Corrado De Gasperis : Understandable.
Zach Spencer: Yes, they — actually this question, the pilot plant in Wisconsin.
Corrado De Gasperis : Yes.
Zach Spencer: Is it still on target to be acquired 100% in 2024?
Corrado De Gasperis : Oh, yes. So we — look, we act as if we completely own and control the Wisconsin facility. There’s two payments, 1.75 million each, which we will very comfortably make when in 2023, on 2024. So the answer is 100% yes.
Zach Spencer: Okay. And another listener is drilling down on the hyperspectral sensors. What is the use case? What are the sensors connected to is that constantly analyzing a stream of data or only when specific materials are recognized?
Corrado De Gasperis : Yes. So I’ll explain very straightforwardly what I understand, okay. So I was just at the Prospectors & Developers Association of Canada, the PDAC Conference in Toronto two weeks ago. GenMat was there in full force. Comstock was there in full force. What my biggest takeaway from that conference, which, by the way, was 30,000 people strong for the first time since pre-COVID, last time I was there, was pre-COVID and I was blown away by the number of people and companies. Some of the largest companies in the world, including Rio Tinto, a keynote speaker, spent their entire keynote on advanced technologies, including data analytics, remote accessing and hyperspectral information, hyperspectral was everywhere.
So what’s happening in the mining industry? Finding new minerals is becoming increasingly more and more difficult. Finding new minerals is becoming increasingly more and more expensive. There is no sustainable solution even for the majors to continue on the same path. So for us, having a much, much more powerful, we believe, the hyperspectral imager that GenMat is going to put into orbit is one of the most powerful, not the most powerful known that’s out there, okay? But that’s only part A of the equation. Part B then is using ZENO to analyze, sit through the rapidly what’s in that data. What it will result in is much more precise by drill targets, much more focused exploration programs, discerning existence of mineral signatures that are maybe impossible to discern today without that kind of processing and computing, not just power, but predictive math.
So getting more data with more powerful imagers is a part of the equation, but how you analyze process and draw predictions and conclusions from data to me is the secret sauce. So said a different way, we can service Comstock. When I say we say, I’m saying, GenMat can service Comstock today, because of the mountain of data that we have, they can service large companies. at the PDAC conference they were talking basically only to the platinum sponsors, the big companies because these are the companies that have sophisticated data analytics. These are the companies that have sophisticated resource expansion of resources internally. The juniors aren’t doing it. So I think that — let me — one more theme on this, you could scan mining districts specifically, you would answer that question, a customer would say, like, for example, Comstock would say, hey, I want you to scan my 12 square miles.
I want a hyperspectral scan of the entire district, right? You charge them for that bandwidth, you charge them for that compute and then you charge them for the value of your interpretation. So that’s how we would think about one way of approaching it. But you could do that for agricultural resources. You can do that for government resources. I mean it doesn’t just apply to mineral identification. So again, it’s ubiquitous. I mean what they’re putting in position is remarkable. Now other people have and can put hyperspectral images in the space, okay? GenMat can acquire their own data. GenMat could acquire data from others. So that — although it’s powerful, although it’s focused, it’s less critical than the actual engine, the AI engine that can generate the mineral targets and generate the mineral discoveries.
I hope that’s helpful.