Incredibly excited about Maia. And you can think of Maia and data productivity cloud as the platform that's already in use across hundreds of organizations, thousands of data engineers and use cases. But now alongside Data Productivity Cloud, we have Maia, which is the digital labor that comes with Data Productivity Cloud to augment, enhance, accelerate, and improve the quality of both life and outcomes for our human data engineering users and customers. Maia is a digital data engineering team, and I really mean team. A set of personas, a set of expertise that can do the work for us and, therefore, in ways we've really never been able to dream about previously, accelerate and enhance our productivity as data engineers. Data engineering is a necessary and creative and skilled thing that that we as data engineers and Matillion and our customers do, but we've never got enough time to do all of it. Our own survey suggests that at least eighty percent of enterprise data teams feel underwater. We've never got enough time to do it, and some aspects of what we do is incredibly interesting and important, and other aspects of what we do can be repetitive and gritty and just necessary. So Maia brings along a team of of digital data professionals to work for us data engineers, whether that's, modifying a pipeline that we've already built, maybe dragging a field through from source all the way through to destination on an otherwise already working set of data pipelines. Or maybe it's I need a data pipeline that does this. Please get the data roughly from here and here, and please just build me the finished result, and I'd like the finished results to look like this. And Maia can go away and do all of that work for you. But what it means is that each of us as data engineers becomes, first of all, just massively more productive, massively more capable. But I suppose if you really think about it, we each also become managers and team leaders with a team that works for us, a team of AI data professionals that can build pipelines. And I think that was really important because of the points in history that we find ourselves in. Maia, of course, is an agentic data engineering team. But AI is having another huge effect on us as data teams because there are already tens and hundreds of agents within our organizations, and soon there's gonna be hundreds and thousands of agents within our organizations that help our companies go faster and do new exciting things, but each of them will require data. And so if we think about the amount of data engineering we need to do, as these agents grow across our organizations, the workload on us as data engineering teams will grow alongside them. We can either not do it, and in that case, we will slow down our organization's ability to use AI to compete. And if we choose to do that and our competitors do use AI to compete, then that's a serious threat indeed for our companies. Or we can try and scale our human teams, which will require so much more resource, so many more people, or we can use technology like Maia to make each of us five and then ten and then one hundred times more productive and actually limitlessly productive as we have teams of AIs working for us as data engineers.