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. 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. One of the questions and one of the feedbacks we've gotten, we got a lot of questions around the impact of AI on 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 brief kind of round of fire introductions to the projects that we are presenting right at the very end, so y ou 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. That's obviously not really possible if you are dialing in, so w hat we suggest to everybody who's dialing in, if you have any questions and you want any deep dive, 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 a orange dot. The reason is that I forgot the CHAPTERS lanyards. 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 a CHAPTERS Group, and you have all the liberty to approach these people. The two of you are getting dots as well, sorry. 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. Let's start with where we are. This is a really, 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 Fintiba, and a pretty big chunk of the group was still in what we call Other today, so b usinesses that are not software or financial services. In 2023, as you know, and as we talked about the last couple of years, we made some hard decisions on how we want to transform the business, a round how we want to look like, and more importantly, what we don't want to do. And the result, you see here. Our software groups of public sector and enterprise have grown substantially, both in relative share and also in the absolute size of the business. Also, our financial technology segment, which is not only the Fintiba business, but two other businesses, Expatrio and Coracle that we acquired, are now a very substantial part of the group.
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 Stephan 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. 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. This is our indicative split of revenues between financial technologies and the public sector and enterprise VMS. You see the piece of the pie of the financial technology segment increasing in 2026 over 2025.
But what might not be so intuitive, or which might not be well understood, that if we look at profitability, this is actually a very, very big part of our profitability at this point. At least for us, Marlene and I and the board, like, this happened very quickly. It was clearly intended, and this is why we did the moves that I will walk you through in a second. 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 t he 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, w ell-functioning anarchy, well-performing anarchy, but really no, just a loose collection of businesses. 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. Today, we want to explain to you how that works. One phrase we use internally is we're trying to build industry standards.
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. I think over time, 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 a high conviction that that's what we want to do in the future. I want to walk you through one example where this played out particularly well. You see the split from us. 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 the financial technology, which you know as the segment with the three brands, Fintiba, Coracle, and Expatrio. That is 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 value which I hold most dear in my heart, which is we grow together. I always talk about the story of you see a pie on the table and there are two different types of people.
One person sees it and says, "Oh, listen, how can I optimize my largest slice of the pie, and how can I eat most of the pie?" The other type of person that says, "Okay, listen, how can I optimize for this?" At the beginning of the year, I thought about hiring a chief of staff. We don't really have any office space left, so there was no, 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. For this presentation, I asked Claude to the point I am 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 is what AI does. This is what AI came up with.
The situation we found in the blocked account space was a zero-sum opportunity, right? You had three companies competing for one piece of pizza, three dogs. It is mine, no, mine. Back off, it is 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 are wearing chef hats. More pizza, more fun, more wins. Every dog leaves full and happy. What does it mean? It means if you essentially catalyze the energy that you had on what is there, it very much limits your thinking. When you catalyze the idea of growing the pie, this is where the real magic starts, and I want to be specific on that opportunity in financial technologies. Now, what is that business doing? This is ChatGPT.
If you type in ChatGPT, "I need a blocked account in Germany," that is the product we are providing. It is a special requirement for the visa process. You need a blocked account, and you need insurances around that, and the three businesses provide that. ChatGPT gives you this result. You can go to those four players, Expatrio, Fintiba, and Coracle, or you can go to Deutsche Bank. In 2025, we had enough capital to consolidate three of the four, which we did. So, brace 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. 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.
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 five years. 50% stay for longer than five 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 t he first entry point with financial services in Germany is us. It is 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, y ou got to imagine they had developer teams that were building the same product to be competitive three times over. Everybody needed a certain product, and you build it three times because everybody needed to offer the product. Now, you can use the same development resources, and you can build three different products that you can monetize. Which sounds very trivial, but it is just how this works. We are now essentially resetting the focus of this business away from this is a blocked accounts insurance business to this can actually become a really end-to-end financial partner. In the future, that business that you see at the top of the water to essentially become the funnel to something much, much larger.
Now, this probably, as an investor, sounds all super sensical and makes a lot of sense, but I want to share some of the operational hiccups and decisions you got to make and also some of the opportunities that we found in doing it. So, when we combined the businesses about a year ago in May of 2025, 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, like, the best strength 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.
The philosopher Mike Tyson said everybody has a plan until they get punched in the face because operations happened. What we found out is the teams did not get along at all. It is not to blame. That is something that you see. What we saw is we had very different standards of compliance, IT processes, efficiency across the business. It is very, 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 of how they ran the business. This is a product where you must absolutely not screw up, right? So, those are people from Pakistan borrowing money from their family to wire to Germany to us in the hope of a new future in Germany.
In that process, we got to make sure that we do not channel any people into Germany that do not belong here, that we do not lose any money along the way, we do not 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. This is a regulated business. Mistake tolerance is absolutely zero. Some of the founders, some of the businesses saw this differently, and we had to make changes. What we also saw, and you saw that with the announcement, the idea was for Bastian, who many of you know, to become a Vorstand at CHAPTERS Group and essentially run this group, which he did at the beginning. Bastian also became a father at the time, and his wife got really ill, very quickly with a newborn at home.
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 it looked differently at the time. 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 do not really work together, and we got to find a way." Come in Manuscript Method and the way we run things. What I did, essentially, everything starts with a strategic plan. 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 of 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 find the right level of engagement. That needed to happen, w hile at the same time, figuring out how is team structure going to work. I got really, really positively surprised because some of the talent that naturally I did not know before, in particular at Expatrio, in particular Toby, who some of you know, Toby Fischer, the now CEO of the group, really impressed me, not only by their personality, but also with the approach they have.
Because the mindset they had, which we now adopted, and I've grown a lot of confidence that this is how you can run situations like this now, and AI plays a role, is 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. At that point, and 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-built development, and this is going to happen within months, not within years." 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, much quicker than we initially thought. And this is what we ended up doing. What 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. In theory, nobody would disagree, right? 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, like, p eople 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.
In the past, it used to be Fintiba, Expatrio, Coracle, 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. 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 Fintiba, Expatrio, Coracle, but there's a group name, which is Lumeira, which we implemented in December. So, going forward, you will have to get used to this graphic, which is the financial technology segment, where the big part is the Lumeira group. Why do I tell you this story? I tell you this story because this is a big part of our CHAPTERS identity.
The genesis is really, really interesting. This started as a minority investment, 20% stake, May 2021 in Fintiba, a tiny business, and t his has now become an industry standard with the best compliance standard for students into Germany and a huge driver of organic growth. I think that's the inspiration we want to have. 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. 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.
One of the big reasons why we, "had to adjust our guidance" is because the execution versus the initial plan is just happening a lot quicker than we originally thought. This is also the reason why we're doing this, because this is driving organic growth. Usually, people think about consolidation, they think about pricing, and they think about the platform synergies, and 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. We've done this once. I think the financial results, probably not super obvious last year, are now becoming increasingly clear.
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. Hopefully, in relative terms, something else is much bigger. I think that's the story of CHAPTERS Group the last couple of years. I want to give you another example. 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. 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, with a lot larger share in one segment. That's the messaging here. What gets me really excited is to stand here, maybe next year is a bit ambitious, but maybe in two years, stand here and show you a much larger pie chart with larger absolute numbers and financial technologies and hopefully, a nice large, who knows what's going to be, software segment. Thank you very much. Now, you have all the good story, I'm handing over to Marlene for the hard numbers.
Thank you. Thanks, Jan. I'm going to try to keep it interesting. What I want to talk through a little bit is what does creating an industry standard actually means in terms of numbers? Where do we see the impact? Where does it come from? What does it mean for our EPS growth? I'm also going to talk a little bit about the kind of early days of industry clusters in our VMS group. Starting with Lumeira, obviously, t hat is the industry cluster we have built, wh at we see for Lumeira is organic revenue growth in 2025 was strong with 9%. We expect 20%+ for 2026. Some of that is volume growth, which is great. People still love to come to Germany, which is great, so t hat obviously adds to revenue growth. But more importantly, it's a review of a positioning of the three brands.
It's a review of the pricing of the three brands. You are in a fundamentally stronger negotiation position towards all your key partners if you double the size. We also see an increase in the conversion rates, as Jan pointed out. The key products still are blocked account and insurance, and the conversion rate of how many blocked accounts students actually also take an insurance product, that is increasing over time because we are applying the best-in-breed approach we see in one of the three brands for all of the companies. That is the key drivers between organic revenue growth for Lumeira in 2026. And almost more importantly, this is what we expect in terms of EBITDA growth.
As Jan pointed out, we are putting this into one platform. That means that organic EBITDA growth for that segment, we are expecting to jump from roughly 14% to 40%. A lot of this is reduced and focused marketing spend. Immediately, I think three days essentially after signing, what the team did shut down is Google Ads. All of them were spending a lot of money on winning market share from each other. We stopped that, and that is hundreds of thousands savings per year just by not spending that money anymore. Paying commissions to partners, all these kinds of things where they just competed with each other, w e can stop that now, and that adds a lot of margin. Also, we had to streamline the organization. There were some positions that were just doubled across the group. This is not easy decisions.
This is not fun decisions. But it's necessary decision. The team took them quickly, executed on them quickly. Also, that adds to margin. Then, in terms of suppliers, what we realized after closing, other than the accounting system, every third-party supplier solution out there, if there were two options, Fintiba went for A, Expatrio went for B, for every single thing they needed. Also there, we are obviously consolidating. We can renegotiate contracts. You just have typical merger synergies that add to an organic EBITDA growth in 2026 of more than 40%. Now, what we want to do is to achieve strong organic growth in the long run, not only with the merger synergies, but really over time. That is where the, not tip of the iceberg, the bottom of the iceberg comes into play.
If you can focus all of your resources and all of your thinking on how do I build the next cool product for my client, what you can actually achieve is long-term volume growth, and y ou can actually achieve long-term revenue growth. I think we've built a cluster now with Lumeira, we've built a team at Lumeira that can actually do this. Creating industry standards is the ambition, and I think for Lumeira, we are absolutely there. For our vertical market software segments, it's still really early days. I still want to give you a bit of an idea, what are we doing there? How we're thinking about it? What is the effect on the numbers? Many roads lead to Rome, obviously. First one is PSI Transcom and VAB. Both of them in the mobility sector.
It's software solutions for public transport, for bus, for trains, et cetera. What they also both had in common, that they were a little bit of the ugly ducklings in their previous corporate setting, a little bit unloved maybe by the previous management. We acquired both of them in 2025, merged them into what today is Peak Mobility, and P eak Mobility is becoming the shiny, beautiful swan. Are we there yet? No. I think most of you have seen it in our numbers. Peak Mobility, the combined businesses, they didn't have a good year 2025. We found a lot of things in terms of processes, in terms of culture, in terms of cost efficiencies that needed to be fixed.
The team on the ground did a fantastic job in doing this, and I want to point out a few examples and give some numbers in order to give you an idea of what the extent of this can actually be. The first one is a super easy one. We did a corporate merger. They moved into one office. We save a lot of rent. This is a really easy decision. Everyone loves it. The new office is nicer. It's more modern. If you want to have a laugh, ask Jan about his first experience in the old PSI office. That's the easy one. The not so easy one is the pricing review and the value-based pricing project we are currently executing on.
This, as an investor, actually as headquarters, this sounds so easy because they haven't raised prices for ages, and you're adding value, and you just have these talks with the clients, and you tell them how great they are, and you go. They're clients, and they hate it if you raise prices. They have lawyers that actually say that the contract doesn't really allow for that. They push back, and then they start complaining about what you didn't deliver in the last year and how the hell are you willing to raise prices. This one is really, really hard to execute for the team on the ground because they are having these uncomfortable conversations. I'm seeing the numbers.
They are having the uncomfortable conversations. Therefore, 2026 alone, and this is not the end of it, we will probably see an impact on EBITDA of almost EUR 1 million. Then, the most difficult one, obviously, is the part on team size. As said, both companies came from a corporate setting, and there's a certain way of working in these old corporate settings that I wouldn't call efficient, necessarily. That also means that you have a team that is bigger than needed, and that also the people on the team are not always the one that you actually need for the new era. So, the team took the very tough decision on really downsizing the team. Again, this makes perfect sense that are uncomfortable conversations, not just with the people, but with everyone, a lot of lawyers involved. So, this is really hard to execute on.
This is what the team did over the past six to nine months, and this is tangible EBITDA effect in 2026 alone of EUR 3.5 million, EUR 3.6 million, with additional measures adding to that. We are expecting Peak actually to become EBITDA profitable in 2026. Now, you might ask, "What's industry standard about this? Isn't that just your good old Manuscript Method playbook you should do with every company?" Yes, it is. It is the groundwork that we need in order to actually build an industry standard. You can't start adding businesses or products to something that isn't running like a well-oiled machine. This was needed in order to move to the next phase, which is strategic M&A in the mobility sector. This is four deals in the mobility segment. All of them are in due diligence. All of them with a closing date expected for 2026.
And all of them have some link to Peak Mobility. They provide the same product, but in a different geography. They provide an additional feature that would actually help. All of this is really not just, i t's great deals on a standalone basis, but it actually really adds to the value of the industry cluster in mobility. Then, the second thing that we are going to do is become an innovative player in the sector. Your competition is they've all been around for a very long time. They all are doing things the way they used to do. We now can do things differently. We now can actually think about what products are missing for our client.
What the team is currently working on, and Lennart is here today and will give you a bit more detail on that as well, is a software solution for essentially fleet AI-powered fleet management, which is particularly important when you have electric vehicles, like actually Hamburger Hochbahn has driving around Hamburg. This is something that can bring us into a position where we become the product leader, the innovative leader in a very specific vertical. Many roads are leading to Rome. The other example I want to give is our ecosystem in the emergency response solutions. In 2023, we did two acquisitions in Austria. Both of them were providing or still are providing software solutions for fire departments. syBOS is an ERP solution for fire departments, and blaulicht is an alarming solution.
Essentially, instead of having that good old thing on your belt, you can get an alarm on your iPhone. That is an additional solution with blaulicht having a super strong position in the Austrian market. We acquired both of them in 2023, did a corporate merger. They are now called SOLARYS. This is not that much of an industry standard. Now, it becomes interesting with the acquisition of DIVERA in 2024. DIVERA is an alarming solution for fire departments and other emergency response providers that has a very strong position in Germany. Now, we have a very strong position in Austria in the alarming solution. We have a very strong position in Germany with alarming solutions, and we have an ERP software for firefighter departments. Three products all in the same industry.
It's a little bit, because we're in Hamburg, we figured we take Hamburg as examples. Freiwillige Feuerwehr Hamburg is now a client. This is a project that started by both companies in 2022. That's four years ago. What Freiwillige Feuerwehr wanted was essentially a combined product combining syBOS as their ERP solution with DIVERA as their alarming solution. Now, you need an API and all the connections, et cetera. You need a real product. It's very difficult to work on something like that if you're still competing for alarming clients with the rest of your business. So, took ages, n ow, they are a client and that is what we really like. They have a prototype they presented at INTERSCHUTZ 2026 as one team, one product, and now, w e have something to sell to new clients.
The cherry on top, we saved probably EUR 35,000 or so in expenses for the fair because we only paid it once. Now, what we are thinking here is building out an ecosystem, adding solutions for industrial alarming, adding solutions for dispatch centers, so a nything that is emergency response- related that could add to that industry cluster, and we are looking for targets in that specific niche. So, very short, back to the jungle of numbers. I think that has always been our complication. Two things are difficult for us is we know that M&A will happen. We don't know when M&A will happen. We just, the other day, we signed a deal. It's a fantastic deal, actually. It's signed.
We were ready for closing, and now the EU Commission has to approve the deal because it's so mission-critical that the European Union has to confirm that we can be the buyer. That's three to four months until closing. I think it's great because not only the software is mission-critical, but the end market is so mission-critical that the European Union has to confirm, but three to four months adding to the timeline. When M&A will happen is super difficult for us to predict. That leads to it being a bit difficult to predict what will happen in 2026, 2027, 2028. I think kind of on the long-term effects; we have a very good view on how the model can actually work. Now, this is the M&A pipeline. These EUR 11.4 million will stay on signing for a little bit longer than we expected.
We currently have EUR 110 million in deals, deal volume under due diligence that the teams are working on. Not only is that a lot, but we also really like the quality of these deals. We are looking at bigger deals than we used to look at. We are looking at deals that are adding to an existing industry cluster. We are doing very focused M&A in fantastic niches with really nice companies that we are looking at that also meet our criteria on kind of revenue, recurring revenue that are ready for an AI-featured world. It's really good quality of deals. That makes us really exciting but make this one a little bit harder. Everything I'm going to tell you now is before M&A because frankly, I just don't know what is going to happen, but a few effects of building an industry cluster on our numbers.
What you probably have noticed is that our entry multiple or the multiple of capital invested compared to EBITDA jumped quite a bit from 2023 to 2025, from 6x to 11.5x. A lot of that is driven by Lumeira, kind of that is a transaction where we paid more than our average 6.5x- 7.5x. But what we do see kind of looking at the long-term plan, that we will very quickly go back to a multiple below 6x because we essentially operate it down. What you've also probably noted is that the share of adjustments in our EBITDA was fundamentally higher than it used to be this year. Again, a lot of this is driven by transactions like Peak, by transactions like Lumeira.
They have been demanding to the people working at Lumeira because this is a huge change for them. You need to make sure that the team on the ground is actually satisfied. There have been a lot of adjustments in relation to Peak because we need to turn that ugly duckling into a swan. It has been a lot. It has been unusually high this year. We do expect this to normalize back to normal levels in 2026. Again, I am going to make the big disclaimer of, and whatever happens in terms of M&A, because that might change things. Now, we also had negative earnings per share in 2025. This is essentially down to the fact that while we adjust for all these one-off effects in EBITDA, they are in the operating results. We do not adjust for that.
This is a number where we adjust for all the accounting stuff like goodwill depreciation, et cetera. But this is really a number that is net income of all the companies belonging to the group, multiplied with our share in it, and then we add it up. So, 2025 has been an exceptional year. What I presented last year that for 2027, we expect a range of EUR 0.80-EUR 1.10 per share in 2027, and that expectation has not changed. What has changed is our view on organic growth. You saw we updated our guidance, and last year, I presented this sensitivity table, and I have put 10% organic growth in there, and I felt really bold. I felt really daring. I felt like, okay. We saw then that, obviously, organic growth is the oxygen for our earnings per share, and that has not changed.
What has changed is that I actually dare to do a sensitivity analysis with organic EBITDA growth above 10%. What you can see here, and this is five-year CAGR 2027- 2032, w hat you can see here is the organic growth we can achieve is so fundamentally important to our organic growth, to our growth of earnings per share, much more important than the entry multiple we pay. Much more important than the entry multiple we pay. If we can find great businesses with fantastic growth profiles, we can pay a higher multiple because that operates down very quickly. You do not have to get worried that we will get crazy and we start buying at 13x all the time. Marc will never allow that. I think it goes to show on how important organic growth and hence how important building industry standards is to the entire model.
Now, Jan gets the fun part of telling the story, I get the hard part of telling the numbers, I always say. Now, the really hard part is to get to the 20%, because all of this makes sense in Excel, and it is a complex Excel model, but it is Excel. Someone actually has to get organic growth on the street, and that is not us. That is our platform teams, and that is, in particular, Marc and the COO team and the CTO team that are supporting our platform teams where they can to actually get to those numbers. So over to Marc, who is hopefully going to tell us how we get to 20% per year.
Thanks a lot. 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. We heard from Marlene, and that is 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. How can we actually do that in practice with a little bit more detail around a French example? I know they are now also out of the World Cup, but I still miss them, and we will still have them on the slide, and o bviously, what is playing the role of AI in that regard.
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, customers are locked in these B2B software solutions. It is 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. It takes a lot of time, and you are operationally depending on that software. People typically do not 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 EUR 1.5 million, and we are the market leader, so s ometimes these niches are really, really small, but what it makes it attractive that in those very small niches, you only have a few players and you do not have a lot of competitive intensity. All the big guys like Oracle, like SAP, they do not find it economically useful to develop software for such a small niche, so t he supply scarcity is protecting our companies from a lot of competition. Lastly, but not least, most of the clients of our software business have what I would call a "good enough" inertia. They do not need the best UI. They do not need crazy additional functionality year-over-year.
Frankly speaking, they are sometimes also pretty happy with okay service. All of that creates a low churn profile, very predictable revenue streams, and we end up buying those assets at pretty okay multiples. It is a good business to be in. 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 exit those businesses is a very good fit to what those owners of those software businesses actually want, t hat 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 things 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's happy. The key thing here on the slide is the defensibility of those assets is not something that we add. The defensibility of this asset 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's 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, EUR 3 million revenues, bit more than EUR 1 million in EBITDA, 30 people, and they provide software for nurseries and after-school caring of kids. Imagine you're 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. We used what we call the diagnostic Manuscript ratios to figure out where is the issue of this situation.
Issue was sales and 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 overstaffed in terms of professional services and we have to let people go. That was where I was at the same time last year. After this workshop, I was traveling to France to sit together with the team somewhere in France, middle of nowhere, like 50 km outside of Marseille, and t hat was 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. 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 cut the team?
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 the software, and then they charged EUR 5,000 for a one or two-day workshop, which is kind of okay. The problem was all the change requests that coming out of this workshop were actually included in this EUR 5,000. Sometimes, they spent like, work for like EUR 20,000 and didn't charge a dime for that, which is suboptimal. The other thing is they had a couple of larger cities as customers, and these larger cities, they were just having dedicated support people, and they called them all the time.
We figured that some individual employees worked three days out of the week for a particular client, and they didn't charge anything for that person. What we did decide, we concluded we wanted to introduce two Manuscript modules. One was a tool called Red Space. Another was a value-based pricing normalization. The Red Space 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, and then you have columns for each employee that should deliver professional services. At the beginning of the week, everything is like red, hence the name Red Space. 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. If they cannot bring in enough work, it remains red. The team is meeting every week, and everybody in the team is looking at that sheet, and everybody's obviously a little bit embarrassed if all is red. People are really calling customers, "Can I do this work? Can I do that work?" After a couple of weeks, the situation typically looks like that. Some people have it all green, some people have some red, and some people have still a lot of red. What then the team does is to understand what is actually this person doing all the day, although it's not billable work.
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. Then, the leader needs to step in and change that customer relationship, that we either stop this or have the customer pay for that. The business also did come up with a functionality in the software. They call it the green button. 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. As you can see, there is a gold level that's + 60% maintenance revenue charged if a customer was on gold level.
Of course, those nasty cities always overusing those professional services didn't have a choice not to take gold. 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 tell that to the team. All those initiatives led to a lot of individual accountability taking. You can see here, these are the monthly professional services bookings that we did achieve when we started introducing this somewhere here to this, and we increased that by 160%.
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. I'm very grateful that we have a lot of such people nowadays in the business, no matter in France or in Germany. That's how this whole Manuscript Method, call it 1.0, actually is working. Now, what happens with AI and what happens if we would just continue with implementing the Manuscript Method? I think what happens with AI is that AI is not attacking directly the different modes I mentioned on the first principle slides, but it basically attacks the underlying factors that lead to those modes. Why are there high switching costs?
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. With all the automation that AI is offering, that migration effort, ladies and gentlemen, is decreasing. 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 costs so much to develop for that small market, the orchestra 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 that later with some examples that Toby is sharing. It's actually amazing how quickly you can come up with a fully-fledged ERP solution.
We did that internally as well. Now, the good enough inertia, the satisfaction with the status quo, that will also, depending on the particular businesses, hold back for a while. 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 product, 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 the low migration effort. What we also should not forget is that oftentimes our solution has been introduced to the customer organizations maybe 10, 15 years ago.
The guy that has been introducing this software from a customer side is getting older and older. At some point, that guy will retire. Then a new guy comes in without all that history, with all that relationship. The new guy will maybe say, "Oh, I want to have a cloud solution. Oh, I want to have" Oh, ha. Yes, I should have the microphone on my lips. Exactly. These are kind of breaking points. If we are not the one that delivering top AI-enabled services and solutions at that point, then we're losing out. That is if you all become 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.
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 is much easier for us to deploy new software versus a new player. We have a lot of advantages, but we really need to move quickly, and that is why we did come up with this RAFO that you might have heard of. This stands for Red Alert for Opportunity. It is kind of our AI strategy that should help our existing companies to move quicker and to AI transform those businesses. 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?" My answer is no, you are not. Why is that? Because these red situations also create something that I told Greg, one of our investors from Sator Grove just before the session started. Oftentimes in these little niches, you do have just a couple of players, as I mentioned. It is like the four tiles here on the carpet, and that market structure is very hardly ever changing because of these low customer churn profiles. Now, AI is changing that, and those players that do not move will basically being washed away. There is also 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. There is a great opportunity for us if we can bring our existing companies to move quicker than the existing players.
I think what we also should take away from this slide is that the AI, through these red areas, is actually breaking the division of labor by these companies no longer can protect themselves because those things break. There is actually a responsibility and opportunity for the whole co or the platform in our situation that we help those companies to transform. That implies that the Manuscript Method needs to transform itself to just improve businesses, like as I just have explained by the example of this French business, but to actually transform the companies. What does that mean? If we are talking about transformation, I think it is good to remember that most of our companies actually have quite some time until these red situations come in. Why is that? Because they are mission-critical. Mission-critical is a very high-level term.
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. Oftentimes, there are critical parts in the main workflow of the customer. They accumulate a lot of valuable data. Oftentimes, 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. 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 do not get paid if the software does not send this information.
They use it for the canteen forecasting to know which child has which dietary preference. Either you have enough meat or not enough vegetable. This is an operational issue for those customers. 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 truth? It is the software of our business. If they do not provide that regularly, the mayor of this business will violate the law. These are basically networks of operational anchors, and that is one of the reasons why customers shy away to replace those businesses. What happens if we acquire and collect businesses in the similar industry? In this situation, we actually have that 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 are working or a little bit older children that might be disabled or they might unfortunately have lost their parents. There is software to manage that. We have two businesses. We call them Heart One and Heart Two. We do have two businesses that take care of elderly people. Some elderly people in France, they cannot pay their taxes because they have a little bit dementia, and there are guardians that take care of these types of people . They use software, and that is King One and King Two. All of these businesses have operational anchors, hence they have low churn profiles. All of these businesses will at some point be disrupted if we do not move and AI transform those businesses.
We are doing that with the RAFO strategy. Toby will talk about this in a second or in a minute, and I think that is great. We can create additional AI modes if we AI transform these businesses. What we also can do, and this is something that we actually did today. I learned from Thorsten Ahns , 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. The Full House is a so-called CCaaS software, which is managing the registration process of when there is a citizen in France in social need, they have to register themselves, say, "Hello, I have a problem." The hello, I have a problem software is Full House, has about 20% market share across France.
The beautiful thing, a part of, yes, they also have some operational anchors. They are in the process of re-platforming their solution. Why is this relevant? It is a great opportunity for us not only to help them to re-platform their solution with what we learned over the last 12 months around agentic coding, and also thanks to Toby and team, but we can actually now coordinate similar re-platformings in some of the other businesses. 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 citizen are stuck in the process, and that allows us to provide a lot of automations, allows us to provide a lot of analytics, and that allows us to provide a lot of cross-selling into this space, and e ssentially will allow us to create an industry standard in this particular niche. Therefore, the acquisition of this Full House will make the whole system much more stickier and therefore, essentially make the whole companies more valuable. I think we acquired this business for like 5x EBITDA. 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 ingredient that we need here is that we can actually only do that if we can re-platform some of those businesses, and t hat requires that we opt up or level up our R&D departments to really apply agentic coding and to become much more quicker in developing software. That's something that Toby will speak to in a minute. I think the probably last slide here is finishing off where I say where we should underwrite going forward. On the left-hand side, I think we should underwrite or continue to underwrite 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. This is paradise pricing.
I think that should be something that we really should do. On the other hand, when we're doing M&A, we should have open eyes for transformability. Now, transformability transcends 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? We're currently developing a checklist to better assess that. It also is important to understand what's the tractability. Tractability means is 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. That will also instruct what we are doing with this business respectively with the leadership team. Also, the tractability is concerned when it comes to customers. We have some customers that basically tell us, "Go away with AI. We don't want to do anything with AI." Such a business is, for me, less attractive as opposed to you have a sector that is really appreciating AI, and so tractability goes both ways.
I think going forward, we really need to invest in the repeatability of all those transformations, because the more often we are doing that, the more proficient we are becoming. I think if we develop muscles in that regard, that will become a key differentiator in the future. We are already seeing it right now that some of the companies we are talking to in the M&A process, when we are talking about with what we are 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. The new power is keeping the focus on making this transformation happening quickly, more quickly than the potential decay. By compounding this across eight platforms, we are basically leveling up the Manuscript Method from just improve stuff to transform stuff.
The key ingredient that I currently see is that we need to double down on making our R&D teams to agentic coding factories. That is the groundwork we already started doing. Toby will show a couple of examples. The next area that we tackle actually in parallel is to do the AI transformation in other departments as well. Okay, that is it.
Yes. Hello, everybody. I am Toby, a new addition to the team compared to last year. So, Marc already gave me two questions to answer in this talk. Let me start with some general remark about software and AI. Every company today is an AI company. Everybody comes with a cheap narrative. That is not what I want to do today. I do not 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.
In Q1 of 2025, 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. 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. Marc started to hire the first parts of the core AI team. In Q4, I joined as the CTO. 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. That is the AI maturity framework we developed in Q4. I will talk about this in more detail in the later slides.
We also came to the conclusion, okay, we have to change something in M&A. The first level of adjustments to our M&A guardrails 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 2025 was the Opus moment. So when Claude Opus came out, we quickly noticed, okay, something has changed. 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.
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 RAFO 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 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 greenfield projects that were really out of scope for CHAPTERS companies before AI.
I want to explain to 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 sizes, 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 transformed. 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 or SDD 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, gives direct answers to customers. 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 a majority of the support requests. 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. 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. What you see here on the slide overall is the result of a 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 in the AI core team.
This, overall, gives us a clear and comparable assessment of the portfolio over all of these capabilities. As you can see, foundational changes are the clear winner. After that, R&D and support are the areas where our companies invested most and reached 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 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. The last point is you can use it to set goals, and that's what we did. We planned that until end of the year, all of our OpCos have reached 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. 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 least three points. This is how we track, this is our result, you heard our goals, and that's also something you can track me against for the next year. Let's come back to RAFO. 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 protects us today. We have systems of record, regulatory modes.
We are embedded in the transaction of our customers, which are rather slow-moving. All of these are parts of the iceberg that is melting. Now, RAFO 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 Marc 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. 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 and 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. 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.
That really speeds up because we are not just sharing ideas on the level of blueprints, but actual implementation. Then, theqre's agentic coding training. We set up a bunch of trainers. We went to AI events. We searched for people that are AI and agentic coding maniacs and have done nothing else in the last two or three years than finding out how can I create my code with agents. We convinced 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 departments 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.
That's what the gap that the trainers can fill, because they come into the companies, they stay there two or three days, and after that, e verybody has seen it works also in our 20-year-old stack. That's when movement happened. There's CHAPTERS Momentum. At some point, we thought, okay, our companies have long-term plans, they have migration plans, they have a roadmap 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 commitments 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, whilst still keeping their strategic plan. 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.
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. 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 else we have to do. Okay. Let's go on and come back to the second topic, the agentic coding factory. What we 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 coding factories we have built, is Software24.
This is maybe, in some sense, the least glamorous place where you can try to use agentic coding, because Software24 offers WIN-CASA. This is a more than 20-year-old Delphi-based solution for property management. As I said, it is a legacy stack, legacy technology, so it was not clear from the beginning if AI would work here too. But what Software24 did, they built this impressive coding factory that really produces code end to end. What do I mean with that? It is easier to understand if you compare to what most developers do now. They sit in front of an agent, and they use prompts to steer the agent to produce the code they want.
What Software24 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 great specification, taking in everything from customer, other stakeholders, then taking this to the next agent that builds a technical specification plan and really a detailed implementation plan for the implementation agents that still work together with humans in the moment to steer them in the right direction. 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. 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.
Software24 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 years of the months they were still building that, June was the first month where the factory was really full in action and you see this step change in agentic coding. Even in a Delphi environment, we can produce more than half of our code now already with agents. Now, to Peak. We have seen it works with legacy. Does it also work in big organizations? Peak is 160 employees. We heard about this. It is 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 agentic coding training came in. There was a lot of effort within Peak to really now pick up this topic. We saw that the agentic coding rose from 13% in January to now 49%, and this is in June. June is again the first month also in Peak where the agent took over the humans in terms of produced code. Now, what do we see on this slide here? We did an analysis of what the people are actually doing with the agents. Are they doing the simple boilerplate stuff that is just running down code? The answer is no. Actually, the agents do the hard part. What you see here on the x-axis, it is more difficult, this is really more cognitively challenging. The y-axis is more complex.
These are changes that require code changes in large parts of the code base, different places, and so on. What is of course obvious, if you have to change at a lot of places, hard stuff, you let it to an agent, and that is what people do. What I find really fascinating are those two points here, because these are bugs and problems that have not been touched by anybody before. It is a new category. Let me give you one really fascinating example. Peak builds control screens for the control room of public transport companies. There are often double digits of control monitors there. 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 window's main window loop and solve it. After two hours, Claude was able to solve things that were bothering us for 20 years. AI is not just solving things that took more time in the past, but it is actually solving problems that nobody was able to solve before. Let me come to the last example of the agentic coding factory. We now have seen it works in legacy. We have seen it works in big engineering organization. The last question is, does it also work on the greenfield, and should we do it?
This is the example of Parity. Parity is one of the oldest companies in the portfolio. For over 40 years, they have been building ERP systems. Their current products are really legacy, really end of life. 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 Software24 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 the task. It's really impressive to see what four people can do now. In the first presentations with the customers, they said, "We 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. Software24, for example, they are 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. We are living in a new cool world now, from my point of view. 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 Marc has told us in his talk, our companies were at some point founded with real customer intimacy because a 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 it and came from the founder mindset eventually to operator mindset that is focused on incremental improvements, monetization, and so on. Growth was limited, and then that's what you do. Also, the founder somehow leaves and often leaves 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 has 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. I also think it will require to take in new talent at some point.
That's something where we as the HoldCo can also really support our companies along the way to get the right people in now to build this bright new future. Seeing beats listening. Now, it's time for the marketplace. I give the mic to, yeah.
Before we start, I want to tell you a story. 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. 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 open my phone, in February, you know, the [SAS Kalypso]. There was an investor, they're not here today. They will remain unnamed. They wrote me an email and said, "Jan, we got to talk to you." I wrote them back, I said, "Sure, I'm on vacation, and we're in close period. You know Andreas.
Those are three really good reasons why we should not talk today, but does it maybe have time?" They're like, "No, we have to talk as soon as we can." I say, "Okay." Small investor. "As soon as we can. Immediately, Jan, please." I say, "Okay." I go to the hotel, kids come up, "Daddy. Daddy." My wife. I was like, "No. No pool. I have this important investor call." I dial in three partners. They look at me. I say, "How can I possibly help you? What's on your mind? Something's wrong." The lead guy looks at me and said, "Jan, what about AI?" It's funny, right? Because this man was just scared. You just hear all the news and you hear what's happening, and you see the rumors, and you see everybody talks about some vision, 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. You have people who are not 100% convinced, who are like, "I got 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 also how the future could look like. We want to make that very, very specific. 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. We'll rush through them. Everybody on the webcast, 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, but one-on-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 was 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 dots because, of course, although I'm not presenting a product, obviously, everybody else on the CHAPTERS team is hanging out as well. We are here for all your questions and make use of that. Again, we will only leave if the last one of you leaves. Usually, we have some good staying power. Until the last question is answered, we will be here. Let's start this little experiment. I'm going to start with my safe bet. Andreas, I think you're first, and you're going to give us an exciting example about AI in the public sector space.
Thanks, Jan. I'm Andreas Philippi of Altamount. We actually started Altamount 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. I think today our ambition for Altamount 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 EUR 45 million- EUR 50 million revenues plus five, six, seven 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.
We believe AI, it's super important. We're all super pumped off of the power of AI. I want to show you that in a second. 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. We're really ready to accelerate the growth. I think one important part to just show you how important AI is for Altamount, we set up a dedicated entity. We called it Altamount Ignite, with the objective to ignite AI in our portfolio companies. This unit, Altamount Ignite, is run by Lennart. Lennart, where are you? Here. Lennart will be showcasing next door one of the use cases that he deployed together with Peak Mobility.
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, and 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 1,200 buses driving around during the day and at night, all these buses have to park somewhere. They come to depots. 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 depots. This is a global phenomenon. This is something where Peak is really strong.
Now, if you have a diesel bus, it's pretty easy. You fuel the bus within seven 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 six hours, seven hours to fully charge a bus, and sometimes this is happening during the day. If he's doing a ride, he cannot just easily get recharged. He 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 the next day, where this whole charging maintenance of these buses is fully integrated, for the depot.
It really becomes a dynamic depot, and Lennart will be showcasing that in the room next door. Pretty exciting case. A second use case that we have, it's a company, it's also a portfolio company of Altamount called icomedias, run by Christian. Here, Christian Ekhart. Very nice company. It's a secure workflow automation for critical infrastructure, but it's mainly for the police in Germany. What icomedias is doing, they provide the so-called Onlinewache in Germany. This is where the 80 million or 60 million adult citizens in Germany can file a police report. My handbags got stolen, my car has been crashed, somebody killed somebody or whatever. Just do 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.
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 on what to do first. This is a very manual process. 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 icomedias, what they now have developed and are just about to deploy is a fully automated prioritization engine. It reads all the different police reports, and if there's murder or a bomb threat or whatever, it's getting fully prioritized. Some of the others, they are just held back until 7:00 A.M. in the morning, then the night shift doesn't have to deal with that.
It's a very powerful solution that's also decreasing the cost for the police, which the police, I think, is very happy about. Christian will also showcase you on the live portal later on. Thank you.
Amazing. Thank you very much. Next up is Jascha. Long story, so the very first software company ever bought by the entity then called MEDIQON was a business called Parity. It was amazing because it was the first deal, and everything that could potentially ever go wrong in acquiring a software company went wrong within the first couple of weeks. Now, 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. You're not only running Parity, but you're running one of our largest platforms now, and 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.
Thank you very much, Jan. Indeed, Parity was a great school for me to learn everything that might 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. We are market leader in this market. We have about 900 dealers online, which means that about 1 million of service appointments are going through our system each year, while the booking process is still quite manual, like most likely, the dealer has a customer at the phone and types everything manually into the system.
There is 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 the dealer, and then the end customer books an appointment directly into the system by using natural language. What did Momentum specifically enable about it? I will keep it short, just 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 projects.
Those were recommended by Tobias and financed by CHAPTERS. 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. This makes it possible to set up an appointment whenever a service assistant, a mechanic, is available and the customer wants to have it. Finally, about the timeline and some economics. We started with that in May 2026. 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. Once they are satisfied with the solution, we of course 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 EUR 350,000 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. I'm glad to be there for your questions.
Now, I'm handing over to Matthias of Waterkant, our literal neighbors, because the Waterkant office in Hamburg is very close to the headquarter of CHAPTERS Group. Whenever I talk to some of you know I'm on the phone walking outside. Usually, that means that 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. Yeah, 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 for us was not that easy. Basically, we wouldn't have the idea whether that target would be a good fit to our guardrails, which you see on the right-hand side, and whether it would really pass or sustain in a shark tank. We came up with an AI solution, with an AI lens, or we call it, which will find out whether a target is really interesting to us. But just using AI is always not the kind of goal you want to use, because it will give you randomized output. So, we had to train up the AI.
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, it could be a core platform, whether we could treat it as more as an add-on. Most importantly, the AI also needs to understand how software is bought in that segment, and what do our customers really need in that segment. Of course, the AI also needs to know what are the current market trends to actually score the companies in an initial setup. 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?
Gives us a lot of context of that company in a matter of seconds instead of just digging through it and having first false meetings with targets who are not really interesting to us. If we do it on a bigger scale, for example for logistics software, we did it before actually entering the market. We can actually see upfront whether we want to go into that market. We see on the top right-hand side, we see that we have lots of core platforms available and which of those can be really addressed and 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 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.
Thank you. I was opening LinkedIn one 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 you were doing a capital round. Then we first had our first, I think, meeting where we met in Berlin on the roof terrace. It was funny because I said, "Well, it's so nice writing with you." He's like, "Yeah, that was my bot." I say, "Okay. Well, I'm not entirely sure you're a human. You might still be a robot, I don't know, but certainly your outreach was professional and very much to our liking." You've been as an investment part of the CHAPTERS family 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 and you too. Yeah, maybe first of all, I was a professional athlete. Now, I have a company, Fuxam. Where does Fuxam comes? Fuxam comes from the future of exam. That was our initial idea, because five years ago, I've based in Berlin, I was there in the university, and I thought, why we are still write exams with pen and paper? This was my initial idea, and now, we build it a much larger solution. We build it a full SaaS solution for universities, the complete operating system. You're thinking now why this guy does that? Because I recognize, because we make a market research, because that was the only thing what I as a student could do because market research and do something like that.
We saw that 120 university, there was a paper that 120 universities using 1,800 different software solutions. When I recognized that, that was the moment where I saw that we need an all-in-one solution, and that is exactly that thing what we are doing now. We have now 30 customers all over Europe. We have six modules. We are building now 90% with AI. We was very glad that we was, I would say, funded, born in the age of AI because it helped us very good to build an all-in-one solution. Now, we have six main products with Fuxam Apply for application management, campus management, learning management system with a mobile version. We have the examination tool, which was our initial idea, and our alumni management, and we are doing a lot of other very, very cool things.
I'm glad to see you, maybe, and talk to you maybe later, and we see us there in the back corner.
Thank you so much. All right. This is particularly exciting because Kältehelden is one of the longest additions. Here's my clicker. Of the CHAPTERS Group, and it's the 3% bar and Other that you saw at the very beginning. You could wonder, it's the only non-software financial technology business we have left. What's going on there? It's kind of amazing. You build, Stef, you build 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, and 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 costs involved, the savings are incredibly real. You've adopted a very playful and very experimental mindset to improving and implementation, it's incredibly exciting to have you.
Thanks for the introduction, Jan. A little bit disappointed that you did not introduce us as the Kältehelden, the coolest cooling company in town, as you used to. But I'm fine with it. We can talk later by a beer. So, we're the Kältehelden. We are 23 people in HVAC market in Germany and Hamburg, and I think AI is one of the biggest opportunities in our industry that maybe ever happened. 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 are missing. That's a heavy problem for us because on those informations, we build our invoice. We probably lose money when our technicians doesn't write the report properly. We started with the AI Hub to create our service report buddy. It's a GPT that works along with the technicians, and they don't have to type in everything now. They just talk about the problem they had. It can be completely chaotic. AI structures this, and the most important thing it asks. If AI sees there's something missing, it will follow up. We did something that the GPT also has technical knowledge. It's not a master craftsman, but it's kind of an experienced journeyman.
If there's a young technician just saying, "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 buddy will give him hints where to look, where to search for the problem. Afterwards, we will get a clean report and just put it into the ERP system, and afterwards, we can build our invoice on it. Yeah. So, more complete reports. That's pretty important for us, and to be honest, it's just the beginning. We have a lot of problems, a lot of themes in our company, in our industry, that can be solved and that can be more efficient by AI. And so yeah, all people can focus on keeping the future cool. Thank you.
Okay. Our last example and presenter is, you know, the few examples I bring around what we do and 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. Here it is. John.
Yeah. Thank you very much. Last but hopefully not least, I've got something very operational for you today. Basically, 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. 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, and 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. 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 used 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, 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 this very clear logic applied to that. It knows our products, it knows the questions the customers raised in the last couple of months. We know what's happening in sales, which conversation they have been. 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. It gives us, as a very small company, a lot of hours, so 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, but it's in the AI Hub, so it means it can be shared across the community of companies we have on the one hand. And 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, nothing like that.
I just have one prompt, looks like ChatGPT, we all know that. 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. 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, 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, there's one thing. Remember the slide that Tobias showed with the founders and the operators. Most of the businesses were started by founders who saw a market opportunity and a software tool at the time, like Microsoft Access, some early Java solution. They said, "Listen, nobody understands the gun license market better than I do, and I'm going to solve this problem." 80% of the businesses that did that, who never scaled, we don't see. They never scaled. The ones that found product-market fit, they grew very capital efficiently with no external investors, and they 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, right? Efficiency, improvement, get the succession right, get pricing right, get things stable. I think that's changing. 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 intimacy, that's a word we use a lot, you can actually reinvent your business.
You can, 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 that rather than just run them, we can 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'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. Marlene wants to say something that's important.
Some directions.
There are fridges with drinks.
Good.
The bathrooms are at ground floor and a bit hard to find. Don't get lost. We have, who's in here?
Condition and Fuxam.
Fuxam and Condition in here. I'm going to be in here. You're going to be in here. Marc's going to be in here. Toby's going to be in here. Andreas, where are you? Down the hall. Down the hall. Jascha, where are you? Also, down the hall. Okay, but mingle around, ask all the questions you want. Whoever has a dot has something to say. Thank you very much.