All right, good morning, everyone. Thank you for joining us today on X Spaces. As always, my name is Paul Cecil, Vice President of Strategy here at ReAlpha, and I will be moderating today's Airtime session. Airtime is an ongoing series where we bring ReAlpha leadership into the conversation to talk directly about how we're building the business, how we think about operations and technology, and how those decisions show up in the real world. Today, we're focusing specifically on AI, not in an abstract sense, but how it's actually being used in mortgage and real estate operations today, where it's delivering value and where expectations tend to get ahead of reality. Before we get started, just a quick housekeeping note: because ReAlpha is a public company and we're currently in a quiet period, there are certain topics we won't be discussing today that include stock price movements, valuation, insider trading activity, potential corporate actions, or any non-public financial information. That's standard and expected, and it allows us to keep these conversations focused and useful for everyone. As always, please see our forward-looking disclaimers that are pinned in the comments below, as well as a link to our investor relations page where you can find our news, updates, and press releases. Joining us today is Mike Logozzo, Chief Executive Officer of ReAlpha. Many of you have met Mike or know Mike from previous X Spaces, but for those that don't, Mike's been with the company since day one, serving in multiple leadership roles, including CFO, president, and COO. That experience gives him a full view of the company across finance, operations, and strategy. His background spans consulting, financial services, and operating roles in regulated environments, and his focus at ReAlpha has been on execution, integration, and building systems that work in the real world. We're also very excited to welcome Vijay Ratnakar, ReAlpha's Chief Technology Officer. Vijay is a technologist with nearly two decades of experience leading enterprise transformations across fintech, consumer products, and healthcare. Prior to joining ReAlpha, he served as Senior Vice President of Innovation and Development at CoreLogic, where he led teams building AI, automation, and blockchain solutions. At ReAlpha, Vijay leads product, engineering, AI, and technology initiatives with a focus on scalability, system intelligence, and practical deployment. In addition to his industry work, Mr. Ratnakar is an Associate Professor at Columbia University, teaching blockchain and AI. We're thrilled to have him here today. With that, thank you again for joining us, and I will turn it over now to our CEO, Mike Logozzo, to kick things off. Thank you, Paul, as always. And Vijay, thanks for joining us. Let's start with your background. You spent years building enterprise systems across multiple industries. Why don't you tell us a little bit about that? Sure. And again, thanks, everyone. Thanks for joining. See, I have spent most of my career building large-scale enterprise platforms for financial services, insurance, healthcare, and consumer technology. So basically, what has happened is these are environments where we not just display information, it also moves money, verifies identities, and carries real legal and regulatory consequences. Right? So this background is exactly what we need for ReAlpha. And as you know, this space is highly complex, and it requires enterprise-grade technology. Also, my background involves most of my time has also gone into building high-performance teams and mentoring startups and then creating products, which makes a difference. So scaling all the way from a small innovative idea to launching this into production and then raising funds and making sure that it is deployed to thousands of users. That scalability and innovation has been a key part of my background and career. Great. Thanks, Vijay. And I'm sure not a lot of people are aware of this, but you teach blockchain and AI at Columbia. So when you do, tell us about the concepts you think are most important for students to understand before you try to apply these technologies in the real world? Sure. See, one of my key interests has been emerging technologies. That's how I learn, and I have been continuously upgrading myself almost very often, and especially in this AI world, we need it more often than previously. So I've been teaching now for almost eight years, previously UT Austin, now at Columbia on emerging technologies, basically, and blockchain, so the biggest lesson that I tell when I'm teaching over the years is technology doesn't create value. It's the systems we build, and it's the right technology we use is what creates value. For example, when we take off AI, right, so you can choose a model. I mean, on its own, it's just math, right? So if you consider blockchain on its own, it's just a ledger without workflows, right? So the real value happens when AI is embedded into decisions, where decisions are made, how money moves, how customers experience a service. So these are the things I usually teach. And also, just to give some examples of what we have built at ReAlpha, Claire isn't just a chatbot, right? So in technology terms, you can just say it as a chatbot. But no, she's an AI homebuying concierge, right? So again, we have engagement bots to work with our customers and leads. And it's embedded into lead conversion. So it's not just conversation. So similarly, we have an AI loan officer assistant tool. So it's just embedded into how loan files are built, right? So not just document processing. So when you consider these use cases, that's how AI becomes a business. So this is what I teach. So yes, you need to understand technology. You need to get excited about the technology. But also, you need to know how to build systems using this technology, which actually creates value, how to build products using this technology. So that's what my focus is on. That's what I teach in these universities. Thanks, Vijay. And I know a lot of people are here to learn about the AI component as to what you do. Before we do that, can you tell us a little bit about how you think about technology from an operator's perspective and not just from a technologist one? Yeah. So I mean, this goes back to a lot of my experience in the last 20 years, so dealing with scalability, right? So when I think as an operator, I think about bottlenecks at scale, right? So for example, when you think in our case also, right, so it's not where one deal gets stuck. It's where thousands of deals slow down, right? And again, if you're using a system, it's how to ensure that when thousands of users are using the system, how to effectively manage it. So that's how I think as an operator. So I mean, when it comes to the real estate industry and mortgages, delays in document review, lead follow-up, or compliance don't just hurt one loan. It just creates the drag across the entire pipeline. That's why we focus on AI and biggest friction points like engagement, document prep, underwriting support, and handoffs between teams, right? So when we remove this friction, the whole platform accelerates. So that acceleration and scale is how we think as an operator. Great. As everybody knows, AI is everywhere right now, especially in real estate and mortgage. But what a lot of people are seeing is what I refer to as kind of surface- level or even hype. From your perspective across industries, where is AI actually being used in production today? And then also, where do you think it's still hype? Okay. See, I mean, generally, when we talk about the tech sector right now, the trajectory of AI is very similar to what happened with the internet. This is before me. And then what I have seen closely is the cloud computing, like the early days of cloud computing. There's a lot of excitement, a lot of experimentation, and a lot of noise. So this is where we have to think through what is the right way we need to approach this, right? So where AI is already delivering value is invisible parts of business. This is what I would say. So that is automating workflows. We had automation before. Now with AI, it has gotten really better and fast. And processing massive volumes of data, that was one of the big bottlenecks previously we used to. It used to take six months, one year to build data analytics projects. Now you can just plug in the LLM to a variety of structured, unstructured data and then process massive volumes of data and then get insights from that. It's supporting customer interactions, so being more bot-based to more human-level conversations and following up. So various ways AI is really taking off. And then developing platforms itself. So as an AI tech company at ReAlpha, we develop a lot of innovative technologies. Every day, we are experimenting with how we can make experience better for our customers, how we can improve a particular business area. So we need to use AI in our own toolkit to roll out the software really quickly and really fast and use AI to make it better. And then just making organizations as a whole efficient. So this is where AI is delivering real value. Like in our organization, I mean, it has really worked well. And we are seeing results of it through the products we are creating, through the efficiency we are seeing in various of our use cases. And I mean, if you really do this, where you're getting is gains in productivity and cost structure, right? So end of the day, that's what matters. So as long as you are growing for a company like us and you are increasing your productivity and reducing cost on this, so it adds a lot of value. I would say when it comes to hype, where hype is really about AI replacing entire professions or human decision-making becomes irrelevant, right? So that is the AI hype according to me. So it's not there. In reality, the most powerful use of AI is basically force multiplier. So it makes people, teams, systems more capable, but it doesn't eliminate the human judgment. So that hype is still there. And right now, I don't see that. The big shift where we see right now in AI is moving from feature on top of the product to becoming the core infrastructure on how digital businesses operate. And real estate and mortgage is one of those where innovation has been going on now for a while. And then we operate as a digital business. And that core infrastructure which operates our business makes a big transformative, I would say, solution. So that's where AI is real. Great. Thanks, Vijay. Now, you've worked with large enterprises and regulated industries, and you and I are both in mortgage and real estate now, so let's talk a little bit about that, so specifically, what makes deploying AI meaningfully harder than in the other sectors? Sure. See, when it comes to mortgage and real estate, it is uniquely hard for AI because there's a cross-section of identity. We collect a lot of personal information and process information. It comes to money, right? So end of the day, we also process and help process transactions through mortgage, legal contracts, and regulations. So when you have these four pillars where we touch upon in every transaction, it becomes really complex. So every transaction depends on basically data coming from dozens of independent sources like payroll systems, bank statements, tax authority, credit bureau, property records, so all in different formats. So we get different formats, and then all are governed by very strict compliance rules. As a regulated company, we need to ensure that how we capture data, manage data, and then use the data is completely governed by compliance rules. That means AI isn't just spotting patterns, right? So it needs to understand these documents, apply regulation or regulatory logic, or help in decision-making, preserve audit trails. We do need to preserve audit trails and produce decisions that can stand up to legal and regulatory scrutiny. So this is why it is harder. It's not easy, right? So I mean, if you're just building a social media app, yeah, maybe, but in this highly regulated and regulatory scrutiny, we have to measure up to that. Now, that complexity is what makes it really difficult, but also it creates defensibility. It can get AI to operate in a reliable environment. You're not just building a tool. You're building, see, in our organization, what we are trying to do is we are building a highly regulated operating system using AI and technology. That's extremely hard, and it's not just for us. It's for anyone to replicate also, so with all the experience with the use cases we have, I think we are in a much better position. Yeah, I agree. Although it's complex, if you could do it, you could crack it. That really is a differentiator for us as well, or for whomever can crack it. At ReAlpha, we talk a lot about infrastructure as well, not just features. So tell us a little bit about what that means in practice. So when it comes to how we are envisioning this, it's the operating system of the company, right? So AI, we are thinking of AI as the operating system of the company. So for example, let's see some of the products we have rolled out last year, right? So when a lead comes in, the engagement bot doesn't just chat. It also qualifies routes and it logs data, which is very important. And when a buyer searches through Claire, it's not just showing the listings. Claire understands the intent of the buyer. It guides through next steps, and then it provides constant information, your 24/7 concierge. And also, when a loan file is created from all this loan officer assistant, what it does is it doesn't just read documents. It extracts data. It checks rules. It prepares files for underwriting. So there's a lot of things happening. So that is what, when I say infrastructure, it's not just one touchpoint. It's multiple touchpoints and handover across different teams, right? So also, where we are using AI heavily is as an AI tech company. Our engineering teams use AI to build, test, and deploy faster. So AI is embedded into our entire software development cycle. We are using AI more than most of the companies. And then also, that's the infrastructure that every workflow, every handoff, every data flow is all AI assisted. And that's how we can get scale, margin, and defensibility. Okay, great. I think this will be so when teams are deciding whether to continue investing in an AI initiative, what questions should they ask internally to avoid chasing what I refer to as vanity projects? Sure. So see, when we are looking at AI projects, we are looking at throughput, accuracy, and cost. So basically, in our case, we think whether does it process more deals with the same amount of time or even faster, and then with the same team. Does it reduce errors and rework, right? So does it lower the cost per loan? So these are the things you need to ask. I mean, it's the same in any industry. So it's throughput, accuracy, and cost. As long as we are making a dent in these areas, we should be continuing with that initiative. Now, see, one of the things is I use the term called as wrappers. We don't want to just create LLM wrappers. And if you create just LLM wrappers, what happens is you're just feeding all the information to ChatGPT or Gemini. And all it is doing is it is processing it, right? So any initiative we decide also is core IP, which helps us and which helps us compete in this industry and which builds our intellectual property. So it starts with choosing the right LLM. It starts with selecting the data we use, how we train that data, and then eventually how we build workflows and process from the AI. So that is very unique. So yes, we need to consider throughput, accuracy, and cost. Also, we are differentiating ourselves by building cutting-edge products, which gives us competitive advantage. So when you have these scenarios, yeah, then yeah, any AI project or any initiative can be really successful. Now, I have seen some failures previously if you don't select the right LLM, if you are automating a business where you're not getting any cost efficiency, or if your architecture is not right, right? So, on how you're processing information, then it can really fail. So yeah, at the end of the day, you need to look at accuracy and cost and then using the right architecture to build your solutions. Thanks, Vijay. And you already kind of touched on this. Many people have said, hey, they're worrying that AI will replace them or their jobs. And you said, hey, if you use it correctly, it could actually be a force multiplier. What I wanted to touch upon is really more about AI creating more complexity. And in your experience, where does AI genuinely reduce friction instead of adding it? Yeah. See, I mean, based on what we are seeing today, AI just replaces busy work, right? Not people. It's just replacing busy work. So in mortgage, just take up an example of in mortgage, it's huge amounts of time was wasted previously on copying data, chasing documents, following up leads. And AI does it more efficiently. And this makes sure that people do what they are very best at. That is judgment, trust, and complex decision-making. So this is where it really helps. So when I talk about this, see, it gives us cleaner data, better visibility so that we can work faster with fewer errors, and then we can scale and grow faster as a company. Great, and you already touched on this point as well, but mortgage and real estate, they are highly regulated. Tell us a little about how that changes how we design and deploy our AI, and I know that you and I have even talked about that. Mortgage and real estate isn't at the national level. It's not even at the state level. We're finding it at the county levels, so from a complexity standpoint, it's ultra high, and it would be great to maybe talk a little bit about how we navigate through that. Yeah. So in our space, right, I would say it's not enough for a system to be right. It has to be able to show why it was right, right? So when we design our system using AI, we design to ensure that we have traceability and then explainability. So even when you see ChatGPT, it goes through process on how it thinks, what sources it connects with. So we are building platforms very similar to that. We have to know what AI is doing. And it has to explain from day one how we have arrived at a particular recommendation. So every flag, every decision, and it can be traced back to a particular data or a particular logic or a particular touchpoint when it comes to human level. So that is how we are thinking to ensure that we are building this in a highly regulated market. So it's all about, again, it's also about how we store the data, right? So how the models are trained and then how the outputs are reviewed. So we are really focused on building more smarter models and building a compliant operating system that our regulators, partners, and customers can trust. And when you do this, I think so we can be highly successful. Thanks, Vijay. And last question. On a personal note, given your experience across consultings, enterprise, academia, and now you're working at an operating company like ReAlpha, what excites you most about building in an environment like ReAlpha where the tech decisions directly affect real customers? Sure. So see, what excites me the most is we are not just building the software here. We're actually building a new operating system for how homes are bought and financed, right? So that is the most exciting thing. We have seen this industry being complex and still a lot of traditional, but we are hoping this operating system we are building makes a big dent on how homes are financed and bought. Now, as a team, we have assembled a team and companies who have deep understanding on mortgage operations and real estate. And also, we have our technology teams who have advanced AI and data engineering skills. So that combination lets us move from different experiments to real transformation. So it's like working with this team every day, understanding the challenges and resolving problems, and then coming up with innovative products in a really quick way instead of waiting for months and years. I call it weeks. That makes a big difference, and it's also very exciting. So on the technology side, as I earlier mentioned, building these Claire engagement board, loan officer assistant, and internal AI tools we use for a variety of activities. Every day, we are adding new features over there, and it is learning and how it is learning and then providing us real productivity. That gives us a really compounding data advantage, and it's incredibly powerful. And then when a team completes a feature and then rolls it out, and then we see results from it, that's very satisfying. And, previously, it used to be just based on my experience being in the consulting world for the initial first half of my career. Yeah, it used to take three months, six months, and even for a product to launch one year, two years. And now we are seeing these results in a really fast way. So it basically drives us to think bigger and move faster. And once we embedded this AI across the full lifecycle of our company, right, we can launch really entirely new products and new business models. So it's just improving today's business, how we operate, and then working with the team. That excites me the most. Awesome, Vijay. And thank you for sharing your insights and your feedback with us. Very much appreciated. With that, we're excited to take your questions. Paul? Awesome. Yeah, thanks. Yeah, thanks, Mike. And thanks, Vijay, for the great discussion around technology. We have a few minutes left here, and it's looking like mostly we have some kind of comments and engagement from our investors here. So first one is from Daydream TR33536. It says, "Thanks. Please keep us posted with everything going on with the company and to Daydreamer." We appreciate the engagement, and we will continue to try and be as transparent here as possible. Mike, do you have anything to add to that? Yeah. Thanks, Daydream. And as Paul said, we'll be as transparent as we can. We are in this quiet period. It happens or so every quarter. We happen to be in it now. But we're executing the plan. We put our investor deck out there at the end of last year, and we're doing what we said we would do. And as we have meaningful announcements or disclosures, we'll go ahead and make them. And we'd be happy to follow up and talk more about what those are to give you more insights as to what we did and how it impacts the company. Awesome. And as one of our consistent engagers and question askers, we have that man, Billy, with a comment here. Vijay, thank you for the vision and execution. I appreciate your time and insight today. And Vijay, thanks for the Billy, thank you for the comment as well as the GIFs you left in the comments or GIFs if you prefer that pronunciation. But we appreciate the engagement as always. And then lastly here, we have a question/comment from Not a Dead Cat One or the Air Army. Thank you, Mike Logozzo and Vijay and the rest of the ReAlpha team for keeping us updated with company updates, especially with an AI product. Yeah, I'm just going to say you're welcome. We'll continue to do so. If you thought that having Vijay on talking specifically about AI was valuable, we'll look to do more of these. With Vijay, we'll have maybe some very specific topic-related spaces. And then we'll also start bringing some other folks from the executive team on to talk about maybe mortgage or what's going on in real estate. And hopefully, it'll give everybody a good idea and understanding and ultimately confidence of the executive team here from a thought leadership standpoint and our ability to take this company forward. And we had one last question here that has come in in the last minute or so from Jared. Care to touch on any SaaS plans? And Mike, maybe you can touch on that and then Vijay. I would say that we're always looking at opportunities. However, we don't want every opportunity to be a distraction if it's not aligning with our current vision. We're focusing on our current vision, more of the traditional B2C, being there for the customer, the home buyer. But building a great product certainly opens up opportunities for other types of solutions, and we'll certainly evaluate those when those opportunities come. Awesome. Thank you, Mike. And so we are now at 12:30 P.M., so we're going to go ahead and wrap up. We We approach the end of the session here. Thanks to everyone for joining us today. Thanks to Mike and Vijay, and thanks for spending the time with us. If you'd like to stay informed, as always, about our future Airtime sessions, because we do plan to do more of these and other publicly announced events, you can follow ReAlpha on our official social channels as well as signing up for email alerts on our investor relations website at ir.realpha.com. That's where we post our event notifications, replays, and our public disclosures. And a recording of today's session as well will be available shortly there. Thanks again. We look forward to continuing the conversation in future Airtime sessions. Thanks, everybody. Thank you.