It is my pleasure to share an introductory conversation with Mr. Denis Laviolette, President and CEO of Goldspot Discoveries Inc. Please note, this is not sponsored content. Learn more on the company's website here, https://goldspot.ca
Peter Bell: Hello. I'm Peter Bell, and I'm here with Mr. Denis Laviolette, CEO of Goldspot Discoveries. Hello, Denis
Denis Laviolette: Hi, Peter. How are ya?
Peter Bell: I'm okay, thanks. Pretty exciting times for Goldspot. I gather a public listing is just around the corner.
Denis Laviolette: Just around the corner, exactly. We're hoping that sometime next week it should be active. We're done, the QT now – the money's raised and it's up to the lawyers and the exchange gods to release the halts, and let us trade.
Peter Bell: Wonderful. Here we are in February, 2019. And the qualifying transaction, I wonder, any information you can provide on that?
Denis Laviolette: Sure. We closed at $7.5 million, which was an oversubscription of about a million and a half from what we set out to do. We had a $6 million goal, and we did far better than that with $7.5 mill. It was exciting. We had a really cool group come to the table, Triple Flag Mining. Triple Flag is owned by Elliot in New York. It is fairly large. And they've been very active in the last year acquiring royalties and streams, spending upwards to $800 million or something like that – very active. They're privately owned by Elliot. They came in for a big check, about $3 million, and that was a real cornerstone investor for us. Lots of familiar faces and support in our stock. We've got great shareholders. It’s a really exciting time.
Peter Bell: Amazing. Triple Flag Finance out of Bermuda, I've heard the name a couple times. They are becoming a more important player in the junior mining business, I think.
Denis Laviolette: They're creeping up there. They're doing a lot of great deals. They're aggressive. I think it's a good sign for the industry, too, because it's showing a large hedge fund in New York taking a keen interest to load up in the current market. If that's not a sign that we're at a bottom and we're on our way up, I don't know what is. It's a good feather in the cap.
Peter Bell: And the long-term vision, looking at the company presentation I see a slide saying "We are a technology company, not a mining company." Are you gonna be a 43-101 issuer, or is this something else?
Denis Laviolette: No, we are a technology company, primarily. We'll be a technology investment-type company. The idea here is creating a hybrid – Goldspot is somewhere between a service company, a royalties company, and an investor. We're really just a new mousetrap to give the modern investor a new way to play mining. I think that the mining space is particularly unsexy for most of the millennial audience.
There's a huge amount of wealth that was created through the cryptocurrency boom and bust, if you will. The medical marijuana space, too. There was a huge windfall. I'm a millennial and I’ve got lots of buddies that were calling me, "Hey, do you know any good weed stocks?" These are school teachers! It was an insane amount of wealth that was generated all of a sudden. You never hear that from mining. There was a bit of that in the last bull market, for sure. Now, I think that a lot of this younger generation that has the wealth and is looking to invest is an unconventional audience for mining. Gone are the days of the broker. I don't know a single friend of mine that has one – I'm the only that has a broker. They're not gonna be calling them and saying, "Hey, you made a good win on your weed deal with your Qtrade account with your online research – your YouTube research, you should be buying this mining deal. It's in the Golden Triangle, and they got 10 grams per ton over 50 meter." Who cares? The new investor doesn't know that.
And the existing mining crowd is so fractured, it's so broken. I'd be curious to see if a lot of that traffic comes back in. It'll be new people. They need something else that they can grasp onto. The AI, machine learning, is a new twist, it's a new edge in the space.
Peter Bell: And the deal structure, too. The monetization strategy for Goldspot has some creative ideas at play.
Denis Laviolette: Well, look – we ripped a page out of a lot of books. And we wrote a few pages ourselves.
When we started this whole thing, we found a team out of INRS who were just tremendous – tremendous talent. A really smart group of young people. They had excellent geoscience skills but, by and large, what they were exploring for was the application of machine learning on geoscience data and that resonated to me. That was incredible. Together, we competed in the Integra Gold Rush Challenge and took second place. We said, "if it works on a local scale and if it worked at Integra, then what can we do on a regional scale?" The idea snowballed from there.
On a regional scale, let's look at regional data. Instead of training an algorithm on one deposit, let's train it on 1,586 deposits in the Quebec, Abitibi. And instead of using one company's data, let's use everybody's data – whatever's in the public domain – and see what we come up with. We came up with a prospectivity map for the province and then overlaid all the existing claims that were active there in the province. We really got to see who was who in the zoo. We saw some small companies with tiny market caps that had great ground. We saw some huge companies with terrible ground. We saw some of everything in between. And we thought, "If you had a crystal ball, how do you monetize it?"
We got to thinking about capital market side and all that data – can we build something to look at all that capital markets data on top of all that fundamental data? That's really where the concept of Resource Quantamental was really born. We've cultivated it since then and aggregated this insane amount of information on every company, every press release, everything, to come up with this Money Ball approach to mining investing. Can we pick, pound for pound, the best explorers in the space? Who has the best data? Who's most poised for discovery? And then, can we come up with a strategy to leverage this team and this technology we have into those companies, as well as some capital?
We knew that this was a bottom of a market, or that we're in a bear market now, so if we can start building a basket of equities and royalties in a time like this using an investment approach and leveraging our technical talent, our services, and our team, then we can really come up with something unique for shareholders.
Peter Bell: And that much is clear. I've encountered a few other groups here and there that have some aspect of AI or machine learning – some kind of unique technology that they're using on large ground packages, but nothing quite like what you guys have put together. And just seeing Integra resources there, as one of the clients that you've already been able to secure with revenue-generating services. Revenue, imagine that, in a junior mining company.
Denis Laviolette: Well, we’re a technology company.
Peter Bell: Thank you!
Denis Laviolette: We've got revenue. A lot of that comes in from a lot of the major clients. That was another idea, "How do we prove to the market that this technology's worth anything? How do we prove to the market to take a shot on this?" We thought that we could pick up a really good project with lots of data, then raise a bunch of money to try and prove it on that project, but then you end up hitting this wall where you need to convince people that are resistent to change in a market climate where nobody wants to invest in anything that's not their own to take a chance with you – try out a new technology. In the mining business, we've seen a lot of technologies come into it and a lot of have failed. There have been a lot of failures to launch.
We thought, how do we get the industry to adapt this technology? So, we target major companies and mid-tier companies in a service capacity. If we can show the market that the big companies will put is through the paces and try this technology out and it’s successful then the junior companies will see the appeal and the market will respect it. That's exactly what we set out to do. A lot of what we've done up to this date is really just brand-building.
The Yamanas of the world, Hochschilds, the Sprotts, the McEwens, the Integras – this is all just to get us out there and prove to the market that there's a lot of value in this technology. To have those companies start out as service clients, then turn around and actively invest in our company is key. And not just in this most recent round, but also historically, too. After we finish service projects, they say, "Wow, we want a piece of this," and, "We got more work for you to do."
It's been a great source of cashflow. We've been able to preserve our treasury almost entirely from inception and grow out of cashflow. We started off with six members, then we went down to four members, and now we're at 22. We've grown and all that is basically from free cashflow. It's been really cool – we're actually going to market with a business, which is unique in the mining space.
Peter Bell: And it's reflected in the amount that you were able to raise there, as well. That's an important part of the primary markets, the going-public stage. To be able to rally that kind of support from some of the most sophisticated investors in the world is very noteworthy.
Denis Laviolette: Well, thank you. It hasn't been easy, I certainly assure you that.
Peter Bell: That's good to hear. Maybe one kind of question about all these service relationships. I wonder to what degree those are really bound by confidentiality and how much we'll ever really hear about success there – exploration success. Sometimes in a bigger company, a small success may not really make a headline for that company whereas it might be very important for you. Will the world hear about it?
Denis Laviolette: I am so glad that you picked up on that. Not very many people do. It's certainly important. Going back to the previous exploration and how we wanted to prove this out, we thought that if we got our own ground or worked with some junior, picked out a whole bunch of targets, and all that then we would know that news would come out into the market. When we went after the big companies for validation – we ended up getting validation through investments, so it's fine – this was a problem. What moves the needle for a big company isn’t the same as a junior. They're not juniors, they're not starving for press releases.
To say, "Goldspot helped us to fill this one hole over there that ... They hit a zone that they said was gonna be there." They need 50 holes in that sucker for it to hit a critical mass and move the needle for these companies. They're not motivated to put that info out and that ended up kind of biting us, a bit.
To have them come in as shareholders, shows the market there is some validity in what we're doing here. Absolutely, that was one of the problems we struggled with. By choosing bigger companies to work with, they're not as motivated to put the news out. And rightfully so. It's gotta work for them, too. They're not just gonna put out a little something just to help us.
Peter Bell: All the cash in the treasury now helps you to be pointed in going after some of the smaller groups who you think may have potential, but don't have the funding to do the work. They may help you get some of the success stories to really wave the flag about, publicly.
Denis Laviolette: Exactly. That's the whole point.
And with Resource Quantamental, our AI machine that basically analyzes the stock market, the fundamentals, and financial side all together to pick the ponies for us to bet on – that's the whole thing. If we pick out the best ponies we can work with and then go to those companies to make a strategic investment and provide them with this tremendous team that we have, which is already paid for by the big companies – those salaries and G&A is already covered – then we can utilize that for the junior companies and put that money to work helping make those stories better. They're gonna be motivated to spend the money that we give to them in order to test some of our targets. That's gonna really be what I think is going to set us off in a big way. You can imagine, with $10 million we're gonna have a lot of stabs at that.
Peter Bell: Absolutely. And it was the first time I'd heard about regional programming Quebec. It seems that that is a hotbed for this kind of research as a very information-rich area.
Denis Laviolette: Quebec's a hotbed for AI, in general. Traditionally, their climate is so encouraging. The way the Quebec government funds exploration is very Quebec-centric, but there is a lot of the capital in Quebec. The Caisse and everything is directed at supporting Quebec exploration. They've been very good at that. They have a high amount of transparency with good integrity in their data. With the machine learning, the advancements that are made in the tech side, Quebec is really a leader. I think Microsoft is building a new AI facility, actually, in Montreal. I heard 5,000 employees or something like that. That's phenomenal. For AI in Canada, the number one hub Quebec – Montreal.
We're seeing a lot of that leave Silicon Valley and move to Montreal. It's really cool. It's kind of a double-edged sword – you end up with this great jurisdiction with great data, geologically, a strong mining community and also a really good place for innovation and technology and artificial intelligence, machine learning.
Peter Bell: And just the layers of geological information, too.
Denis Laviolette: Oh, there's no shortage. You have incredible geophysics over the entire province. Great geological mapping, soil geochemistry, stream sediments, drill holes, everything is there – it's all in the public space. If you venture off into Nevada, then you don't have any of that. It's super top-secret.
It's an interesting time for Goldspot. What we do is really effective in some jurisdictions and we can really hit a home run. Then, in some other jurisdictions, it's not. They may be great mining jurisdictions, but a lot of the data is very secretive. Nonetheless, one of our most successful projects was actually in Nevada.
Peter Bell: It sounds like an example of the partner keeping data secret before the discovery and afterwards.
Denis Laviolette: Exactly. It's secret on both ends. The whole thing's a top-secret game.
Peter Bell: There's a lot of money at stake. It can be understood to some degree, but it just sets Quebec apart as leading place for the mining industry, globally.
Denis Laviolette: And it's not limited to Quebec. All of Canada is quite good. And other jurisdictions, too. A lot of the African jurisdictions are incredible. We're working on one project we'll be announcing shortly and it's great. It's got a huge wealth of data. The government with the IMF and World Bank all pay for tremendous surveys to be flown to collect all this super-sophisticated data and attract new investment dollars into some of these areas and stimulate the mining business. That data's readily available and free, in most cases.
Peter Bell: The beauty of being able to do it right from the beginning in some of those circumstances, too. Lessons to be learned by those governments from Nevada versus Quebec for – how to set up those government polices from carte blanche.
Denis Laviolette: It's nice to have seamless data sets. That's a real luxury. We never get that. As a company, we end up having to massage the seams, level, stitch, and homogenize data – whether it's geophysical data or geochemical data whatever. In some of these places where the government is paying or another external body is paying and supplementing that, you end up with these beautiful, seamless data sets from end to end that cover the entire country or large swaths of the country or province. And they cover a multitude of deposits. For AI, that gives you a tremendous training set to work with.
Peter Bell: That's so important. One of the things that's always fascinated me about geological data is the different scales and the fact that you can have a dataset that covers the entire country , within that, you can have a higher resolution stuff of the same methodology and both of those data sets can provide different insight.
Denis Laviolette: Totally. Some of the pushback we get as a company is from people, skeptics, who say “How is it gonna work? I can see how it worked for Brownfield's exploration, but it's never gonna for greenfields." Well, it all depends on the granularity of the data and the granularity of the product you're looking for. Whether it’s regional data or more local, it's the same data type. If we're working with regional data and are training on multiple deposits, then we might not be able to show you where to stick the next drill hole, per se, but we can certainly show you where you should be exploring or where you should be following-up with a more detailed program. That's valuable.
It might be able to tell you, in an entire geological district, what's the best land to put together? What's the best land package to have? Where's a total waste of time? Then, you can hone-in on those areas with more granular, more expensive surveys in more focused areas. All of this covers all different aspects of granularity, as you go from a really high view down to really a zoomed-in, project-scale view.
Peter Bell: And people who know the mining business have some familiarity with that, but there's a broad cohort of people who don't have any sense of the process of exploration – all the twists and turns that Mother Nature lays down when some of these mineral deposits are created. Figuring it all out is a massive undertaking. Let alone production! The mining industry is just awe inspiring.
Denis Laviolette: It's a very complicated space. It's full of complexity from top to bottom. Production's challenging, almost nobody makes money. Almost no discoveries become mines. And the odds of discovery? As you go down the list, the odds of seeing something through from the exploration side to the initial discovery, all the way down to actually what goes into production and what actually makes money is incredible.
Peter Bell: And with all those challenges, it becomes so much more clear how important it is to, at least, have the foundations of the company set up the right way. For a technology company, you mentioned the team having shrunk down from six to four and now up to 20 plus. The list of names and the credentials in the deck is impressive.
Denis Laviolette: We're so lucky. We've got a really unique blend of talent. That's what we focused on. We recognized very early that machine learning and AI is not enough. The actual data science side of the equation is not enough. We couldn't just have a whole bunch of computer nerds trying to figure this out and then put in data. The data is full of mistakes. You have to understand the data to use it. So, we ended up starting to assemble this rockstar team. Not just geophysicists, for example, but geophysicists who don’t only understand geophysics – maybe they also have a PhD in machine learning and data science or math. You need that special blend of domain expertise with that data science edge to really thrive. Cejay Kim, a Director calls it collecting in Pokemon and that's really apt. They're all special in their own little way and they all have this special blend of skills. And that's really what we've focused on.
Whether it's a PhD structural geologist that also happens to publish papers in the data science world or code on his spare time or her spare time, we have incredible people. We have quantum physicists that are helping us on the financial side because there's a lot of stuff that different disciplines can bring to the mix. We're really bringing all of these people together. Every deposit, every data set, and every project is different. It's not one code. When I'm going around marketing, everybody has a solution, “Denis, you're doing this all wrong. This should be a licensing model.” Or, “it needs to be a SAS model,”. Or, “you should be creating a software and licensing that software.” First of all, they have no idea of what the market share in mining is in terms of software. The biggest software company in the mining space, it's I don't know, what? It's probably a $10 million, $15 million company.
Peter Bell: Or it's SAP forthe accounting software.
Denis Laviolette: Right. We're not building an AI QuickBooks that the whole world can use. This is mining; it's small. If you add all the top gold companies together in the world, you're still not market cap of Apple. It's not one of those situations. Saying that it's got to be a licensing model or a software model doesn’t work. It's not a one-size-fits-all problem. Every single property and every blend of data sets is unique. Whether it's different geophysical data types, or different frequencies or whatever. If you just try to build a software with this automatic “click a button” machine learning solution where you feed in a whole bunch of Python, off-the-shelf ML algos, it's not gonna work. I'm sure somebody will do it. I'm sure somebody will try to promote that, but it's super flawed. We literally have to custom build everything we do for every client and every project.
Peter Bell: I was trying to imagine what that process might look like and I just gave up pretty quickly.
Denis Laviolette: You get more practiced at it, which is cool. We get better at it. There's a lot of stuff that we carry from one project to the next, in terms of our workflow or the way that we work together. We're getting more efficient and are going to be able to do a lot more. We're doing more things every time. You almost wish you could go back to the first project you did with all that you know now and try it again, but it's a business.
Peter Bell: Again, that comes back to some of the deals that you've done with the partners in a service model there. Very valuable experience.
Denis Laviolette: The great part is that they've watched our evolution, too. They've watched us evolve from the first project we did to the last project that we did in some cases. Some companies have given us five projects to work on. For others, we’re only on number two or number one. It's great to have that feedback. It's great to see how you learn to deal with people. We’ve learned how to deal with geological teams, some of the push-back and skeptics. It's interesting.
Peter Bell: I can't imagine the resistance you encounter along the way.
Denis Laviolette: There's quite a bit. All in all, I think people are waking up to it. It was the same resistance that block modeling company had when Datamine or whoever was first creating software. Block modeling was the approach of the day to optimize the resource calculation business. I remember starting when it was all done with a plotometer and polygons on long sections. It was a manual process. There would be one computer in the office and that was the block modeling computer. Everybody was afraid to touch it. And every now and then, there'd be a specialist that would come around – that was the block modeling guy or lady. They would come there and just sit there at that PC, run it for like three days in a row – nobody breathe on it – and then, all of a sudden, it comes out with the resource calc. At first, people were saying, "The block models are all wrong. You gotta do it by polygons." Anyway, it's all a learning curve. Technology's there to help us, and it will.
Peter Bell: Amazing to hear that you've seen that evolution in your own career.
Denis Laviolette: That's a testament to the business, my friend. The fact that I'm not that old. The fact that I had to do things on paper when I started out. We had a light table. Everything was on light tables. And I'm watching the company spending huge money. I was with big companies and small companies. We were spending big money on geophysical surveys and IP surveys. We were withholding information from the geophysicists to test them, like, “don't give them everything. Don't show them the targets that we have ourselves or some of – let's see if they find them on their own."
We were holding stuff back to test this witchery that was geophysics. And the geophysics companies were trying to convey this super sophisticated information, this 3D multi-variant information to us. We had dial up connections! There's only so much you can transfer that way. Of course, we'd get a CD in the mail, which was good, but nobody looked at that. The dust would grow on that. You'd get these four or five color maps to look at – first vertical derivative map, second vertical derivative map, horizontal derivative – they'd explain to you what it means and you'd go, "Yeah." And then you'd overlay it on your light table with the rest of your stuff you printed out on a plotter, which would take an hour, and you'd overlay it on the light table to see if you could see a pattern. This was not that long ago.
Peter Bell: If you squint hard enough you might see something, right Denis? If you squint just right, you might see it!
Denis Laviolette: Right. The eagle face – the eye of the eagle. Everybody was picking out shapes. Do you see that little horsey shape? “That, right there, we should drill that.” And we would drill it. And then we would miss. Then, we would throw the geophysics in the garbage and say it's a geo-fantasy. The CEO would talk to the market and say, "We tried IP and IP doesn't work here." Or, "We tried mag and mag doesn't work." And that would be it.
As an industry, we spent an ungodly amount of money on super-sophisticated data that totally stumped us. And it stumped me, too. I'm not pretending like I was an expert or anything. I wasn't, but I recognized that we're spending this money and there's all this information that’s not being utilized.
Peter Bell: A personal bug of mine that I often find myself debating with people is when they say, "Drill, drill, drill." And I'm like, "Hold on. Do we have any data?" Respecting the different layers of data for an exploration thesis doesn't seem to be a real big part of a lot of people's approach in the exploration business. And it's wonderful to hear that that's your premise here.
Denis Laviolette: It's a data-driven approach. We don't believe in betting on the jockey. Not necessarily. The fact that that's still a thing is amazing. People are bona fide mine finders? “Well, this person, he's a great geologist or she's a great geologist. He or she has found mines, has found deposit.” As soon as a discovery is made, they get appointed to the Board of the advisory community and all of a sudden that person comes in to save the day. “We've got a great mine finder now on the team.” It's the data that does the finding.
It's the data that does the finding. They're not gonna change the process of blanketing your whole property with geochemical sampling and-or geophysical surveys. The whole concept of “mine finders” and the fact that we bet on that is completely preposterous. I get it. There's some truth in it, in the sense of running the company, being frugal, and having access to capital – that's true. But, when it comes to someone being technically superior to another because they were lucky, I don't believe in that. I believe that there are great projects out there with people that no-one's ever heard and don't get any capital. We want to find those companies and do a deal with them to bring them capital and a team that they can't afford to make that discovery happen.
Peter Bell: And to help them look at the data in a different way. They may have blinkers on. They may not be able to step back and look at all the data that you have put together. Another set of eyes may help them in some of those tough situations.
Denis Laviolette: Totally. And it's amazing when you get involved because you start engaging these people – the geos or whoever else is involved – and they go, "Oh, wow! This is useful. Let's try this..." And it's like, "Great idea. Yeah, let's try that. That might work." Then, we get a product and now they understand it. This is not a thing that's supposed to threaten every geologist's job. We're not gonna put geologists out of work. AI is not gonna solve all our problems. No, it's just another tool. It's going from paper cross sections to 3D models. And a step further to AI handling and finding patterns in this unfathomable stack of layers of data that no human possible could ever look through all at the same time to find patterns in.
Peter Bell: As you say, the data that is not particularly friendly to work with. I have some experience with data analysis myself and the time and effort and expertise required to massage and trim it to get the stuff all lined up with itself is very challenging.
Denis Laviolette: It's where we spend 90% of our time. Homogenizing, cleaning, fixing data, tidying it up so that we can get it to a stage where we can use technology to help us – technology can even help us clean but you've gotta be careful doing that. Certainly, there are ways to tidy things up and simplify things and we see it all the time. Structural data is a pet peeve of mine. You may have world-class structural experts come to a project, a structurally-controlled deposit or something – they'll come in and do a big forensic review, and generate a hundred page report. A top-shelf structural geologist has put in hours and days and months of time to generate this hundred page report, which is super complicated with all types of measurements. They sit down with the geological team and flip through the report, explaining it to everyone. For that blink, glimmer in time and space, everyone in the room has their eyes open – everyone's nodding and totally with with them. Then, that person leaves. And you're stuck with this big report.
You see it hit the shelf, nicely bound, and within months it is collecting dust. No-one has used that information. That information just died on the shelf. Those measurements are not gonna be worked into the model. Every geologist has a job to do, they have tasks to do day-in and day-out. They don't have time to go through there and pull out all that and try to draw it all up. It takes forever.
We're not trying to create something that people don't know. We’re just pointing them onto the fact that they already have this information. I am ashamed that we're not using that information, so let's work together to use it.
Peter Bell: And for the companies that do work with you, there's not some software that they just use – is it fair to say it's a little more involved than that?
Denis Laviolette: Yes. We use a bunch of software. Our geophysicists will use conventional geophysical software, but we do a lot of processing externally because those software packages are not equipped with some of the tools we need. We have to write our own scripts and things to crunch the numbers. Sometimes we use cloud computing and things like that to make that crunching process a lot quicker. Then, we simply output the product back into that conventional software so we can visualize it. That's a lot of what goes on. It's not as simple as one silver bullet software.
Peter Bell: Good. Getting stuff out of one place and back into another is everything. The heavy duty AI stuff that you would've had to build outside of all the existing things, I can't quite imagine all the different software you must have. Keeping all that organized, checking all that for bugs – I guess that's where the 22 people on staff comes from.
Denis Laviolette: Yes, there are 22 people. Nine with PhDs. All of them are way smarter than me. They are champions. And you know what? There's still gonna be mistakes. There's still gonna be false anomalies. But if we can go from having 100 targets to boil it down to 10 where only two work, then that's still better than what the industry's doing.
Peter Bell: Yes, it's clear. It's compelling! There's a huge opportunity here. It's gonna take some bold vision to go after it, as is apparent from this company, Goldspot Discoveries Inc.
Denis Laviolette: Thanks.
Peter Bell: You're welcome. It's a pleasure to be talking to you. I wonder about some of the technological or scientific breakthroughs that are obviously happening somewhere among the team – I wonder if any of that will really see the light of day?
Denis Laviolette: Eventually, it will. I think that some of the stuff we can do now is worth sharing. We can take four or five different geophysical surveys or even just one geophysical survey that's picking up petrophysical properties of the rock, which we always neglect to analyze when we hand it off to a geophysics firm to oversimplify with a couple two dimensional maps. We take that raw data, crunch it, and we can do some miraculous stuff now. Even some of the simple things like generating a really detailed geological map at the click of a button with nothing else other than geophysics. It can give an incredible geological map or a three dimensional model as a product. You have this incredible geological map that, instead of having to walk line by line to map outcrops and all that, you can now just take all that geophysics and crunch out this super high detailed geological map. You don't know what to call certain things because they were grouped according to their petrophysical properties but you can revisit those things. It can save so much time. Instead of having to walk line by line, you just go and find out what was this type of rock for this anomaly? Add a label to it and then do some validation work to get to where you need to be. That alone will save the industry huge amounts. Those are little internal projects that we've built that eventually will see the light of day, but right now we have them for our own usage and for the use of our clients.
Peter Bell: Targeted ground truthing, it sounds to me.
Denis Laviolette: Exactly. Instead of just having to just blindly walk line by line by line and map outcrops. And this is great in areas where you don’t have much outcrop, too.
Peter Bell: With satellite imagery, too. I'm assuming there's some pretty high resolution and a variety of types of imaging there.
Denis Laviolette: Yes. In South America – the Andes or anywhere that has got low vegetative cover where multi-spectral or hyper-spectral data can be used to some high-degree of accuracy, it's like having geochemistry over every square inch of land. You can cover hundreds of deposits and use machine learning to figure out the fingerprints, then see where else those fingerprints re-emerge. It's incredible which kind of projects you can generate just by doing that. Multi-spectral imagery, hyper-spectral and polychromatic imagery satellite – it's all super useful stuff. And you can bring all these worlds together. That's really what we're about, bringing all this tremendous stuff together and brainstorming how to make it work – how to make use of it all and let the computer do what computers do best, which is crunching numbers and finding patterns.
Peter Bell: And the world is your oyster to some degree. You can pick tools for the right area and go to it. I wonder how you narrow down such a broad search space.
Denis Laviolette: From the Resource Quantamental perspective of analyzing every company and all this information on the public domain, picking out the best companies to work with, a lot of times we've got our own biases. We put a preference on certain districts and certain jurisdictions because we know we can bring in those type of tools. If you're in some jungle-dense area where multi-spectral imagery is out then the company hopefully has geophysics. If they don't, then that's out for us. There's not much we can do right now, so they'll rank lower. Even with all the things that meet our criteria, there's so much out there, we could do work for a thousand companies.
Peter Bell: This is it. 22 people may sound like a lot, but I am sure those people are more than busy. And that's the question of corporate leadership here too, yourself as president and CEO setting those priorities.
Denis Laviolette: That's really been the challenge. Scheduling. We've got tremendous amount of interest with lots of major companies coming to us who want to do something. We're looking at it, trying to juggle the bandwidth and. We can take a couple of major companies because they can bury us in data and work, and we could supplement that with a few junior companies where we're a little bit more nimble and a bit easier to work with but no less accurate. We can still add a lot of value there. Pound for pound, what's better for us? What's the better plan?
Now, we're trying to juggle a lot of that. We've grown our team to address some of that and we're figuring it out. We're filling it out. I'd love to be the person to say, we've got it all perfectly planned out but it's not like that. It's a business and sometimes you have to throw yourself into the deep end and try to swim.
Peter Bell: Sounds like a barbell strategy a bit, too. The stuff with the majors there to keep bringing some revenue and then some of these targeted investments with juniors. If that doesn't just provide a wild upside for Goldspot, I don't know what would.
Denis Laviolette: If we could just keep our G&A offset totally by major contracts and utilize that capital to put to work in the juniors for royalties and start building an equity basket, then that's really what it's all about. It will be just a tight-rope walk as we grow – how much can we do? And right now we're noticing that the strength of our team is the fact that our geophysicists, structural geologists, our geochemists, and our resource people are all working together. And I'm not trying to cap-out our business and say there's a limiting factor where the bottleneck is collaboration or that we have some magic number where we cannot grow past that. I think we can grow, but I'm very cognizant that a lot of our advantage is taking this multidisciplinary approach. Having all of these disciplines under one roof is very important.
Peter Bell: It very well may be that the bottleneck is the people or even collaboration, however you measure that. The future of this company may have some kind of a modular shape to it where some of these people who were involved in some of these early campaigns get involved leading other groups within the company, maybe with a regional focus or client-specific groups. There are quite a broad range of possibilities here in terms of organizational structure for Goldspot.
Denis Laviolette: It's ever-evolving, right? You want to departmentalize and compartmentalize, but you don't want to lose that advantage of having that multi-disciplinary cross-communication between groups. Anybody that says they can predict this about their business and say exactly how it's going to play out is wrong. As we grow, it's all a process. You're cognizant of it and you cross the bridge when you get to it. We've done that since the start.
Peter Bell: Wonderful. Congratulations. I wonder if there are any deals with juniors that have been disclosed at all yet?
Denis Laviolette: No. There are a few in the pipe that will, hopefully, be disclosed soon but nothing at the moment yet. Stay tuned.
Peter Bell: Roger. Thank you. The market will surely be puzzled over what to make of all this. Who was associated with the launch – you mentioned Triple Flag, but is it an RTO, IPO? What's happening there?
Denis Laviolette: It's an RTO with the CPC called Duckworth, which is a capital pool company. They had some cash. We chose this path thinking that it was a quicker and easier way to be public. Inadvertently, it ended up being the same as probably a traditional IPO. I think we've demystified the allure of choosing the RTO path over the IPO path. I think it's six to one, half-dozen to the other. In the future, anybody looking to take their idea public would be wary of that. I think everybody involved has been absolutely great, but I think that there's just as much molasses to swim through with the RTO processes as the IPO process.
Peter Bell: With the crowdfunding and everything going on out there now, it would seem to be a changing landscape to some degree.
Denis Laviolette: I hope to see that come into the mining space. I know a few different groups tried, right. Red Cloud tried to launch some sort of crowd funding platform.
Peter Bell: It's one thing for getting distribution numbers up, but it doesn't really help with the big money raise. That's where cornerstone investors like Triple Flag for you was pivotal.
Denis Laviolette: And the regulators, I think, need to wake up to a lot of the red tape around funds’ involvement. It's getting very difficult for funds to invest in a deal like us. We had to go through some fairly unconventional channels for capital. The retail audience with their ability to invest in deals like ours, they're just not really dialed-into that yet. This is a growth-pain period. I think we're going to see a huge shift, as we talked about earlier. I didn't know anything about crypto or blockchain. I'll be the first person to admit that, but I got my education on YouTube.
I went to YouTube and I watched some videos and I was like “Okay, now I get it.” That's where people are going to get educated. That's where people are going to consume information. Like I said, I have a broker, but very few people I know have brokers. A lot of people do their trading on their own. The traditional way of private placements and all of that is a little bit flawed. We've got to reinvent something there. How are you going to get accredited investors? There's a lot of people out there that are accredited investors, but they don't know it. And a lot of people don't understand these mechanisms of how to play all that.
It's going to be a really interesting time for the mining community, overall. I think we've got a whole new audience to captivate. Hopefully stories like AI and Goldspot allow for a new generation of investor to get sharp in the tooth on mining deals and exploration. I hope they get an appetite for it. Look at what Steve de Jong is building wit Vrify. Have you seen that?
Peter Bell: Yes, I'm a big fan. Very important.
Denis Laviolette: It's great. Everybody's got to drink the Kool-Aid and then, eventually, the new mining investor is not going to be looking at a cross section that they don't understand in a press release with a whole bunch of tables for drill results that they can't spatially visualize. There's all this nonsense that is not designed for the modern day investor at all. The new generation that wants to watch YouTube videos and understand things better. I did a Palisade Radio interview today with Kareem earlier and we were talking a little bit about this. I said to him, what’s the number of people that will listen to this interview? Maybe 1000? Maybe 2,000? Put Eric Sprott or Frank Holmes on Palisade Radio and they may get 5,000 views, which is pretty good for a mining show on YouTube. If you look at some guy that's YouTubing about cryptocurrencies or medical marijuana, you'll see hundreds of thousands or millions of views. We scratch our heads and wonder why is gold at a bottom? Why are mining companies so under-valued? For one thing, it’s a horrible business in terms of our ability to make money. For another, we are not captivating anybody with this.
Peter Bell: Same as it ever was! The crowd will come. It does what it does, right? It chases whatever's hot. Mining will have a time again.
Denis Laviolette: And I hope it's Goldspot. I hope Goldspot is going to come in and play Mining Money Ball, like the movie. Just completely dominate and shoot the lights out in the space. And if that helps people go on and invest on their own in individual deals, then that'll be good. I think it'll take something to attract new investors.
Peter Bell: I agree. And I've been watching for leaders and groups that could be leaders in a run. I think about the business you've set up here and am imagining three successes in 18 months. Or hopefully less time! Imagine three successes in six months – the first six months of being public. That would just surprise everyone, I think.
Denis Laviolette: Totally. Even then, we'll always have the skeptics. They'll sa that AI resulted in a discovery, but how much is really there? “It's not a resource yet.” What's that royalty worth? “The royalty portfolio is worth nothing.” There will always be so much mud that we're going to have to wade through to get to the other end, but I definitely think that a few discoveries will be transformative for our value. I just think that it'll take some serious time before everyone really turns around and respects it.
I didn't do that much road showing to be fair as this whole RTO process took a very long time to finance, but it wasn't because of lack of interest. It was just because of some of the groups had a lot of due diligence to work through. That's why such a long delay from when we announced a process to when we closed. On that path, I was meeting with people and who were saying, “This is great, but what are you worth? You're a technology company – what's your recurring revenue? What's your this, what's your that?” They were trying to hang us up against other models that they know, which is natural. I was an analyst, too – that's what you do. And when you can't hang up the company against something else it's not worth anything! It's not worth anything until it's worth everything. Everybody was scratching their heads about Facebook, right? Facebook was nothing until it was worth everything. That's the truth. Amazon, it's the same. So is Netflix.
Peter Bell: That's it. And the change is accelerating – all these things that we know for our time today in the world. Amazing.
Denis Laviolette: You wake up and blink – it might be too late, right? The way I look at it is simple, and I know for sure that a lot of our shareholders are in the same boat – we know AI is coming into the space. We know for sure AI and machine learning is going to turn the space on its head. Right now, Goldspot is three years at it. We've got 22 people, 9 PhDs, and we're dedicated to ushering in part of this change. If you believe that AI will come into the space, then there's nobody else to bet on. We're the first movers. That's what keeps me going. I sleep easy at night knowing that we're working on something. We're working really hard. There's going to be some people that are going to poo poo us – it's going to happen – but hopefully the proof is in the pudding. And the numbers speak for themselves. We can show the market what we're doing now and we've got the money to continue with that.
Peter Bell: Denis Laviolette, CEO Goldspot Discoveries Inc. Thanks very much for talking to me today.
Denis Laviolette: Thank you very, very much for having me. It's been a treat.
Peter Bell: I look forward to next time. Goodbye.
Again, it is my pleasure to share an introductory conversation with Mr. Denis Laviolette, President and CEO of Goldspot Discoveries Inc. Please note, this is not sponsored content. Learn more on the company's website here, https://goldspot.ca