Sysgroup (LSE:SYS) — Investor Presentation

0:00 Good morning, ladies and gentlemen, and welcome to the SYSGROUP Investor Presentation. Throughout this recorded presentation, investors will be in listen only mode. Questions are encouraged using the Q and A tab situated on the right hand corner of your screen. Just simply type in your question at any time and press send. The company may not be in a position to answer every question received during the meeting itself. However, the company can review your questions submitted today, and we'll publish those responses where it's appropriate to do so. Before we begin, we'd like to submit the following poll. And if you give that your kind attention, I'm sure the company will be most grateful. I'd now like to hand over to the management team of SYSGROUP.

0:32 EJ, good morning. Thank you, Mark. Good morning, everyone. My name is EJ Chae. I'm the executive chairman. To my left, there's Owen Phillips, our CFO, And on my right is Ross Humphrey, chief AI officer. Thank you for your time today. As you're aware, we announced last week that we raised 11,000,000, pounds, which was oversubscribed with both existing and new investor participating. I must also add that the management and the board invested £600,000 in the race as well. Today, we wanna provide the opportunity for retail investors to participate. Having run three PLC during the past twenty years, I'm dating myself here, I really appreciate the support the retail investors give the companies.

1:24 You put the time to get to know the company well and are truly long term investors. I really appreciated the support you gave me while in the twelve years I was at SCAPA, where most of you stayed through the entire twelve year journeys through the ups and downs. But I think that that we all came out well, when I sold the business three years ago. Just, give you a little bit of a a background about myself for those who don't know me. Prior to SYSGROUP, I was the chief exec of SYSGROUP PLC, a Manchester based global manufacturer health care industrial business. We took it from 170,000,000 to over 300,000,000 in turnover.

2:13 The market cap, when I joined was 24,000,000, similar to what SYSGROUP today, and sold it three years ago to, US Multinational about £450,000,000. So very good return and a great journey from a personal level as well. Prior to that, I was a group chief exec of another PLC called Volux Group, global manufacturer of power cords and cable assembly. And before I joined the industry, I started my career in finance starting with the Blackstone Group, then I got my MBA from our business school as well. So when I sold the business three years ago, I was reflecting on what I wanted to do next. And having ran industrial manufacturing business most of my career, I wanted to be part of a sector that was growing, that was dynamic, and that was, exciting and, obviously, quickly focused on technology.

3:16 And then within that, it was evident that AI is where all the activity and all the investments and and innovations were coming from. So I think I quickly recognized kinda what the potential of AI could be. You know, having spent the last twenty years running small medium businesses in The UK, what AI, could benefit. But I also recognized the challenges, of a small medium businesses in terms of how to engage with the technology and the limitation. So with that in mind, I went through, I would say, a list of about 300 companies, in The UK that kind of fit some of the basic criterias I had.

4:07 I went one by one, knocked at the doors, and started talking to them. Out of the 300 lit in the list, 11 were publicly listed company, and one was SYSGROUP, which ticked a lot of the boxes, and I thought it was a good foundation for us to build the thesis, behind the SYS today. So when I, took over, SYSGROUP in April of last year after acquiring 14% of the business, I I wrote a check for over 2,000,000 of my own personal money, well, to become an investor, in the business.

4:54 SYSGROUP was a a roll up MSP, typical roll up MSP, through series of, acquisitions, six from 2016 to 2022. It provides primarily hosting services, cybersecurity, managed support, and consultancy. You know, it's about a 100 employees in five offices across The UK. From a financial perspective, in the, announced RNS we put out about a month ago for the year past ending March, our revenue was 22.7. Adjusted EBITDA of 2,000,000. It has a recurring revenue of over 70%, which I think it's a a significant positive.

5:45 Not a capital intensive business, so 80 cash conversion, and it had net debt of 3,400,000. I think one of the the biggest asset that the business brought was its customer base. It has around 500 customers that are active, 90% of those which will be classed as an SMB. There's no significant customer concentration, the largest being about 6% of the revenue, and they're all long standing. You can see some of the logos on the right hand side. They're actually very good name, very decent name. And having said that, we they only buy only few of the offerings that we offer.

6:31 And what that translate from our perspective is significant opportunity to cross sell. And I think we have been able we've been able to evidence that the last six months passed. So, you know, when I joined the business twelve months ago, we've been very busy since. So these are some of the changes in the transformation we want, not only from a strategy perspective, but also in the execution, the leadership of the group. Out of the 13 people that constitute a senior leadership team, 11 have been replaced, including the CEO and the CFO, with a team of eight, including six new talents on a relatively cost neutral basis.

7:17 We also recruited of a an AI team led by Ross here of software engineers from blue chip organizations such as JPMorgan, AWS, McLaren, Valens, and such To complement the capability that already existed as well as the AI, we recruited a team of cloud experts to become hosting platform agnostic to help customer, you know, transition, migrate, and support, in their environment, whether that's a AWS, Azure, or g cloud. And I think the validations of the capability and the confidence of the people we brought in, I think, is reflected in the next two bullet points.

8:06 So in the short amount of time that we've been focused on this deep in this strategy, We achieved AWS selects tier partner status, which is the second level, in the short amount of time. And for those who follow and understand how the partnership program at AWS or any of the cloud provider works, that is a very meaningful and significant validation that supports not only our ability to grow, but in terms of our ability to engage with the broader market. Two weeks ago, we also announced that we've been designated as a outsourced partner for AIML by Softcat, which is the large UK largest value added reseller to provide AI, ML. As you know, Softcat are very much a reseller of both software and hardware.

9:03 They do not really invest in technology per se, but they rely on a partnership network to offer broad range of capability to its vast customer. For us, that opens up another channel of opportunities and customers that we can tap into. And, again, I think the fact that Softcat will partner with someone like us, I think, is really validation of the, reputation and the credibility of the team that we've been able to bring on board, which I think Ross will touch on, later in the presentation. We're also rebuilding our sales organization. One of the new recruits that I brought in, Henry Quartz, is a ten year veteran at Softcamp where he was, involved in the or building the sales organization in their Bristol office and their London office, and he is replicating that success Softcat has achieved being very much, sales driven, growth focused entrepreneurial, culture.

10:12 We have recruited three additional salespeople from Softcat that, joined us, in our journey. And I think green shoot of that working and and and taking place is really evidenced by what we've been able to deliver last six months, h two of last year, from a growth perspective. We had 13 and a half percent organic growth, which I think in context of, really, I think, flat to no growth in the last five years at ex acquisition, really, I think, validates that we brought the right people, and we have the right strategy in place. So this is the picture of the team that's in place.

11:02 I wasn't allowed to join because I'm too old for the picture apparently. So, again, it really shows the you know, if you look underneath some of the backgrounds of the people as well as the capability, I think it this is probably the the biggest asset we have and the strength in the business there, really a management team that is really capable and experienced. I think, you know, I don't have to say much about the hype around AI market and what it can do. I mean, here are some of the bullet points, you know, which I think most of you seen in, pretty much every periodical and journal that's out there, you know, as farfetched as that they're gonna take over the world. But from a in the context of business, in terms of the benefit, you know, in terms of the productivity gain, somewhere between 20 to 40% of business, in terms of what it can do from a manufacturing productivity, in terms of what it can do to enhance and augment the sales and growth and marketing.

12:07 So, you know, it goes without saying AI is here. And and then if I were to steal a line from Eric Schmidt, who's ex CEO of Google, in recent interview, he said he doesn't think AI is overhyped I mean, is underhyped. Sorry. He's overhyped. He's actually underhyped. I think being in the thick of what's happening in AIML space and some of the development and the investment we're seeing, I think it's it's amazing what's going on, and they it's amazing what the potential AI will have on a business if they were to embrace it. I think the challenge, having said that, is that I think UK, in particular, are behind in the rest of the world in terms of the adoption and understanding of of AI.

12:59 And, you know, as as I said before, you know, from my previous experience of running SMB for the last twenty years, I totally understand the challenges they're going through. I think on the first level, I think it will apply to probably most people, out there is that we have a very limited understanding what AI is. I think it it vacillates all the way from the Terminator to ChatGPT or Copilot. And I think people struggle, okay, what kind of strategy do I need in place for my business specifically? Which really quickly leads to kinda the financial implication of it. What is the value proposition?

13:41 What's the cost and return on investment? And and, equally, you know, how does it integrate to my existing infrastructure? You know, a lot of the invest owners and and CEOs are thinking about how much investments they made in terms of their own existing infrastructure. And every consultant who they bring in about in their conversation with AI will quickly say, well, you gotta move everything to AWS or Azure or or cloud and and obsolete and and then the in all the capital investment they make. So there's a lot of kind of financial hurdle people need to overcome before they really kinda say, okay. I wanna jump on the AI bandwagon. But even at, you know, past those two hurdles, you're kinda stuck, okay, looking at your internal capability, but also external capability. I think today, there are a lot of people who professes themselves as an AI expert, which I think is challenging trying to understand whether credible or not. And equally, looking at your internal capability who are used to managing hardware to really understand what how AI can be integrated to their

14:54 business. And and I think, you know, the next slide really, I think, captures the challenge of someone who's looking to navigate really a complex and fragmented AI value chain. I think if you were to Google right now and say AI expert, AI partner, AI supplier, I think you'll get a, really, a smorgasbord of different capability. And there's a combination of actual fragmentation and the skill set required to deliver end to end AI because it transgresses not just the tool itself in a software machine learning, but also the hardware, the infrastructure.

15:40 But post implementation, the system and the maintain maintenance of the a model going forward. So what we wanna do is really, I think, simplify the customer's perspective into six different technology stock. And in essence, really taking the approach of looking at the situation from a data perspective. End of the day, data is what fuels AI, and the the the value proposition is really understanding how does data get collected, how it gets hosted, how it gets connected, how it gets protected, what's the security around the system, and the support and monitoring to really achieve the value add of an AI.

16:28 So for us, simplifying in the context of a data journey is what we're trying to do. So with that, let me turn it over to Ross who can really walk you through what's our proposition, but also kinda talk about what's different about us than the rest of other people out there. Yeah. So hi, everyone. I'm Ross. I'm chief AI officer. So a bit of background. I joined the company six months ago with a team of people to deliver AI services for SYSGROUP. We've expanded up to eight people now with a range of disciplines, with the mindset that we're going to become the partner of choice for small and medium sized businesses on their AI digital transformation journeys.

17:15 So the way in which we're looking at doing that is to offer an end to end solution for customers, looking at their use cases and helping them really discover what they can do with AI, and how they can use the technology. As he just pointed out already, this is very much a data centric, engagement that we do with customers, and I'll go through some details on how we do those engagements, in a moment. But we want to meet our customers where they're at. So the way in which we do this is by, building a solution that is agnostic to the hosting environment they're in. So we realize the complexity of SMB businesses and the way in which they run their IT platforms. Often, have some on prem, some cloud presence, and we really need to build worlds that facilitate where the customer is hosting their their current infrastructure.

18:06 So the way in which we've done that is we've built a proprietary framework, and this hosts the machine learning solutions that we build at scale. We can do this in a way where we are very attractive from a cost perspective, but also performance, from a influence perspective. So we take the burden of training onboard ourselves, using the platforms and the economies of scale that we have, and I'll go into more detail on that in a moment. But, essentially, what we've made ourselves is an aggregator of machine learning models. So we possess the skills internally to build our own custom, machine learning models, but we also realized that there is power in using existing solutions as well. So what that ends up doing is positioning us somewhere in between a SaaS product where we have a platform that we deliver our solutions on, but also a professional services, organization as well where we have very highly skilled people, that can deliver the outcomes that our customers need.

19:00 So the way in which we service our customers on an ongoing basis is also important. So we realize that over time business requirements change, and so do the performance metrics of organizations, and machine learning models that we build today might not be fit for tomorrow.

19:16 So what we do is we offer a managed service that looks at the infrastructure, but also at the quality of the model as well. And we work with customers to train that on an ongoing basis to make sure that they're getting the results that they that they require. Next slide, please. So the way in which we deliver that is through a four stage process. The first piece of this, process at the moment is very, very important. So we're at a very early stage in this journey with customers, and the technology itself is fairly unknown to a lot of the smaller and medium sized businesses that are looking to use it. So what we help them do is define what success in AI machine learning looks like. So we'll sit with them, look at their business, look at their business goals and their KPIs, and really help them understand what AI and ML can do for them.

20:03 Once we've done that, we come up with a use case and a business case as well, and we evaluate what type of data they have within their organization. This is done on a ninety minute free phone call with a customer, and at this point, they've spent no money with us at all. So it's a very low barrier of entry to engage with us as an organization. And at the end of it, they have a good idea of where they can take their machine learning journey, and where they should start. The next piece of this is a paid engagement, which is our data engineering engagement. This is where we use a blend of automated tooling, and our professional services team to go into an organization and look at the size of the data estate that they have, but also the quality of the data within it. And what this helps us do is inform how the organization is run at the moment from an operational

20:49 program process perspective, but also what type of dates do they have, and how are they managing that at the moment. Off the back of this, we then have a very good idea of what dates they have and the problem they're going to solve, and we then move into a engagement around the machine learning, model of what they should be using to solve that problem. So we can select the models and train them, and we do that using our platform that we built out internally, which I'll talk about in a minute. We can then test that against the KPIs that we defined in step one with the customer, and produce a solution that solves their problem. Once we've done this and the customer's happy that it it solves their problem and has signed it off, we can move that solution to a production environment, and we're very focused on this as a team. At the moment in the market, there is a very high focus on doing proof of concepts and pushing those out. Whilst they do offer some value, we actually believe that moving our customers to a production solution, as the primary focus is the best, for the customer. So once we've done that, we then manage that on an ongoing basis. So we're looking at the quality of the model and then also managing that underlying infrastructure as well. And what this tends to do is whilst customers go through this process, they have more, more of an understanding of what the technology can do, and they come up with more ideas. So as you produce the results that they, you know, can see and tangibly use, they often come up with more use cases, which in turn results in more work for us as a as a business as well.

22:18 So the way in which we deliver this is our kind of unique value proposition. So we realized that the kind of consultancy, business of, you know, having one person to deliver one engagement and scaling that up in a linear fashion, doesn't really work. So what we've done instead is we've built this underlying deployment platform, that we can host, our models on and train them as well. And there's a number of benefits to this. So the first one is that we're platform agnostic. So like I've already mentioned, one of the big hurdles that a lot of customers have is that they have these very fragmented environments or they're just not very modern. Right? So they have an on prem environment at the moment, but they want to engage in machine learning and the technology and the benefits that they can offer.

23:03 So what we've done is built this platform agnostic hosting platform where we don't really care about the underlying compute. Obviously, there are some hard restrictions on the performance that we need to discuss with customers, and we do that. But we're very, agnostic and can move, with

23:20 customers. You know, this is something at the moment that is actually a big problem in machine learning space. Customers are maybe six months into a twelve month migration, for example, and we can step in, deliver machine learning in their current environment, and then when they move to the new cloud environment, we can move with them in a very seamless, and cost effective way. The the next part of this is where we are able to, build our platform and integrate it very easily, through the API layer that we've created. So we've got a lot of integration points with, you know, familiar AWS, the store environments, but also with off the shelf, SaaS platforms as well. So, you know, CRMs, ERPs, that we've done work with, in the past, and we've realized that there's a a hurdle that a lot of customers have integrating this technology. So often the machine learning work when it sits on its own somewhere and produces a spreadsheet that, you know, isn't really part of a a business process. So this framework helps integrate with the customer's environment and helps to integrate with their current processes that they have.

24:23 So the reason this is scalable for us as a business is we're not doing this work time and time again. We have an underlying delivery platform, and that enables us to move very, quickly as a as a team and not do repeat work. And that means we don't have to scale in a linear fashion. We can use this underlying framework, in order for us to, deliver projects. When people often hear this, they often think of big product teams that sit behind platforms like this. And the way in which we built this is so that it's very light from a platform development perspective, and very easy to maintain. So when we push the updates out for security reasons or performance reasons, this is done to all of our customers at the same time. This is not a customized piece of software. It's only the models that are customized to the customer and their solution. And that means that we can build this and keep it very deformable and very secure on an ongoing basis without having a huge number of people, to support that.

25:18 Case studies. Yeah. So And anonymize it. Okay. Yeah. So in terms of where we've seen success with this delivery model so far, we have two case studies to talk around. So the first one is a very large online retailer in The UK that we've worked with. They came to us in November, so it's actually my second day in the company, and had a conversation with us around leaving SYSGROUP. So that's our on prem hosted environment, and they were looking to go to AWS. Historically, as a business, we did possess the skills required to do that migration, and therefore, they used another partner to start that process over a two year period. They spent £500,000, with that partner and unsuccessfully migrated their work over to AWS. So they were obviously not very impressed with the current, partner that they had, but also kind of disenchanted with moving to cloud. So our team had a look at the had a look at the platform they were trying to migrate from SYSGROUP and worked out that we could do the work within three weeks, delivered that very, quickly to the customer, removed all of their production workloads across, decommissioned the existing SYSGROUP platform, and, obviously, the customer was very, very happy with this. So this is obviously a good example of how those new skill sets that we bought into the company benefited our customers, but also the opportunity it gives us as a business moving forward.

26:41 So off the back of that, they then signed a twenty four seven support agreement with us, which I think is a validation of the good work we've done. And over time, they then started looking at how we can do more, work to SYS ROUP as a consultancy and professional services business. So it's a a good validation. We're engaged with their c level executives across the organization now, and we're looking at how we can push their data journey forward and how we can start delivering machine learning workflows with them as a business. And talk about the revenue profile, how we were able to kinda Yeah. Yes. The revenue we're gonna lose. Yeah. So the the the revenue profile, you know, led by by way of example. So a 100% of the revenue was on our hosted platform, that the customers paying with us, previously.

27:30 That then moved to a very different model because, obviously, moving to a hyperscaler, the hosting charges then dropped for us as a business and moved to the hyperscaler. And what that means is that we had to kind of realize the difference between, you know, let's say, a 10010%, you know, after that migration. And the way in which we did that, obviously, was taking on that twenty four seven support agreement, and that pulled it up significantly. So, you know, offering value rather than just a hosting environment to the customer. And then we've actually surpassed the original revenue that we were making from that customer through a number of professional services engagements. So this is where, you know, the spend that they're spending on the hosting platform is almost multiplied up, by the amount of professional services work that we've done with them. And the customer is actually in a much better position, know, off the back of that. So whereas they were paying a 100% for for just the hosting, they now pay, you know, slightly more than that, but they have a much better environment that's more performing.

28:28 And from a retailer perspective as well, you know, their transition from, non paying customers to paid customer has also gone up, which is very good. Next, the next case study that we have is a machine learning, like, only engagement that we have with the customer. So they came to us with a very open remit of, like, what can AI do for our company, and, you know, what can we use it for? We sat with them in a room, went through some of the problems they were facing as a business, and we quickly came up with a number of ideas that we could, we could work on together. So the next stage after that, we get an initial engagement to look at their data. And the data that they gave us was of the highest quality I've seen in my career so far. So over a period of twenty years, they diligently managed that data on an ongoing basis, showed us what they had internally, and off the back of that, within two days, we were able to come up with a proof of concept that showed the value that we could add as a business. The car dealers. Yeah. Yeah. The car it's a car dealership. Yeah. Sorry. I should have added.

29:28 So they were looking at, as a use case, how they could increase the customer spend, and how they could target customers at the right time and target them with the right products as well and the right marketing material. So once we've done proof of concept, we moved to a production, rollout of a, slightly more complex solution. And off the back of that, they were able to, in a natural language, so the way in which they communicated with each other within the dealership, communicate with the model that we had built, and, get the answers as to when someone was most likely to buy, what they were most likely to buy, and when they should target them given a number of inputs that they had from a number of different systems in their dealership. So I think that shows that, you know, within a very short space

30:12 of time, within six months, we've added value to a number of customers, not just those ones as part of this group. Yeah. I mean, I think an example, you know, in the conversation we had with the owner was you know, he said he comes in in the morning. He looks at the inventory that he has in the lot, and he sees there's a you know, this is a Range Rover dealer. I have three Range Rovers with a black color and certain trim and thinks, oh, you know, mister Smith bought the same likes that trim, bought it two years ago. He should be up to re you know, upgrade his car to the new model. And, you know, traditionally, he asked the salespeople, hey. Have you called mister Smith? And he has to manage that way. And his challenge was like, why can't the sales guy get up in the morning and speak to and say, who are the three customers I should call or 10 customers I should call today based on the lot the cars in the lot? So these are the kind of the dynamic conversation the salesperson can have and have hyper individualized marketing that they can put in place that I think just will accelerate their revenue. So it's really, I think, interesting use case. And, you know, the tractions we've been you know, the feedback we've been getting from not only that customer but other partners who are aware of it has been really exciting.

31:35 Okay. I think this is repeated. Right? Just I can see. Our engagement model, I think I alluded to earlier in the presentation, is that how we go about to the customer. And it's really, I think, reframing what we're selling. I think if you look at the typical MSP, the way they engage with the customer is looking to sell specific product and or services, whether saying, hey, mister customer. Do you need more hosting?

32:00 You need to upgrade your protection. How about connectivity? You know, we're representing a different security software now. You would be interested. You know, which I think works, but from our perspective, I think that we're looking at a differential from a data perspective because data is the lifeblood of AI. I think what's different about business today and going forward is that every business generates its own set of data, whether that's through ERP system, CRM system, HR system, websites, different database, the internal communication, whether that's Slacks or Teams, and even flat documents. We have technology now is that we can convert PDF, and the machine can read it. And that is a very valuable commodity that every business generates every day. And what you do with that is the huge competitive advantage that we're trying to harness for our customers and have them recognize that. And for them to really do that, really, is to understand how data gets collected, but most importantly, how it needs to get hosted, stored, protected, connected, and put our security and monitor to really get to the analytics where the AI kicks in and be able to contribute and augment and generate productivity for the businesses.

33:28 So for us, our salespeople will go in talking about your data source, mister customer, then that allow us to touch every technology stack that that encompasses the IT environment that all business, exist under. So for us, really, I think if you combine AI as a leading as a as a kind of the Trojan horse in terms of the conversation we initiate with our customer, in terms of, are you interested in AI? What can you do with AI? And let us help you get it in AI. Naturally leads to us looking at the every element of the environment and create a flywheel. And a great case study is, you know, there's a customer, kinda quasi government entity that that Ross engaged literally first day at work. I mean, we're very keen to really matter as many customers as possible because we knew there was this tremendous demand of people wanting to not learn but really get engaged on this.

34:32 So this is a company that is focused on aggregating cell and gene therapy solution across the different industry, and they store a lot of data. And they really were keen to do something with the data. So the first meeting at Ross, this is kinda November 7. I mean, it's it's kinda getting very specific, but we do that to explain to you kinda the velocity of revenue you can generate. So November 7, Ross showed up, spoke to their management team in terms of what we think we can do. Two days later, we were wheeled out to their Turing Institute, which is a government entity that proves out any AI consultants or AI suppliers. Month later, we got our first order to do, you know, a health check on their data environment, which led to kinda first looking at their entire environment and identify some of the upgrade potential, whether that's, you know, component server component, the patching that is required to couple of the devices.

35:37 All of these are different revenue stream and, you know, ultimately, kinda leading the different elements, every stack that we talk about. Last year, they spent with us about £200,000. In six months since their first engagement that Ross had on November 7, they had spent incremental £600,000. Again, this impact of flywheel being able to be their partner of end to end choice, not on a just AI, which some people do, or just on on the infrastructure. We wanna be able to help them in terms of the outcome of the solution they're looking for, not the different the plumbing and the electricals to get there.

36:27 So, hopefully, we gave you some of the use cases that we're looking for. And and, again, the idea that we wanna be able to have all the arrows in our quiver to support our customer is important. So to that end, you know, m and a is part of our our strategy. So let me turn it over to Owen, and he can walk you through some of our thoughts. Good morning, everyone. My name is Owen Phillips. I'm the CFO. I've been with the company three months, and I'm I'm really excited to be here on this journey. M and a is a key part of our strategy, and that's part of the reason for the fundraising, and I'll I'll talk to you to to use the proceeds in a moment. In terms of m and a, we're looking across the across the spectrum of of the area we're in, but we're really looking for businesses that have something we have a skill set that we currently don't have that we can use to bolster our offering and that have a customer base that we can therefore, you know, cross sell our existing, package, solution into them as well as as well as cross sell them into our existing. We're looking at a range of businesses. We're actively looking at some at the moment.

37:30 We're generally looking in the region of circa 5 to 10,000,000 revenue at the moment. These will all be EBITDA accretive, so adding profit to the bottom line. We're looking at a range of areas, particularly looking in the security security sector at the minute. And and we think there's some really great companies out there, and we think that we've got price businesses as well. So as I say, we we remain confident we we'll be able to do at least one acquisition in the next twelve months, we're hopeful. And and, really, it's a key reason for the fundraising. As I said, we wanna have the cash on hand so we're good to go and really, you know, really move fast. So that that's our m and a strategy. If I jump to the next slide, I'll give you an over overview of the fundraising, and in part it is to raise money for M and A strategy.

38:16 So as I say, the primary place is to raise approximately we put out that 10,000,000. We're going to achieve 11, which we're absolutely thrilled about. As I said, certain members of the senior management team are participating. We're putting in, I think, it's around 600,000. And in terms of the use of proceeds, which is the bit we're kinda getting really excited about is is raising these funds to to really get going. We're gonna spend 2,000,000 on an internal transformation project. We want to be one of we want to be a true AI adopter and really walk the talk. We want to be a a best case example to our customers, to our employees of the use of AI in the business. Now so we're going to do a major internal transformation project. As I say, we're all we're all evangelists. We we we think this is really the future, and we think that we'll be able to gain so many efficiencies from really putting AI in place.

39:10 We've got circa 30 use cases at the moment. We've hired a project manager, and the team's already got going. We've actually got one AI engineer starting today. So we're really excited about this as a way of way of driving efficiencies and driving the bottom line, but, also, we wanna talk to customers and say, yes. This is how we do it. Come come down to our offices and see what we do. So this is really an area we're excited about. So so I'm personally inviting all of you to come. So in a year's time I know Owen says don't do this, but I'm gonna do it anyway. Year's time, all of you guys are invited. We're gonna have a Capital Market Day to our London Tower Bridge office. You come in. We'll send you a we'll hand you an iPad or a VR, screen where you'll have a virtual life of what's it like to be an employee at SYSGROUP.

40:01 You'll be greeted by an avatar, go through an induction process, you know, for different members. You can ask question, interact act actively. Then you'll be going to a room where you'll be met by Heinrich, where you'll be kind of through be lived through virtually AI assisted enhanced day of a SYSGROUP salesperson. You'll be getting talking to it through, you know, chat GPTs in a natural language saying, hey. You know? What are the things, top three customers I need to call on? What are the products? What are the topic I need to discuss? What are the some of the conversation I had? Will they pop up? Equally, same thing on procurement and on and on and on. So, you know, you'll actually get to live and see what the use cases of AI is, and we're quite excited about that.

40:54 So on top of that, we're going to use a further 2,000,000 to meet our final earn out payout in q two for the Truststream acquisition from from 2023. There'll be no further earn out payments due then, and that'll leave a balance of 67,000,000 retained cash, which we're gonna use for working capital and, crucially, m and a opportunities, which I went through on the previous slide. On top of that, as a final point, we're putting in place a management incentive plan. We recognize that retaining a motivating staff is absolutely crucial in taking the boots on this journey, and so we're gonna have a value creation plan and a performance share plan for employees. Okay. So what I'll do now, there's been some questions submitted ahead of time, and, equally, there are some questions that are coming up as we're giving the presentation.

41:46 So let's see if we can do first the presubmitted. What is the forecast? This is for Owen. What is the forecast for the next two years, and what are the drivers? And I appreciate, you guys don't most of you probably don't have access to the ZEUS research last week. Okay. So our intention is to achieve 25,000,000 in revenue and 2,200,000 in adjusted EBITDA. And then the following year, we're targeting 30,000,000 revenue with 3,800,000 adjusted EBITDA. There's obviously a a a big increase, so that's f y twenty five to f y twenty six, and that will really be driven by sorry. A big increase in revenue, which were driven by, as as we've discussed, the flywheel effect, but also our consultant led AI, approach really, you know, gaining momentum is our intention.

42:40 There's a there's an increase in in EBITDA as well, and that will really be the efficiencies coming from, as I said, our internal transformation project combined with really, you know, momentum gaining generating in the sales teams and the AI teams. So so that that's our that's our objective. As I say, we're we're we're excited to to crack on and get going. I mean, this year and next year will be would make the heavy investment. I mean, you know, from a headcount perspective, we expect to go from 100, you know, end of year. 100 to a 100 about about a 125 by the end of the year. Yeah. So, I mean, that's, you know, that's probably the investment we're making before the revenue kind of kinda kicks in and the margin kicks in in two years from now. Right?

43:27 '26. Good. Yeah. Yeah. Next question for Ross. What differentiates you from other AI companies or MSPs? Yeah. So I think in terms of the differentiation, I think it's the fact that we've got a very highly skilled group of people. So this skill set at the moment in The UK is very, very it's very, very scarce. And what we've done very quickly is build a team a fairly significant team in terms of size, but particularly in capability, we have a very widespread of skills across the team, you know, networking, hosting, cloud, AI, ML, data science, and we've done that very, very quickly. So the the skill set is definitely something that differentiates us. As I spoke around in the presentation, we focus very much on production rollouts of AI and machine learning as well as proof of concept.

44:19 But also, we tackle some of the very, very hard topics that customers have. And because we can deliver across that host of, platforms that that he just spoke around, you know, across networking, security, hosting, etcetera, we're very uniquely positioned to offer, machine learning to our customers, you know, irrespective of the environment that they sit in at night. And, can anyone else replicate this? So they can. I guess, you know, there's nothing stopping someone hiring, you know, a group of people very, very quickly. The reality is they probably can't do that. So the the team that I put together here very quickly, half of them joined with me. The other half have worked in business with me before and know of me.

45:07 So it's very much a reputation led employment cycle that we've been able to take advantage of and hire a team very quickly. You know, if I was to do this without that, it'd probably take eighteen to twenty four months. So I think we've got the first mover advantage not only on the platform we're building, but also in the people that we've managed to bring on board as well. Okay. So there are other questions that I I think similar to the questions that's been asked that I kinda combined into that into those. Some on screen question. I guess that there was a question about my role as a executive chair. So let me just roll up and say, could you give more detail on your role as executive chairman? How involved is this day to day are you?

45:57 Said, you are not in the photo of the leadership team, seriously, not because you're too old. Having said that, you know, I think the average age of the people on that screen's in the thirties, and I'm almost in my sixties. So, you know, there is a big gap in terms of age. So I don't think that's too of a of a misguiding answer. Having said that, now, look. My role, I think, you know, the most difficult and I think time consuming bit is behind us in terms of which is the putting the team together. I think that that really you know, the idea is easy to come up with the thesis, but, really, it's finding a team that can implement it. It's been really, I think, the biggest accomplishment if I reflect back on the next twelve years last twelve months. Sorry.

46:47 And, really, I think, you know, we didn't do justice in terms of just giving more details in terms of of the backgrounds of the people, but, you know, Heinrich as a CRO who's been ten years a successful career at Softcat to lead that and join, you know, join the the journey and and bring few other people into sales who are very commission driven, if you know this soft cap model, to to leave that opportunity and and start all all new, I think, really, I think, to the potential that we've been able to do. And I think, again, as Ross just alluded to the reputation that Heinrich has in not only in that company, but in the industry. So Ross, I mean, despite his age, he's kinda the one of the old timers on ML.

47:33 You know, when he started his career at JPMorgan, I think he was one of the first to kind of get into the ML space, and and he says he's the first one to have installed GPU capacity in UK. So, you know, whilst this, you know, is, really exciting, it's very new. So being in the industry for fifteen years does give you a lot of credibility and experience. And I think, again, that's validated that that people that is being able to attract. I think it's you know, you'll hear the challenge in the AI space is really being able to get the right talent and how difficult it is. And I gotta say, you know, they are knocking on our doors. You know, we're not using recruiters. People are just hearing words-of-mouth to join us, and I think that's quite exciting and and for us.

48:23 And and and Owen here, I mean, you know, he joined us from Matillion. That's the last unicorn come come out of, I think, UK. I mean but definitely Manchester. They they went from start up out of Warrington to 1,500,000,000 valuation last year. So, you know, for him, you know, he thinks that we'll have a similar trajectory growth, and I hope he's right. And, you know, and that's just touching few of and and I think there's another question below, which is about how well is the integration of Truststream going. I think it's going really well. And and, again, part of that being, you know, the owner and the founder, Paul Sullivan, after he made his earn out, decided he's gonna stay on. He was very much when I first met with them when I joined ready to leave.

49:10 He's saying, look. I'm waiting for my earn out check to clear, and I'm out of here. But I think the fact that he is signed up to be our CTO, and, really, I think, know, he's working harder than he did when he was he started his trust stream. And he's very excited. He's invested in in the equity raise, and he's very motivated. And I think, you know, again, the team that's been to get put together, not just those who I just mentioned, but you know? And Wendy, Beck, Samaya, Charles of the world, I think it has been the hardest and the most important part of my job. And I'm still very active in the business, but I think it's my job going forward is to make sure I don't get in the way as much as trying to tell them what to do. I'm never gonna tell Ross how to design his machine learn like, you know, language model machine learning models. And and so, you know, I think I know, you know, after twenty, thirty years of managing business, I'm I'm more aware what I don't need to do versus what I need to do.

50:20 What's the next question? I think it was one more question. Right? Actually, the question is the new AI team in Heinrich Salesforce appear to have very different sales approaches. How do you see them working together? How do you ensure they are coordinated? Actually, I I think I very much disagree. I think as we talked about in terms of having our engagement perspective to be data focused because that is the outcome that drives the outcome, which is all about AI. I think it's critical that those people work in hand in hand, and the engagement that our salespeople have in terms of how do we help you leverage data, is a critical component, or is is it kind of the framework that AI falls in in the within the six stocks that we're talking about. So, actually, I do think it's a it's a kind of very symbiotic.

51:22 Next one. No. Go down, please. A lot of questions. Great to see you putting your money where your mouth is. Is that in has been investing in AI. Can you give us comfort that investment is fairly balanced between the bleeding edge and conservative? Well, I mean, you know, the only comfort I can give you is that in twelve months, you're gonna come and see it yourself. I mean, I think if we were not confident and and comfortable that what we're doing is right, I won't be sending an invite now. You know, I think, hopefully, when you come in and see, you will get to feel and see what, you know, AI is about I think it's beyond kind of the the, you know, the kind of the the the smoke and mirror a lot of people are talking about today.

52:08 And and I think what's exciting for me on a personal level is to be able to become the lighthouse of AI because I think, you know, we need in UK something that we can be proud of and just put ourself as a forefront. And I think that will put us so far ahead in terms of what everybody else in the world's doing that is a truly AI business. Just one another point in terms of, I guess, the the first question David asked in terms of, you know, whether I my you know, I guess, you know, wondering about my engagement. I mean, look. Title aside or whatever or not, whether I'm in the picture or not, I think what you can discount is that I have, you know, significant portion of my liquid wealth in this business as well as I further invested on this equity raise. So I'm highly invested and motivated to make SYSGROUP successful as the rest of the management.

53:01 Okay. Next question. Where is it? There's a no. Oh, I think in terms of the as you as you propose your AI pitch to more customer, what areas of the pushback you're getting from the customer? And I think the three areas that we highlighted in terms of understanding what AI is, understanding the financial implication, and and kind of having a trust partner that they can take on the journey. And that's why kind of the sales cycle that Ross went through in terms of where we are focused very much on the outcome and not coming in and and more incrementally, you know, proving it out and charging the customer along the way instead of kind of the typical approach today saying of the big four or the Capgemini or McKinsey saying, oh, give me a million pound before I come see you without really understanding what the outcome will be.

53:58 So I think that is kind of the big pushback, and I think because how we are approaching and framing our proposition differently than how it's been done historically, I think goes long way to addressing some of those pushback that we're seeing. Ross, you wanna answer that? Yeah. So question is, is the way you do data analytics different from what the market offers? Do you actually possess your own analytics software? If yes, can it be patented? So the framework that I went over on the on the presentation, so that's the unique way in which we deliver deliver our solutions. So the framework, can do that on the proprietary, and we built that in house.

54:52 And as a result, we're going through the process of how we secure that at the moment. So can't answer the bit. Can it be patented because it's pending that process, but the answer is, like, we are committed to protecting our own IP. I mean, I think, Jeremy, the the key thing for you to appreciate is that the value that we bring to the company is that our solution is based on the customer's data. It's not on a generic data. So, really, the value we get from our solution is the fact that our software, our our models, and our architecture is taking advantage of the information that the company generates, and we're providing solution on the back of that. So it's not in the the process, the model itself, the software, but how we can understand where the data and what data do the customer have and understanding the business requirements of the business to be able to help them with the outcome.

55:58 The next question. There's one more. So we'll do one more question because I think we're coming up to an hour, and we don't wanna get over an hour. In terms of m and a, is it possible to talk about valuation in the sector? Can you please give me an idea of likely mix of organic versus acquisition growth in the business? I mean, I think in terms of so the multiple, I think I gotta say it's more of a biased market right now. And and I think if I can just put it against my own experience of where, as I said, you know, when I started this journey, put together a list of 300 companies and start talking to them. The expectation and and the valuation, I think, is somewhat parallel as what you see in the public market. I think a lot of the business, I would say, in the scale that we're talking about is, like, five to ten ten times EBITDA.

56:50 I think that's probably where it's a right number. That's probably where we look at. And and in terms of the mix of organic growth versus acquisition, the forecast that Owen has given is purely on an organic basis. Been clear. That excludes acquisitions, the numbers I gave you earlier. Okay. And I think with that, I think let's kind of end it here. You know, one hour. Hey, Jay. Thank you very much, Owen and Ross, for your time and your generosity with the Q and A. Hey, Jay. I know that investor feedback will be particularly important to you, and I will shortly redirect those on the call to give you their thoughts and expectations. But just before we conclude today's meeting, I may just come back to you just for a couple of closing comments, and then I'll redirect investors to give you feedback.

57:39 Thank you, Mark. First of all, I wanna thank you for joining us, taking an hour of your time and and probably times beforehand, to get to know the business better. I I appreciate this is new space for a lot of you, but it is quite exciting. I think it is a technology, a and I think it's an area that is, on the cusp of transforming a lot of the businesses out there. And there's a significant demand and requirement. I think we bring a unique solution, that no one else is doing it. I think we have put together an amazing team of people with a very broad range of skills that is needed to, I think, participate in this space and deliver the outcome that customers are are looking for.

58:29 From my perspective, I wanna I was very keen that as part of the race, we make a a opportunity for the retail investors who have supported SYSGROUP in the past to come along, in the journey and not be left behind. So, hopefully, you know, a lot of people will take up on the opportunity. I do think it's a it's a great investment, and I think it's a quite exciting journey we'll get on. So, again, thank you very much. And, you know, we will very much be engaged with the, retail community. We will be going around, every results to, obviously, every platform that we can think of to engage you. And in your feedback, please do tell us what's the best way to communicate with you. I know with the the the on the regulatory, you know, the dynamics is difficult to provide and communicate as often as quick and and as openly as we can with everyone.

59:30 So, again, thank you very much for your time, and, Tom, thank thank you for your support. That's great. AJ, thank you very much indeed. Could I please ask investors not to close this session as we're now automatically redirect you for the opportunity to provide your feedback in all that management can better understand your views and expectations? This may take a few moments to complete, but I'm sure it'll be greatly valued by the company. On behalf of the management team of SYSGROUP PLC, we'd like to thank you for attending today's presentation. I wish you all a very good day.