Great. Thank you very much. Everyone can hear me okay? Okay. This is weird, but I'm I'm here for it. Okay. It's like a silent disco kind of thing with all the blue lights. So chief product officer at OneModel. But before that, I was a HR guy. I spent fifteen plus years in all the different areas of HR, And I just happened to be the guy who also understood Excel and could could, yeah, like, add up, basically. So I was I was building analytics databases and and such, and I could use pivot tables, and everyone else was like, oh, that's witchcraft. So so that's how I kinda fell into the world of analytics and workforce planning. So yeah. So I did a lot of that for the federal government in Australia and and then worked for a little company in Brisbane, Australia called Inform, which was then acquired by SuccessFactors, who was acquired by SAP. So I was in a company of a hundred and thirty people doing analytics and planning and having a great time. And all of a sudden, I was in a company of three thousand people, which was fun and crazy. And then I was in a company of a hundred thousand people. I would say, okay. It's not fun anymore. It's hard to get stuff done. So back to our model now, we're about Dan first connected with us when we were two people. Now we're a hundred and thirty people. So it's still fairly small, but, yeah, hunting above our weight, think you'd say. So what do we do? We pull data together from all of the different systems that you might have, HR, business, and operational systems, and basically try and get you accelerated access to quality data to make better, faster, more high quality business decisions. Some of our customers, and many of them are here at the event as well. What's what's really cool is that in terms of, you know, punching above our weight, you know, we have the blue chip, you know, companies of of the US and and internationally. So, you know, organizations like GE Aerospace, you know, John Deere, you know, New York Times, Kellogg's, Colgate, Deloitte, Like, well, you Corning. You know? These are these are brands that everyone works with every day. So it's pretty exciting. We learn a lot in partnership with those organizations. And everyone at this conference doesn't need this. I'm gonna blast through a few of these kind of context setting slides, but reach me afterwards and you can get access to all this stuff. But my famous saying is, you throw a rock into the Internet and you'll hit a thousand articles that talk about the ROI of having a data driven decision making culture. And, yeah, the yeah. This is an example from Burson, but there are there are there are many more. So, yeah, if you if you want help building a business case, then also reach out. We've got each of this kind of content that that can assist with that. Similarly, what's interesting is analytics everyone thinks analytics in the context of their lifespan, their their work lifespan. But analytics in the people space has been around in earnest since the eighties, but also back to the fifties and and before. So it's not a new space. There's a lot written on it. If you wanna know how to do people analytics, like, are books out there to tell you how to do it, but there are lots of reasons why it's hard. And, again, I won't dwell on all these. Many of these will be familiar with. You know, integrating their data from all the different places, getting it right, getting it high quality, getting trust, getting usage, managing security and privacy. All these things are just hard. So so that's obviously kind of, you know, why we why we exist. So we have a platform that pulls data from all the different systems, then provides this kind of, you know, in the circle there, this kind of, you know, way of then creating an integration space, a modeling space, the ability to wrap it in AI, the ability to then create predictions to do kind of, you know, rich content delivery and wrap it all in security, and then push that out through our content layer or feed it into any other space that you have. So if you use Snowflake or, you know, Google BigQuery or Azure or Amazon, then we connect in with those databases as well. So we're trying to be part of the enterprise AI. But we're here to see stuff, so we're here to see real product. Let's jump in. Yes. Someone was saying to me for I have a lot of tabs. Yeah. This is one of my browsers. I have a lot of tabs running, so hoping my laptop stays alive for this session. Let me know if it's too small if you want me to zoom in, but I can I can do that? It's good. Okay? So okay. So we've got a lot of content in the app that I can go into, but I'll start in the AI side because that's the topic for today. And then we'll go back into that content layer if if that's of interest to you. So the first thing that we can do is we can have a bunch of prompts. So this is a They a chat based interface into the application. So if anyone's got a question, they can just ask it here. But sometimes, people don't even know what to ask or don't even know how to form a a prompt. So here, we can say, what questions can I ask? So what you know? And that'll kinda give you a bit of an inventory on what the data coverage is as well. So here, we can then see what all the different topic areas are. So I can say, oh, okay. I'm interested in recruiting. Recruiting. Check it out. Oh, okay. What are the questions? I wanna know about their Funnel. Let me tell about the let me look about the process there. So now I've clicked that, and this is gonna give me a really nice ready to go answer to that question. So I can see and it's all interactive. I can see different stages through the funnel, the conversion rate between stages, the time in stage, total time to fill, all that sort of stuff. So in one glance, you can get a total summary of what's going on. For one or two, I could click and drill deep, and we'll do that later on. We go into, like, a a deeper space there. But for a quick answer, I'm straight there. What's What's great, though, is that I can actually then form novel questions. I can ask any question that I want. So I'm gonna try and type and talk at the same time. You're gonna fail, but I'll I'll do my best. So I could ask, show my external hires for the last twelve months. Hopefully, there's no too many typos there. So so what we've created, it is an ability to integrate any data into the platform and then automatically answer any question on all of that data. So it's not like this has to have been pre figured out her save. Right? Or just within a particular locked catalog. So one customer can have a completely different dataset to another customer to another customer to another. And so here, I've got some some data back, which is great. So I've got a trend. What's really cool about this is you can see at the top here, I've got the definitions, but I can also expand this open, and I can see how that query was assembled. So from that trust and transparency perspective, if if anyone was in Haley's session first up this morning, you can see exactly what's going on at all steps in the process. The machine will always be open where you can drill in and see what's happening. I could also then change this. I could add new metrics and change it around, but, obviously, I'm in kind of voice mode, but I could just easily jump out of voice mode and get into tactical mode. I can then pin this out. I can save it to my real insight library. I can put it on Storyboard, share it with someone. So it becomes an immediate usable asset. I can, you know, jump into it and use different views. I could drill to detail. So, you know, I could see the definitions and the formulas. So so all this is now interactive and real and available to me. But what's cool is that I can now just keep expanding upon this. So I could might wanna look at that by gender. So I can just basically keep expanding my thought process as how, oh, I wanna slice it by this. I wanna drill into it by that. There we go. Now I can actually keep on going. I can say, oh, and run a forecast. So all the tools that we have in our platform are accessible to the user through this interface. So you don't need to understand how to create a forecast. It'll just do it for you. And if I, you know, if I click on a regular data point, I get the information about that. If I click on a forecast data point, I get information about the forecast. So this is a defensibility layer. When someone in the room is things thinks they're, like, really smart, and they're like, tell me the p value of that correlation or whatever. And you're like, yeah. You're be doing headlights. You can always click and show those numbers. And so here, we can see what type of algorithm was used, whether it was a, you know, a curve fit or a arena or whatever other algorithm the the model decided to use for that. Okay. So let's go into another example. So now that I've got the data there, I can ask questions about the data. So I can say, is there Any? Seasonality in this time. So things that you might have questions about, you can actually now ask in kind of an interrogative way. And so this says yes, and it then explains it all. I won't go through all the text there, but it tells you all about the seasonality and why and and wherefore, which is pretty cool. Now let's do another similar example. Back to the visualization mode. So I put the mouse bouncing around here. Okay. So now I'm gonna ask a question about terminations. Ignore my typo. Hopefully, it'll figure it out. Great. Got that right? Now I can ask questions about this. But more broad, I can say, how did this compare to other tech companies in the USA. So what we've created here is a very broad integration to Booking dot ai. And so what we're doing is we're able to use that as a researcher system, basically, for everyone in the company. So instead of you then getting your data here and then having you jump off into your private chat chippy tee session and follow-up with questions and whatever, you can just ask all those questions here. So and you don't even need, like, your separate licenses for that. It just runs within my model, which is pretty cool. Okay. So now I can then go beyond this. I can say, what are the key drivers of turnover? So this is now gonna re query the one model application. But now it's gonna say, okay. You're interested in drivers. That's a predictive question. We've got a predictive model that does that for you. And what it's also doing is it's providing interpretation of the model as well as stacking the drivers. What's cool is that you've got then links to follow-up questions, underlying, you know, data that's that's more detailed, and I can just drill into that if I want to. So here, I'm in a storyboard now that's all about this topic, and I can learn about it. I can see, again, on this explainability side of things, what is the model? In this example, it was a gradient boost classifier model. It could have been random forest. It could have been a linear regression or something else, but we can also see the quality of the model. We're showing the f one score. We're showing precision and recall. And I can draw further on this if if you want to come and see us at the booth. We can go crazy deep here. But what we're trying to do with the application is layer complexity. So the average person can get really easy quick answers to everything, but then the person that really wants to go deep can really go deep. So here you can see we've got all the all the parameters. So instead of just the top few drivers, now we're also looking at drivers that are push and pull, things that are keeping people, things that are driving people out of the organization. And then we can group and categorize it by roles, by levels of risk, all that sort of stuff. So so so really just an example of how then the storyboard library can be can be surfaced for people just by asking questions as opposed to kind of, you know, kind of blindly browsing around, which could be fun. But Yeah. So you can kind of get to the data in kind of those those two different ways. Okay. So mindful of time. So we're doing lots of cool stuff in the platform, and I'll show in a moment how we can get data in and then all that sort of stuff. But imagine if I'm not in the platform. Imagine if I'm in ChatGPT, and that's your enterprise AIs. Then here, I could maybe say, okay. Show my turnover for the last twelve months. So what's happening is this is gonna connect to our model. And it's gonna use single sign on so it'll know who you are and what your role is and what your level of security access is, and it'll give you your data in the place where you're doing your work. So In effect, you don't even have to use the one model interface at all. You can just be using Copilot or Gemini or Chertipity or or something else. So it's doing a bit of thinking, and also we're sharing Wi Fi. So here it comes. Okay. Great. And it actually has pre built a chart for me. It's giving me some insights. That's pretty cool. What's great about this is that you can then do anything you want with this data. Right? Because we're now in this this flexible world, but we've had a trusted secure handshake to get access to it. So I can say something like, write Five Top points with insights for HRBP. There we go. So we can use all the power of DLLM in that context. And then I can do, you know, all the all the regular stuff like create a presentation with the chart insights, and some recommendations. I didn't finish typing before I hit the button. Anyway So it'll do all the things. Okay. We'll come back and look at the presentation whether it creates in a minute. So back into the application, just quickly bounce through a couple of things before I kind of get close to wrapping up. Okay. So in the application, we have all the tools to add data from wherever it is, connectors to all the major systems, plus the ability to add novel connectors on the fly to any API within the product. To model it, to connect it, to export it, and to leverage it in this content library. We also have a working version of Copilot, which is kind of similar to what you just saw with ChatTPT. In this example, you can then be doing it within your productivity apps. Right? So you can get that content straight into Word, or to PowerPoint, or Excel, or or wherever it is you wanna be. So I won't go through all that now. Come see us over at the stand if you if you wanna see that in detail, but that's happening this quarter, so pretty excited about that. We also have a project running right now that we're prototyping with some of our newer customers where we have a full agent based AI that does all the data integration work. So, normally, our team, our data engineering team, would help customers integrate all their data, write all the logic in the code, and and figure out how to join it all together. We now have AI that can do that. So something that might take you a few weeks as an individual contributor, data engineer, Now it will happen in a matter of minutes. They'll write thousands of rows of code and just deliver all that storyboard content straight out of the box. So if you're interested, we've got a new playbook on readiness for AI, and you know how to find me. Thanks for your time. Awesome. Huge round of applause for Tony. And I don't think we have time for questions, but their their booth is right there, so I'm sure you won't be mind being bugged and and Great presentation. Thanks. Awesome stuff. Thank you, everybody. Enjoy the rest of the day.