CHAPTERS Group CMD 2026 | Long-Term European Software Sourcing Allocation Strategy Outlined

Welcome everybody. This is a very new format for us. We have twice as many people here in the room as last year and it's the first time we're doing a webcast. So this means that a few of the aspects here are a bit of a hybrid format. And the way the day will roll is we're going to do a few rounds of presentations and then we're going to host an event format that we've done internally a lot which we call the AI marketplace because one of the questions and one of the feedbacks we've gotten, we got a lot of questions around the impact of AI and software businesses and we felt the best way to explain to you and kind of align your thinking with our thinking is to just actually show you what we do. This is going to happen after the event for the webcast participants. So you can participate, we will do a brief kind of round of fire introductions to the project that we are presenting right at the very end. So you will get a glimpse of it. A downside of that format or an upside if you're here is that it gives us a lot of room for Q&A with a lot of people from Chapters here in person. So any question you possibly have about our business you can ask here today. Now that's obviously not really possible if you are dialing in. So what we suggest to everybody who's dialing in, if you have any questions and you want any deep dives, get in touch with Andreas after the capital markets day and we will put you in touch with whoever you want to talk to in person and we can do one-on-one sessions again. You're probably wondering why do I have an orange dot? The reason is that I forgot the Chapters lanyards. So a way and like an impromptu way to identify everybody who's working for Chapters. Everybody who has a dot on their collar in different colors from the presentation kit next door is working for Chapters Group and you have all the liberty to approach these people and the two of you are getting dots as well. Sorry. And ask all the questions. This is going to be open end. We will only leave if the last of you leaves and you can ask all the questions that you want. So let's start with where we are. This is a really interesting inflection point of the business. What you see on the left is the cut of the business in 2023. The business was about half software. We had a small investment in a business called Fintiva and a pretty big chunk of the group was still in what we call other today. So businesses that are not software or financial services. Now in 2023 as you know and as we talked about last couple of years we made some hard decisions on how we want to transform the business, around how we want to look like and more importantly what we don't want to do, and the result you see here. So our software groups of public sector and enterprise have grown substantially both in relative share and also in the absolute size of the business and also our financial technology segment which is not only the Fintiva business but two other businesses Expatrio and Coral that we acquired and now a very substantial part of the group. And importantly, because it seems so minor, what we have left in other is actually one of the more exciting businesses that we have seen and one of the more AI forward businesses that we have in the group with Stefan here presenting today. So I think we've truly transformed the group.

Now this is continuing and I want to point out one specific example in my presentation that will work as a proxy to explain how we think about building a business and that's our financial technology segment and I will use that as an explanation and you will derive a lot of conclusions for our software group from that. So this is our indicative split of revenues between financial technologies and the public sector and enterprise VMS. So you see the piece of the pie of the financial technology segment increasing in 26 over 25. But what might not be so intuitive or which might not be well understood is that if we look at profitability, this is actually a very big part of our profitability at this point and at least for us Malain and I and the board, like this happened very quickly, like it was clearly intended and this is why we did the moves that I will walk you through in a second. But still it's important to reflect on how the group is changing and where the gravitas of the group is today. When I personally got started in building Chapters, I'm an investor at heart, right? So the natural tendency is to get started with an investment holding mindset. Very influenced by Warren Buffett, very decentralized, thinking like an investor only. That's how we really got started at the beginning. I always say we want to build an operating system built on autonomy. Really at the beginning it was built on anarchy, well functioning anarchy, well performing anarchy but really just a loose collection of businesses. And the real insight we had and that really started in 2023 with one of our largest shareholders who brought us that idea was to say there is actually something more you can do which is not to be just a capital allocator of businesses but you can actually build a group that is worth more than the sum of the parts because you're actually improving the businesses that you are running and if they join part of the group they become worth more. And today we want to explain to you how that works. One phrase we use internally is we're trying to build industry standards. So historically we have acquired businesses in particular in the software space as long as the recurring revenue is good and the churn is low and it's a very well-defined niche in a vertical we would acquire that business. And I think over time we've both because we have built almost by accident certain industry clusters but also because we've seen the benefits of building these little platforms in specific industries. We have some good traction of building industry standards and high conviction that that's what we want to do in the future. And I want to walk you through one example where this played out particularly well.

So you see the split format. It's a bit of a more sleek design now, but the numbers are the same from the annual report. The three segments I want to touch on are the financial technology which you know as the segment with the three brands in Oracle and XPro that's how we presented this in the past. Now you also know me that in all of my slides and all of my presentation with the team or investors I always talk about the three values that we have. I will spare you with that right now. But I will talk about one which I hold most dear in my heart which is we grow together. And I always talk about the story of you know like you see a pie on the table and there are two different types of people. One person sees it and says oh listen like how can I optimize my largest slice of the pie and how can I eat most of the pie? The other type of person says okay listen how can I optimize for this? Now, at the beginning of the year, I thought about hiring a chief of staff. Now, we don't really have any office space left, so I decided not to hire a chief of staff. So, I trained Claude, the chief of staff agent, and uploaded all of my thinking and how I communicate in my presentations. And for this presentation, I asked Claude to the point I'm now going to make to put that into a graphic incorporating these values. I think the result is absolutely terrible, but I want to share it with you because that's what AI does. So this is what AI came up with. The situation we found in the block account space was a zero sum opportunity, right? You had three companies competing for one piece of pizza, three dogs, it's mine. No mine, back off, it's mine. Both losing dogs leave hungry. I think what we achieved and now the magic of AI happens is this. So the dogs are now a lot more proactive. They're wearing shaft hats. More pizza, more fun, more wins. Every dog leaves full and happy. What does it mean? It means if you essentially canalize the energy that you had on what is there, it very much limits your thinking. And when you canalize the idea of growing the pie, this is where the real magic starts. And I want to be specific on that opportunity in financial technology. Now, what's our business doing? This is Chat GPT. If you type in Chat GPT I need a blocked account in Germany. That's the product we are providing. So it's a special requirement for the visa process. You need a blocked account and you need insurances around that and the three businesses provide that. It gives you this result. You can go to those four players, UA and Coral or you can go to Deutsche Bank. In 2025 we had enough capital to consolidate three of the four which we did. So grace for impact. And really what this created is that historically the entire market structure always focused on essentially two products. It was the blocked account and insurance. And there was a lot of competition. There was a lot of marketing spend. There was a lot of fighting. And it was really fighting around that one pizza piece. And I think what got lost in that process is that misses the entire point because 50% of international students coming into Germany stay for longer than 5 years. 50% stay for longer than 5 years because they find love or work or both. Those are usually people that come to Germany to study and then find good jobs here and they have no prior relation in any financial services in Germany and the first entry point with financial services in Germany is us and it's kind of obvious that the big opportunity and the big prize at the end of the rainbow is to change perspective and essentially become the partner for all financial products for internationals in Germany for the duration of their stay which in many cases is forever.

Now what we achieved with that market consolidation is that I mean you got to imagine they had developer teams that were building the same product to be competitive three times over. So everybody needed a certain product and you build it three times because everybody needed to offer the product. How you can use the same development resources and you can build three different products that you can monetize, which sounds very trivial, but it's just how this works. And we are now essentially resetting the focus of this business away from this is a block account insurance business to this can actually become a really end-to-end financial partner and in the future that business that you see at the top of the water to essentially become the funnel to something much larger. Now this probably as an investor sounds all super sensical and it makes a lot of sense but I want to share some of the operational pickups and decisions you got to make and also some of the opportunities that we found in doing it. So when we combined the businesses in about a year ago in May of 25, the thesis we had at the time was to essentially keep the three businesses separate, migrate one quicker than the two others and then over time figure out what the platform strategy should be. The idea was also to approach a mindset to kind of learn the best strengths from all businesses and from all platforms and make a best of all worlds approach and kind of go slowly and with a measured approach and take our time to figure out what the target operating model is. Now the philosopher Mike Tyson said everybody has a plan until they get punched in the face because operations happened. So what we found out the teams did not get along at all. It's not to blame, that's something that you see. What we saw is we had very different standards of compliance, IT processes, efficiency across the business. It's like very different. We found out that in one of the businesses which we ended up shutting the brand off, there were some really serious compliance issues with how they ran the business. And this is a product where you must absolutely not screw up. Right? So those are people from Pakistan borrowing money from their family, wire it to Germany to us in the hope of a new future in Germany. And in that process we got to make sure that we don't channel any people into Germany that don't belong here, that we don't lose any money along the way, we don't lose any data along the way. All these things got to be not here. They got to be absolutely top notch. And the mistake tolerance is zero. It was a regulated business. The state tolerance is absolutely zero. Now some of the founders, some of the businesses saw this differently and we had to make changes. Now what we also saw and you start out with the announcement, the idea was for Bastion who many of you know to become for Chapters Group and essentially run this group which he did at the beginning. Now Bastion also became a father at the time and his wife got really ill very quickly with a newborn at home and I made the decision I said listen you're out immediately, I take over, you do your thing, this is more important. They're fine now by the way but they were like it looked differently at the time.

So last September I was in a situation where I said okay I got various compliance problems, I got an incoherent IT strategy, I get teams that don't really work together and we got to find a way. So come in manuscript method and the way we run things. So what I did, everything starts with a strategic plan. So you come up with an idea of how should the business look like in three years, what are the big drivers, what are the really big value drivers or how the business can move to the next level and we use a tool that is called policy deployment to track progress against that. That's not something that gets adopted overnight, you need to teach policy deployment and you need to put the processes in place and you need to convince the team and you need to fight the right level of engagement that needed to happen while at the same time figuring out how is team structure going to work. Now I got really positively surprised because some of the talent that naturally I didn't know before, in particular at Expatrio, in particular Toby who some of you know Toby Fisher, the now CEO of the group, really impressed me not only by their personality but also with the approach they had because the mindset they had and which we now adopted and I grown a lot of confidence that this is how you can run situations like this now and AI plays a role to say listen let's get to target operating model just way quicker. We had a lot of, I mean maybe we're a little shy, maybe we're a little cautious, we haven't done it before but we were just giving this time and I think we were just delaying a decision and at that point a really big driver was the CTO of that business, a guy called Pablo who's amazing, who came up with a strategy to say listen we're going to streamline the IT stack mainly through AI build development and this is going to happen within months not within years and that's what we ended up doing. So I appointed new leadership, we changed the plan, put new leadership in place, put new incentives in place, we have a strategic plan, we implemented policy deployment and we started executing much quicker than we initially thought and this is what we end up doing. So it used to be a three brand strategy is now a two brand strategy. What used to be two and a half platforms over time migrating became one platform quickly and this just creates so many efficiencies. I mean in theory nobody would disagree, right? So in theory nobody would disagree that that's the right strategy but in practice this is really hard to execute because you have a lot of people care about the worlds that they have created in the businesses and they want to treasure that which is very human, right? But you got to change the mindset to this is what we have done and in the past it used to be Fintiva, Expatrio, Coral but there's now a new way. It's not about whether you are better or worse, but it's all about how's the target operating model and how can we get there and this did work out and I think it's an amazing inspiration for how we think about industry standards. A logical consequence of that is also that in future presentation we will not say this is a financial technology segment with three brands called Fintiva, Expatrio, Coral but there's a group name which is Lumra which we implemented in December.

So going forward you will have to get used to this graphic which is the financial technology segment with a big part is the Lumra group. Why do I tell you this story? I tell you the story because this is a big part of our Chapters identity and the genesis is really interesting. This started as a minority investment 20% stake May 2021 in a tiny business and this has now become an industry standard, the best compliance standard for students into Germany and a huge driver of organic growth and I think that's the inspiration we want to have. So this business is going to do fine, right? It's going to grow. The efficiencies are not nearly like we will have another period of significant efficiencies in front of us. But this is really much more. This is an inspiration of what we can do in so many parts also in the software group and we've already started this. So there are already some of those early clusters simmering where we have already a couple of companies that are doing something similar and the idea is now for us to scale this and replicate what we've done here. And the big reason, that's one of the big reasons why we quote unquote have to adjust our guidance is because the execution versus the initial plan is just happening a lot quicker than we originally thought. And this is also the reason why you're doing this because this is driving organic growth. And usually people think when they think about consolidation, they think about pricing, and they think about the platform synergies. So that's true. But also what's even more true and what's even more relevant is you can just serve the customer so much better. You can just use your development resources and you can use all the resources you have to really redefine an industry and become a true industry standard. And we've done this once. I think the financial results probably not super obvious last year are now becoming increasingly clear. And the vision I have or what gets me really excited is that one day we stand here and we reflect on how the financial technology segment is in absolute numbers dramatically larger than it is today but hopefully in relative terms something else is much bigger and I think that's the story of Chapters over the last couple of years. I want to give you another example. So it's usually very hard for people to make decisions around how something may look like in a year and people focus mainly on what is here today. And what we do to the team and what we do within our board communication is always reset focus on where are we going towards and not where are we coming from. Because what really matters last year is the decision that the pie chart that you saw around profitability is just a lot larger share in one segment. That's the messaging here. And what gets me really excited is to stand here next maybe next year is a bit ambitious but maybe in two years stand here and show you a much larger pie chart with larger actual numbers and financial technologies and hopefully a nice large who knows what's going to be software. So thank you very much. Now you have all the good story and I'm handing over to Maline for the hard numbers.

Thanks a lot, Marlene. I have three topics. One topic is transformability. One topic we already mentioned is industry standards. And of course no VMS presentation without the topic of AI. So we heard from Marlene and that's the top part of this slide that obviously the manuscript method seems to deliver with the improved guidance. We also heard that we have also in the VMS space first indications that building industry standard does make sense. Now how can we actually do that in practice with a little bit more detail around a French example? I know they now also out of the World Cup but I still miss them and so we will still have them on the slide. And obviously what is playing the role of AI in that regard. So before I introduce a concrete example, I like to take a step back and look at why we all in this room like vertical market software from a first principles perspective.

You can see on the left hand side why we like investing in vertical market software. Obviously the well-known notion of high switching costs. So customers are locked in these B2B software solutions, it's a lot of effort for them to move from one vendor to the next vendor. You have to train new people. You have to buy new software. You have to do a lot of customization. Takes a lot of time and you're operationally depending on that software. So people typically don't do that. Even if I tell people to hijack the prices and move the prices up, they still remain clients. Another thing that we really like is although those vertical market software markets are sometimes really really small, remember we still have the market leader of orchestra management software doing revenues of €1.5 million and we are the market leader. So sometimes these niches are really really small but what makes it attractive is that in those very small niches you only have a few players and you don't have a lot of competitive intensity and all the big guys like Oracle, like SAP, they don't find it economically useful to develop software for such a small niche so the supply scarcity is protecting our companies from a lot of competition. And lastly, but not least, most of the clients of our software business have what I would call a good enough inertia. They don't need the best UI. They don't need crazy additional functionality year over year. And frankly speaking, they're sometimes also pretty happy with okay service. And all of that creates a low churn profile, very predictable revenue streams and we end up in buying those assets at pretty okay. So it's a good business to be in.

And if we combine what we can offer to these companies, we can offer much cheaper capital compared to those sellers of those businesses or buy and hold forever approach of being a public company, not a private equity, not being required to active those businesses is a very good fit to what those owners of those software business actually want. That is stability for the legacy they built over decades. Now the special thing that we bring in and I think we even more bring this in since the last two years is we collected a lot of best practices how to improve those businesses. So it's the improvability that we bring to the table. We call it the manuscript method. It is policy deployment. It is pricing. It is a lot of additional thing that we collect and share in the group to improve the businesses and that eventually makes up the 22+% guidance. So the classic power move is buy a durable asset, improve it, and everybody is happy. The key thing here on the slide is the defensibility of those assets is not something that we add. The defensibility of these assets comes when we acquire the businesses.

I think we will see later in the presentation why this is maybe changing with AI. Now a concrete example I always appear here trying to come back to last year's session. Last year session I already talked about the French business that had some problems. It was this business somewhere in southern France that we acquired. It's a public sector VMS business, small business, 3 million revenues, bit more than 1 million in either... school caring of kids. So imagine your parents in France, you have to work and you want to have your kids being taken care of. Our software is managing this from a customer's perspective. We acquired the business and then after the acquisition, we realized oh the business is not collecting any new professional services bookings. What's going on? We used policy deployment. We did come up with some countermeasure plans. We assigned some additional people to the sales side of the house. Nothing worked. And we used what we call the diagnostic manuscript ratios to figure out where is the issue of this situation. Issue was sales of marketing ratio was off, professional services ratio was off. The long story is either we bring in new professional services bookings or we need to conclude that this business is overstuffed in terms of professional services and we have to let people go.

So that was where I was at at the same time last year. And after this workshop, I was like traveling to France to sit together with the team somewhere in France, middle of nowhere, 50 kilometers outside of Marseilles. And that was like a very cozy meeting room with a lot of people with a very small window and it was really really hot but we had a lot of coffee. So what we did is we discussed what is the situation of the professional services. Is this business really not having enough professional services? Do we need to call to the team? And what we figured out is that it's not a problem of the business not delivering professional services. The problem was that there was no charging of this professional services delivery in an appropriate way. What they did quite often is they had some health checks. So customers wanted to improve the usage of software and then they charged like €5,000 for a workshop for a one or two day workshop which is kind of okay. But the problem was all the change requests that came out of this workshop were actually included in this 5,000 and sometimes they spent like work for like €20,000 and didn't charge a dime for that which is suboptimal.

And the other thing is they had a couple of larger cities as customers. And these larger cities they were just having kind of dedicated support people. They called them like all the time. And we figured that some individual employees worked like three days out of the week for a particular client and they didn't charge anything for that person. So what we concluded we wanted to introduce two manuscript modules. One was a tool called Redspace, another was a value based pricing normalization. And the Redspace tool is a really nice tool. It basically gives you an Excel sheet. Just visualize it a little bit. You have a column with the days of the week. Then you have columns for each employee that should deliver professional services. And at the beginning of the week, everything is like red, hence the name Redspace. And the people, the individual contributors then have to fill each day with professional services work. If they achieve a day full of work, they can turn it green. If they have no clear confirmation from the customer, but maybe they can do some work for a customer, it's yellow. If they have some vacation, it's gray. And if they cannot bring in enough work, it remains red. And the team is meeting every week and everybody in the team is looking at that sheet and everybody is obviously kind of a little bit embarrassed if all is red. Right? So people are really calling customers, can I do this work? Can I do that work?

And after a couple of weeks, the situation typically looks like that. So some people have it all green, some people have some red and some people have still a lot of red. And what then the team does is to understand what is actually this person doing all the day although it's not billable work but then they figure out situations like I just mentioned that a customer is constantly calling that poor employee and the poor employee cannot defend themselves not to deliver services. So then the leader needs to step in and change that customer relationship that we either stop this or have the customer pay for that. And the business also did come up with a functionality in the software. They called it the green button. So whenever a customer called they could press the green button and the green button was recording the time they spent for that particular customer. As a flanking strategy they also did a pricing normalization. So they introduced a new service level with the customer and as you can see there's a gold level that's plus 60% maintenance revenue charged if customer was on gold level and of course those nasty cities always overusing those professional services didn't have a choice not to take gold right. And we did also some communication for the team because the team needed to understand why we are now treating the customers in a different way. The team first time ever realized how much revenue and how much profitability the business actually is making because the former owner never told that to the team. And all those initiatives led to a lot of individual accountability taking. And you can see here these are the monthly professional services bookings that we did achieve when we started introducing this somewhere here. So this we increase that by 160%.

And all of those things is probably a combination of certain tools we share within the teams and the people on the ground that take those tools and execute those tools and these are people that are different than me and I'm very grateful that we have a lot of such people nowadays in the business no matter in France or in Germany and that's how this full manuscript, call it 1.0, actually is working.

Now what happens with AI and what happens if you would just continue with implementing the management method. What happens with AI is that AI is not attacking directly the different modes I mentioned on the first principle but it basically attacks the underlying factors that lead to those modes. So why are there high switching costs? So one big answer to this question is that it's just bloody painful from a migration effort perspective to move from solution A to solution B. Now with all the automation that AI is offering that migration effort ladies and gentlemen is decreasing. So that will erode. Will high switching costs ever go down to zero? Probably not. But they will erode. The supply scarcity. So having a small niche and nobody coming into this niche because it cost so much to develop for that small market, the pestra example I think that will break because with every frontier model we'll see the build costs just go down and down and down and we can see that and you'll see later with some examples that Toby is sharing. It's actually amazing how quickly you can come up with a fully fledged ERP solution and we did that internally as well. Now the good enough inertia so the satisfaction with the status quo that will also depending on the particular businesses holds back for a while. But when you have a situation where the AI startups or the existing providers of competitive solutions are providing more and more value using AI in their products at some point the delta between our solutions if we don't do anything and the competitive solution will be so high that this delta kind of making sense if you combine it with low migration effort. And what we also should not forget is that often times our solution has been introduced to the customer organization maybe 10, 15 years ago the guy that has been introducing the software from a customer side it's getting older and older and at some point that guy will retire and then a new guy comes in without all that history with all that relationship and the new guy will maybe say oh I want to have a cloud solution. Oh, I want to have... Yes. And I should have the microphone on my lips. Exactly. So these are kind of breaking points and if we are not the one that delivering top AI enabled services and solutions at that point then we're losing out.

And that is if you become like a lazy incumbent there is another option. We have the same possibilities as the AI startups using the different tool sets, the agentic tools that are at our disposal. So if we can bring our existing companies to now move quickly because we have several advantages, we do have the distribution to the customers. We have these customer relationships. We are basically clear from a procurement perspective. It's much easier for us to deploy new software versus a new player. So we have a lot of advantages but we really need to move quickly and that's why we did come up with this VA you might have heard of this stands for red alert for opportunities kind of our AI strategy that should help our existing companies to move quicker and to AI transform those businesses. Now if I go back to my first principle slide, you might say, 'Oh, this is red. Switching costs go down, the niche is no longer unattackable and you have a yellow on the good enough inertia.' Am I in the wrong movie here? And my answer is no, you're not.

And why is that? Because these red situations also create something that I told Greg, one of our investors from a sader graph just before the session started. Often times in these little niches, you do have just a couple of players as I mentioned. It's like the four tiles here on the carpet and that market structure is very hardly ever changing because of this low customer turn profiles. Now AI is changing that and those players that don't move will basically being washed away and there's also like a possibility and opportunity for our companies to actually expand their market share not only against these AI startups but also against the existing players. So there's a great opportunity for us if we can bring our existing companies to move quicker than the existing players. And I think what we also should take away from this slide is that the AI through these wet areas is actually breaking the division of labor by these companies no longer can protect themselves because those things break. So there's actually a responsibility and opportunity for the whole co or the platform in our situation that we help those companies to transform and that implies that the manuscript method needs to transform itself to just improve businesses as I just have explained by the example of the strange business but to actually transform the companies. Now what does that mean?

If we talking about transformation, I think it's good to remember that most of our companies actually have quite some time until these red situations come in. And why is that? Because they're mission critical. Now mission critical is a very high level term. So if you go down one level and you ask yourself what actually makes mission criticality of these software businesses, I would like to introduce a concept that I call operational anchors. What are operational anchors? Operational anchors are data and functionality in the software that if you remove that the customer really has operational problems to deliver. So often times they're critical parts in the main workflow of the customer. They accumulate a lot of valuable data. Often times these operational anchors are also connected with each other. By the example of this French business, all of them are example of that French business. So for instance, our software is collecting the attendance data of the children in these nurseries and this information gets sent to the city and then these nurseries they get subsidies. They don't get paid if this software doesn't send this information. They use it for the canine forecasting to know which child has which dietary preference. And either you have enough meat or not enough vegetable. I mean this is an operational issue for those customers and the mayor of that particular city has a legal obligation that every child is enrolled in school. Guess who is actually managing that source of proof? It's the software of our business. And if they don't provide that regularly, the mayor of this business will violate the law.

So these are basically networks of operational anchors and that's one of the reasons why customers shy away to replace those businesses. Now what happens if we acquire and collect businesses in the similar industry and in this situation we actually have that I already was talking about this business for early childhood caring. We do have when we are taking the vertical as citizen in France in social need being it parents that need to taking care of their kids while they're working or a little bit older children that might be disabled or they might unfortunately have lost their parents. There's software to manage that. We have two businesses. We call them part one and part two. And we do have two businesses that take care for elderly people. So some elderly people in France they cannot pay their taxes because they have a little bit dementia and they are guardians that take care for these type of people. They use software and that's king one and king two. All of these businesses have operational anchors hence they have low churn profiles. But all of these businesses will at some point being disrupted if we don't move and AI transform those businesses and we're doing that with the rather strategy. Toby will talk about this in a second or in a minute and I think that's great.

We can create additional AI modes if we AI transform these businesses. But what we also can do and this is something that we actually did today. So I learned from Torson aunt who is heading up the group in France that we acquired and closed and signed a deal today. No joke we call it the full house. Now the full house is a so-called TAP software which is managing the registration process of when there's a citizen in France in social need they have to register themselves at hello I have a problem and the hello I have a problem software is full house has about 20% market share across France and the beautiful thing apart of yes they also have some operational tank bankers they are in the process of replatforming their solution and why is this relevant? It's a great opportunity for us not only to help them to replatform their solution with what we learned over the last 12 months around Atlantic coding and also thanks to Toby and team. But we can actually now coordinate similar replatformings in some of the other businesses. And while doing that we can synchronize the data model and we basically can allow that a person coming into via the full house is then basically assigned to an appropriate order of our businesses and we can do that in an automated way. We can also when we synchronize the data model basically have a platform that always knows where the individual citizens are stuck in the process and that allows us to provide a lot of automation, allows us to provide a lot of analytics and that allows us to provide a lot of cross-selling into this space and then essentially will allow us to create an industry standard in this particular niche and therefore the acquisition of this full house will make the whole system much more stickier and therefore essentially make the whole companies more valuable and I think we acquired this business for like five times. I would argue we could have paid probably a bit of a higher multiple for that and it would have still have making sense.

The key ingredients that we need here is that we can actually only do that if we can replatform some of those businesses. That requires that we opt up or level up our R&D department to really apply coding and to become much more quicker in developing software. And that's something that Toby will speak to in a minute. And I think the probably last slide here is finishing off where I say where we should on the right going forward on the left hand side I think we should on the right or continue to on the right to acquire networks of operational anchors. We should do that in the verticals we're already present in to become a more dominant player in those verticals. This will obviously give us much more pricing power as you can have seen that house for me. I mean this is like pricing like this is paradise pricing and I think that should be something that we really should do.

And on the other hand when we're doing M&A we should have open eyes for transformability. Now transformability I transends to we need to understand for each acquisition what is the headroom meaning what is the current situation and how can we move this business into a transformed version of themselves what does it mean in terms of team size what does it mean in terms of processes what does it mean in terms of pricing and we're currently developing checklist to better assess that. It also is important to understand what's the tractability. So tractability means how easy we can do this transformation or how hard is this transformation. For instance, we found that in some of the business where we have leaders that really leaning in into the AI transformation, everything is much more easier done as opposed to you have someone that just doesn't believe that AI is meaningfully impacting his business and that will also instruct what we're doing with this business respectively with the leadership team. But also the tractability is concerned when it comes to customers. So we have some customers that basically tell us go away with AI. we don't want to do anything with AI and such a business is for me less attractive as opposed to you have a sector that is really appreciating AI and so tractability goes kind of both ways and I think going forward we really need to invest in the repeatability of all those transformation because the more often we're doing that the more proficient we are becoming. And I think if we develop muscles in that regard that will become a key differentiator in the future and we already seeing it right now that some of the companies we're talking to in the M&A process when we're talking about with what we're currently doing in our companies in terms of AI it already becomes an attractive feature of us because they can see that we can help them to manage the AI transformation. So the new power is keeping the focus on making this transformation happening quickly, more quickly than the potential decay. And by compounding this across eight platforms, we're basically leveling up the manuscript method from just improve stuff to transform stuff. And the key ingredient that I currently see is that we need to double down on making our R&D teams do agentic coding factories. That's the groundwork we already started doing and probably will show a couple of examples. And the next area that we tackle actually in parallel is to do the AI transformation in other departments as well. Okay, that's it.

Yes. Hello everybody. I am Toby, a new addition to the team compared to last year. So Mark already gave me two questions to answer in this talk. So but let me start with some general remark about software and AI. So every company today is an AI company and everybody comes with a cheap narrative and that's not what I want to do today. I don't want to show you a vision slide with some agent symbols on it but actually show you what we are doing in the portfolio, how we track it and what we do in the broad sense of the portfolio to really move the companies toward the AI transformation we are aiming for. Let us take a step back and look at what has happened over the last 18 months. So in Q1 of 25 we started the first AI working group got people together from all the opcos that were interested in AI, found the enthusiasts, started something that later turned out to be our AI champions league. Then later in the year in the manuscript days we already discussed how we can identify in our companies the right positions where we can apply to add value for our customers. Eventually we noticed okay we need central capacity central knowhow and Mark started to hire the first part of the core AI team. Then in Q4 I joined as the CTO and we decided two things that we first need to do is formalize our view on AI and the way we see AI in our portfolio and that's the AI maturity framework we developed in Q4 I will talk about this in more detail in the later slides and we also came to the conclusion okay we have to change something in M&A and the first level of adjustments to our M&A guard rails was done already in the fourth quarter of last year and there was another refinement in March of this year. Also in the fourth quarter of 25 was the OPUS moment. So when Claude Opus came out, we quickly noticed okay something has changed and the way we have to look at software development really changed and what is possible previously in years is now possible in weeks, months or sometimes even days. And we took a step back and decided to really accelerate our efforts, put up the velocity and that was the birth of the RAFO framework and our RAFU initiatives that were ready and already starting to be rolled out when then eventually the market also understood that there is something happening and the stock price reacted. But at this point we were already ready and rolling out Rafo which we will see in the rest of my talk. In quick succession we started all these initiatives.

We started momentum competition AI hubs. We started the agentic coding training and now today we already see results in the companies where we see more and more opcos where agentic coding work has taken over human work. We see green field projects that were really out of scope for top chapters companies before AI. Yeah and I want to explain you in the next couple of minutes how we got there. All right. The AI maturity framework.

If you have 60 companies all of them are really individual they have their individual markets different types and so on. How do you assess if your portfolio is moving and if it is moving in the right direction? What we did there is the AI maturity framework. We separated our companies into 27 distinct capabilities. And for each of these capabilities we developed a transformation lane as we call it. Here on this slide you see as an example one capability from the support area where the company moves from beginner to assisted enable and transforms. That's the same for each of these 27 capabilities. So as a beginner in support, you maybe have a first chatbot pilot. It captures some ticket information, gives a little help. On the assisted level, the AI already drafts answers to common questions, routes requests to the right employee, still with a human in the loop. On the enabled level, that's where we think the company already has a knowledge layer and has a clear knowledge base in the company that is fed by different sources and is the basis for agentic help that already end to end cares about the most common cases, give direct answers to customers and then in the end there is a transformed level where agents really have responsibility in the support flow and take care end to end of the majority of the support request. So this is one example how such a transformation lane in one area of a company can look like and we did the same for 27 other capabilities. We developed one lane that we call foundational lane and the rest of the areas are related to the standard operations and functions of a company. What is the foundational lane? Those are the basics. So this is do you have AI policy? Do you have an AI representative in your company? Do you have basic AI training for all of the employees? Do they know the EU AI act regulatory requirements to work with AI? All of this is the foundational lane and that's more or less done in most of the companies by now. So what you see here on the slide overall is the result of the self-assessment of all of these companies which was then challenged by the platform representatives. We are closely aligned with our platforms. They have the same view on AI and these levels as we have an AI core team. So this overall gives us a clear and comparable assessment of the portfolio over all of these capabilities. And as you can see, foundational changes are the clear winner and after that R&D and support are the areas where our companies invested most and reach the most steps in terms of this maturity. However, this does not mean that our accounting departments don't care about AI, but that our opcos took a deliberate decision to focus on support and R&D because they see the most immediate advantage there.

Now that's a decision that is taken at the edge and that should be taken in the edge because the opcos know this better. What we can do as a whole holdco is to enable them and provide them the tools like the maturity framework like the training and so on to take smart strategic decisions today and to give them the tech and the training that they need to actually decide is it rather support professional services or whatever that brings us forward most. Last point about the maturity framework. You can use it to build your strategy because you have development lanes and can plan how do I want to go along this path. You can use it to measure the portfolio. And the last point is you can use it to set goals and that's what we did. We plan that until end of the year all of our opcos have reached the assisted level for everything foundational. They have to reach the assisted level in R&D because we are software vendors and that's certainly the biggest point for us in AI and then apart from that each opco needs to choose at least three development lanes where they see the most advantage for their company but we demand that they move at at least three points. So this is how we track this is our results you heard our goals and that's also something you can track me again for the next year.

Let's come back to Rafo. So you have seen where we are. You have seen how we measure this and now let's come to the details of what we are actually doing to drive the progress. On the left side you see what protect us today. So we have systems of record regulatory mode. We are embedded in the transaction of our customers which are rather slow moving. All of these are part of the iceberg that is melting. And now Rafu comes in and helps us transform these companies to harden these modes and turn the iceberg into stone again. What is often the starting point of this AI journey or has been in several opcos is Mark coming to the opcos doing something we call the future back workshop where we take a larger part of the company and discuss with them a scenario where it's the year 2030 and they have been disrupted by AI going from this point back we see what happened on this wave so you are disrupted and from the starting point we build up again a positive vision how you can be part of the AI winners in 2030. This format has proven to be really good to drive adoption and to get the people started. But it's not just talking. AI is also know how in tech and that's why we have developed the AI hub an open-source-based AI solution that gives you software but also GDPR compliant infrastructure something most companies would maybe struggle to set this up we found one solution that we apply in all of the companies we roll it out so there's no central AI hub from chapters but every opco has their own individual AI hub can shape it to their needs, but they don't have to reinvent the wheel. And the AI hub gives us the opportunity to take use cases that work in certain opcos, take it up to a central repository and then ship it into the whole group and that really speeds up because we are not just sharing ideas on the level of blueprints but actual implementation. Then there's coding training.

We set up a bunch of trainers. We went to an AI event. We searched for people that are AI and agentic coding maniacs and have done nothing else in the last two or three years. Then finding out how can I create my code with agents. We convince those people to come into our companies and tell our people how can you do it because what we learned is most people in our R&D department understand that AI is great. They understand that there's a huge opportunity but there is some uncertainty if it really works in their 20 year old stack and that's what the trainers can fill because they come into the companies they stay there two or three days and after that everybody has seen it work also in our 20 year old stack and yeah that's when movement happened. Then there's chapters momentum. At some point, we thought, okay, our companies have long-term plans. They have migration plans. They have a road map with their customers, and we don't want to disrupt that. We want to keep them moving along their strategic path. However, we don't want to be left out with all the cool new AI features. So we set up the momentum process where companies can combine a really great business case together with hard customer commitment to get funding from the holdco structure to make sure that external forces can help them to have AI features in their product now and today while still keeping their strategic plan. And this has been quite a success and we will hear more of something that came out of this later at peak. Then there are people. So we said the change needs to happen on the opco level. So we assigned a champion an AI champion in each company and we also decided every MD needs to be an AI leader. So we take every MD into training sessions where they not only learn how they can transform their company but how they can be a more effective leader using AI every day in their work as an MD and that's something that we also found really useful and beneficial because once the MD has seen in his everyday life what difference AI can make they are ready to push it into the opco and then there's not so much as we have to do.

Okay, let's go on and come back to the second topic, the agentic coding factory. So what you have seen before was mostly related to how can we transform the rest of the companies. My first example and I will show you a total of three agentic

The coding factories we have built is Software 24. This is maybe in some sense the least glamorous place where you can try to use agent coding because Software 24 offers KAZA, a more than 20-year-old Delphi-based solution for property management. It is a legacy stack, legacy technology, so it was not clear from the beginning if AI would work here too. But what Software 24 did was they built this impressive coding factory that really produces code end to end. What do I mean by that? It is easier to understand if you compare to what most developers do now. They sit in front of an agent and use prompts to steer the agent to produce the code they want. What Software 24 does is they have a set of distinct agents that work together across different systems to have an end-to-end development workflow that starts from creating a great specification, taking in everything from customers and other stakeholders, then taking this to the next agent that builds a technical specification plan and a detailed implementation plan for the implementation agents that still work together with humans in the moment to steer them in the right direction. But this is then automatically reviewed by a review agent. There are end-to-end test agents that in the end make sure that this does not only work in unit tests but really works on a running and built machine. And then, to really make it an end-to-end process, the agents also create the release notes, they update the customer documentation, they update the internal documentation, and all of that. And Software 24 has shown us that this is possible today with a legacy stack and old software. In the lower part you can see the impact of that because in the first months they were still building that. In June was the first month where the factory was really full in action and you see this step change in agentic coding. So even in a legacy environment, we can produce more than half of our code now with agents. Now, Peak: we have seen it works with legacy. Now does it also work in big organizations? Peak is 160 employees. We heard about this. It's a huge development organization distributed over Berlin and Poland, so several sites. The case of Peak is interesting because in March they were still at the beginner level, basically not really started with agentic coding. Then the agent coding training came in; there was a lot of effort within Peak to really pick up this topic. We saw that agent coding rose from 13% in January to now 49%. In June, again the first month also in Peak where the agent took over the humans in terms of produced code. What do we see on the slide? We did an analysis of what the people are actually doing with the agents. Are they doing the simple boilerplate stuff? The answer is no. Actually, the agents do the hard part. On the x-axis, it's more difficult, so this is really more cognitively challenging, and the y-axis is more complex. These are changes that require code changes in large parts of your code base, different places, and so on. What is obvious is if you have to change a lot of places, hard stuff, you let it do agent, and that's what people do. But what I find really fascinating are those two points here because these are bugs and problems that haven't been touched by anybody before. It's a new category. Let me give you one really fascinating example. Peak builds control screens or the control room of public transport companies. There are often double digits of control monitors there, and the software had a bug that let this 12-monitor layout crash from time to time. This bug was there for 20 years. Nobody was there to solve it or able to solve it. Finally, with these new options, one programmer sat down and said, 'Hey, I have Claude now, maybe he can figure it out.' It took two hours. In the end, it was two lines of code. Claude was able to find this obscure bug in the Windows main window loop and solve it. So after two hours, Claude was able to solve things that were bothering us for 20 years. So AI is not just solving things that took more time in the past, but it's actually solving problems that nobody was able to solve before. Now let me come to the last example of the agent coding factory. We have seen it works in legacy, we have seen it works in a big engineering organization. The last question is: does it also work on greenfield, and should we do it? This is the example of Parity. Parity is one of the oldest companies in the portfolio, over 40 years. They have been building ERP systems. Their current products are really legacy, really end of life, and they took the step back and thought, 'Should we really use AI to modernize this old software, or should we just go greenfield?' That's what they did. They put together a small team, put together a coding factory very similar to what Software 24 has built, and just started. The results are quite impressive. They planned with three sprints that should be three weeks each. They finished each sprint within one week, so just a third of the time. After three months and just eight person-months invested, we have a ready product shipped to six pilot customers. 91% of the code is generated by agents. The whole development velocity is possible because the agents are kept in place by 2,000 AI-written automated tests that really guard the harness to keep the agent on task. It's really impressive to see what four people can do. In the first presentations with the customers, they said they expected this not before one or one and a half years in the future, and they were really flashed that this is possible now in three months. We have a lot of other companies in the portfolio that are now thinking about greenfield rebuilds because the economics of this has completely changed. Software 24, for example, is now accelerating their legacy product, but they are also working on building a new, nice, non-Delphi greenfield thing. I'm really excited about this opportunity because, to be honest, a lot of the companies were in a situation before AI where there was no way out of the 30-year-old stack. It was just not economically feasible, and we are living in a new cool world now. That brings me to my last topic before we can finally get to the marketplace. Some outlook for the next year and something that I see as our big challenge. As Marquez told us in his talk, our companies were at some point founded with real customer intimacy because the founder noticed there is a need in his niche and he can fill this gap with technology. The companies grew, they matured, they focused on shifting and came from the founder mindset eventually to operator mindset that is focused on incremental improvements, monetization, and so on, because growth was limited. The founder somehow leaves, often when we buy the business. Now I see it as our challenge to in many cases revert the mindset back to a founder's mindset because the environment has fundamentally changed. The opportunities for growth, the opportunities to build new solutions in the market for our customers have drastically changed in the businesses we have. That's a great situation, but we have to make sure that our companies actually take it. We will work with them, but I also think it will require taking in new talent at some points. That's something where we as the whole company can also really support our companies along the way to get the right people in now to build this bright new future. So, listening to people, seeing people's listening. Now it's time for the marketplace. I give the mic to... Yeah.

Before we start, I want to tell you a story. So my family, we spend January and February in Cape Town most of the time, and I go back for business if I have to. It's the same this year. So I had something to do in Europe, did what I had to do, and then flew back and landed in Cape Town. I always have my phone shut on long-distance flights. I opened my phone in February, you know, the SAS Calypso. And there was an investor. They're not here today, they remain unnamed. They wrote me an email and said, 'Yeah, we got to talk to you.' So I wrote them back and said, 'Well, sure, but I'm on vacation and we're in a closed period, and you know Andreas, those are three really good reasons why we should not talk today, but maybe it has time.' They're like, 'No, no, no, we have to talk as soon as we can.' Okay, small investor. As soon as we can, immediately, please. It's okay. They go to the hotel, kids come up, 'Daddy, daddy.' My wife is like, 'No, no, no pool.' I have this important investor. I dialed in. Three partners look at me and say, 'Well, how can I possibly help you? What's on your mind? Something's wrong?' And the lead guy looks at me and said, 'Yan, what about AI?' That's funny, right? Because this man was just scared. You hear all the news, what's happening, the rumors, everybody talks about some vision of what might potentially happen. The learning we took away is that when we communicate both with clients internally, some of the people in the organization that are also skeptical, we have people who are not 100% convinced, who have objections, or investors like you. We've made the choice to be super specific and tell people very specifically what we do, what the limits are, what the resources are to get there, and also what's easy and how the future could look like. But we want to make that very, very specific. So what we're doing now is a bit of an experiment, which is going to be a bit wild. The idea of this is that I'm going to call on people from the team who will make a brief roll of thunder, two-minute pitch on who they are and what they want to present. I'm going to say something that will make them uncomfortable and hopefully funny to set the stage, and we'll rush through them. Everybody on the west cast, if you find someone particularly interesting, write Andreas, we're going to set up a one-on-one, possibly group them into one-on-ones, split one-ones. Everybody who's here, both here in the room and also at the end of the hall, there are several rooms where we have screens and we have time and later a beer. If I were you, I would spend a lot of time with these operating folks and just quiz them, ask them about the business, ask them about the projects, ask them about the limits of implementation. I still have to identify a few more thoughts because of course, although I'm not presenting a product obviously, everybody else on the Chapters team is hanging out as well and we are here for all your questions. Make use of that. Again, we will only leave if the last one of you leaves, and usually we have some good staying power, but until the last question is answered, we will be here. So let's start this little experiment. I'm going to start with my safe bet. Andreas, I think you're first. You're going to give us an exciting example about AI in the public sector space.

Thanks, Yan. So, I'm Andreas Philippe of Alamount, and we actually started Alamount two years ago in this room. We introduced it. I think some of you folks have been here at that point. Two years ago in July 2024, and I think today our ambition for Alamount is really to become the leading public sector software company in Europe. That's what we work for every day. I think AI is a big part of that. In the last year we acquired six companies. This year in 2026, we expect 45 to 50 million in revenues, plus 5, 6, 7 more acquisitions this year, which will obviously increase the revenue number for this year. When we started, we had some hypotheses. To be honest, AI back in 2024 was not one of them. To be very honest. But we believe AI is super important, and we're all super pumped about the power of AI, and I want to show you that in a second. So, as I said, in the last two years, we built a platform, we built a great team, we had a lot of hypotheses that we mostly have been validating, added some of them like AI, and we're really ready to accelerate the growth. I think one important part to just show you how important AI is for Alamount, we set up a dedicated entity. We called it Alamount Ignite with the objective to ignite AI in our portfolio companies. This unit Alamount Ignite is run by Leonard. Leonard, where are you? Here. And Leonard will be showcasing next door one of the use cases that he deployed together with Peak Mobility. So we had it a couple of times here before. Peak Mobility, public transport provider in Germany, it's the market leader, I don't want to say the world market leader, but one of the global market leaders for depot management. It's a mysterious word, depot management. You go out on the street, you see all the buses, the red buses, driving passengers from here to anywhere else in Hamburg. In Hamburg, there are 2,200 buses driving around during the day. And at night, all these buses have to park somewhere. They come to depots. And in these depots, they are being maintained, cleaned, the wheels are changed, and everything that needs to be done. This is a pretty traditional process. In Hamburg, it's software-based, but globally we're below 15% digitization in these steps, and this is a global phenomenon. So this is something where Peak is really, really strong. Now, if you have a diesel bus, it's pretty easy. You fuel the bus within 7 minutes and it's ready for the next day. With the e-buses that are coming more and more into the cities, it's much more difficult. It takes 6, 7 hours to fully charge a bus, and sometimes this is happening during the day. So if it's doing a ride, it cannot just easily get recharged. So it needs to go to a depot, and this needs to be integrated in the schedule and the planning of the buses. This is what Peak is offering, and we're the first in the global market who are currently developing a solution where we have a dynamic route planning for the next day where this whole charging and maintenance of these buses is fully integrated for the depot, so it really becomes a dynamic depot. Leonard will be showcasing that in the room next door. Really exciting case. A second use case that we have, it's also a portfolio company of Alamount called Ecomidas, run by Christian here, Christian Ebert. Very nice company. It's a secure workflow automation for critical infrastructure, but it's mainly for the police in Germany. So what Ecomidas is doing, they provide the so-called online police station in Germany. This is where the 80 million or 60 million adult citizens in Germany can file a police report. 'My handbag got stolen, my car has been crashed, somebody killed somebody or whatever.' Just the filing of a police report. You have two options: you go to the police station or you do it online. That's the two options you have in Germany. And we are providing the online channel. So there are a lot of police reports coming into the system 24/7, and somebody at the police station has to read all the reports and prioritize what to do first. This is a very manual process. And then at night, if there's a police report coming in at night, there must also be somebody at the police station who reads that report, which is quite expensive to have people sitting there 24/7. What Christian and the team of Ecomidas have now developed and are just about to deploy is a fully automated prioritization engine. So it reads all the different police reports, and if there's a murder or a bomb threat or whatever, it's getting fully prioritized, and some of the others are just held back until 7:00 a.m. in the morning so the night shift doesn't have to deal with that. So it's a very powerful solution that's also decreasing the cost for the police, which I think the police is very happy about. Christian will also showcase this on the live portal later on. Thank you.

Amazing. Thank you very much. Next up is Yasha. Long story. So the very first software company ever bought by the entity then called Medicon was a business called Parity. And it was amazing because it's the first deal and everything that could possibly ever go wrong in acquiring a software company went wrong within the first couple of weeks. It was amazing, everything you could potentially creatively imagine would happen happened. Not only did that business recover in remarkable ways, but that experience also brought us you. And you're not only running Parity, but you're running one of our largest platforms now, which has grown tremendously over the years. You've been with us since 2019, which is an amazing journey. I'm handing over to you to present what you're doing in the bicycle motorcycle market.

Yeah, thank you very much, Yan. Indeed, Parity was a great school for me to learn everything that could happen in a software company, but this is not the topic today. What I'm going to show you today is an example of an ongoing Chapters Momentum initiative at our opco CSB. So CSB offers an ERP, or often called dealer management system, specifically for motorcycle dealers, and we are market leader in this market. We have about 900 dealers online, which means that about 1 million service appointments are going through our system each year. The booking process is still quite manual, most likely the dealer has a customer on the phone and types everything manually into the system. So there's quite a huge amount of digitalization potential, and this is why we decided to set up with the Chapters Momentum initiative a project, and we are now building an AI-based digital service assistant for CSB. The idea is that the service assistant can be implemented into the customer's website, and then end customers, so our customer is a dealer, and then the end customer books an appointment directly into the system by using natural language. What this Momentum specifically enabled about it? I will keep it short from a top-level perspective. First of all, we of course gained a lot of speed as the idea of Momentum is that we take external capacities into the project. Those were recommended by buyers and financed by Chapters, and those external guys were able to ship the feature very fast. So there was no disruption of our existing roadmap. Secondly, we have a way more capable product now through the use of AI, which from a high-level perspective means that we found a solution that the AI is directly connected to the on-premise ERP system, so it can talk in real time to the dealer and is always up to date, and this makes it possible to set up an appointment whenever a service assistant and mechanic is available and the customer wants to have it. Finally, about the timeline and some economics. We started this in May 2026, and it took us only three months and about 20 days of internal capacity to build the first MVP that is fully tested running internally at CSB now. We are planning to deploy this to the first batch of customers. We have about five customers that committed already to pay for the solution. They are waiting for it, and once they are satisfied with the solution, we plan to further push it into the overall customer base. Based on some estimates we made before, we assume that we are able to create an additional revenue stream for CSB of 350,000 euros per year for subscription fees for only that small add-on. That's it so far. If you are interested in more details, feel free to stop by later. I'm somewhere over there in the back in one of the rooms, and I'm glad to be there for questions.

Now I'm handing over to Matias of Vatakant, our literal neighbors because our office in Hamburg is very close to the headquarters of Chapters Group. And when I talk to some of you, I'm on the phone walking outside, and usually that means they are watching me, what might be going on in front of their screen. You're presenting something that is kind of interesting, not operationally related, but around how we source companies.

Thank you very much. I'm going to talk about the scorecard for M&A purposes. Before AI, we used to have to dig through all sorts of information which you see on the left side. Just from the website, finding out whether a target can be interesting to us was not that easy. Basically, we wouldn't have the idea whether that target would be a good pitch to our guardrails which you see on the right-hand side, and whether it would really pass or sustain in a shark tank. So we came up with an AI solution with an AI lens, as we call it, which will find out whether a target is really interesting to us. But just using AI is not the kind of goal you want to use because it will give you randomized output. So we have to train the AI. But we did it for a specific sector. We need to train the AI what is a key definition to us in that sector, whether it's a core platform, whether we could treat it as an add-on, and most importantly, the AI also needs to understand how software is bought in that segment and what our customers really need in that segment. Of course, the AI also needs to know what are the current market trends to actually for the companies in an initial setup. So that's how one scorecard could look like. Basically telling us what's the owner structure, can we actually buy it, does it check out all the quality gates which we have in place, and gives us a lot of context of that company in a matter of seconds instead of just digging through it and having first meetings with targets who are not really interesting to us. So if we do it on a bigger scale, for example for logistics software, we did it before actually entering the market. So we can actually see upfront whether we want to go into that market. So we see on the top right-hand side that we have lots of core platforms available, and which of those can be really addressed. Actually highlighted up here are the ones where we were actually in talks with them. As you already see, there are also some optimization layers which we could add on in the later stage. Finally, what that scorecard gives us is a fast analysis and a good personalized dedicated outreach with high response rates and a high conversion rate. Of course, it gives us the market overview which I've just shown you and how we can complement those targets in the long run, and maybe for some cases actually build an industry standard in the long run. I'm happy to talk to you later on and looking forward to your questions.

So, I was opening LinkedIn all night and I got messages from Julian, former professional athlete now turned SaaS founder. I kept on writing with Julian, or what I thought was Julian, and we were agreeing on a call because we were doing a capital round. Then when we first had our first meeting, we met in Berlin on the roof terrace. It was funny because I said, 'Well, it's so nice writing with you.' And he's like, 'Yeah, that was my boss.' So I was like, 'Okay, well, so I'm not entirely sure you're human. You might still be a robot. I don't know. But certainly, your outreach was professional and very much to our liking. And you've been as an investment part of the Chapters assembly for some time now. And you're doing many exciting things in AI.

Yeah, perfect. Thank you so much. I'm very glad that I'm a human in front of you. So, yeah, maybe first of all, I was a professional athlete, now I have a company called Fuksam. Where does Fuksam come from? Fuksam comes from the future of exams. That was our initial idea because 5 years ago I was based in Berlin, I was there at the university, and I thought, why are we still writing exams with pen and paper? This was my initial idea. And now we built a much larger solution. We built a full ERP solution for universities, the complete operating system. You're thinking now, why this guy does that? Because I recognized, because we made market research, that was the only thing I as a student could do. Market research and something like that. We saw that 120 universities, there was a paper that 120 universities use 1,800 different software solutions. When I recognized that, that was the moment where I saw we need an all-in-one solution. And that is exactly what we're doing now. We have now 30 customers all over Europe. We have six modules. We are building now 90% with AI. So we were very glad that we were, I would say, funded and born in the age of AI, because it helped us very well to build an all-in-one solution. Now we have six main products: Folks Apply for application management, Campus Management, Learning Management System, we have a mobile version, we have the examination tool which was our initial idea, and our Alumni Management. We are doing a lot of other very cool things. I'm glad to see you maybe and talk to you later. We see us there in the back corner. Thank you so much.

So this is particularly exciting because Kelton is one of the longest editions of the Chapters Group. It's the 3% bar and other that you saw at the very beginning. Now you could wonder, it's the only non-software technology business we have left. So what's going on there? It's kind of amazing. You built an HVAC business here in Hamburg from scratch with our help since 2020, which is now one of the largest HVAC businesses here in northern Germany. I don't know what happened, but recently you caught the AI bug. What has happened in this very blue-collar business is unlike anything I've seen before, and it's absolutely amazing to watch. It has a very clear lever on profitability because there's just a lot of variable and people cost involved, and the savings are incredibly real. You've adopted a very playful and very experimental mindset to improving and implementation, and it's incredibly exciting to have you.

Yeah, thanks for that introduction, Yan. A little bit disappointed that you did not introduce us as the coolest cooling company in town as you used to, but I'm fine with it. We can talk later by a beer. So with Kahne, we are 23 people here in Hamburg, and I think AI is one of the biggest opportunities in our industry, maybe ever. That's why we started to look at our problems and search for solutions. One of our biggest problems is that our technicians need to write a service report after every service call. After a long day on a rainy rooftop, they need to type it in on a small tablet keyboard. To be honest, a lot of those reports are incomplete and a lot of information is missing. That's a heavy problem for us because on that information we build our invoice. So we probably lose money when our technicians don't write the reports properly. So we started with the AI Hub to create our service report buddy. It's a GPT that works along with the technicians. They don't have to type in everything now. They just talk, and they talk about the problem they had. It can be completely chaotic. AI structures this, and the most important thing, it asks. If AI sees something is missing, it will follow up. We did something that the GPT also has technical knowledge. So it's not a master craftsman, but it's kind of an experienced journeyman. If there's a young technician and just saying, 'Okay, problem is solved,' the GPT will ask, 'Okay, what was the problem? What was the root cause?' If the technician doesn't know what the root cause was, the GPT will give him hints where to look and where to search for the problem. Afterwards, we will get a clear report and just put it into the ERP system, and then we can build our invoice on it. More complete reports are pretty important for us. To be honest, it's just the beginning. We have a lot of problems, a lot of themes in our company and in our industry that can be solved and be more efficient by AI. So all people can focus on keeping the future cool. Thank you.

Okay. So, our last example in presenter is you. Usually, the few examples I bring around what we do in the group, I usually bring up the gun and fishing license registry business that you might have heard from me from time to time. So, here it is. John.

Yeah, thank you very much. Last but hopefully not least, I've got something very operational for you today. So, Casey, what is it what we do? I asked AI to do a picture as well, and I think it's telling you exactly what we're trying to achieve in Germany, and that's public safety. So our software solution helps the authorities to keep criminals and other bad people away from guns. As well, we do that in terms of nature, so we help Germany to look green. In terms of numbers, this means we have around 30 employees, around 450 customers, and that's around 90% of the relevant market in Germany of gun and fishing and hunting licensing control. This means around 5 million firearms in the hands of 1 million people. That's basically our business. We like to talk to our customers, and we have something which most customers and most companies have: customer success managers. Those people need to prepare for their customer calls. We found out that this is roughly 1,200 cycles a year at least, and at least 18,000 minutes spent in our small company just gathering information from different systems. So you know what's up with the customer, what tickets did they raise in the last couple of weeks, what opportunities do we have, what do they talk about with our support teams, is there an opportunity for an upsell or something like that? What we did, we needed something which reduced those 18,000 minutes because it was around six systems you need to look into, you need to do manual research, and if you're not that seasoned it may take a while. We use the AI Hub for that. So we have a compliant opportunity now to link all of our internal systems to the AI Hub and create a very easy report within seconds about everything which is happening at a customer for the last couple of weeks, months, a year, whatever you want to see. This enables us on the one hand to have very good conversations with our customers. It makes sure we think about the right people and the right things, and it makes sure we have a very clear logic applied to that. It knows our product, it knows the questions the customers raised in the last couple of months, we know what's happening in sales, which conversations, and so it gives the customer support manager a good idea of what is happening at the customer at the moment. This takes less than a minute. So it gives us, as a very small company, a lot of hours, 300 hours or more, just on time back in terms of better conversations with customers because we don't need the time to research, we have the time to talk to the customer. It's a very small operational example that is in the AI Hub, so it can be shared across the community of companies we have. We have a couple of other things coming out of that because we don't use it in this specific use case. I actually use that before every conversation I have with a customer because it takes me less than a minute, and then I don't need to log in to different systems. I just have one prompt that looks like ChatGPT, we all know that, and I get a report with very easy to read traffic lights, green, yellow, red for different categories like sales, support, all the other stuff. If there's something I like to know a bit more in detail, I get into the report and have all the data ready at hand in just a couple of seconds. So this for us is the biggest opportunity apart from AI, agentic coding, and all the other things we've seen today in an operational area between customer support and sales.

Amazing. Brilliant. Thank you. I'm back there. So, you very soon you're allowed to stand up and grab a drink. Thank you for your patience. I hope it was directionally interesting. If there's one thing you want to take away from this presentation, remember the slide that Sophia showed with the founders and the operators. Like most of the businesses were started by founders who saw a market opportunity and a software tool at the time like Microsoft Access, an early Java solution, and they said, 'Listen, I understand the gun license market better than I do, and I'm going to solve this problem.' Then 80% of the businesses that did that never scaled, we don't see them, but the ones that found product-market fit grew very capital efficiently with no external investors and became the businesses that now become part of Chapters Group or other acquirers. For the longest time, when we acquired these businesses, we ran them with an operator mindset: efficiency improvement, get the succession right, get pricing right, get things stable. I think that's changing. So when I look at 2030 and how we want the group to look like, it's all about reigniting that entrepreneurial fire and appreciating the distribution and the great reputation and the great market position we have in all of those markets, but make clear to the operating leaders that they now have a new super weapon which is AI. If you're close to the customer, customer intimacy, that's a word we use a lot, you can actually reinvent your business. And because we also know a thing or two about M&A, you can also use M&A to really build an industry standard, a really magical business in a certain vertical in a pretty short time. Our focus is to kind of reignite that fire. We can really reinvent the businesses rather than just run them, reinvent and recreate these businesses because of the powerful tools of AI. The best way to figure it out and get a sense for it is if you now, first you're allowed to clap and thank the team. Thank you very much. Then you can stand up and get a drink and go after everyone at Chapters. Malina wants to say something that's important.