OneSoft Solutions Inc. (OSS) Earnings Call Transcript & Summary

Greetings and welcome to the OneSoft Solutions Inc. webinar. It is now my pleasure to introduce your host, Mr. Brandon Taylor, President and COO. Thank you. Mr. Taylor, you may begin.

Thank you. Good afternoon, everybody. I'm Brandon Taylor. I'm the President and CEO of OneSoft Solutions. We are a SaaS software company focused on recurring revenue. Our solution, Cognitive Integrity Management, or CIM, as you'll hear me refer to it as, is a mission-critical enterprise-level solution for oil and gas pipeline operators. It's been validated and deployed by our clients who are Fortune 500, 100, and in one case, a super major. We now have over 51,000 miles under subscription, roughly 246 integrity engineers, user solution daily as our primary solution for work. Our mission is to predict pipeline failures, save lives and protect the environment with the assistance of data science and machine learning. We're playing a pivotal role in the digital transformation that is currently underway in this industry today. And key takeaways on this slide, specifically, OneSoft has a $50.6 million market cap, super-strong balance sheet with 0 debt and $9.7 million in cash with high insider ownership of 34%. For those who've been following the company, please bear with me if you've heard parts of our presentation before, but I will be some new developments as we go through it for new followers who may not be familiar with OneSoft. Welcome. OneSoft has been listed on the TSXV for over 21 years with lots of history of success. I would encourage you to visit our website to view that chronology. But most importantly, our management team has been working together since 2004, and we've been working with Microsoft for over 18 years. In the fall of '15, OneSoft was bucket as one of 9 companies to participate in their very first machine learning and data science accelerator or incubator. And since launching, we continue to collaborate with their industry team on strategic initiatives and work every deal with their field sales teams around the world. The relationship is now stronger than ever. In fact, independently this year, just recently, we are awarded the Partner of the Year finalist to runner up for Application Innovation on Azure. Now that's an award that is worldwide, and it really speaks to the volume of the solution that we have in place, and we believe we have a very significant competitive moat with no other machine learning solution and commercialized to date, and we don't really see anybody in our rearview mirror. PHMSA who sits under Department of Transportation, Stanford Pipeline Hazards Safety Materials Act, is a U.S. regulator that regulates oil and gas pipeline operators today, and they require them to report data to them on an annualized basis. And the report shows some very discouraging metrics over the last 20 years. On average, a pipeline incident occurs somewhere in the United States every little over 1.5 days, 1.8 in '18, 1.7 in '17 that leads to injuries and fatalities. And the cost can be very significant with 2 of the major failures over the last 20 years, over $1 billion each. We believe that the industry -- the only possible way to stop these failures is specifically through data science and machine learning. Today, the industry has been asking operators to collect data with the tool called Pipeline Inspection Gauge or PIG. You see that in the left of this slide. Operators, this tool has odometer wheels on it, an array of sensors based on the technology, whether it's magnetic or UT, there's different tool vendors that make these hardware devices. The operator, pipeline operator will contract with these, they insert it into launch valve. The product actually pushes it down the pipe and then they take it out and aggregate this data. If you're a liquid operator within the U.S., you have to run these devices on your pipeline every 5 and gas every 7. After they get this data, they deliver to the operator an excel sheet. And typically, engineers will then take that excel sheet, look when they previously ran this. And compare these, and they're looking for anomaly growth rate. So they're manually trying to or macros within excel, compare these numbers. It's very inefficient, it's very costly. Most importantly, they filter out about 95% of the data, just to give you some sense for our customers, including super major running this process using Microsoft Excel. Just to talk about how this is done within our solution, I want to give you a little background. I have a demo to basically walk you through that. But following the accelerator, we engaged with Phillips 66, who was our first beta client. They gave us a bunch of their data to analyze, very progressive company. And we -- they've built a system in-house over the last 16 years that managed most of this process, except they didn't have any machine learning or data science. When they gave us the data in 2016, they went -- did excavations, prove our results, went live with us in January where they give us 10,000 miles of their pipe in January of '17. And then through that year, understanding what our solution did. They actually transferred their IP, their intellectual property from their on-premise solution to us, gave us funding. And with Microsoft, we move that to Microsoft's Cloud, gauged a couple of other pipeline operators in early '18 to make sure that what we built was both horizontal and wide enough across the entire industry, so we could scale it. We did that and launched that solution, which is, what we call, Cognitive Integrity Management [ 300 and Lake '18 ], and that's the version that we have continually iterated on since then. Want to play a little bit of a demo and walk you through what this does. So this is the solution. Operators only need a browser. You can see that we have a bunch of power BI, dashboards and heat maps. It shows you all kinds of different metrics related to the entire pipeline system from an operator, where it is? You can actually do what Microsoft calls natural Q&A. So you can ask as long as the data is in there, any type of questions, how many anomalies are growing at 50% and it handles the whole planning process, so you can actually plan all of your tool runs that you're going to have to do over those 5- or 7-year periods. And then you can literally just drag and drop files right into the solution. So whether that's the preliminary tool run or the final. Once you drop it in, all of our algorithms will run automatically. So it kind of shows the depth. And then most importantly, after those have dropped in and we've adjusted and normalized data, you can go and do what we call integrity compliant, which is basically our -- my regulatory compliant immediate 60-day, 180-day, industry specific, we share these conditions across all of our customers. So it's kind of best practice within the industry and the company specific. And then finally, our algorithm can run. And they'll know this within minutes. So from the time that they get that tool run, they'll know which conditions or potential threats on their pipeline and where they're in regulatory compliance. And we have a bunch of viewers and different kind of analyses that they can do within the system to basically do true engineering versus just data manipulation. So it shows them where that threat is and they call these HCAs or high consequence areas. And you can actually see different patterns based on what we're showing. So you can see these patterns, you would never see in Excel. So they're able to analyze this low-level corrosion where this joint pipe is and they haven't been able to do that before. So that kind of gives you a sense on what the solution does, and it continues to evolve every week. As Microsoft determined prior to us -- inviting us to participate in their accelerator, they quickly identified that this is actually a $1 billion opportunity before we knew that it was. If you look at the U.S. market, there's roughly about $637 million spent on an evaluation. That's our sweet spot that green piece of the pie. Some portion of inspection, some on maintenance, globally the $1.1 billion opportunity just on the green slides, and we'll talk a little more about this as we drill into it. Our initial focus is on U.S. specific, lots of regulations and intentional, tightly regulated environment. There's roughly 2.7 million miles of pipe in the U.S., 600-and-so and our immediate sweet spot that has this in-line inspection of the data that comes from that tool. Our current focus is at the beginning of this year. We have early adopters, those on what we would call the left side of the chasm. In March, we started to formalize that. We believe now our solution is wide enough and deep enough that basically, it's going to be hard-pressed to have new operators come on and not -- and we not have the functionality and the solution that's needed. So we've actually started in March of this year, formalizing the commercialization of CIM. And most importantly, sign new customers, sign them up quickly and onboard them quickly. I'll talk about this in the next slide. But in March, we hired a new VP of sales to start that evolution process. And we're actively working with Microsoft continuously on deals. Our partner model is working pretty well, in fact that we're actually looking in Australia and a whole bunch of global opportunities through partners now. And our consumption economic model, this year, we're actually doing more. We build crack facilities to be able to run crack tools, and we're actually getting additional revenue through those, and we expect to have more of those as we go. We just started a proof-of-concept about bringing on their data sets in. So as you can anticipate, they run these tools, they do repairs, they have tight property data. And then there's a bunch of other data sets out there that we're now starting to adjust and starting to experiment on data science projects related to if their correlation around soil, depth to cover the pipe, those types of things. So all those things are happening now. And then most importantly, PHMSA is enacting new regulations as the digital transformation occurs as data management. They want to make sure you can do analytics, is your data correct, was the tool good? And all of that is basically being tracked within our opportunities. We have roughly over 300,000 miles identified in the sales pipeline. Actively, we have production trials on the [indiscernible] And the next slide on what we've done to basically optimize and make this more [ Donald style ] kind of commercialization process. So let's talk about that slide a little bit. So for new for 2020, again, I mentioned this early adopter. We've moved more into commercialization. So we've hired a new VP started in March, new VP of Sales. COVID has had an impact as well as to the oil in the February, March, April time frame. What we wanted to be to make sure is we are in a position as we came out of that, which we've since done, so we've hired type of Tech Sales Manager, Lead Gen team. And we've adopted a whole new strategy as far as what we'll call the evolution to commercialize this at scale. And 2 main components there that we're driving towards is a production trial, and we've really got that down pretty pat. And as part of that, a key component is cost savings, both explicit and implicit. The way that process works is, we have an initial meeting from there. We can give an NDA sign, a production trial, and they will deliver data to us. One week later, we schedule a meeting where we do a kickoff, and this is where the prospect would take us through their existing process. Whether that's excel-based or some other type of system. Then following week after that, we basically show them results, which include our cost-saving model and their data in our solution. So from start of that process [indiscernible], it's about a 3-week cycle. The cost savings is really important. I'm going to talk about that in the next slide. But what we've really done is standardized and made that so we can repeat and scale that process across. And the Lead Gen, super important, we actually have now got to the point where we're averaging almost 2 met first, what we call first meetings. So we're filling that top of the funnel and making sure that we can basically bring that down as we go through. And we want to talk about all of the things that are happening globally as well. So we have evolution on not only the Microsoft relationships but partners, very active in Latin America, Argentina, Australia. So we're moving in those directions, which is driving us to metric potential language, all of those normal things, so we can scale this globally. Our cost savings and business value. I'm not going to talk about the specific each one of these buckets, they apply to CIM, in general, on where operators can save money. I think the main primary categories that we want to talk about is that there hasn't been a production trial where we haven't be able to take each one of these buckets and whether you mix and match them, there's not cost savings. So integrity compliance is an example, is where our solution once we love their data, we'll have identified threats that they may not know about. And we communicate to those, and we assign a value to that based on PHMSA's average cost of an event. Dig Management is the same thing. We'll identify digs, though have tell us which digs they've done and CIM would have not suggested those digs. So we can save them money on where they wouldn't have had it on a dig. And then we've even gone further, if you look at our repair fraction, there's links in this PowerPoint, if you want to download it, where we've shown mathematically that basically we can reduce that's doing the right dig, and we can show it did do the right dig. So every 1% that we show them the reduction in that ratio is a 1% improvement into their dig budget. And just to give you some sense of the numbers, we'll flush these out. We've done averages across our customers, but we've seen production trials for [ Syncon ] and save them $1.5 million to $6.5 million on an annualized basis. And then now they go do SME work to validate these finding. We'll go to the next slide now. So really our focus, and we're communicating this more in our MD&A, is that on this dotted line, for us, this has been ARR. We left the green line on this slide because we believe that over time, we'll continue to have these one-off revenue hits, but our real focus on a day-to-day and everything that we drive from an operation's perspective is to try to drive that number. We're really trying to basically double our CIM ARR year-over-year. And we announced a few weeks ago and our last thing is that, we've got our innovation lab up to speed. So you've seen that release where we're basically trying to fast-track the identification development and new prototype solutions. And as we do that, we're specifically focused on those projects that can increase our TAM as well as immediately drive CIM ARR. So we'll do those kind of projects, so we can actually get driving additional CIM ARR, so we can basically focus on this line. And you can see the numbers, and we'll go over these too much. You can have slides those and if you have questions, we can talk about those. I mentioned Innovation lab. We basically established this, this year. And we wanted to make sure that we could focus that and identify that existing, as you can anticipate, we're new into these operators and not only just what we show, but there's all kinds of other, what we call white space, and we wanted to be able to set structure up and not disrupt our CIM ARR business. So we established Innovation labs, so we can make sure that we keep aware and keep comprise of new opportunities that could then further increase both TAM outside of oil and gas, so water sewer. And additionally, within CIM. So different data sets, different technology, all of those types of things. We did set that up, and we have multiple projects underway, and we're seeing lots of good traction related to nonpiggable pipe. And COVID, unfortunately, caused that a little bit, but that will get started back up. We get tool runs going where we can bring that data in, start analyzing it and different partnerships that we see within this space here as far as what that TAM looks like. Investment highlights. Just to talk about this first-mover advantage. I go to conferences, we look at competitors. We definitely have a first-mover advantage. We feel just from cloud alone. We probably have a couple from picking up Phillips' IP, 3 to 5 years to 16 years to build that. We are ingesting this data, and we're looking at DaaS kind of model, so Data-as-a-Service. Today, we have over 60 million pipe features. We've iterated on our algorithms over 65 times. So lots of opportunities and patterns that we'll start identifying that other operators or that just in regulatory environment aren't even known because there's no data at this volume. So we're sharing that learning across all of the operators. We started with our conditions a couple of years ago. We had 95, have over 200 conditions that are shared across all of our operators. So it's super important to see that. And we're less -- being less concerned on cloud now that more than ever. I mean there's a definite trend on digital transformation. Super high operating margin from a SaaS perspective, our models show greater than 50% up at a $10 million revenue. And we've got partnerships that come into us quite frequently, somewhat very encouraging. We had a very clean cap structure. And the management is aligned with the shareholders. Insiders own 34% and 23 of it is owned by management. So hopefully, that gives you a sense. We've left 5 minutes here on for Q&A. And I've got that open. If we do have questions, feel free to post them, and I'll respond to them in 4 minutes.

I had an e-mail from Dan, yes. We -- our PowerPoint, we can send you that, Dan, I've got your note on that. Any other questions? Doubling of rev does -- the question is, the doubling of the revenue target, does include the deferred revenue recognition? No. Our doubling the revenue, like when we get to do that is a little more challenging. It depends. Some of that is that the operators description. Is those -- the people that do follow it, typically will sign multiyear contracts. And at the time, they typically will pay us upfront and then we start onboarding them. We have spent the last year working tremendously on our onboarding, and we're hoping at a large operator. Our next kind of engagement will do a 2- to 3-month thing. But we'll get the cash, the revenue will start and our licensing model from the subscription component. We can recognize that, but there's onetime fees that we have to wait until we onboard. And then there's the loading through the year. So our revenue objective is really to focus on not deferred, but just the recognized revenue deferred is not going to be the doubling. I think hopefully that answers the question. Another question is, working opportunities how far? These are in various stages. Historically, as we've moved through this commercialization strategy, we have really optimized that so that we can maybe start getting some guidance at future points when we feel comfortable about that, that process is working. But do we have prospects that are at the first meeting to -- in process of legal reviewing that NDA and the production trial agreement to some that have already given us data and we've done results. And once you've given results and just everybody understands in this industry, this is a very typical time and it's a budgeting cycle. So they're looking at these kind of things. And they're looking at bigger long-term strategic things that they're trying to do just around digital transformation, other projects, et cetera. So we're not giving any guidance on how many are -- of them are within each stage. Price per mile can be in 5 to 10 years. We got a lot of stuff going in innovation lab that suggests that, that is going to change. And what we don't know and we're not giving you guidance on, is going to be double, triple? Are there any things that we can? So we don't know yet. We know that our consumption economic model is incrementally increasing that per mileage that we can charge, and we have made some adjustments to our pricing model. We do have per log and per mile, and we've done some changes to that. So it's hard to say if we could get definitive on -- and we're trying to get that so people can model out what that per mile can be. But we have to be further along to be able to project that and answer that question. Next question is, has there been a lot of activism on Keystone, Excel and Enbridge? Actually, it's been very positive for our business because it basically means we're running on older pipes, and they have to keep them in business for longer. So we like that. That's where our value is, is based on these tool runs on the old pipe or discorrosion and stuff like that. So I think that's the end of the question and time, I should say, as well. So Keith, it's 11:25. I know you wanted to wrap up then. I think that will be -- Thank you, everybody.

This concludes today's webcast. You may disconnect your lines at this time. Thank you for your participation. Have a great day.