Thank you everyone online for joining us today. My name is Daniel, I'm the CEO and founder of Upsales, and we are extremely excited to present our new AI agents for you today. I will be joined by David Dernulf, our CTO, and Kristina Fridheimer, our CFO, and then we will end with a Q and A. I wanted to start just by talking about what started this whole project. We by accident listened to a podcast with this guy, Jason Lemkin, who is somewhat of a legend in Silicon Valley. A tech entrepreneur who now runs something called SaaStr, which is the biggest SaaS or software event, one of the biggest ones in the world. He was on a podcast talking about how he had a couple of really well-paid people on his sales team who just left with one day notice, and he started thinking about, "Okay, my business is not growing. My sales team is not as predictable and stable as I want it to." He started experimenting with AI agents. Around one year later, this is what they managed to do at SaaStr. They replaced almost 10 people with AI agents, and they went from negative growth to almost 50% growth. It's a very interesting story. It might not be applicable to every company, but I highly recommend everyone to listen to it. What happened at Upsales when we listened to this podcast was that our first thought was that we can do this. We can help our customers do the exact same thing, to reach incredible levels of efficiency and really use AI to be more than a chatbot or an assistant. Actually, at the same time, my colleague David was in the second iteration of building the new AI assistant in Upsales, and the idea was to build a, you know, an AI assistant, a chatbot. After we started talking about this, it turned out to be something way bigger, which we are very excited to present today. I just wanna talk a little bit about this also. If you have ever tried launching an agent or use AI to do anything useful in your sales team, you might have some bad experiences that the AI doesn't work as good as you want it to, and the numbers back it up. Most of these projects show no financial return. It's the data out there of most companies who have tried deploying agents are quite depressing. Our view is that this is not a technology problem. There are cases like Jason Lemkin, and there are many more out there. The models are good enough. The problem, it's a workflow problem and an iteration problem, and I will talk more about what I mean by that. I think what we see is that the number one mistake when deploying AI agents is to treat it as something you set and forget. You treat it as a traditional piece of software or a traditional system integration. And the problem, as many of you know, is that AI models are non-deterministic. You know that they can do something, it will hopefully be useful, but you don't know exactly what will happen every time. What we see is that the companies who are able to get the kind of results that Lemkin was talking about, they do 30, 40, 50 iterations. It's hard work tuning these agents, giving them feedback, and making sure that they are doing exactly the right thing. And the most companies give up after iteration two or three. You try it, you get a little bit encouraged at start, and then you get a little bit discouraged, and then you come to the conclusion that AI doesn't work. It does work. The models are good enough, but you have to have some stamina, and you have to go through the number of iterations to make the AI do something useful for your specific use case. Dernulf will talk more about what this means in in more detail. Why do we think we can do this? Why, why can Upsales be the company delivering the same kind of results as Jason Lemkin is talking about? I think, number one, our very long track record and experiences of nerding around sales processes. We've learned from the best. We interview the best people in the world. We have worked with thousands of companies implementing our sales and marketing software. We've seen what works, we've seen what doesn't work. What we're doing is we're taking all of this knowledge and putting it inside these new agents. Generic AI is generally useful for some things. What we are building is something very specific for the core sales and marketing use cases our customers have. The third reason why we think that we can do this is we have had a almost 20-year track record of working with not only selling the software, but also selling the data. Because the biggest problem in our field, in sales and marketing tools, is that the data is broken, and you, your strategy to get the right kind of data is to have the users enter it manually into the system. If you tried it, you know it doesn't really work. We've always worked with third-party providers to make sure that our tools and system contains the real quality approved data that you cannot scrape online. You have to buy it from a third party. When you have all of this, these finely tuned agents together with actual data, it's a lot easier to make them do something useful. This is just one example of many, just to make it a little bit more concrete what we see, our customers will be able to do with these agents. We're moving from a chatbot waiting for a prompt and an assistant waiting for a question to autonomous agents working 24/7, taking initiatives, doing stuff, preparing stuff for the team, contacting customers, booking meetings. We move from supporting the work to actually doing the work. This is one example of one of the agents we will launch. It monitors every single customer you have 24/7. It keeps track of everything going on, and it finds the early warnings that usually exist before you lose a client six or 12 months later, and it gives you a strategy and a proposed plan. All of this, they never take a break, they never go on vacation. They are running 24/7. The elephant in the room, the question that is too polite to ask out loud, do we think this will replace the sales team? I don't think so. I agree with what Lemkin says in his podcast. I think many of the agents that are out there today that are doing useful sales work, they are better than the bottom half of salespeople. They are better than the mid-pack, but they are not better than your top performer. We see that the work of the agents is enabling really good salespeople to produce even more by removing the non-meaningful, repetitive admin work. The ones who lose are kind of the ones whose value is the administration because that kind of work is quickly being replaced by different types of agents. Before I hand over to David to show you what this looks like in practice, I think when I talk to I'm out seeing our customers every week, and when I talk to them, I think that a couple of things happened in the last 18 months that was not really true 18 months ago. The models are good enough now. They were not good enough 18 months ago. The cost of running them have dropped significantly. If you look at the kind of reasoning power and intelligence you get for a dollar today compared to 18 months ago, it's a huge difference. You can experiment and you can be a little bit creative with AI without kind of breaking the bank. I think the platforms that will win are platforms like Upsales, where you connect to real data and make sure that these agents are not trying to go out on the internet and just find something and hope it's useful. I think the CEOs and the sales leaders I talk to, who are winning, they have like a 24-month plan, and they realize that this is a sprint and, if you're not doing it now, your competitors will. What we recommend when we talk to our clients is to not try to have a huge, super ambitious AI project. Find something, find one process, start somewhere, and just iterate until it, until it's as autonomous as it can get, and then move on from there. I see a lot of companies kind of getting stuck in the planning phase. I think I will stop there and hand over to David for some of the real stuff. Thank you, Daniel. All right. Hello, everyone. My name is David. I've been working at Upsales for, I think, over 15 years now. I've been CTO of Upsales since April this year. I still work hands-on in the code base. I want to continue a little bit and talk about what Daniel said. I want to talk about what I've seen happen for my line of work in the last 12 months. Okay. About 12 months ago, we used AI, of course, during coding, but AI was nothing more than, like, a better auto-complete. Today, I can write a spec, and it doesn't have to be very detailed, and I delegate it to the AI, and it completes the feature while I just go grab a coffee, basically. I come back to work, and most of the time, it has completed the task very well. The thing I get paid for has changed, and that's not, like, a prediction. It's the reality. That's happening now. Nobody at Upsales is writing code by themselves anymore. That's just not how it works anymore. AI does that. I mean, we see it first as developers because we're closest to the tech, I guess. I think the harnesses around the models and the tooling for coders are probably like at the cutting edge, which is not the case for most other lines of work yet. I think the same shift is coming for all white collar work basically, and it's coming really, really soon. It's not gonna be five or 10 years from now, it's gonna be starting this year, next year. Yeah. That's the real AI revolution. Like Daniel said, the models doing this kind of work, they're already as capable as most humans by any reasonable metric, I would argue. They can reason, they can articulate their motivation, they can make decisions. There's an obvious question here. If the machine can already reason at the level of an educated professional, then why does everyone's work still look basically the same anyway? I'm sure most of you or probably all of you has used ChatGPT, some probably use it every day or some other provider. It hasn't really changed how your work gets done. It's just a tool that you reach for and you use it sometimes, it's not the foundation of the business. Why is that? I think that gap is probably the most important question in tech right now, I'll tell you what I think the answer is. The model is not the product, the model is basically just the brain, the brain alone can't do much unless you provide it good context, memory, and a way to take action. That's what we've built. It's not a smarter brain, it's basically the body for the brain in sales. We built a platform, a foundation where AI isn't the feature, it's basically just how the work gets done, it has to solve two specific problems. A model is only as smart as the data that you give it. If you ask a generic AI, "Should I call my customer today?" It has no idea. It doesn't know who you are, it doesn't know what company you're talking about, or anything like that. Obviously the information exists, and you could just paste it into ChatGPT or your personal favorite of provider, and you would get a pretty good answer. The bottleneck isn't whether the context exists or not, it's who assembles it and when. No salesperson is gonna pull like financial data, news data, activity history into a prompt to an AI for every decision they make because they have a job to do. What we've done is we've built a layer for them that does this for them. It knows the context needed, it fetches it, delivers it at the right moment. The user will never have to think about that. Like Daniel said, at Upsales, we've been collecting this kind of data for over 20 years now. Proprietary company data, financial data, credit data, monitoring of news, et cetera. That's our edge, and I think that's really hard to copy. The next problem is the intent. I'd argue that most people don't know how to write a good prompt for the AI, nor should they have to. I don't think anyone should have to do that. I mean, I write prompts every day, and it's a bit annoying, to be honest, and especially annoying when it doesn't understand what I mean. Asking a salesperson to learn how to prompt, I think that's just bad product design, honestly. With our 20 years of building sales software, it means we know what good sales work looks like and bad sales work as well. We know what decisions move the deal forward, and we've baked that information into the agents themselves. Our customers do not have to teach our agents how to sell, even though we might encourage them to prod them in one direction or another. They already bring two decades of best practice with them from day one. All right. Even if you have a good platform, someone still has to ask the AI to do something. This is basically what Daniel talked about before. You sit down, you type, you get an answer. The human is still the trigger. The obvious next step is an AI working without being asked to, basically. You set up an agent, it has a job. Make sure every salesperson on the team has at least 10 leads to act on at any given moment. It will silently watch, and once the lead count drops, it will act. Find candidates that fit the ICP, draft outreach, and surfaces it for approval. Nobody told it to do that today. It's just doing its job, basically, like a good colleague. Like Daniel said, I mean, this doesn't mean salespeople stop prospecting. It just moves the work, basically the grunt work. They stop spending their morning searching LinkedIn. They start spending it deciding on which of these 10 leads should they focus on right now. The job doesn't disappear, it just gets better. Okay, let's see. Let's try to make this a little bit concrete though. I'll walk through a salesperson's workflow here. Currently today, they need to prospect, do outreach, have the meeting, recap the meeting, maybe update some data, maybe they send a proposal and try to close the deal, right? Some of these steps will happen multiple times, obviously. Yeah, with our platform, the agent will do the prospecting. It will propose outreach based on our sales experience and also based on the voice and tone of this particular salesperson. Once the meeting is set, it will help you prep for the meeting by giving you a brief with yeah, maybe an agenda and whatever details about the deal you need to know at that time, basically. Once you've had the meeting, the agent will send an automatic email summary. It might draft a proposal, it might update the deal data, or it might add a contact based on what happened during the meeting. At the same time, we have agents working on the platform, making sure your data stays clean, everything is updated automatically, duplicates are flagged, and forecasts are adjusted. All of these things needed to be handled by a human person up till very recently, most often the salesperson. Now agents handle them, and the salesperson does the part that needs them, the conversation, the judgment, and the relationship. All right, enough talking. Let's see if we can get this going. Is this yours? Daniel's maybe. Let's see if I can I gotta log in here. Okay. Okay. We'll start over here. This is basically the start screen, and it looks like any regular AI assistant, and obviously it works like that as well if you want it to. In this case, I'm gonna ask it to show me my pipeline. It pulls up some data from the CRM, cross-referencing activity history, checking external signals, and computing a risk score here. I get a basic brief pipeline summary, as well as some deals that are in risk, at risk. Then I ask it here, "Okay, actually, could you send this to me every Monday?" It's like, "Yeah, sure. I'll send it to you every Monday." I'm like, "Okay, but, could you also flag anything that looks weird when you look at the pipeline, and make sure to notify me about that?" It says, "Yeah, sure. I'll watch for anomalies, sudden stage changes, unusual deal slippage, et cetera." You can obviously just ask it again to be like, "Yeah, I don't care about deal slippage. I don't care about that. Just focus on this." You will get this report every Monday basically. I think this can probably replace a huge part of our current analytics platform, but we'll see. Daniel and I talked a lot about the prospecting and outreach. We have an agent working during the night here, and it found four candidates for us to review. Click it, you get a list here of the prospects. Tink, Klarna Group, Trustly, Lendify. We can see that it found some news about it and added the credit score from our proprietary data and the contact person we should contact, it writes a draft here, and I can just approve and send. Read this one as well. Looks good. Approve and send. I'm gonna skip these ones for now. Then we have the data hygiene, maybe the boring agent, but we have this cleaning crew agent. It runs a sweep during the night looking for duplicates, maybe like opportunities that lack contact persons or stakeholders, or finds deals that have been stuck in a stage for long. You can click it and see what it proposes you do about it, basically. It gives you reasoning what to do, then you can approve or you can reject or request changes. Same for all of these. Obviously, like, you can ask the chat to do basically anything, if it thinks it's gonna take a long while, it might decide to run it in the background. If you know that you don't wanna discuss anything with the agent, you can just tell it up here by giving it a task. In this case, I have, okay, it's very ambiguous and not very good task, but that's the thing. People just write whatever is on their mind. Check and see if we know anything new about Stegra, you run the task, and it starts working immediately. Looking at CRM records here, looking at our data, then it goes on the web and fetches, sees if there's anything, any news on their site, basically. Once it's complete, it synthesizes all this information into a short summary here, where you can read, okay, they had a Series E closing last week, new VP Revenue Operations hired, and headcount is up. Like, okay, I don't care about that, you get the point. These are just a few examples of what we think this can bring to our customers, basically. Let's see if I can switch back to my slides. What I showed you right now is just the foundation, the same architecture obviously extends a lot further. Today, the agents do work alongside the conversation. It does drafts, recaps, preps, follow-ups. Tomorrow the agents will handle the work around the work. We will generate, like, proposals with the correct pricing, correct branding, legal terms, sales material on demand prepped for this specific client, agents that know when to ask a manager for approval, when to escalate to legal, or when to involve another colleague. I think also in the near future, we'll have agents that don't just work around the conversation, but they will help during the conversation, so basically live coaching in the moment, in the meeting, suggestions in real time. Going further than that, I mean, I think the limit is just your imagination, and I think we'll find many really cool use cases going forward. All right, I'm about to wrap up. I think a lot of people might be nervous about AI, I understand that point. I mean, the press talks a lot about what AI might take away from us. I want to leave you guys with the opposite framing, because I think it's more accurate and also more interesting. Just like Daniel said, I think the work is just moving up the stack, basically. The repetitive parts, the boring parts, the parts you do the same thing every week, those are going away. What's left for humans is what's always been the most valuable anyway, the judgment and the relationships. I think the days of a salesperson spending their day updating records and chasing dead leads, those are gone. They'll be spending it deciding which of the leads to pursue, as I said, and how. That's a better job and a more valuable one, and it's the job our platform is going to make possible. I think the companies that figure this out first, that harness the context, the initiative, will be the ones defining business software in the years ahead, and we intend to be one of them. Thank you. Here comes Kristina. Thank you. Welcome. Thank you. Hello. I'm Kristina, and I'm the new CFO of Upsales, and I've been here for only a couple of months. I'm not going to share my 23-year-old story from the inside. I'm going to share what I've seen since I came to Upsales. The first thing I noticed was how this company is actually built. I'm gonna talk about three things, how we work, why we're independent, and how we see the market. This is the foundation. We have been profitable every year. We have no debt. We have well above 90% recurring revenue, and we have had organic growth since 2003, so since start. All the investors are familiar with the, these numbers, but I want to spend a minute talking about what this does to our customers. By building the company this way, we can stay close to the people who actually use the product. We haven't chased trends. We haven't made big bets. We've listened to our customers and solved real problems year after year. This is also shown in how we deliver the product without a big consulting team in the middle. I think that's what makes Upsales unique. Discipline isn't something we just put on the wall. It's our operating model. 23 years of running a profitable business teaches you something. It teaches you what works and what does not work, and what to leave behind. Second thing I noticed is the independence. It's not that common for these kind of companies. We're founder-led, we're listed, and we have never needed to raise capital, and we have no exit clock. We also have a founder, CEO, and largest shareholder all in one person, Daniel. He's been all in for two decades in Upsales. We have no fund waiting to flip, we have no bank telling us how to run the business, and we have no outside agenda. That makes us pretty special. For investors, this is a really solid financially stable business. For customers, we really do our decisions based on what's right for you over long term. This is where it really matters, I think. Our old business funds our new one, so the core business funds VET. We do not have to raise money to chase the AI market, we do not betting the company on agents. We have a profitable debt-free recurring revenue business that pays for what comes next and what David has built. It means we don't have to choose between protecting what's already in the company and building what's coming next. A lot of companies do need to choose, we do not have to. The market situation. There's a narrative right now that SaaS is dead. A lot of people are talking about SaaSmageddon and that AI takes over. Truth is way more interesting than that, I think. We do not think that the market is shrinking. We think it's shifting. The big players in this category were built for big deals with many partners involved, but that makes their projects slow and expensive. The architecture that worked and that built our companies or competitors also is the things that traps them today. AI is changing what customers want to buy. They do not longer want big projects, a long run rollout or a small, like, partner army of partners anymore. They want something that just works for their business fast today, and that's where we come in. Yeah. Companies have bought business software for many decades, for one simple reason. You can't build everything yourself, and you can't do that with AI either. You can, but do you want to spend time on it? I don't think so. Tools that run your business are worth paying for. The ROI is still there, and no company will stop investing in tools that run their business or their revenue. They are too busy running their own business. To wrap this thing up, I want to just summarize with three things. We have built the company with discipline, quite hard, and we have also kept it independent. The market is now shifting to something that suits us very, very well in the future. Let's go to the Q and A. Daniel. Nice. All right. We will open, Inge, do you have the questions from online, right? Yes, I do. Yeah, it's possible to use the chat online if you want to ask a question as well. Do we have any questions in the room? 20 years of discipline. Where did you start? Like, what was your starting point? My starting point, I think like many entrepreneurs, it's kind of a combination of a coincidence and maybe some courage. I was actually working at a company who didn't have a tool like this. I'm an old developer myself since I was 13. I built a tool. By accident, I was on a client visit actually at a company someone here used to work at who became my first client who needed something like this. It was humble beginnings. I think that culture is still in Upsales to like grow with each client and do the hard work every day. Thanks. Yes, some questions regarding the AI and the roadmap going forward. We have used a few AI agent from you. First of all, it's nice to hear, I don't know, nice, but I mean, you need 20, 30 iterations for it to work. That is also good for you to bring to the product with the customers. We weren't told that, so I mean, now we're scrapping one. I mean, the users have lost faith in it and so on, since it's providing shitty answers and so on. That's one thing, just something to bring with you. Others, what you think about for us, I mean, our view on AI agents and transcription and so on, when you have a quite small niched business It's really, I mean, this transcription, they choosing words that doesn't exist and so on. It's really, I mean, how to build good agents. I mean, I want to put our lingo and so on into it so it can learn from that, because otherwise, the business, the users are losing faith in the agents because they are just providing shitty recaps and all that is just nonsense. Any reflections on that? I think, I can ask. Yeah Do half of the answer, and I think David should. Yeah The other half. I agree on the lingo. My favorite one is Upsales is always Upsale in our meetings. Yeah Sometimes, so I know the problem. I think Daniel will talk about how we solve that. Yeah One interesting thing is, like how fast the market is moving, because the platform you're talking about. Yeah Is the agent platform that we launched, like a year ago. That is, it's a white label partnership with another company, a very fast-growing automation company. We were thinking when we signed that, should we sign like a one year, two year, three year? It's like it's so strategic, so let's sign a three year contract with this company. It's almost already obsolete, I would say, that platform, because what we saw is that technical products require too much from the typical customer of ours. I think that's one of the big reasons why we're building what we're building now, where you get your agents by talking to the platform without having to become a technical person or use an engineer. I think with regards to the other part, I think you're better to talk about that. Yeah, maybe. I mean, I'm very sorry that that has been your experience, and I want to be clear that the agent platform we're talking about today, like Daniel said, they have nothing in common. Like, one of the issues is, like Daniel said, like when you need to iterate, we need to iterate on the agents, and they need to learn. They need to have a memory, we need to be able to provide, like customer-specific context. Also for specific agents, we wanna have to provide like agent-specific context as well. I think, one of the best way to improve this is to let the agents have a memory, that way you can say, "Never translate that to this again. We talk like this, and you should never do this. That way it learns over time, and you can make sure to never have it repeat the same problem again. It's easier to do this than you might think. We have done it, you're gonna see how it works very soon, I would say. Hopefully this will be a much better experience than what you had last time. I hope that answers. We'll see. Some. We'll see. I mean, of course, it's important in the projects to really stress the importance of the iterations because, I mean, typically the users out there, they give it a try or two. If it doesn't work, psh. Yeah. I mean, we will provide a lot of agents, and they will be trained already. Like we will iterate on them internally and make sure that they are good enough. In order for you to get the most out of them, once you, like, add your specific context and you have your specific needs, yeah, you might need to iterate even further. You can also build your agent from scratch, and in those instances, it's very important that you try them out and see what works and what doesn't, and just tell it. You just write to it and say like, "Yeah, don't do that. Focus on this. Never focus on that. I would say this is the first time in my 23 years that these guys are ahead of the sales pitch. In all software companies, usually the other way around, you go out and you talk about the future and all the cool stuff you're releasing, and then engineering guys are trying to catch up. If you saw the video on LinkedIn when we invited to this event, it's, I think it was also Lemkin who wrote this sometimes, that founders typically have like an very annoying sense of detail. And I'm always the guy who finds the 10 first bugs in new releases and I was literally blown away about the first demo, which is many weeks ago now. You will be impressed, I promise. I'd like to add on that as well. I mean, I work as an engineer, and I have for my whole career, and I don't like to say things that are not true or promise things I can't. I'm very confident in this feature and what we will deliver and build in the coming months. I'm very excited. Take that to the bank or something. Just so I understand. How is this going to hang together with the normal Upsales that we use every day? Is this something special? Is it separate or is it integrated into one, into Upsales normal one we use every day? Yeah, it will be integrated. Yeah. Currently, it looks just like I showed you, where you can't really see anything of the regular Upsales. I think what we'll do is, we'll show the top bar or something so you can navigate around to other parts of the system as well. Yeah, it's part of the regular system. I think we had an interesting discussion when we were talking about this, when we started looking at the design for this. I mean, a lot of SaaS companies, software companies, when building AI, they add it somewhere. Like, it's an assistant somewhere in the corner, but the product stays the same pretty much. We had this kind of metaphor of, like, if we were, you know, a group of 25-year-olds in a dorm room in Silicon Valley somewhere with a goal of, like, taking all of Upsales customers and making Upsales go bankrupt, what product would we build then? That has been kind of the idea, to challenge everything. We have a long experience, and we have a lot of knowledge, but we have to reinvent a lot of stuff. What will software look like in general five years from now? It will not look like software looked like today or a few years ago. It's very integrated, and it will replace a big part of the product as it exists now. Yeah. I just wanna add quickly, I think there might still be a case where we do add the assistant in different part of the system as well, because I think that might be useful as well. We will still have the, like, workspace view that you saw here. Yes, we have a question from online as well. How do you see these AI agents accelerating ARR growth? Yeah, maybe that's a question for me. You can do that. I think when we are talking, especially to our existing clients, I think in almost every meeting, there's like five, 10, 15 use cases, like problems to be solved and ideas to be explored. I think for existing clients, there is, like, a lot of work to be done and a lot of more value to be created. And for new clients, I think it's about staying competitive, making sure that we have the best product. And the best product in 2026 is to have the best AI. That's the bet. And of course, we are not done. We will never be done. We will continue doing this, yeah, as long as the company exists. It's always a matter of innovation. I have more of a trend watch question. What will great salespeople look like in five years or in three years? I had an interesting conversation at lunch today actually about this. I think great salesmanship, like 20 years ago when we started Upsales, I think it's the exact same thing today. I think it's about, you know, knowing your product, understanding your customer, being able to build relationships. I mean, I think the kind of negative view that people typically have of a sales guy, I think that kind of sales guy was never a great salesperson. I think it's kind of universal and timeless. They hopefully will not spend their time adding phone numbers and email addresses into a CRM system. Yeah. I wanna add to that as well. I think, like, the agents will make you look good, but then you have to show up in person and talk to people, and if you're not a good salesperson, it's not gonna work either way. I think AI amplifies. It amplifies your strength, but also it kind of very painfully displays your weaknesses. More questions? Yeah, I heard one in the back. Yeah. Hi. First of all, I just wanna say, congrats for this big launch and this new feature. I mean, it seems amazing really. Thank you. We're happy to be an Upsales customer. One thing, I don't wanna be the most boring guy in the room, but about the GDPR and the data, could you tell us a little bit more about it? How do you share the data, and how does it work? Don't reply. No, yeah, that's a real concern, and we've thought about that obviously. What we're doing currently, with these kinds of products that we have launched today already, is you have to sign a, like, separate contract where you agree that this data will be subprocessed by third-party providers. I'm looking into finding providers in Sweden which keeps the data here and that are GDPR compliant and ISO 27, that have all certifications, so we don't have to worry about compliance. To be honest, there's one big problem, and the problem is that America has the best agent, the best models. The open source ones are catching up. I think, like we both talked about, like, the models, the models aren't a problem anymore. I think we can get by with having not the top-tier model, but an open source one that is compliant, GDPR-wise and otherwise. I think what might happen is, like, you might have to wait until you get the compliant version, and then you might get to choose if you wanna be completely compliant and, or if you wanna go crazy. An important point is that we never train on any customer data. All your data is your data, and we don't do anything with that except store it for you and do what you tell us to do with it. I think it's also interesting, I mean, a lot of companies moving away from US-based software. I think it's not because of GDPR, I think it's more because of Trump and his adventures. We see some very promising European companies like Mistral in France, which are building models, which is catching up to both Anthropic and OpenAI. Yeah, time will tell. Yeah. Thanks. We have another question from online. What kind of behavioral changes do you think the sales teams need to do if you want to successfully work with AI agents? Just go with the flow, man. I think they need to do a little bit more than that unfortunately. I mean, what I see, again, talking about the best sales reps I've worked with and the best sales reps I've seen, I think they have a kind of systematic way of thinking about how they do their work, how they do sales, and I think that's the kind of behavior you need to be able to get more leverage from using, like, any kind of a tool, but AI in, in particular. I think the, the boring answer is to, like, you need to be a little bit more interested in processes and tech in general. We have an example at Upsales now, a guy in our sales team who's 22 years old, has, like, zero technical background, and he's, like, building tools using Claude connecting to Upsales because he had the opinions about something lacking in the product. He has zero technical background, but he has a lot of drive and curiosity. I think, like, expanding your views and be more curious is an important trait. What is your predicted cost for using AI models, like cost, like for tokens, et cetera, for the next 12 month? So there's no specific number that we communicated, but I mean, I think this is a challenge that every software company has now, because the AI agents, they can provide a lot more value than traditional software. I mean, the cost is variable. That's just how it is. As a customer, you want predictability in what will I actually pay for. We don't have a perfect answer yet, but we are looking into, like, how to find a fair pricing structure that allows the customer to keep some kind of predictability. Yeah. I mean, we will add, like, limits per organization and agent and user. You can be sure that, okay, the maximum cost is never going to exceed this. Because obviously it could be possible that you build an agent that does a lot of work, but it's not really valuable, and you want to avoid that obviously. I think some limits will do the trick. We have one more in the back. Hello. I'm Lars from ProHearings. Very interesting. Fantastic. Thank you. You did not address the outreach. I come into my office, I'm a great sales guy, I get the 10 prospects. What about the next step, the outreach? How can the models predict this guy you should talk to on LinkedIn, this guy you should email, you have to meet this guy, you could suggest a Teams meeting. Yeah. Expand a bit on that. Yeah. It was part of the demo, but it was really small, and I probably just skipped over it. Basically, how to decide what to write and when is basically just like, how do you know that? You know it because you should always reach out I don't know. I don't know sales. You know sales. Like, you have to teach it, and we have to teach it. And you correct it, basically. It will provide a draft for an email, say, and you look at it and you think, "Okay, this looks pretty good, but I would never write it like that. I would write it like this." Then you correct it, and next time it knows that. It provides a better suggestion. You might correct it just a little bit, like, "Okay, never do that. Just do this." It gets better again. It learns how you like to communicate, and that's something that we can't ship, we can't ship that for you. I mean, we can't make it talk like you. You have to make it talk like you. I would guess that's basically how it works. You can always, like, provide a lot of context. If you have sales material already, you can just dump it in there to get a good starting point. I think the view you should have is, like, see it as an employee. Like, how would I onboard a new rep on my team? I think it's pretty much the exact same thing. The good thing is you will never have to tell it the same thing twice. Yeah, that's a good analogy. Yes. We have, one more question from the, online audience. Have this been tested together with customers? If yes, what feedback did you get, good and bad? It's like select few customers, together with our internal sales team. We're kind of rolling it out gradually, because we want to make sure that we don't release a thousand agents that go crazy on RepayWalk. I think the customers that have tried it are kind of our most enthusiastic, most technical customers, and the feedback has been very good. Again, I think we're really onto something, because typically we need to iterate many times before getting to where we are now. Yeah, I'm quite impressed by how fast we got to where we are now. Yeah. The release plan is to just continue throughout Q2 and gradually release it to more customers. Great. We also have a question here. You mentioned moving faster than your big American competitors. Have you compared their AI agents to yours, and how is yours different? You want me to? Yeah, I don't remember saying that. I mean, we've tried all of them, I would say. We're not alone in trying to do this. I think the two advantages we have is number one, we have the data. We have the data, and we have the 20-year advantage of working with real kind of data. That allows the agent to be more relevant every time. It gets you from iteration one to 10 without having to do the iterations, I would say. The other thing is that, I mean, there are many kind of in our category of software pipe dreams, I mean, the sales guy selling a CRM will say that it's gonna work perfectly and you're just gonna grow your business if you just buy our CRM. All of us know that's not really true. Knowing what works and what doesn't work and all the experience and putting that into the agents, I think that's the main difference. I think it's time to wrap up. Thank you everyone for coming. Thank you everyone for joining us online. See you on the rooftop for a few drinks. Nice. Thank you.