One of the things too that I think we hear a lot about that I wanted, I've heard you mention it obviously in today's market, AI is all the rage. As a product I think people anticipate it, but I've heard you mention using machine learning and AI capabilities as you develop enzyme pathways for Invizyne. Can you just elaborate on that? Is Invizyne an AI company? Invizyne is a user of AI. We are maybe not on the hype cycle of calling ourselves an AI company. The same way back in time when people they thought they were first in on internet and they invested in all the people digging down internet cables and they ended up missing out on the value proposition that was actually the applications of the internet. There was the social media, the Googles, and so on of the world. We see the same thing going on right now where a lot of people is throwing a lot of money into the tools and then they're giving us those tools for free and we can apply them and generate value. So we are users of AI and machine learning and because of what I've been working with before, it's a pretty natural thing for me to introduce to a company because we have been using computational power and machine learning for generations now, generations of startups now. So it's not as new to us than it is maybe to the world. What is very unique about us is that we have a very, very potent data source for how to optimize on enzymes. And I actually personally think it's more important in the AI race to be the owner of the data and the signal that you're optimizing on than the actual tools. So that's how we are positioned. No, I agree with that. I mean, I think I've made the case probably way too publicly that AI is not really a product. It's a service and a tool. And the value of that tool gets dramatically enhanced when you have proprietary data, right? When it's exclusive. That large language model is only as good as the data that's in it. And if everyone has access to it, it's not going to be as valuable. So are you using machine learning and AI to optimize enzyme pathways you already know about? Or is it to investigate potential enzyme pathways that you haven't learned about before? Or is it a combination? So it's definitely a combination. But to be concrete about what we do the most is when we run enzymes outside of the cell, so not inside of the cell where there's the whole protection of the cell and all the support mechanisms, the enzymes need to be more stable. We want our enzymes to be super specific in what they do. And we want them to be fast as some of the features we're looking for. Think of an enzyme as a little work horse that is put up in a production line. So the first workhorse, as they break the things down into the right building blocks, the next ones put them back together in the right order. We want all the guys in that chain to be as efficient as possible and not suffer from being in this outside environment where it's free of all the problems from the cell. So that's what we are optimizing towards. So each enzyme, we run basically data on and computationally find out the genetic optimizations we can do that we then test in the lab, feed that data back into the algorithm and basically optimize. So it's a way of removing the bottlenecks in the production. Yeah, I think it's also important to talk a little bit more about the underpinning technological principles, right? Because I think when people hear of new technologies, they think it's something that's maybe unproven, untested, not well known, not well studied. That's definitely not the case here with Invisign. We talked about the origin story. It was discovered more than 10 years ago out of UCLA with Dr. Jim Bowie and two of the other founders, Tyler and Paul. I believe I might be correct in saying some of this enzyme engineering is the recipient of recent Nobel prizes in chemistry. So can you just kind of speak to really the tech foundation underneath Invisign, how that's being leveraged and how some of that is uniquely owned by Invisign as well? Yes. So there's actually a really cool background story here. So first of all, the last four Nobel prizes in chemistry are the four technologies that we are combining. That's the state of the art kind of argument. And I think that's a pretty solid argument. So those are technology directions that allows for certain things when you optimize. It's things like CRISPR and gene engineering. The last Nobel prize that was given is basically using computational power to foresee the structures of proteins. In our specific case, it's the protein of enzymes. And we use all of those fundamental sciences. What we do uniquely is that we bring them together and use those tools. And the last, you can argue that the last Nobel prize is literally AI use in proteins. And we use them specifically for enzymes. So we are in the privileged situation of having the data source that is specific for enzymes. So we are positioning ourselves to be really good at that, both because we are really good at it now, but also because we are in a competitive situation where we become better and better and better the more we use it. Back to the core of it is then we have had some core inventions that the founder team realized at UCLA. And we have licensed out and then we have built on top of that. So we also have a good dose of secret sauce on top of that. So we have patents, we have core inventions that are from UCLA, from ourselves, and then the secret sauce on top of. So you can argue just in layman language, we are really good at optimizing enzymes and make them survive outside of the cell and work these production lines. And why does that matter? Well, it's a new generation of access to natural resources or chemicals. So it allows us to allow us for a new way where we don't use oil as a starting point and we don't have to run out and cut down a lot of trees or bushes or other plants or bacteria. So it's a new generation of how we make chemicals. And as we talked about, obviously with a focus here in the beginning, but it does apply to anything chemistry. And Lou, what is chemistry? It's your good life. It's what you're wearing right now. It is the sofa behind you. It's the paint on your walls. It is the colors you see in all materials. It is also in your food and in your medicine, in the fragrance and the flavors that makes the good life. Yeah. Chemistry to me is that first class in college that was pass fail. And I got a 53 on the exam and with the curve, that was a B plus. So it's, I don't have the fondness of memories of chemistry, but, to your point is it's into everything. And I think that's what's tangible here to investors and potential shareholders. I think what also is tangible as you start explaining really the underpinning technology and what it's capable of that you immediately realize that there's probably endless possibilities in terms of directions you can go as the CEO of this company in terms of, Hey, what do I want to commercialize? How do you approach a go-to-market strategy? How do you rationalize that endless possibilities into commercial realities?