Albert Mining Inc (CVE:AIIM) Artificial Intelligence Platform Mining Project Generator
Albert Mining Inc (CVE:AIIM) (OTCMKTS:MJXFF) (FRA:L7C2) is a pure project generator using AI and existing industry data to locate potential discoveries. Director André Larente describes the company’s technology platform and its pivot to an asset ownership model. Albert Mining uses legacy data sets and its proprietary software to analyze mining data using AI. This proven technology has already led to 27 discoveries of resources such as silver, gold, nickel, zinc, and lead. Using this technology to pinpoint deposits reduces project territory sites by 95 percent. Initially, Albert Mining operated as a service company. Analyzing customer data, Albert Mining would then supply those customers with data targets. In what Larente calls a “rebirth of the company,” Albert Mining now earns an ownership percentage of the evaluated assets. As a result, the company’s profitability is significantly higher than it was under its the previous service model.
James West: André Larente joins me now. He’s a Director of Albert Mining Exploration and Services, trading on the TSX Venture under the symbol AIIM. André, welcome to the show.
André Larente: Thank you very much, James.
James West: André, Albert Mining used to be known as DIAGNOS, and I know this because I interviewed DIAGNOS I think almost a decade ago, and so whereas I am quite familiar with the business model of Albert, formerly known as DIAGNOS, why don’t you give us an overview of the business model of Albert Mining Exploration and Services.
André Larente: Today. So we started in 2005, working on our technology platform, and we started really early, probably end of 2005 with customers and all that, and our business model was to really give a service contract to a customer. They would give us their data, we analyze the data, we’d give them back the targets.
We started like that, and we created, we probably had about 60, 70 customers based on that business model. What we saw later on is that business model, as that evolution from 2005 to 2018, the business had changed. We had the crash, lasted way too long, and now we can see it’s coming back, so the business model has to be changed to a more proper environment today, where we will actually now analyze data in a certain territory or region or country, and look at the targets and see and identify which companies do the targets land onto. And then, actually, approach them in a different type of deal where we could actually have royalties, we could have shares, options, and also some cash.
James West: Sure, okay. So you’ve previously done this as a service, and how many discoveries has this technology actually successfully made?
André Larente: Well, we’ve documented 27 discoveries.
James West: Since 2005?
André Larente: Since 2005. We discovered kimberlites, gold, copper, silver, and nickel.
James West: So this is proven technology.
André Larente: It’s been proven.
James West: So using – so it sounds, you’re using artificial intelligence to examine legacy data sets derived from previous explorations to improve the chances of discovery. I guess that causes exploration budgets to be a lot more efficient, as well.
André Larente: Yes. We can actually reduce the territory size by 95 percent by using our tool, because we’ll actually focus on where the targets are located. On top of that, the technology that we’ve built actually really eliminates a lot of noise by using existing drill holes from the past. So the past historical data that everybody says is very important, we actually use that as our basis to move on to the new targets.
Without those targets that have been drilled in the past, well, you can’t really find new ones. And in every contract we’ve done, or every area we’ve been, there’s always more negative drill holes than positive drill holes. So we actually, that’s the technology to actually perform a little bit better, by either using positive drill holes or a mix of the two. And that’s our secret sauce.
James West: So when you say a negative drill hole, that would be classically referred to as a drill hole that didn’t yield any intersection of any target mineral.
André Larente: Any minerals, yeah.
James West: Right. So those negative drill holes, in your data set, they’ll become the sources of information for sort of stratographic identification of different sort of geological events and formations?
André Larente: Yes, absolutely. Without the negative and the positive drill holes, you can’t really use the artificial intelligence to its highest level, because if you only have negative drill holes, it’s difficult to train the engine to actually find positive ones because it has no example of it. But in a negative drill hole, there’s a lot of data that’s very valuable; it tells the engine, don’t go when there’s a setting of variables such as the negative drill holes, and then focus on the positive ones.
James West: So I guess the benefit of artificial intelligence applied to these data sets is, it’s able to extrapolate, in more nuanced patterns algorithmically, that the human mind and eye couldn’t necessarily arrive at just by a cursory look at the data?
André Larente: Yes. I mean, it’s now a 15 year old concept, where you want to analyze your data and actually try to extract the good features of it. It just takes a long time to understand the data. Mag is mag, but when you get into the mag or any geophysical data, there’s thousands of variables. So as scientists, we need to understand what these variables can do, and when they’re mixed between themselves, you know, how do they react, and what really creates copper, silver, and why is the gold hiding in this type of rock versus another type of rock? So it’s very valuable.
James West: Interesting. Now, I’ve heard of another company, or there’s some other companies out there, that purport to do the same thing; they use data sets to identify drill targets and they’re relatively new. What is, like, what other competitive matrixes are out there in terms of this artificial intelligence that might be considered competitive to you?
André Larente: Well, we’ve been monitoring the market the last 15 years, so it’s not a – it’s not like there was a lot of competitors. For a long time, we were probably, you know, the priest in the desert, and I welcome new competitors, because it actually shows to the market that there is value in analyzing the data with science.
So it just makes our business case much easier to sell than before. Also, I think that if you look at the oil and gas market, they’ve been using science for a long, long time. Without that science, these tools, they couldn’t find oil and gas. So now I could probably say that I think the market, the mining market, is ready to use these kinds of tools to actually now have a better chance of finding new deposits that can go into production. That’s very important.
James West: So I find it very interesting, your new business model. Formerly, you did this as a sort of cash for services, typical service-industry model, but now you’re doing it in such a way that you’re earning into a percentage ownership of the asset that you’re exploring for.
André Larente: That’s it. So what we feel, anyway, is that by analyzing the data and looking at the different properties, the different owners of these properties, we will have a better view on where the targets could potentially be.
Now, the chances of finding the targets with our technology is much higher, so we feel it’s around 70 percent. So there’s only 30 percent margin of error. So if we have a very large region, it’ll be much easier for us to, how would I say, tune in to the right properties and actually go into the property owners and make them a deal where, if they’re not using it, well, we can actually come up with some financing, finance the drill holes, and acquire a large portion of the assets. And that will create more wealth for DIAGNOS – for Albert Mining.
James West: Okay, so let’s use the 27 discoveries that this technology has already made as an example. What would you say, the very grossest estimate, is the average value of each deposit?
André Larente: Well, I want to be clear that when we find targets, sometimes you need to find a lot of them together to actually come up with a deposit. So from the 27 different discoveries we have, probably three of them could really be, you know, potentially bigger discoveries. So discoveries that you could probably put in production, because they’re close to an example of an existing plant or something like that.
James West: Okay. So are we talking billion-dollar discoveries?
André Larente: Well, I can’t put the numbers on, but I mean, it would at least be half a billion and more.
James West: Okay. So let’s just say that 3 out of 27 were half a billion; that’s 1.5 billion total value. What percentage of that value could you have captured in this new business model?
André Larente: We would have captured at least 30 to 40 percent of it, where compared to before, we received almost nothing. A cash fee, but that’s it.
James West: Right. So it’s a much more lucrative model, then, now that you’re actually interested in getting a piece of the action.
André Larente: Getting a piece of the action, I think, is going to also generate more growth for the company, more employees, and more science will be developed to be more specific. Our engine is very generic; we can actually look for kimberlites, for copper, for silver, for gold, but if we had a lot more resource in terms of cash, we could actually build very specific models for each of these different types of minerals.
James West: So are you analyzing data sets right now?
André Larente: We’ve, yes, we are. I can’t tell you where, but yes, we are. We’ve analyzed some very large ones. The largest we’ve done was the Planard in Quebec. So we actually analyzed the whole database, from the existing camps all the way up to the north. So the Planard, for us, was a huge program; it took a little bit more than a year to analyze the data. So it’s not something that’s, you plug in the numbers and you have a number coming out. So the data analysis is very, very long, and very tedious.
But what we did there is, we did all the base metals plus gold and also kimberlites.
James West: Yeah. Albert Mining is kind of undergoing a bit of a Renaissance now. You’ve got new investors, you’ve got a lot of new investment coming into the company, you’ve got new management coming into the company. Is it kind of like a new day for Albert Mining?
André Larente: I think it’s a reborn of Albert Mining, or the old DIAGNOS, if you want. I think with the new investors that are now behind us, that believe that the technology can help them identify targets and potential mines, it’s important now to actually put the technology to use, and we know it works, and we’re very excited.
James West: Sure. How much money has gone into the development of this technology since the beginning?
André Larente: We’ve actually invested about $4.5 million in actually software development, so the code, the actual programs behind all of this that are actually the heart of the system. So $4.5 million, we’re going back to 2003. So at today’s value, it would probably be at least $10 million to $15 that you would have to invest to actually build a platform like that.
James West: Okay. So let’s say I’m a public company owner of a mining company; I’ve got some great promising ground, I’ve got lots of historical data; what’s my next step to get in touch with you to try your system on my data?
André Larente: Actually, I would invite everybody to PDAC; we have a booth there, come up to the booth and we’ll actually explain to you how this is done. We have four steps that has to be done to get to the results, it’s very simple to understand, very complex in terms of the software, but the actual workflow is very simple to do.
Come to the booth or call us, and then we’ll be very happy to call you back and give you an estimate.
James West: Okay, André. That’s a great intro. We’ll leave it there for now, we’ll come back to you after the next discovery and continue the conversation. Thanks for joining me today.
André Larente: Thank you, James.
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