Converge Technology Solutions Corp. (CTS) Earnings Call Transcript & Summary

Good morning, everyone. We're going to get started in just a minute since we have people online waiting for the 8:30 start. So apologies for the slight delay. So good morning, everybody. Thank you for being here. Thank you for those of you listening online. Good afternoon to those of you overseas. My name is Lorne Gorber. I'm just quickly going to introduce our day. We have this great series, 2 great speakers this morning. We'll wrap up the morning after the 2 speakers with the Q&A. That will include Greg, Shaun Maine, Avjit as well. So really happy to have you all here. Before I turn it over to Greg, just to kick things off, famous slide, the disclaimer, everything we say about the past is true, everything we say about the future is up to you to decide and evaluate. So maybe without further ado, we're a couple of minutes behind here already. So I'll turn the podium over to Greg Berard, President, Global CEO.

All right. Am I up? All right. Good morning, everyone. Welcome to our first Coffee and Converge series. We appreciate you taking the time to be here. I know everybody is busy -- very excited for today. I won't talk long. I know you guys are here to listen to Chris and Shaun really talk what we do with our clients -- there we go, what we do with our clients and the high-value solutions we're driving on a day-to-day basis. For those that don't know me, my name is Greg Berard, Global President and CEO, here at Converge, started with Converge back in 2018, was the fifth acquisition and now 35 later. I'm in this role and excited about what's to come for Converge and our future. I just want to do quick introductions for the 2 presenters. So Shaun Bertrand here on the right, runs our cybersecurity practice. He will be the second presenter after Chris Foster, who's our CTO of our advanced analytics and artificial intelligence practice. So Excited to have them here today, both from recent acquisitions. Shaun joined us in March of 2021 through the CBI acquisition. And Chris joined us in October of 2022, through the Newcomp acquisition, which was the fourth analytics company we acquired. A lot of people have already asked me, are we doing these monthly? Are we going to do these quarterly? We don't know yet. We're going to do some surveys and get some feedback from you guys after this and then we'll make some adjustments to the agenda, and we'll focus on what you want to hear about. So more to come on that. We'll get through today and then let you guys decide how often you want to see us and whether we do them in person or remote. I'm going to spend a couple of minutes talking about the Converge strategy. And I think my first message to everybody in the room is our strategy has not changed that much, right? We've been acquisitive. We've made 35 acquisitions, but every time we acquired a company, our strategy was to leverage those clients and cross-sell into those accounts to gain more wallet share and drive more value with those customers. That hasn't changed. The only thing that's changed right now is we're not acquiring, and we're focused on organic growth in a couple of different ways, bringing in more sellers, driving more solutions, building new partnerships, building new solutions and continuing to have those high-value conversations with our clients. In terms of the transformation, right, we've seen it happen over the last couple of years. We're continuing to see it happen. We're continuing to have different conversations with the clients we acquired, and you'll hear a lot about that today. You'll hear about what we're really doing around AI. You'll hear about what cybersecurity has brought to the Converge company and what conversations we're having with our clients around cyber and how that aligns and ties to every other conversation we're having around application modernization, around cloud platforms. So there's a lot you'll hear about today and the overall strategy of us at the end of the day, being the end-to-end solution provider for our clients. And that's what makes us unique in the marketplace. There's no one out there that can do what we do and go as deep as we can across all those practices. A couple of points I want to make on this slide. You've seen this slide, I'm sure many times, but I think it's important to reiterate the structure we've built both from a sales perspective and practice perspective is what makes us different. When we acquire companies, we have integrated specifically in North America, every sales resource into the region where they belong. And we do that to ensure they maintain that client relationship. They're still the face to the client, but they can tap into everything we do as an organization. They can leverage folks like Chris and Shaun to go have those high-value conversations. And that's important. It makes us different in the marketplace. The second piece is on the right-hand side of the slide here, the amount of technical resources and the amount of partnerships we have allows us to be that end-to-end solution provider for our clients, right? We can go in there and have conversations and bring in the thought leaders and the technical resources that can truly have a thought leadership conversation. And the sellers can tap into all of that across North America and that's been very, very key. The other comment I'll make is around the partner relationships. We have 10, what we call our strategic partners, but that's at a global Converge perspective. Within each practice, and Chris and Shaun will talk more about that today, we have other strategic partners that we're partnering with on a day-in and day-out basis. And that's where we get into the technical solutions and capabilities that we bring to the table. When you think about this structure, and we've talked a lot about it and it's evolved, right? We launched the practice structure in February of 2020, right before COVID hit, but we've continued to make investments in these areas. We've continued to acquire companies to make sure that we can go deep and wide with our clients and have those conversations. We've continued to invest organically in technical resources to support the sellers. When we talk about regional presence and regional capabilities, our ability to put solution architects on the front end of a sales cycle in region with our account execs so they're in front of our customers, is a game changer for us. The other piece is when we talk about integration, I think I've already had 2 questions already on ERP today. When we talk about integration from a sales and services perspective, every technical resource in North America now resides in one of these practice areas. They're no longer part of the companies we've acquired. So from a people-integration perspective in North America, sales is in the regions they belong, technical resources are in the practices where they belong. And that helps us align with our clients. It helps us align with our vendors so that we can have that thought leadership conversation. The other comment I'll make on this slide is around partnerships. If I would have showed you this slide in 2020, and we have a different logo slide on what partners that we go to market with on a day-in and day-out basis. That's evolved over time, right? When you talk about analytics and AI, we wouldn't have shown Snowflake 3 years ago. We wouldn't have shown Tableau or Alteryx, that's evolved. Why? Because we want to stay at the forefront of the IT landscape and make sure that we're talking to our customers about what's important to them and what's important moving forward. Shaun will talk about Snowflake and Okta and other partnerships that we've developed based on the CBI acquisition in March of 2021. The key for us is to be vendor-agnostic and client-centric. We want to be able to make sure we understand what our clients want to do and we can build the right solution that makes sense for them. And that's going to be key, and you'll hear about some of that today. Two other points on this slide, and then I'll turn it over to the smart guys in the room. When we talk about AIM, you've heard us all talk about assess, implement and manage. Each practice area will continue to evolve and make sure they have the right partners, the right solutions and the right offerings that we can take to our customers to ensure we're on the front end of the sales cycle, helping them understand what they really want to accomplish. And then we have the skills internally to implement those technologies and manage it long term. That's a transformation. It's not going to happen overnight. It's continuing to evolve. We're continuing to build new offerings, and we'll get there. The last piece, which is critically important for us is as we've built up this foundation, our ability to bring thought leaders from across each of the practices is what our differentiator is. When you can put Shaun and Chris together in front of a customer and talk about how they want to build AI applications and wrap security around it, having one partner to do that is what our clients want. That's changed over the last 3 to 5 years. You've seen a lot of consolidation happen in the IT marketplace. They want one partner that they can trust, that they can rely on and that can build them a true solution end to end. And that's our goal as an organization. And I know standing up here today, we have the platform and the resources to be that end-to-end solution provider for our clients. So with that being said, I want to introduce Chris, our CTO of our advanced analytics and AI practice and have him talk about some real-world examples. So thanks again for the time today. Chris?

Thank you -- can everybody hear me? It's not on -- so yes, I'm Chris, I'm based in Calgary. And like Greg said, I'm the CTO of our AI and analytics practice. That means I get to talk to our vendors and decide where we're going to invest our time as far as how we go to market with who we hire, how we partner with those vendors, how they like to work with us and it's fun. I get to see all of our deployments across North America and decide what do we focus on. Now when they asked me to speak today, I wasn't actually really sure what to talk about. There's not a whole lot going on in the world of AI and analytics, right? Obviously, I'm kidding. It's wild. And to be in this role and have to stay on top of everything that's happening daily, hourly, the developments that are being made is just crazy. And a lot of the discourse out there is that this stuff is scary or it's going to save the world. So somewhere in between there lies the truth, and that's what I hope to go through with you guys today, help you understand where are we spending our time? Where are we investing? Where are we winning? Where are we learning, but ultimately, when you look at the market, everybody is a little bit scared. And I think there's a lot of discourse out there that says, just shut down AI. And that is not the case. And I'll explain why that's not our perspective. If you follow this, you guys probably know about this letter. This was signed by I guess, almost 35,000 AI researchers and scientists now that basically says we need to pause development. This stuff is moving too fast. It's dangerous, we don't have enough governance around it, we can't control it. They're kind of treating it like a nuclear weapon. And so is everybody familiar -- does anybody know this name on the bottom right-hand corner here? I'll know who's a nerd. Okay. He's a -- if you think of a Venn diagram of a philosopher and a computer science kind of guy, it's kind of the forefront of all of the AI discourse. And basically, he said it's not likely that this stuff is going to kill us. It's the most likely outcome. If we just continue on the path that we're on, he wouldn't sign this letter. He said it wasn't restrictive enough. So this is the world that we're operating in right now. And I'm going to spend the next 20 minutes helping you understand where the real business opportunity is in this. Is it ChatGPT? Sure. That's part of it. Has AI been around since the '50s? Definitely. And so this isn't new, just the language around it is new. It has exploded though, and this is what we're dealing with every day. So in my job, it's too small. And I made a point so -- you can't read this. But these are all the AI vendors out there. And this is what we have to stay on top of, and this changes every day, right? They bubble together, some of them grow, some of them disappear. And so where do we place our bets? And that's not just a Converge question that -- all our clients are having to deal with this as well, right? And so we're spending a lot of our time just being MythBusters and saying, "Okay, let's cut through all the LinkedIn posts about how this is the best and newest and greatest and we do something nobody else does," and this is what I spend my day doing is going through this so that they don't have to. When we talk to our clients, there's a couple of themes that emerge. So I'm going to try and go from here to something a little bit more focused here. There's a couple of trends that emerge. This is really where we're spending most of our time. In fact, it's really in these 4 categories. So designing for AI, this is relatively new. This design thinking concept of how does AI actually fit into the real world. When I put this into my dad's hands, what happens? How does their behavior change, right? How do we ensure that whatever decision we're telling them to make -- if we're telling them to turn left, how do we know that, that is going to lead him somewhere safe? And so we sit with our clients, and we do design thinking sessions. We sit with them and say, "Okay, if you infuse AI into your HR application, what does that actually mean culturally? How do we need to train people? How is this going to integrate with what they're used to doing?" We talk about synthetic data. Okay, this is where large language models and generative AI live. We've actually been synthesizing data for a long time, right? So if we need to generate our own test data, need to generate an image or a forecast or something. We're using historical things that we've trained a model on to generate something into the future, right? But now that we have so much computing power at our disposal, we can generate a lot faster. That's all ChatGPT is, right? That's all large language models are. All they're good at doing is forecasting the next word in a sentence. That's it, right? That's as powerful as these things get. Digital twins. Anybody familiar with this term? Yes, especially if you -- here we go. Especially if you live in the manufacturing space, right? So instead of doing like destructive or nondestructive testing on a turbine, for example. We create a digital twin of that physical entity and we can perform tests on it, right? We've collected enough data off of the 150 sensors that sit on a piece of equipment where we know what happens when conditions change, if vibrations increase or temperature goes up, and we can test things in the digital world as opposed to doing it in the real world. All of you have a digital twin as well. You know this because any time you go on Instagram or you see an ad, they follow you. They know your profile. Google Maps knows so much about you that they can actually draw a heat map of the path that you're going to take on any given day, we're predictable creatures, right? So each of us have our own digital twin. And optimization, this is a problem. This is something that we've been using AI and just analytics for, in general, right? This is linear algebra, so saying, we're going to optimize for some specific outcome, whether that's safety or production or throughput. But again, now that we have so much computing power, we can optimize in real time. So this is we're spending a lot of time speaking with our clients about and that puts us in this role where we're not just AI evangelists or developers or coders, we actually sit with them and we close the doors and put the blinds down and they say, "Okay, what is this really? How are we actually going to use it?" And so we end up in this trusted advisor role, and we're thinking a lot about that because we end up spending our time with not just the technical team. In fact, it's rarely the technical team, right? Use cases for analytics originate in the business or they say we have to tighten something up or reduce risk or stop accidents from happening or increase safety on our well sites. And so we've thought about each of those personas and we're really organized. When we go into a meeting and we know who we're meeting with, we have a little playbook knowing what that person is actually caring about, right? So whether it's somebody in sales and marketing or an executive, what is their perspective? And how are they actually going to use AI and care about it. We actually can't be everywhere at once, although I wish we could. So we need to teach our clients to go evangelize and build an AI culture themselves, right? So they have to educate their staff on how to use AI in a healthy way and in a productive way. They need to be nurturing and teaching people and communicating an AI strategy among their executive team or to their Board and their shareholders. We just helped a company hire an HR Director, and she was saying the first question she got asked is, how are they going to use AI in HR. And she doesn't come from an AI background, right? She comes from a human resources background. And so these people in these roles that aren't traditionally tied to AI or analytics, they need our help. And so that has nothing to do with our software development skills or our math skills. We need to be able to coach her on how to -- what are people expecting now? What does a new grad expect from an HR department when it comes to technology. And so what do we do? Maybe we build a chat interface for an employee handbook, right? So something we're working on and are testing internally. So we're trying to come up with real practical ways of doing this in departments. This is how we build trust our clients so that each of them don't have to fail and trip and fall themselves. They can learn from what everybody else has done. So we gather this whole treasure chest of things that you should do and shouldn't do. And so we're warning. This is how we fulfill our role as an advisor, we say, "Watch out, when you're building these things, there's some risk behind it. This doesn't mean that you shouldn't do it. It just means that what are you able to tolerate?" So we give them a whole book of things to watch for and in this case, this allows them just to move forward and know that we've thought about some of these traps on the sides. We obviously have to build a team to go do all of these things. And so I tried to distill all of our capabilities down to a couple of categories for you. How did I do? 3 is pretty good, I guess. So we've always had to go and get data from where it lives and put it somewhere -- none of that -- that has never been more important than it is now, to get all the data in one place because the wider the data set we have, the better we are at predicting what's going to happen, right? So we can use that very wide data set for more advanced analytics. People want to see and interact with data. So data doesn't just live in a database. That's not its final home. We need to put it in the hands of somebody who can actually make a decision with it, right, or look at a trend, identify a pattern. Data means nothing until you can take an action on it, right? And so our job is to put that in front of people in a way that they understand it. And then obviously, we want to use all that historical information to go look for patterns and predict the future. And this is where our AI and data science team comes in. I figured I might as well splash it all up there. What you don't see here are all the skills that wrap around this, right? So software development, for example, things that weren't -- maybe weren't traditionally data and analytic skills as you know -- you needed database skills. But now we actually end up building applications a lot of the time. So if anybody has any questions in the follow-up here about some of the tangential skills we've had to build to go support a traditional analytics organization, let me know because that has changed over time. Now, we can't do everything for everybody. And so we really need to decide where to focus. And so Converge, we've decided we want to focus on this part of computing, which we call cognitive computing, essentially, things that humans do, right? Seeing, hearing, interpreting, translating, searching. And we're trying to use our data powers to go enhance or augment the human experience. And so this is the subset of AI that we really focus on. And when I talk about our use cases and success stories in a little bit, they're going to be focused around these things, okay? I was alluding to it a little bit earlier, but applications are a great way to deploy AI capabilities. So not everybody sits there and coding Python to do AI, right? In fact, very rarely is that how AI is deployed. And so we need to wrap it into an application, whether we embed that into Salesforce or into the ERP or we build a specific application. So like here, for a bank, we built this deep learning model where it kind of does this PDF search, and it does -- builds a graph in the background and it connects all these documents together for this real holistic view of deals in this case. For manufacturing, if you think about what it would take to go and gather data -- how to manufacture, it's actually complicated, right? Every piece of equipment has a sensor on it. Those sensors might belong to Siemens or some other building management system. It's on a completely -- the HVAC and systems are all on separate automation controls. We need to bring that all into one place. So before we even start talking about how to optimize production and throughput and safety, there's a lot of plumbing that needs to be done. And so I'll talk about this a little bit in a second, but AI for us is just -- is the piece that you see. All of the rest of the plumbing and engineering that has to happen is a huge effort. And so what we're trying to do is abstract a lot of that work from our clients and say, "No problem. Let's talk about the business use of AI." And then we've got a blueprint for going and doing all the plumbing work and these repeatable patterns per cloud. So if it's on Azure, Google or AWS, we've got a pattern to follow. So we don't have to invent these things from scratch every time. We also can't -- we also have to think about how we actually deliver these things and implement them, right? And so -- we've got a couple of services patterns that have emerged. This speaks back to our AIM strategy. So whether we are advising up in the top right-hand corner where we're sitting alongside our clients and doing road maps and that sort of thing. And I don't mean like big fluffy consulting road maps. Usually, our road maps are like 3 pages long. It's very specific about what we should do. And then we do our implementations. Obviously, most of the time, we're sitting beside our clients, everybody is hiring up their own AI and engineering and software developers. There's not -- they can't move fast enough. So it's often this hybrid approach where we sit beside their developers, and we're building and teaching them and learning together. And then our managed analytics practice, so where a group just says, "You know what, you guys -- we don't have the analytic skills or we can't staff up quick enough. You guys be our analytics department for the time being." So those are great ways for us to work with or plug into a client regardless of where they are in their journey. Let's talk strategy for a little bit. I think it's important -- I might as well start with this, who do we compete with? We've run up against the systems integrators more often than not. They tend to have good executive relationships so that maybe they have done some of those 3-ring binder strategies, the ones that are 1,000 pages long, and they've kind of been embedded organizations forever. And so I think it's easy for clients to say, "Yes, you guys are already here, you might as well try this data warehouse project or this AI project." But they end up having -- you guys know this. They hire junior resources. They build them at really high rates. And so we often get the opportunity to swoop in after and say, "Okay, we're much more efficient. We've got real experience. Those guys -- they tried, but they can stick to what they do best." So we come up against them. Regionally, we compete. There's a lot of startups, a lot of like government-funded or provincially funded regionals that we'll bump up against. These are 30-, 40-person boutiques. And so when -- so I came from the Newcomp Analytics acquisition. So this used to be my life, right, because we were less than 100 people. Now we've got much broader shoulders to stand on. And so they might be really good at getting an AI project started, but if it's a cybersecurity question or somebody says, "What's the best AWS pattern to follow here? And how do we manage our costs on the cloud? How do we embed this into an application?" They don't have those skills and we didn't have those skills anymore. So for me, it's been really great because now it's not -- we don't have to compete at all. We have the full story, which is great. And often, our biggest competitor is doing nothing, much unlike cybersecurity, where you can't do nothing in AI, you can, sometimes it's still seen as a luxury, right? And so this ends up being the biggest competitor. Our statement of work is up against nothing. And so they have to decide, is this a cost center thing that I want to take on or we're willing to take on. I think often, they will need a strategy and a road map. So we're doing a lot of those kind of engagements. But yes, often you have to convince people just to move. And I don't know if that's surprising to hear or not. It is for me every time I'm like, how is this an option? But it's a good spot to be in because they think of us when they come back and they're ready. So Greg had this up here. I'm sure everybody's kind of familiar with how we think about our practices in this kind of traditional way, right? So advanced analytics, this used to be my whole world. We've got these 7 practices. We've always kind of -- I have always kind of thought about them as independent pillars of Converge, right? But I think what we have the opportunity to do is -- especially let's think about AI, in terms of AI. There's a lot of overlap in terms of what our salespeople are going to be asked. Shaun, you know your salespeople are going to be asked, how is AI infused into cybersecurity? And these are like problems where we have a lot of overlap, and we can reuse resources. We can invite people to the same events and workshops. And so we're working top to bottom in our organization. So from our executive team, to our developers that are on the streets, just making sure we have repeatable motions. When we talk to a vendor like Dell, somebody I would have never talked to before, right, who now is asking, will you guys partner with us for AI? I need to say the same things to that partner that my colleagues in the infrastructure business are, that Shaun will be talking to them about. So we really need to standardize a lot of this. And then across our practices, we need to make sure that our teams are collaborating. So our presales folks, who are doing demos and doing architecture and solution design that were saying the same things that we are bundling things together. So right now, in the analytics practice, we always bundled together some DevOps work, which is traditionally a software development kind of thing. We bundle in cloud services, which comes from a different practice and so we're appending all of these things that have immediate lift. So instead of thinking about them side by side like this, my view is that Converge is this vertically integrated group of practices where we have clients, these are real humans who work on laptops and with workplace tools like Teams and Office, those applications are increasingly powered by AI and data. All of that sits on cloud infrastructure that we can deploy and support and engineer. Shaun and his team wrap security around it, and all of that sits -- could sit on public, private or hybrid infrastructure that we deploy and support, right? So to me, this is awesome because my story used to stop at data and AI, and I would need to phone a friend to say, "Oh, I don't know. I need somebody to come to do some Google BigQuery development", because that's not our world. But now it is, which is awesome. This might be a little self-centered. But I think we're looking for opportunities for data and AI to be infused into the rest of our practices, right? And so I've been spending a lot of time with Shaun and his colleagues, these people who are running the practices day by day looking for opportunities. So if I talk to our workplace team, yes, of course, they're talking about Microsoft Copilot, right? It's AI sitting next to teams and helping people organize documents and search. So we need -- data and AI needs to be a service to the rest of our organization. And we think sales-wise, this is an easy append into a lot of these accounts, right, where we've got cloud business or we've got digital workplace, productivity tools business or security or -- we can automatically talk to them about AI. And so not only am I spending my time talking to clients about AI, but I'm spending time talking internally to our group about it as well and all the opportunities there are. And you guys can read. These are all real things that we can go talk to a client about today. We thought a lot about how do we do that cross-selling. So we've got a lot of these motions. We did a road show. We're doing a regional roadshow with all of our salespeople, helping them understand how to have this conversation, so they might come from an infrastructure background. How do you talk to a client about analytics and AI? And so we're teaching them to do that. We're doing like mock selling sessions and having them really practice this messaging because it's really important. There's hundreds of them and one of me. So that's how we scale. Now, don't squint too hard. This is real life. So I put this up here kind of as a joke, but this is Microsoft's architecture for easy AI. So you can imagine, I don't know how many services are on there, but there's a dozen plus, right? It is not easy, is the point. And so you can't just go into Azure and say, spin up my AI studio, you can. But it goes this deep, right? To deploy AI in an organization and get it into operations takes a lot of this. There's containers and security and IoT connectors, Power BI, which is where the users might consume it, right? So we are trying to come up with a lot of these patterns, so we can walk into a client and say, "Oh, you're talking about retail -- Customer 360, we've got a pattern to follow for that. Here you go. And here's how we plug it into your CRM, your retail system, all of the sensors that are in your stores, so that you can see the movement of pair of pants as somebody tries it on. We know all of those things." And so that -- this is how we're -- we don't want to do things bespoke every single time. That's a really expensive way to do professional consulting, right? So we bundle a lot of that up and turned it into an offering -- how am I doing on time? Anybody watching? Okay. All right. We're trying to bundle these up. So I'll talk about the walled garden, for example, right? So we were just talking about it this morning. Does everybody just use public versions of ChatGPT, that's not really acceptable for most organizations, right? So we're spending time -- we've got a bundle that says, here's how you spin up your own internal large language model and train it. We understand the costing, what kind of hardware they need or virtual services they need to spin it up internally. Design thinking. This comes back to the design for AI. I don't know if you guys remember on one of my first slides there. But we will sit down -- and these are advisory sessions, where we sit down and people just spill use cases to us, and we gather them up, we put them in a list, and we sit with the executive team or the steering committee, and we say, where should we invest? And then we use those as pilots that first 1 or 2 we real -- those are icebreakers, we really learn a lot about their ability to deploy AI as we do those first pilot projects. So we're trying to be very intentional about how we go to market, and we're trying to do it. We're being pretty agnostic as far as the platform, we do it on. So we need a pattern for each of the major cloud platforms. Governance is -- we anticipate is going to be huge. So AI, it's always been huge. But I think now that we put these tools into everybody's hands, we have to think more about how do we document decisions that are made and watch for bias and risk and understand how they're being used. And so I think we're going to end up investing a lot more in governance. I know David, you and I have talked about this a lot. These are some of the first questions we get from clients who are risk-averse. And you'll notice the word document in each of those 3 pillars. This is all about documentation, being able to show our work, being auditable and understanding. These models cannot be black boxes, that's not acceptable, right? So we're spending a lot of time on governance. I'll talk about partnerships just for a second. So in my role, I get to talk to Databricks or Snowflake or Microsoft and understand what they're doing and how they're going to market. So we spend a lot of time not just understanding the technology but understanding the economic side of how we partner with them as well, right? So what -- when we're talking to an opportunity, how are we going to get paid on this deal? And so there's your traditional resale margin that we can get paid or some of these new vendors will pay us on the back end, referral and collab fees. Now this can be really lucrative because sometimes we just need to participate in 1 or 2 meetings and do a demo and they'll pay us a fee on the back end. Some of those are really big deals. We can participate in ELAs and adoptions, something we could only do historically with some of the real mega vendors now being able to do this with some of our more strategic point vendors. So that's really promising. And multi-vendor. So you probably see -- if you follow us, you'll probably see this in the way that we do our marketing. We try not to just do 1 vendor anymore. We try to have a couple in the room who work together, and they've got connectors to each other's platforms. That's a much stronger story. And then when we have opportunities with clients, we come in and we do that together. We build together, which is great. And they're letting us behind the scenes. So they're saying, "Here's our product road map. So here's what we're going to put into the platform. What do you guys think? What are you hearing from your clients?" And we get input into that. Those are the advisory boards and councils. We're getting certified as training and services partner. That adds legitimacy to us because a lot of the time, these vendors -- everybody familiar with Snowflake? If you hit Snowflake's website once you'll get their ads for the next 6 months, right? And so the gravity changes, people will call the Snowflake reps directly and we need that Snowflake rep to think of us first, right? And so being certified, being authorized, having a lot of these blueprints, they feel comfortable coming to us -- yes, do you have a question?

How many of these are like onetime fees versus recurring fees? Some of them say, renewals? So like...

So like almost all of these models now are recurring subscriptions. There's some perpetual licensing plus like a renewal model in there. But most of these are subscription models nowadays or pay as you compute, right? So like a Databricks or Azure, for example, you're paying for what you use.

So you'll get revenue...

Based on whatever -- yes. And so if we have a great story, I cut this slide out, I should -- I'll show you later if you want. But one of our big pillars is to drive compute. That's how you make friends with the hyperscalers, right? A lot of times Snowflake will go in and they'll sell a $50,000 contract to some client in Vancouver. And then 6 months later, they look at the bill and they've spent $600 out of it, right? So they say, "Okay Converge, please go sit with them and find use cases, get them using that computing power so that when it comes to their contract renewal, they re-up or buy more, right?" And so that's where a real strength of ours is. They don't have -- there's 6 Snowflake reps for Western Canada, for example, they need us to go do that. They can't be everywhere at once. So we're spending a lot of time just driving compute for these guys. Good question. Thank you.

Let's talk about a few stories, and then I'll turn it over here to Shaun. So this one, really interesting, and I had no idea the whole part of the world. But about 80% of the RVs -- do you guys know this? 80% of the RVs are made in Northern Indiana. And these are like Amish communities. Almost every adult male in those communities works in these factories, works in manufacturing. And so you can imagine getting them to adopt technology is a challenge. And so they were trying to give them scanners on the assembly lines. This client makes about 50,000 of these trailers a year. It's a lot on these -- it's 12 production lines, and it is run side-by-side in a giant facility. But the crafts people were breaking the scanners and the -- so this was a very slow -- things are moving and stuck. So they called us in and they said, "Okay, we need some balance here between just winging it and letting them work at whatever pace they want and some crazy technical solution." And so we ended up building the solution we could look at every part and scan, which part was at what station on the assembly line and all the crafts people had to do is hit a button that said starting and stopping. And we were using cameras to tell how far along the job was so we could forecast when that -- it would move to the next station, and this is huge. I don't -- I won't talk numbers, but this was an 8-figure return for them. And so that is massive. When they can make 20,000 extra trailers in a year, that is huge. COVID was wild and getting everybody back into venues like this was a challenge. And so we were asked by -- this is a public one, right?

Yes, yes.

It's Lucira. So basically, they work with the Oscars, with the NBA. And they needed a way to certify people negative COVID tests without testing everybody at the door, without showing -- it's slow to show your phone and get to scan the codes and whatever. So they asked us to build an application for at-home tests. And it ended up being the first FDA-approved at-home test. And basically, all you had to do was take a picture of the test and it would detect if it was negative or not. And that was all shared with these organizations. So that's huge. Being able to get people back into venues like this. I think we accelerated that by a long shot. This is a really cool application. But you can think of all the intersections that needed there, we had cameras and everybody's phones that needed to be supported. We had to collect that data somewhere, share it, API with all these partners like the NBA and the Oscars. So this is a really complicated one. It shows our strength to go just beyond data and analytics. This one's public too, right?

I don't remember.

No, it's not. Nice try. So they make powder -- it's metallurgy. So they make powder coatings. So if you got rims on your car or on an airplane, basically, they make powders for all the coatings. Very strategic client of ours. We helped them hire their first CIO, and they grew by acquisition. So they ended up with this really messy world of applications and devices. And so it didn't even start as an analytics client. We did device management for them or endpoint management, right, trying to manage all these devices from all these different clients. And for our strategic accounts, we have -- what's a STA? Strategic technical advisor? So we have somebody assigned to just think through hard problems with our clients. And so that's -- we identified they really need to modernize their data infrastructure. So they brought us in. We did a data modernization workshop. So we sit down with everybody from every department. And we just say, what are your data needs? Where are you at right now? What's antiquated? What needs replacing and modernizing? And then we go build it. So -- and then we -- I think the first project we took on was modernizing their data warehouse. So great, again, started as a hardware endpoint kind of client with phones and devices, ended up in the analytics world. And finally, this is a giant natural gas company. This started as a -- they brought us in to help with their budgeting and planning process, so our FP&A practice. We actually sat alongside IBM in a lot of those meetings. They needed to replace something called Hyperion, which was their budgeting and planning platform. But in those meetings, after they pulled -- this is my client, so they pulled me aside and said, "Actually, we've got this whole mess of data infrastructure. We're paying Informatica and Oracle combined, about $0.25 billion in the next 3 years for our renewals. We need to do something about this." And so we brought Microsoft in. They wanted to modernize on Azure. So we brought Microsoft in. They paid for the first couple of pilots. So everybody familiar with ECIF? Anyway, Microsoft will fund the project for you. You have to show how much cloud spend you're going to drive, and then they will pay you a portion of that cloud -- forecasted cloud spend to go and do pilot projects and that sort of thing. So Microsoft and Databricks came in. We did a 3-month POC for submersible pumps replacement. I don't if you guys know this, but submersible pumps in a well. They don't fix them. They just run them until they explode. It's too expensive to fix it, pull it out of the ground and lose production for a day or 2, right? So they were trying to decide what RPMs and how much pressure do we put on that pump until it explodes to maximize throughput. So we built those models with them. This ended up being a 3-year project, tons of data engineering behind the scenes to go get all that sensor data and historian data from PI and put it all together. So million dollars of services just out of that, which is phenomenal. I know I'm tight on time. So we'll look forward. We don't see any of this slowing down. I think the hype around AI is real, and we'll consider -- and will continue to be real. I think reality will set in that a lot of organizations, we already know this, and we're already seeing this, they need a lot of the plumbing work done first. They need their data clean and put into one place. And so we can sit there and say, "Okay, we have an AI road map for you, but also we can do all of that engineering and software development for you along the way." Governance is going to be huge. We talked about this. We are going to end up having to find talent for our clients. And so we stay close to the schools and the programs there to try and direct traffic. We've got a talent organization at Converge, and I think that will be more important than ever. Our clients will turn to us and say, "Who do we hire? What are even the skills we're looking for? How do we evaluate who comes on board?" And so I will just leave you with this and that in my year here, it's been pretty awesome. We used to have just part of the story. And now I've got so many friends that we can phone. That oil and gas client, by the way, we brought in our app development team. So our app-mod team. They needed to rebuild their whole DevOps environment in Azure. And so I brought in my colleagues from that practice, and we brought in our cloud practice as well to do a whole bunch of their pipeline automation. So just the fact that we can do that is very exciting for me. That was a client that Converge acquired through Newcomp, and we've been able to go and extend them all into the Converge family. So being able to do even more of that is just super exciting for me. So, Shaun I'll take some questions first. If anybody has any?

Yes. Oh good.

Just a quick question. It's very interesting, all that you talk about here. But what is the true interest in AI for your clients? Because you -- like Converge has a very different set of clients than some of the other larger technology services companies might have. So what is -- are they still ramping up on their technology ecosystem before they even consider -- considering this AI discussions there?

I think they are scrambling, and that is our experience. I was just talking with another insurance company that all of you would know, yesterday. They have 500 people in their AI group -- what they call their AI group. I think that's made up of data engineers and software engineers and a bunch of computer scientists. But they are lost. So their users want to use tools like ChatGPT and other open source platforms. They are trying to do a hybrid cloud -- so they haven't made commitments to AWS or Azure, and so they're going to try and deploy on both. They need to abstract all of that complexity from their business community who wants to go and develop AI-infused applications, but they don't know how. And so a lot of this is still an architecture question. And these aren't even the technology folks. This is like from CIO through the organization. So yes, it's this balance between everybody can think of great ways. Yes, we want to put a chat bot inside of our employee handbook or we want to have a call center automation or whatever but at the same time, behind the scenes, they're scrambling. And I think that data warehousing of the past just is not going to be enough to keep up with the compute demands of AI. And so we're burning this candle from both ends. But that's not just -- like we're building right now. So if you have enough data, we can go and do pilots and build actual applications. And we've got dozens under our belt already. But when you talk about deploying it at the enterprise scale, there's still tons of work to do.

And Divya, one comment I'll make there too, is when we talk about our mid-market strategy. When we say mid-market, it still includes large enterprise clients, not the Fortune 100. That's where we're seeing a lot of larger services projects, right, multimillion dollar services projects that we've signed in 2023, right? So when you think about our customer base, those enterprise clients are driving lots and lots of services with us, and we'll continue to focus there to drive the high-value growth in AI, in cyber, right? To ensure we can deliver on those big projects.

Yes. Yes. A Midwest bank found us on YouTube and their CTO, sent me a note and he said, "Hey, I need help because everybody is asking me and I don't know what to do." And that also turned into a $1 million client for us about a year ago. So just thinking about how to deploy this, dovetails into a whole bunch of services that we are doing which is awesome. Yes?

When you -- when you mentioned that the clients are scrambling a little bit, do you find that they're scrambling more on the plumbing and the infrastructure or on the data and the setup and the cleansing of the data and whatnot?

Mostly on the infrastructure. So a lot of them still have a lot of legacy systems that they are trying to figure out, a lot of point reporting and they -- we like to think everybody has moved to the cloud, but some applications have and some haven't. And then the other half of that is just organizationally and culturally, they don't know how to structure it. Should all of this be funneled through one steering committee that decides where to invest or not invest? Do we let each of our departments like finance or HR or operations, do we let them hire their own data scientists? And then, therefore, just build them a platform to go develop against? So it's a centralized versus decentralized thing. There's a couple of patterns that are emerging out there, but you'd be surprised at who needs help doing this. I think it's still early days. And we're -- just being in the thousands of accounts that we're in right now, we're getting those phone calls every day.

Could you dive into the significance of the Snowflake relationship and how are you have an inside track with them versus some of your peers perhaps?

Sure. Yes. I mean I -- so we end up spending time with their engineering groups. So we get feature previews from them. So they'll say, here's the next 1.5 years of development that we have when they throw a time line up there. And they'll say, okay, so when they -- I won't get technical, but they say, we're going to support some open-source data store like Iceberg. They'll give us a preview of that. We have our own environment. We can go test integrations against it. Greg mentioned tools like Alteryx, which are downstream of Snowflake, a business user might go use Alteryx to go build a predictive model. We'll test how that integrates back into some of these new preview features with them. They give us access to a lot of their assets as well. So whether those are industry blueprints or models that they use to -- so that we don't have to go invent demos from scratch every time and do custom demos. They'll give us a lot of that as well. And then, yes, just spending the time with their engineers, like not their customer-facing engineers, they're product engineers. We get to see what's coming, which is awesome.

How unique is that to you?

You have to be at a certain tier of partner for them to open. And these are by invite only. So a lot of the time, they'll host this workshop in one of their offices and they'll invite a handful of their top global partners strategically to come sit in those sessions, but they do not do them publicly. Yes, no problem.

Yes. So when we're talking about clients potentially being lost with AI and even the best specialists in the field aren't exactly sure what's going to happen over the next year or 2. Not everyone's reflex is going to the problem. Do you see the market being in a bit of a freeze in terms of assessing what's going on right now? Does this slow down other type of work that could be done on the cloud side as well? How is the dynamic in terms of these companies investing in new product given the -- all the AI questions we have?

Tough to say that where it's going. Where it is right now is that it's driving way more conversations. Those are inbound questions, whereas we would have had to go do outbound outreach, right, and invite people to events and say, "Okay, we should have an AI conversation." Now they come to us. And so for us, it's immediate lift in that we're identifying, "Hey, we need to organize your data or get ready for this organizationally." So for us, it's not a -- the hype may die down, but the lasting impact is that all these organizations have realized they're not ready to a certain extent, right? So that will have a long tail. And we're staffing up accordingly. So for me, data engineering and all of that kind of data architecture and design, cloud infrastructure, all of that, these AI questions will drive that in the future.

And Jerome, that's what we're seeing, right? As we get brought in to have the Gen AI conversation or any AI conversation, we're now getting introduced into that account, we're branching off, right? So AI might not happen or the project we're talking about or the use cases we're developing might not happen for 2 to 3 years. But now we're engaging on a cloud platform project or an app-mod project, right? So then we expand from there, and we're in there continuing to talk about as we build this application, here's how AI is going to be leveraged in the next 2 to 3 years, right?

It's just pulled everything to the forefront. Even a couple of years ago, data scientists in an oil and gas company, they were kind of kept buying the scenes, right? They were building models, and they would just kind of throw it over the fence to somebody. Whereas now those skills are being pulled forward and they're sitting in front of people and saying, here's what these models mean. And so any time that gets pulled forward, that pulls us forward in the organization from the depths in the back offices to -- these are Boardroom discussions now. So I'll take that. And it's going to drive it for the next foreseeable future.

There's currently a war for AI-related talent. So how should we think about your manpower in terms of R&D and implementation? Also you mentioned at the beginning of the conversation that you're based in Calgary, is -- AI a major center for Converge is like global strategy for you guys?

Okay. I'll start with the first one, too. So just like a war for talent. You're right, there's some like wildly inflated numbers in the markets that we operate in, I think so far, we're finding it okay to go and grab that talent, the AI talent. We're trying not to be a victim of the hype as well, right? So we are hiring smart people with real life skills. That doesn't mean they need to have worked at Google or something, right? For me, somebody who's willing to learn and build these models and understand and do the training that we ask them to is powerful enough, and we're seeing the results with that. They're able to go out there and build really cool things for our clients. Calgary itself, I don't know. Guys, is it strategic? It was one of the dots. One of the dots on the -- it is a great market to be in. There's a lot of -- there's some cool startups in Calgary that have drawn funding like Neo. I don't if you guys know Neo Financial or Benevity or companies like that, that have drawn talent to the city. So no, it's great. We don't struggle with job posting. They don't last long.

And I think that's where the scale and expertise comes into play too, right? Technical resources like these guys are enjoying being part of Converge because they can work with peers outside of just AI. We're seeing the same thing on the recruitment side. So as we've acquired all these analytics companies now that we have the mass, people want to join the Converge team, right, whereas we're not struggling to find talent. And Chris mentioned earlier, the talent organization, right? That has a whole team of 1099 resources that we've tapped into in the past. That as the projects come on board, we can then bring them on full time as well. So...

How big is your team right now? How many openings do you have? And where do you think the team will be in about a year?

Okay. I don't know what's in our -- actually, I don't have a good count. You probably know better than I do. A little over 100? Yes, yes. And about 20% of that, I would say, are like raw pure data science folks. So these are people with that holy intersection of business acumen and maths skills and computer science skills. So these are our design thinkers and application builders who are doing these. So proportionally, it's a heavy part of our group and then supported by massive data engineering team.

And as we look at headcount, right, that constantly changes, right? We're always looking at what projects we're signing, right, versus having a bunch of people sitting on the bench, what's the pipeline look like and then it's higher as we need the resources from a technical perspective.

That's right. resource by resource, Greg, every month, we go through utilization, who is forecasted to be on what. We have a close eye on this stuff. So yes, we know exactly what's coming from a signed statement of work perspective. We know where we need to lead time to go find either subcontractors, our talent pool or go higher for our teams. So.

We're going to take one more on [indiscernible] then we got to move to cyber. We'll put it up again at the end.

Yes. So just your stance on the open versus close source [indiscernible] declines. How you're approaching it?

It can't be the last question. That's like a 20-minute answer. No. You know what? Open source is great because it's accessible to everybody and you can get started. But it's open. So whatever you put into it is at your own risk, right? So we're seeing a lot of -- so I was talking about the insurance company I had a call with yesterday, they blocked access to it entirely. So these are companies that you would hope are on the forefront of developing with ChatGPT and other large language models. If you go to OpenAI, it's blocked. So their director of analytics told me he has his personal laptop sitting here where he uses it on his personal laptop, and then e-mails it to himself. Shaun, just do you want [indiscernible] -- but that's where we're at. It's lock it down. The open -- building your own walled garden closed versions of these. They're expensive to train too, right? Like GPT-4, does anybody know how much it costs to train GPT-4? $100 million. So it's about 10,000 T100s out they are chips, and they're about $30,000 in the U.S. each, right? So it -- very expensive to train on their own. And so we're seeing a lot of our clients use the public-facing ones not just GPT but like MAMA or whatever, and get started while in parallel, we're thinking, okay, how do we build their own version of this in their own infrastructure, it's controlled and secured. So it's very bimodal right now. We're doing a little bit of both at the same time in each of our clients. Yes. But that was the short version of a really long answer. Okay. Thanks, everybody. Over to Shaun.

Thank you, Chris. Feel like I am following Paul McCartney there. Good job, Chris, fantastic.

Who? I'm kidding.

Is there another reference maybe? I don't know. Thanks, everybody. I know we're running a little short on time here, but I'm going to do my best to talk fast, but not furious, right? So good morning, good afternoon or good evening, depending on where everybody is coming from. Want to thank those in the audience. It's the first time I've been to Toronto in about 12 years. My, what a beautiful city, great culture. It's changed a lot since I was here last. Thank those on the webcast as well, right? So my name is Shaun Bertrand. The way I describe I run the cybersecurity practice here at Converge today. I describe myself as a very passionate resource as it relates to cybersecurity. I've been doing this since I was about 13 years old. Here's my vertically challenged joke. It was about that big then, and I grow about this big now. But I'm excited to share with all of you here today, a little bit of our vision, our team, our strategy and the opportunities for cross practice selling, right? So without further ado, let's get started. I'm a big data-driven individual, right? And I want to talk first a little bit about the cybersecurity landscape and how important data like this is for our customers and for our strategy. We are a data-driven organization. We let data drive our strategy, allowing us to make more effective business decisions. Let's be frank, nowhere in my opinion, is data more critical than cybersecurity, right? So when we look at some of these stats, right, cybercrime is expected across the world, $10.5 trillion annually by 2025. That's more than the GDP of Japan, right? You look at a ransomware attack, I don't think a day goes by where we don't hear about a ransomware attack, right? Back in 2022, every 11 seconds, an organization was attacked as a result of ransomware. By the time I'm done with this presentation, there will have been 163 companies that have been attacked by ransomware. And I don't think ransomware obviously isn't the only attack factor that's out there, but it's one of the most impactful, right? Costing organizations and victims around $265 billion annually, and it will attack consumer devices every 2 seconds. Global average cost of a data breach in 2023 was about $4.5 million, a 15% increase over those last 3 years. So what does this tell us, right? What does it tell us? It tells us that 51% of organizations are planning to increase their security investments. And I think that other little data tidbit over there, the final one, right up your ally, Chris, right? Average savings for organizations that use security AI and automation extensively is about $1.76 million. They can eradicate the adversaries faster. They can leverage that AI automation to discover, and I'll share some more data points with you here momentarily. So a little bit about us, right? Who are we? We are a team of strategists, veterans of the industry, former chief information security officers and resources who are driven to exceed our customer expectations, right? We align to the defined outcomes and the consistent outcomes of our customers. So what do we do? We defend and secure the endpoints, the networks and the users. We test, monitor and manage various different areas of operational risk. We detect and respond to incidents. We help our customers comply with a very wide array of different rules and regulations, and we enhance and upscale to cybersecurity resources, not just within our customers but within our organization as well. So Greg talked a little bit about Converge, right? We talked about our 60-plus locations. We talked about the 4,000 clients, the various different partnerships that we have. Want to talk to you a little bit about the cybersecurity practice, all right? So over 30 years of cybersecurity experience. We have roughly 200 resources that are out there, 4,000 professional services engagements underneath our belt and a lot of different data in regards to the threat hunting and the security alerts that we analyze. So when I look at that, I think what we do? We delight our customers. We delight our customers, proven by those industry-leading NPS scores, and by leveraging our differentiators, which I want to talk to you a little bit about here momentarily. We drive strong retention, right, world-class retention, leading to those strengthened relationships, strategic alliances that we build with our customers that help promote the success with the cross-practice strategy that we have as well. So let's go through our core differentiators here a little bit. I'm proud of the team, right? I really am. And I'm going to talk to you about passion, right? Passion runs through the blood and the DNA of pretty much every one of our cybersecurity resources, all right? It's at the heart, center of what we do. We really don't stop until we achieve our goals. And one of those goals is to exceed our customer expectations. World-class delivery, right? I talked a little bit earlier about those NPS scores, but I feel strongly that we perform at a higher and more consistent rate than our competitors. Specialized expertise. We do a lot, right? We don't do everything. We are authentic with what we do. But here's a good example that I'd like to make on the specialized expertise side. We do a fair bit of defense contracting, all right? So we have around 20 active top-secret cleared resources. I don't usually talk about that a lot. But do all of our customers need somebody with an active top secret clearance? No. But as is given the warm and fuzzies and the trust and the credibility and the confidence that they're working with the right firm. Yes, it does. Compliance and regulation: We have a wide array of specialized resources that focus solely on helping our customers with that diverse set of governance, risk and compliance. I'm going to talk to you a little bit in the future here in a couple of other slides about some of the emerging frameworks within the United States that we feel we can align to strongly. Partners. Our ecosystem of partners is we have -- I think the key to partnerships is strong relationships, right, but also aligning specifically to our customer demands, and we'll talk about those as well here moving forward. Thought leadership. I think this is an important one. What I want to do is I want to empower our customers. I want to educate our customers, right? It's really simple as that. So through workshops, through blogs, through white papers, social media, we are true evangelists that allow our customers to walk away smarter from our publications and materials. And that makes me feel good. It really does. I got into cybersecurity when I was this big. Before it was really the Wild Wild West back then. But the reason I'm in it today for as long as I have been, it feels good. And I know this sounds kind of like cheesy, but it feels good at the end of the day, walking away, knowing we've made a difference. We've helped our customers improve their risk resilience, reduce their attack surface. And at the end of the day, that sense of accomplishment is real. Global reach, a majority of our work is based on North America clients but certainly expand into that global reach, right? We operate in -- we help facilitate work in EMEA and Asia Pac, obviously, here in Canada. And so we're expanding that reach as we speak as well. All right. This is intended to be a little bit of a condensed overview of our practice and the services that we facilitate. So I'm going to start with advanced testing. Advanced testing, otherwise known as the penetration testing team, otherwise known as the red team. It depends on who you ask and what day of the week you're asking in regards to this. But what do we do? Application testing, we focus on emerging threats like AI-driven threats, like ransomware. We facilitate penetration testing, which is a real core competency. And we developed a penetration testing as a service that I'm going to talk to you here again in a little bit. I want to talk about application testing, though, right? Application testing is driven significantly over the last 2 or 3 years, right? Our business is growing in that space significantly. The drive towards application security testing and the growth that we're seeing, it's generally supported by the cloud. What do you think about when you think of the cloud? What's the primary interface that you're interacting with the cloud? It's a mobile application. It's a web application. It's an API, right? And those risks and those threats are detrimental to various different organizations, but it requires a very advanced and unique skill set, a sophisticated skill set to identify those threats before the bad guys do. GRC, right? We focus on things like cloud security, a wide array of regulations and compliance, right? The list -- I mean, PCI, HIPAA, GDPR, some of the new stuff coming out with the ADPPA that I'll mention here momentarily. We are great at what we do in that space. Incident response, as I talked a little earlier about letting data drive our strategy and our vision, one of the organizations that we work with is a group called the Ponemon research institution. World-class research group, all right? Organizations that have -- that have a tested incident response plan, they're doing things proactively before stuff hits the fan. They're going to reduce their average cost by 35%. All right? So that's why we are seeing a tremendous amount of growth in our incident response planning, the preparation, tabletop exercises, and we really help our customers be more proactive with incident response. That said, not even the best-run organization, the most prepared organization, is immune to cybersecurity attacks, right? So in those cases, our incident response team and our partners are well positioned to help eradicate the adversaries, get that business back into a viable state. We talked a little bit about strategy and defense here. So our strategy in defense is really centered to a lot of implementation that we do, whether it's controls, countermeasures, deployment of our various partner technologies. We also have a very strong focus on data and user protection. I mean, data is king, right? Data follows us from a consumer level, from a business level all over. So we have a very refined strategy, great partnership with IBM in that space that we'll talk about momentarily here. Network is the evolution of cloud, on-prem, SaaS, PTaas has evolved considerably. We still focus on those various different networks where the data is located and tying that together as well. Threatened vulnerability. Threatened vulnerability management and implementation I mentioned already. Strategic staffing. I want to talk a little bit about strategic staffing, AKA residencies. It is one of our most prominent services, all right? What we do here is help our customers fill skill sets that they don't have, and I'd say more importantly, what we end up finding is that customers have an initiative. They have a solution that they're trying to resolve, but their team is already under water. Their team is busy just trying to keep the lights on. Our value add is we can come in with the advanced and sophisticated skills that we do and help accelerate the organization's ability to get these solutions implemented and to address their challenges. Managed security. So talk a little bit about managed security. Our value derivative here is quite simple. We help our customers detect and respond to cyber threats. I want to talk to you real quickly, as I talk about enablement and empowerment, I hope a lot of you walk out of here smarter as a result of some of these data tidbits I'm mentioning. The average breakout time, and I'm going to explain that here momentarily. From the time it takes from an adversary to gain initial access into a customer environment, to the time that they compromise the entire environment. So let's say, they send a phishing attack, somebody clicks on that phishing attack. Then the actual adversary is able to get inside the environment and compromise the environment, 90 minutes. It takes 90 minutes from that initial access to the full compromise of an organization. Why is that important? We have 89 minutes within our managed security services team to identify and to eradicate those adversaries and that's what we do. We're different than our competitors here. We're not just automated. Absolutely, we leverage AI and automation, but it's also complemented through the advanced human threat hunters that help more effectively detect and prevent those attacks. The advice, implement and manage. We heard a little bit about that earlier, right? I'm going to hone in on that here on the next slide in a little bit more precarious detail. So let's look at the advice, implement and manage methodology in a little bit more detail. Not every customer goes to this methodology, but this is the approach where we see customers lean in. They understand the approach. What do we do? We advise and assess, facilitating services that help discover and remediate those vulnerabilities before the bad guys do. We unify and we integrate GRC using that risk-centric approach. We then move into the implementation phase, helping customers proactively position themselves with that more risk-resilient perspective and then move forward towards building, integrating and optimizing a diverse set of those controls and countermeasures. And then lastly, managed security, right? We talked about it. We finally position these controls and opportunities in a managed services fashion, whether it's cloud, data protection, detection and response and threat management, and it's real. We are doing this. I know this is an eye chart, very similar to years earlier, right? What I want to do is I want to take you on a journey, all right, very similar to that advise, implement and manage methodology. It all starts in the middle, protecting our customers' brand, their reputation, their data, right? And that's the goal. We start on the left, identifying vulnerabilities, threats and risks for our customers. And as we shift to the middle, this is where we implement and integrate a wide array of different controls and countermeasures that align to the data, the applications, the user, e-mail, endpoints, network and cloud -- and this is where a couple of things come into play. First of all, our partners, right? You're going to talk to -- I'm going to hear you -- I'm going to speak a little bit about our partners and how we are relatively vendor-agnostic. But this is where we can apply the right partner technology to the customers, depending on what their challenges are. This is also where we see the cross practice opportunity really start to develop, all right? So I'll give you a couple of examples. When our application testing team finds those application-related vulnerabilities that we spoke about, many of our customers are very challenged in how quickly they can remediate those threats. I mean I've been in this business a long time to fix some of these vulnerabilities and threats that we find could take months, sometimes years. That's where we bring in our [indiscernible] team. right? So we bring in our [indiscernible] team, they can come in and they can help remediate those things often in days, right? Then we can start talking about more of a strategy-based focus. So why did they get to this place in the first place, software development life cycle, CICD pipeline, integrating security earlier on into that development life cycle, that's what we do, right? Same thing with incident response, right? We talk about incident response. Post incident, what we are very successful with right now is then being able to position our digital infrastructure team into the equation. Because I hear a lot of our customers, after the incident, say, "We need help. We know our network is not as resilient or robust as it should be, can Converge come in and help?" Absolutely, we can. Absolutely, we can. So as we move forward across kind of over into the right, I talked a little bit about the ability to help our customers manage their various solutions and technologies through our managed security services and the strategic staffing piece as well. Partners, our core partners. I wish I could put all of our partners on here, but I could not fit everybody on here, right? But what we do is we emphasize a vendor-agnostic approach. We choose who we go to market with based on the best solutions or those emerging technologies. And relationships with these partners are key. They really are. Whether we're fighting for more points or whether we have an issue or an escalation, we have everything from strategic relationships with the utmost level and the highest layer of these organizations with the CEO, the CIOs, that there is an issue, I can pick up the phone and talk to these individuals right away. And then we have sales ownership and accountability and relationships. And then we have technical accountability and relationships, and it works. We're also really heavily invested right now and figure out who is the next up-and-coming AI ML partner that we want to hitch our wagon to, right? Companies like Anomaly or Vectra AI. We're looking at these in a different set of optics than we did 2 or 3 years ago, and we're excited. But we're also better understanding where is it that some of these existing vendors where we feel very confident that over the next 2 or 3 years, they're going to take this AI world by storm. CrowdStrike is one of them, right? So CrowdStrike just released a tool called Charlotte, which why is it, everybody's got to come up with a name for the AI piece, Chris? Is that just a common kind of thing?

Maybe, I guess.

What does Charlotte do, all right? We have this thing in the cybersecurity world called SOAR, security orchestration, automation and response. It allows organizations to take action on the things that they're seeing in a much more efficient way. Well, that's what Charlotte brings to the table. We've struggled with SOAR over the last 3 to 5 years significantly. So now what you can do is use some of this capability from our partners like Charlotte to say, show me all the vulnerabilities I have in my cloud, run some scripts to go and patch and fix those things. That's what we see AI doing within the partner ecosystem. Where we see forecasted growth opportunities, right? Identity and access management, this is big. Market is projected to reach $34 billion by 2028. It's growing at a clip a CAGR, a current annual growth rate, of around 14%. We're aligned to these growth opportunities today and continue to diversify our technology and partner portfolio. Cloud Security is going nowhere, right? I mean if you look at some of the things that we've seen with Microsoft and the cloud reach over the last week or 2, we know that cloud is here to stay and that we know cloud security needs to be woven into the fabric of not just cybersecurity, of AI and everything else in between, right? That was a big one. If you haven't read up on it, multiple U.S. e-commerce secretaries, state department officials and some other organizations that have not yet been publicly named, cloud security is here to stay. As-a-service offerings. This is the one of the most things I'm really proud about, right? So take our penetration testing as a service, as an example, about 3 years ago, we built this service for a couple of reasons. Number one, we wanted to disrupt our competition. Nothing makes me feel better than disrupting my competition, right? Number two, we wanted to provide strong value to our customers. What we did here is we created an offering that is a cost-effective and continuous solution and what it resulted in is a service that grew in ways we couldn't imagine. Today, we have some of the largest organizations in the world that span a lot of different industry verticals focused on our as-a-service offerings. Data protection, privacy regulations, I talked a little bit about this, but raise your hands. Anybody know what the ADPPA is? That's the opportunity. Who knows what GDPR is? Few -- all right. So the ADPPA is the American Data Privacy and Protection Act. GDPR is a European framework that is designed to have penalties and teeth in it. So if an organization doesn't protect your data adequately, they get fined. United States has been focused on trying to develop and implement and integrate something like this over the last 3 to 5 years, and it's finally starting to come to fruition, right? So we are aligning. Why is that important? And what is the opportunity? Because customers, specifically in the United States, are going to have to prove and demonstrate that they are doing the right things. They've got the right controls installed, their diligence is there, and that means opportunities for us, right? Ransomware defense, AIML, we talked a little bit about. How am I doing on time? Okay. All right. Application security we talked about. Anticipated growth verticals, right? Municipalities, specifically, schools, specifically K-12, right, and even in the higher ed space. It makes me mad when I read something in the headlines about a K-12 school district getting hit by ransomware, right? It completely brings them down. It impacts our children's education and their ability to learn. That's where I get passionate and excited about fixing things. So we feel there is a tremendous amount of space there, and we're actually building some services to give that K-12 and even the higher education vertical an opportunity to do better. Health care is a big one, too. We see health care moving at light speed right now. IoT-based technologies struggles with the way that they have all these different technologies segmented and siloed and controlled, and then critical infrastructure, right? As I mentioned before, we got quite a few folks with active TS clearances, whether that's nuclear or other, gas pipeline, a tremendous amount of opportunities in that space. And then lastly, the SMB sector, right? When we look at our ideal customer profile, it's largely going to be between the mid-market space and within enterprise for cybersecurity but we also feel there's a tremendous greenfield approach and opportunity within the SMB market itself, maybe a little bit to the left or to the right than the spectrum that I just gave you. All right, a couple of stories. All right. So this is a large U.S. airline organization, and we formed the partnership with this organization about 2 years ago as a result of what I'll define as some tactical initiatives. And as a result, we formed relationships at the highest layer of the organization. Board, CIO, CEO. And this is where I want to talk to you about the practice, the cross-practice strategy being real, all right? So as a result of our cybersecurity services, we were then able to land and expand, no pun intended, with the airline into our digital infrastructures team, right? So then as a result, we started to sell them a lot of product, VMware, F5 and Rubrik and we built that strategic alliance. We built that partnership, and as a result, we are very well aligned with this customer, and we're currently executing on a very large cybersecurity strategic initiative to make sure all of their controls and countermeasures are locked down in the manner that they need to be. Global automotive manufacturer. So this one I am also very proud of. Our relationship with this company has spanned about 5 years and as a result of our thought leadership, the advanced and sophisticated skill set that we bring to the table, and also driven from our penetration testing as a service offering. I think it's important when we talk about the result. Every year, this very large organization brings in all of our competition, and they put us into what's called a capture the flag event. And really, it's just a technical validation test to make sure that they're using the right vendor who brings those skills to the table. Every year, we have destroyed our competition. They bring us back year-over-year, and that feels great at the end of the day. Enterprise financial organization. So this organization and firm we've been working with for over the past 10 years. And this is what we strive towards, being an extension of their team, right, being that aligned partner, building that strategic partnership. We do a lot in the GRC space here with them with the framework such as PCI, Sarbanes-Oxley, GLBA, but the result is now that we -- now we are focused on helping them with a very large data protection strategy. So over the last 2 years, we helped them establish the foundation of their data protection strategy, aligning to those various different GRC framework and requirements that they have. And now we're in the midst of actually executing. So when I talk about a team, we've got 5 to 7 resources out there doing nothing but helping them with their data protection practice and strategy. Almost done. All right. So -- this one is important, right? When we talk about kind of closing slide, I got one more after this. Choosing the right cybersecurity partner comes down to what matters most to you and to our customers, right? We're not the right fit for every organization, but what makes us different is -- from other firms, this is often exactly what works for our customers. The collaboration, the focus, the expertise, the portfolio, the services, the solutions that we spoke about, that's really what makes us special, unique and proud of the team that we've established today. So the last slide that I have here is this one. I'll let -- these are customer references, all right? As a result of my colleague here, David Luftig helping us to operationalize the NPS process, right? And this just speaks a little bit about who we are, what we do and what our customers think about as far as our cybersecurity team. We're proud of what we've accomplished thus far. But more importantly, we feel we have a tremendous opportunity ahead to drive the growth, the revenue, the strategy that we share here with you today. And our vision is quite simple. It's to establish the converged cybersecurity team into an industry recognized name, a force to be reckoned with. And I appreciate everyone listening to me talk for the last 30 minutes here. I think I did okay on time. But I hope you all feel the same level of intensity, excitement and opportunities that we do as well. So that's it. A little time for Q&A or do we need to move into your Q&A?

Great job on the presentation. Just going back to the slide about your partner ecosystem. So you'll use CrowdStrike for the EDR, Arctic Wolf for the MDR, Zscaler for browser isolation, whatever. Have you been able to create any of your own proprietary tech along the way? Dashboard, is it's false-positive detection, whether it's something else patch pushing, something else. Have you been able to do that to increase margins?

Yes. I mean one of the things that we did is we developed a partnership with an organization that has allowed us to build our own penetration testing platform right? It's a fair bit of a SaaS, but also some intellectual property and our own built in. And what that platform allows our customers to do is get real-time data as we're finding vulnerabilities with our pen test. That's one of the biggest drawback that we heard 5, 6 years ago is, why does the customer have to wait for the report to find out these critical, jaw-dropping threats? Now it's real time, right? And we're operationalizing that infrastructure and that platform with various different API calls that give them a lot more value derivative. But I also feel there's opportunities with a lot of our other business too. When we talk about the thousands of penetration tests that we've facilitated and the very unfair advantage that we have in regards to the techniques, tactics and procedures that we use, feel there's an opportunity from an AI perspective to help automate some of those things, and we're talking about that today.

As you look at your pipeline today, how much of that is advisory versus managed implementations and then professional services. How do you kind of look at that?

Yes. So advisory, I don't know if you guys want to answer that a little bit. But for us, the advisory, we want to turn ourselves into more of a professional services organization, right? So from the advisory perspective, it makes up a pretty great amount and best percentage of what it is that we do. What we need to be doing better on is driving those opportunities into the managed space. We're doing great in the managed services space. And we talked about the integration of managed infrastructure services with managed cybersecurity services as well, but a great example of what we're operationalizing literally as we speak, is what's called a pull-through opportunity process. So at the end of every single engagement that we do, we sit down as a team, not just cybersecurity but DI, DW, [ AMoD ], analytics, everybody and we understand what are the recommendations that we're leveraging today that can help drive that cross-practice strategy and opportunity.