everyone a good morning. Thank you.