 distributed by its nature. So, and if I understand it, you're not trying to bring all that information into one big data temple. You're going to where the data is. Can you talk a little bit about how you see that working, maybe specifically in the commercial case and even in a legal scenario? How will a customer actually practically exploit your software to solve problems related to who knows what, when, where? Sure, I mean, I think what you'll have is a, essentially as data is either consolidated in certain locations or identified in various ones, you'll have to go and run an algorithmic process to get at the key facts and relationships and you'll have to store and process that information across the various data stores. So rather than sort of necessarily consolidating everything in one place, what we think about is there's data and then there's the analytical or understanding layer. There's the entities and the relationships, which is a spring. A metadata. You can think of it as very, very... A lot of it. Everything is metadata in the model. So it matters not just that a particular person has talked about, but who they spoke with. That's, think of that as metadata, but that's ultimately a very, very sort of feature rich environment which you can run statistics on and to the extent that you can start to summarize those entities and the things that they're doing, you can run the statistics either locally or, you know... We'd like to do some follow up with you guys. Love that we can talk for an hour on this, talk more about the technology and the solutions in place because that's the future what you guys are doing is the future.