Yeah, no doubt. So we're building a very special platform. So we're building an institutional grade A decision quality platform for private equity, venture capital, credit investors that do tremendous amount of research and analysis and modeling on each and every decision that they make. So to some extent we're dealing with sort of the highest standard of investment rigor and thinking that there is. These are folks that are very, very experienced, they're very well trained. They typically have, you know, MBAs and gradu degrees as well as PhDs and some of the underlying sciences that they invest in. You know, biologists are studying biology, life sciences companies and engineers are studying engineering companies. And then various kinds of financial analysts and MBAs are doing the work to kind of vet these companies. And they might be spending, you know, thousands of hours. And if you think about the Malcolm Gladwell book, the famous 10,000 Hours of Time put in to become an expert, these folks are spending the 10,000 hours multiple times over. So we're shooting for a very high degree of accuracy and reliability and trust. And frankly, the way that we have attacked it, Anthony, is first and foremost to build quality control and trust into every step of the process. One of the oldest phrases in the book is garbage in, garbage out. It doesn't matter how smart you are or how rigorous your process is. If you got bad inputs coming in on the front end, you're going to make bad conclusions on the back end. And that's true in personal financial management. And sort of all your audiences making real world financial decisions under time pressure and information pressure, they don't always have all the information they need. They don't always have all the time they want, but they nonetheless have to move forward and make some decisions. And the first thing you have to do is make really careful workflows so that you're not pulling in false or faulty information that garbage in, garbage out problem. We handle that in a variety of ways. I'm not going to give a demo or bore your listeners with a thousand feature functions of the bullet point network AI platform. If they want a demo, they can get one in 10 minutes. But the key thing is source control, source prioritization, making sure that someone is deciding, yes, these sources are valid and we are automatically pairing on our platform the most relevant, most reliable and most recent sources to the questions at hand. If you just type a prompt into ChatGPT or Gemini or something on the web, you're going to get whatever information is on the public Internet and it's not typically prioritized in any fashion. So the first thing we do is prioritize a set of sources and organize a database so that the information being used to drive the answers that you're getting comes from those trustworthy, reliable sources. Second thing we do is we give you visibility into it. We may have done the automatic pairing, but we tell you what sources were used and if you don't like some of those sources, you can deselect them. Say, no, let's not use those, let's use these. Just like you might if you hired a smart young person to work for a human being. You might say to her, hey, I want you to look at this topic, but please don't look at these sources. These aren't helpful or reliable. Let's focus on these. I know these to be good ones. And then you get a footnote or you get transparency. You can see the, the actual chunks of information that were driving your stuff. So that's the very first thing we do. The second thing we spend a lot of time on because our core users and our clients are numbers oriented people, they're typically trying to think about, if I invest at this price, will I make a 5x return over this time period and will that be an acceptable IRR or rate of return, given the risks that I'm underwriting or taking? So numbers matter to them quite a bit. We enable them to connect their own trusted spreadsheet into the analysis so you don't have to worry about AI guesses doing all the calculations. You can use inputs you trust, formulas, you know, because you saw the spreadsheet and outputs that you trust. And so the connection between trusted sources and trusted numbers is very, very important to Us. But the last thing which has kind of been implied in all the answers I've given you about this is you need a person. We call our platform AI+1. The AI is doing its thing, but you're the one. You're the person who has to supervise, control, decide, ultimately edit with nuance and judgment the conclusions that come out. So you can't go and shouldn't go on autopilot where you just sit back and let AI do 100% of the job. But if we can save you 80 plus percent of the time and give you a higher quality, more rigorous answer, that's a win. And if you do that with an AI plus one framework, you can come out far, far ahead. I mean, I think, you know, one of the many issues that people are grappling with today in addition to like, is the answer trustworthy and reliable? Is, you know, is this thing going to help me or is it going to take away my job? Is it going to hurt me in my life? And you know, what I often say is it's unlikely that AI on its own is going to take your job. But you may lose your job to someone who's using AI better than you, who's building in workflows and trusted processes and being a smarter, better user of this powerful technology. And that's what we're trying to help our clients to do. And that's what I think a lot of your audience is probably spending its time thinking about how can I use this to help me to be high ethics, high trust, high control, and to get efficiency and power without having some problems.
