All right. Good afternoon, everybody, and thanks for joining us here at Docebo Inspire in Miami. Got great kickoff this morning with a nice release. I trust you've all seen the information by now. A couple of notes are out there circulating. Got an awesome presentation for you here today. Our speakers include Alessio Artuffo, our CEO, Scott Peacock, our SVP of product, and Brandon Farber, our CFO. Before we dig in, let me remind you that during the course of the presentation, we will be making some forward-looking statements. Please refer to the Safe Harbor statement here on the slide deck and in any of the other materials that you might go to as part of your research for notes that follow. Quick agenda. Alessio will kick it off. We'll do some product demo work with Scott, hit a financial review with Brandon, and then we'll go into the Q&A. As part of the Q&A, my colleague Jason and I will come to you as you get pointed by Alessio and Brandon for a question. We ask that you hold it to a single question, let it work around the loop, and then we'll take second rounds of questions. We will be available afterwards for additional questions once the session officially wraps if you have something further you'd like to check out. With that, I'd like to bring Alessio up to the front, and we can kick off. Alessio. [audio distortion]. Better now? One, two. One, two. Yes. Perfect. Awesome. All right. Hello, everyone, and thank you for being here. Thank you to those that flew in from far away. I know some of you were on the red eye, so appreciate you guys making it here. For those of you that have been in the keynote, there will be some concepts or info that will overlap. The material is slightly different. For those who haven't been, I think particularly during the session with Scott, you'll get to see some of the concepts that we're going to be talking about applied in real live demo. Let's get us started. First of all, a little bit of background as far as where we stand in the state of. Every 15 years or so, enterprise software rises. Every time, the same companies win. In the phase of the on-prem era, yeah, so this early 2000s. Many of you remember those days. It was all about Oracle. It was all about SAP. They owned the data, the workflow. Comes SaaS. Salesforce owned CRM and the sales and marketing workflow. Workday owned HR and the payroll workflows. ServiceNow owned the IT business. Those companies that just freed up an interface really never broke out. It's cutting. Cutting? Yeah. Got it. Want me to turn this off? Yeah. Sorry, guys. One, two. All right. Now we are in the agentic era. The narrative that we're hearing is that SaaS is dead. I think that's lazy. What's affected is SaaS that's built on shallow data and commoditized workflows. What's thriving are companies like Docebo that own the system of record and run the work on it. Docebo at this point sits on two decades of learning data, compliance records, learning histories, customers certification history. What's important to underscore is that none of this data can be fabricated. Just to recap, we own the data, we run the workflow, and now we're shipping the agents of it all. Now, let's dive deeper into what we're building, what we've built, what we're shipping. What I said earlier is that today Docebo is shipping at an unprecedented pace relative to its most recent history. I did bring up a stat relative to our growth in usage of Docebo AI, which we've seen go up 300x over the past few months. That's just the beginning, because the future that we are headed towards and the things that we've announced today make us ecstatic about where we're going. Let me talk to you about AgentHub, MCP in the context of Docebo, and Enterprise Knowledge. I'm going to start with AgentHub. These are proprietary agents that reason, decide and act on our skills graph, on evaluation signals, and that use our Enterprise Knowledge, which captures the wide net of company knowledge that exists in an organization. These agents build courses, they triage compliance, they nudge learners. They run the work that the L&D teams used to do manually. They may do that independently or they may do that with humans in the loop. Eventually, this is my prediction and something that we're preparing ourselves to, they will work with other agents coming from other platforms. The second thing that I want to talk to you about, even though I go to the next side of the slide, is Enterprise Knowledge. I'm going to have more on this topic in the material later on, but for now, simply put, to give you context, we have effectively connected 20+ enterprise-grade systems that live in most enterprises so that our customers can get to that knowledge where it lives. Whether it's SharePoint, whether it's Confluence, whether it's Notion, whether it's Google Drive or Slack or Microsoft Teams, whether it's different CRMs or HRISs. The point here that I need everybody to understand is Docebo is no longer just a course catalog. We make knowledge available so it can feed real capability building. On MCP server, I had a couple of conversations with some of you earlier in the hall asking about it, whether this is going to make us stronger or more commoditized. The fact that we go natively into Claude, ChatGPT, Copilot. When the employees of our customers ask a question about training, skills, or certification in one of these environments, the answer comes from Docebo. What it means is that every AI assistant, okay, becomes a distribution channel for us. When a learner interacts with our data, that becomes an outcome. The more the learners engage with our data, the better it is. When you think about the combination of AgentHub, MCP, Enterprise Knowledge in the context of the LMS combined with skills, this is a unique proposition that nobody in the market right now can replicate. Not Sana, not Workday, not Cornerstone. This combination that I'm presenting is unique. The market wanted us to pick a lane. We built the lane. Look at this image. I think what I just said will become more clear for you all in this animation that is not anymore an animation because it went fast. Look at the left side. The legacy LMSs live there. Nothing wrong with it. Primarily, they check a compliance box. On the skill side, you have the skills intelligence platforms. Great to manage signals, but no way to act on gaps. Without a learning engine to close the loop, if you know that somebody has a gap in a given capability, and you don't close that loop, and you don't connect it to any knowledge or learning source, there still is a gap. On the right side, you have the knowledge platforms, the wikis, the AI copilots. They are helpful for just-in-time knowledge retrieval, but disconnected from, number one, what people need to learn, and number two, from any recorded and auditable validation. Because the fact that you do a search and find that you looked for a policy using a tool like Glean is not recorded anywhere. That's not auditable. It's not a data point. Docebo closes the loop by sitting in the middle of this, by capturing the opportunity that exists to combine knowledge, learning, and skill in one closed loop. That is the magic sauce. We haven't made this up, by the way. This is what our customers have been asking for. It's just incredibly hard to build. So, AI without data is a demo. Well, let me tell you this, Docebo is not a demo. Here's what we own, and I know this is an important topic for the models and for the understanding of Docebo's position in the current AI market. Let's talk about compliance records. These are legally required and very scrutinized in pharma, in banking, in insurance, in manufacturing, in aviation. They are auditable. They're tied to a specific person and a specific date. The thing is, an LLM cannot hallucinate a record like this. It must come from a system of record, and that system of record, that is us. Skills graph. Thanks to the investment in 365, now we hold the canonical map of who has what skill at what level proficiency across the entire workforce of a company. Every agent that touches learning, talent, or workforce planning needs this data to be valuable. If you don't know who is at what level, how do you provide training that is coherent with it? We control that data that is made available via API in the way we decide. Learning history. 100 million learners are part of the Docebo ecosystem. This is the training material that makes personalization actually work. You can't synthesize it, and you simply earn it over time. To the external training data. This is the core business of the companies, the data about the customers, who completed which certification. Right? This is sitting really at the intersection of compliance, revenue, customer success. It's really invaluable data for any company. Just for reference, nearly 50% of our ARR is with companies that use Docebo for internal and external training. In agentic world, it's very clear. The companies that are being squeezed are the ones with no data or light data and a pretty UI. We're the exact opposite of that. We have a pretty UI. One more thing before I pass the baton to Scott and the team. I get this question a lot, and I think it's going to be relevant to understand better how we think about knowledge in the context of the recent announcement that we've made today. The question is, what is the difference between knowledge retrieval and what you call workforce readiness? I think it's a very valid, astute question, and I'd like to handle it head-on. In simple terms, knowledge is not learning. Retrieval of information is not capability. There's tools like a question asked in Claude or a question asked in Glean, you certainly find an answer. Now that we are having access to our enterprise knowledge in Docebo, you can find the answers that come from different sources across the company, and we make sure as a learning platform that you need to come back and ask it again. That's the difference between a knowledge system and a workforce readiness system. Claude can certainly surface, on the compliance side, a policy document, assuming that it's connected to some system. Docebo differently can prove that the employee, Mike McCarthy, was trained on it. On what date, for which regulation, with the auditor-ready record available for the regulated industries. That's the ballgame. You can swap a search tool tomorrow. Nothing changes. I know it because we're doing it at Docebo now. You cannot swap a compliance system of record. Then scope. I think this is an important part to understand as well. Scope intended as in audiences. A tool like Glean or a tool like Claude used in any company is for your employees. Docebo, we designed it to address audiences that are both our employees, but our customers, our partners, our franchisees, the distributors of our customers. I want to then close on the agent side. A knowledge agent. Great summary. Very helpful. A learning agent moves a person from novice to expert, and validates it, and records that information. One, convenience. The other one is tied to revenue, to risk, and to retention. The value is profoundly different. Hopefully, that addresses upfront some of the questions that I know you will have on this topic. I'm looking forward to the Q&A. I hope this is helpful, and I also know that the most exciting part is yet to come, because Scott's going to give a little bit of a deep dive preview of what he showed today. For those that missed it, and for those that didn't, by the end of today, you'll learn how to demo Docebo yourself. Scott, on to you. Thank you. Lovely sound. Perfect. Well, let's hope I don't need both of these mics. I'll turn this off for now. I guess before I get into my kind of core of the presentation, I think it's worth highlighting how we structure this and how we think about the different components of the solution that Alessio was just talking about is, when we think about the workforce readiness platform and how we've structured it right now, I want to highlight that Learn obviously remains at the core. It is the majority of our SKUs and, of course, the majority of the revenue. We're building around it as well, and everything is going to be integrated, right? Although 365 was months ago a separate platform, we already have organizations that are using both systems integrated and passing data back and forth. I can show you a little bit more about what that looks like in practice. Yeah. We'll get right into my kind of first component. Before we do that, for those of you who attended the keynote session earlier today, you notice that it's not just the story of agents, it's a story of us deepening our platform and improving the core experience that all of our customers experience on a daily basis. For those of us who were in the room, I thought it was pretty telling that some of the most simple or basic concepts were the ones that got the biggest cheers. I think at one point I said you could now cancel a certain type of ILT session, and I literally got a standing ovation. I think it's important to keep our eyes on the future and on what we're building and all the innovation that we're bringing to the table. We must remember the people that we're really serving and the challenges they face every day, and the hours and hours and hours that we can save, and how happy we can make them with some of these core improvements. One of which is Docebo Companion. The reason I call this a core improvement is not because it's not innovative, but it's because we want to get it in the hands of every single one of our customers. This is an answer to the customers who didn't have the ability to leverage a massive IT team to build a complete headless experience. It's something we're hearing a lot more about, this idea of embedding all of the information and doing direct API integration, so you don't have to touch an interface. For our customers who wanted Companion, all for them was to say, "Hey, I'm in Salesforce, and my folks need training in Salesforce. My folks need training in our intranet. My folks need training wherever they happen to be, and I don't have a 30-person IT team to build an integration, so we'll turn on Companion." Companion is Docebo's capability of servicing the right training for the right person, no matter where they are on the web, and no matter how they access their work. This knows who I am, it knows what I'm trying to accomplish, and it knows where I am. Alessio rightly pointed out that data is at the core of everything we're doing. The more we rely on AI to answer our questions, the more we use AI on a day-to-day basis, the more important it is that that data is relevant to me. Companion has another reason that we're putting it in the hands of all of our customers. It allows us to understand how those learners are navigating the web, interacting with their systems, and it allows us to build signals. We can say, "Hey, 40% of your workforce watched a YouTube video on MCP servers recently. You should probably create some training on that." In fact, we have an agent who saw that and has already made a draft for you. This is the kind of stuff we're doing by giving more access to more of our customers for things like Companion. Another really core system of improvements that we're making recently is our enrollments rules engine. Again, this is one of those standard parts of the platform that people were so excited to see coming out. This allows our enterprise organizations to scale massive executions of enrollment in the hundreds of millions. When you have a system like ours that is so connected to all of the infrastructure, all of the data, all of the compliance records, making a single change can cause a cascade that affects 100 million different components. We needed a way to do that for the largest organizations in the world, and so now we have it. I wanted to highlight another core component that we're building upon. For those of you who are tracking, AI Virtual Coach is how we've historically referenced this, but as we've expanded our capabilities with AI Role Play, we've changed the name to reflect what you're really doing. Fundamentally, it allows people to create custom rubrics where they can define exactly what good looks like and execute against it. Content Marketplace, last one before I actually get into more physical demos. I know we want to see the products. I wanted to highlight that the Content Marketplace is getting upgraded, which allows us to add tons of new partners into that marketplace and give access to our customers very, very soon. We talked a lot about the future of workforce readiness. We were sitting on the stage about this and the agentic system that we're building here. I want to show some of the components that flow into this, and 365 is a good place to start. I'll jump over to my 365 environment. It's embedded into Docebo, and we're going to start from a learner's perspective. It was an agent calling. It was an agent calling. We're going to start from a learner's perspective. Ashley Anderson here, who is in the system, we are already hooked up to all of the sources of truth that feed the information into the platform. You can imagine we're pulling in her LinkedIn information. She's uploaded her CV. We're hooked up to the talent management system that the company used to hire her in the first place. Of course, we're hooking up to SAP, Oracle, and Workday wherever her performance records exist. This is step one of gathering all of the information for all of the learners in our system. Based on all of this, we can understand the type of job she has, the skills that she's building, and fundamentally, how her job description lines up with the skills that she actually has. She's able to say, "Hey, look, I'm a software development engineer. What's the path that my career could take from here? And ultimately, where are my skills in Java or C++ compared to where they need to be?" This is powerful for an individual because they can build their career and plan what they're going to do next. More importantly, it's powerful for the administrators of our system because an admin doesn't necessarily care about that individual learning path or the individual who's kind of building their career. It's important. Don't get me wrong. Fundamentally, we're building a huge database of all of the roles that exist across this organization, how they relate to each other, the skills that exist within each of these roles, the expected skill level of each of the people that hold these roles in systems engineering, digital business analysis, software engineering. We can take a look based on all of the information we know about this entire audience to understand globally across all of my software development engineers, where are their skills compared to where they need to be. C++ is right on the mark. Teams is a little bit lacking in the debugging skill. That might explain some of the regressions. Java is another core skill that we need to grow on. I also want to highlight the ability for us to now do benchmarking across this kind of job. We can do this from an internal perspective and see who's declaring the skills at what ratio according to the role. Importantly, we can take a look at benchmarks from outside the system. We can take a look at all the different organizations that are hiring software engineers right now and how that has changed over time, and how people are being found and brought into the system. These are some of the smaller components that overall drive all of the data that we now have access to as we get into what we are calling the evidence engine. The evidence engine is our ability to say we understand the distinction between a signal saying this person is interested in this topic, we should build courses, we should have an agent take an action, versus as Alessio was saying, evidence and validation are true understanding that someone has done the actions that we classify as that person being an expert in that particular skill. Of course, all of our customers are different. We serve a huge range of industries, organization sizes, and complexities of organization. What one company says is the prime example of a manager validation. My boss says I'm an expert at presenting. Maybe so. If I did a proctored certification for an AWS practitioner exam, that's the gold standard and I'm considered an expert. We gather all that information, and we can allow our customers to set up campaigns to close the gaps where we know that they exist. People are getting used to vibe coding with Claude. We need to expand that. We need our team to be better and faster with it so we can launch a skills campaign and see who is actually successful. In this case, Maria Chen, James Liu, Priya. The individuals benefit because they build a body of evidence that proves their capacity, that proves their skill, and we are able to say, "Okay, let's feed all of this information into the agents that this person has access to." When I first got ChatGPT, the reason that I moved from Gemini to ChatGPT was because they introduced memory. They introduced an understanding of who I was, and I said, "Refer to me as Captain Kirk, and here's all the information about me." Most people when they're configuring their AI that they use on a daily basis, you're using an MD file in Claude or a couple paragraphs to explain who you are. We have thousands of data points to understand who someone is. We have every time they watched a YouTube video with Companion on. We have every course they've completed and every score they've achieved. We can use all of that to make sure that when our AI answers their questions, it does so with them in mind and their best delivery system in mind as well. Okay. Let's go back to our slides here for a second. I want to also talk about AgentHub because I think it's obviously deeply relevant to the way that we're moving forward here, connecting to this tech stack and knowing the people. I'll slide all the way over just for a single slide. I wanted to highlight again that the way that we're being able to build these agents and the way that our system is going to connect to other organizations, it's really dependent on the company that we're working with, but we're going to be creating agents that understand exactly how our system works. You've heard about the MCP server from our perspective, where we expose certain information that we want the web to have access to. AgentHub also allows us to act as an MCP client, where we're reaching into other MCP servers, gathering the information, and taking all of that data that we have access to and making courses, delivering training, and setting up learning plans for people at scale. Our customers are some of the only ones in the world that can get 20,000 learners their own individual learning plan that respects the way they like to learn, the kind of job they have, and exactly where they are in their career. Okay. I have one last piece. Oh, that was the last piece. I want to hand it over to Mr. Farber. Thanks for coming up. You might want to hold on to this just in case. Yeah. Turn it off? Yeah. You're good. Good job. Roughly 3.5 years ago at Inspire Nashville, we posted our first TAM ever, $25 billion. We're coming here today updating that to $40 billion. That's driven by organic growth into the government space and inorganic expansion into skills intelligence. When we look at corporate learning, it's really split into two different pies. It's the internal audience, onboarding, compliance, talent development, sales enablement, and it's the external audience. It's customer academies, whether it's monetized, unmonetized. It's franchisee workers. It's partners. It's memberships and not-for-profits is the word I'm looking for. From a TAM perspective, slightly less than 40% is internal audience. Slightly more than 60% is external. Let me walk you through why the external opportunity is so large. I'm going to give you four examples of real Docebo customers, how they use the Docebo platform. First two are non-traditional corporate entities. First one is a large sports organization in the U.S. They use Docebo to train their internal employees, referees, parents, everyone in youth hockey. They have 1,000 internal employees, whereas they train 100,000 annual users on Docebo. Their external audience is 90 times the size of their internal audience. Another non-traditional, an independent regulatory body that monitors all the brokers in the U.S. They have 7,500 employees, and they're training 650,000 external audiences, 85 times the size. Now let's go to some more traditional corporate entities. Databricks, at Inspire, long time customer of Docebo. Finding out their internal audience was a little bit harder. They're a private company. I asked Claude three different times, got three widely different answers. They have anywhere between 5,000-25,000 employees. I know it's large. They train 1.5 million users annually on Docebo. That's about 65 times the size of their internal audience on the upper end. They also have one of the most successful unmonetized and monetized customer academy on Docebo. A new customer we signed in Q1, Fortune 100 company. Can't name the logo, so I'm going to say global technology infrastructure company. They're using Docebo just for a partner use case, 100,000 users. More than 50% of their revenues is serviced through partners. If you think about a manufacturing company, you have a company handling part A, part B, part C. They have partners in over 90 different countries. They're using Docebo for just certification of their partners, three different levels. As you move up through training, you get better discounts. This is just one external use case. We don't have the internal. We don't have their customer academy. Large expansion opportunity. This is why we're so excited about the growth in corporate learning. This is why we're so excited about the growth of the external opportunity, and where Docebo plays really well is when we're doing hybrid use case, combining the internal plus external. From a U.S. Gov perspective, we updated the TAM to $3 billion, and that's really just revised data on the updated employee and contractor count multiplied times the average realized sale price. Very simple TAM calculation. There's three reasons why we're just really excited about the government sector. Number one, we have the right partners, from Deloitte to Carahsoft, even niche learning partners such as eSkills. Number two, we're seeing the pipeline now three quarters in a row. We talked about in Q3, we talked about in Q4. In Q1, our pipeline continues to exceed expectations. We have the right pipeline. Now it's on us to execute. Third, we're really just leveraging the innovation we've been bringing to the corporate learning segment for the past two decades into the government sector, and the government sector continues to be the least competitive market we play in. From a skills perspective, this is the first time we're showing TAM, post-acquisition of 365, and it's third-party validated sources on three different use cases: skills intelligence, internal mobility, and talent marketplace. The best thing about 365 is that it's already an enterprise-grade tool. There's already large enterprises such as SNCF, Crédit Agricole. These are companies with hundreds of thousands of employees, multiple different geographies using 365. It is an enterprise-grade tool that's ready to sell now. I'm pleased to say that 71 days after the acquisition, in Q1, we cross-sold 365 to a Docebo customer. 71 days in, and our acquisition thesis is already playing out. We're not only expanding our TAM, we're also expanding our GTM. You may look at this slide and say, "Hey, Docebo, you've been talking about the enterprise space forever. You were in there in 2021." My answer would be, "You're right, but you're also wrong." In 2021, we had roughly 8 headcount in the enterprise space. That's a combination of account executives, account managers, and a manager of the team. Conveniently enough, every one of those sellers was previously mid-market or commercial sellers at Docebo. We treated the enterprise space like we treated mid-market and commercial. We had no different motion. We had no partner network. Zoom forward to today, we have 25 headcount, combination, account executives, managers, VPs, managers. We have a completely different motion. 50% of our closed ARR in the enterprise space touched a partner in Q1. We have a completely different partner network. From a skills perspective, we inherited a standalone selling team as part of 365. They're going to continue to sell 365 to any organizations, no matter what LMS they have. If they have SAP, if they have Workday, we're going to go in there and sell 365. Now, there's been one investor question over the past four years that we just haven't had a great response to. That question is, "Hey, Docebo, when is ARR going to stop decelerating?" We're pleased to be here today to say 2026 is that year. We showed the acceleration in Q1. Understand there's acquired ARR in there. When we look out to Q4, no matter if you look at with acquired ARR or without, we're going to be accelerating ARR. We're seeing it in the business, we're seeing the levers, we're seeing it in the expanded TAM. Everything's in place to accelerate ARR. Another thing is that, when I compare Docebo today to Docebo 12 months ago, we're significantly de-risked. In Q1 of last year, we had AWS at 2.8% of our ARR. We had Dayforce over 9%. Today, that AWS use case is at zero, and Dayforce is slightly more than 3%. Our top 10 customers, excluding Dayforce, is less than 8% of our revenue. Our customer base is significantly diverse. In 2026, we simply have a larger TAM, we have a larger GTM motion, and that gives us confidence that ARR is going to accelerate. If there's one number on this slide that I want you guys to take away, we've talked about enterprise, why we're going the enterprise, and the one number that we've never shown is when we compare our NRR for customers who pay us $100,000 or more compared to $50,000 or less, there's a nine percentage point difference in our NRR. That's even including AWS, Thomson Reuters in the past two years. If we exclude that, the number's even better. If we look at that on a five-year basis, it's even better. But we had small numbers, so I didn't give us credit for the full five years. But naturally, as our business becomes more enterprise-focused, you are going to see NRR move up. We're also still early days in the enterprise space. We only have 524 logos paying us over $100,000. We're only 5% penetrated in the Fortune 1000. We still have a large room to grow in the enterprise and compound growth. From an EBITDA perspective, we've gone from 8% EBITDA in 2021 to slightly over 20% forecasted for 2026. This is really just a testament to the sustainability of our business model. We've never sacrificed our growth levers to get here. It's been methodical. It's been deliberate. It's been a stepping increase in EBITDA every year. We're not only disciplined from a spend perspective, but we're disciplined from a dilution perspective. This morning, I was doing some last-minute research, changing some slides, driving my friend Mike crazy over here, and I was looking at some research from an analyst in this room. Thank you, Josh. His firm looks at roughly 80 different SaaS companies, posts all these different SaaS metrics, and conveniently enough, they had stock-based comp as a percentage of revenue. I sorted that data from largest to smallest, and Docebo was at the bottom end of the 80 names. There wasn't a single name below Docebo. When we talk about best-in-class from stock-based comp, there is just not another company you could find at our scale. It just doesn't exist. We're not only doing that, we're decreasing our share count. 7.2 million shares decreased over this time period. $277 million returned to shareholders via buybacks. We're going to continue to buy back shares at these valuations. This is old news by now, eight hours old. I'm not going to talk too much about it, but the thing I love the most about this being raised is that it's not because Dayforce revenues were higher than we expected. It's not because we magically acquired more ARR than we expected. It's not because FX benefited us. It actually hurt our guidance by about $1 million. It's because our core business accelerated in Q1. On the conference call, you asked Alessio and I, "What's it going to take to be raised? What's it going to take to raise expectations?" We're very consistent. Enterprise. In Q1, our enterprise team delivered. Here's our target operating model. 10%-15% of subscription revenue. We talked about the growth levers. It's really clear. External use case opportunity, growth in the enterprise, government product expansion. From an R&D perspective, it's gone down 1% since our last target operating model, and that's really efficiency from AI over a period of time. While we're not seeing that in 2026, right now, we're actually seeing costs really shift from headcount to compute. While we're getting more efficient, we actually need less heads, but that cost is just moving from one bucket to another. Over a period of time, we do expect compute to come down and realize some savings in R&D, but not in 2026. G&A is unchanged. Our track record speaks for itself in G&A. We've come down from 27% of revenue in G&A in 2021. In 2025, we're 14%. If you just use the midpoint, we still got 4% leverage just through G&A and EBITDA without sacrificing our growth levers. Sales and marketing is actually the biggest change since our last target operating model. We previously had 28%-32%, we're down to 26%-28%. What are we seeing? We're seeing improved performance from a quarter perspective, and there's certain areas within sales and marketing that will certainly benefit from AI. We're talking about more the RevOps, sales enablement, the groups that need to scale up with the quota carriers. I'm going to leave you guys with a hypothetical but a very realistic scenario. If you take our 2026 revenue guide, and then you use the bottom end of our subscription revenue growth, 10% for the next two years, so 2027, 2028. From an expense perspective in 2028, let's assume we get to the top end of all these ranges at 80% gross margin. My math tells me that's roughly 24% EBITDA margins. We're talking about a business that's generating nearly $80 million of EBITDA in 2028. I'm going to repeat that one more time. This is a business that will be generating nearly $80 million of EBITDA with best-in-class stock-based comp and a declining share count. On to Q&A. Just go right there. Would you please just- Next If you would please just give your name and firm. Thanks for doing this, guys. Matt Van Vliet from Cantor. I guess when you talk about not only the growth in head count going into the enterprise, but maybe walk through some of the mechanics from an operational perspective that are changing. What things are you doing differently to attack the enterprise market, but that can still leverage down to the mid-market and commercial for more of a shared services approach? Brandon mentioned this in his talk track. I would say operationally speaking, the changes that we've made to the enterprise engine are across a few dimensions. By the way, we also have here Mark Kosoglow, our CRO, that is implementing a lot of the things I'm talking about. Mark, feel free to jump in and take the mic if you want to add anything. Let's see if I know enough about the enterprise business, okay? Don't be shy. First, to me, it starts with the people. Brandon mentioned it earlier. We were running the enterprise playbook with folks that used to do commercial and mid-market sales. The first thing we did, we were intentional about upgrading our skills profile at all levels, sellers, managers, directors, all the way up to VPs. The entire organization of enterprise sales has been rebuilt in order to get to where we are today. We brought on board people that were experienced enterprise leaders and sellers in the industry already. The majority of people that we brought on board had sold very large deals with the Cornerstone, Workday's of the world, and they knew the enterprise game cold. The second thing we did, and this is not a, I would say, a short-term initiative. It's the work of being very intentional over the past couple of years. We built a very strong partner network to really be involved and be ahead in these accounts. When you want to enter in Google and similar companies, it is undeniable that the Accenture of the world and the Deloitte of the world have a head start because they live inside those companies. They run transformation projects. They run large consulting and RFPs for these companies. Having established different degrees of partner program with these companies is something that, for sure, is giving us a lot of return. The third thing that I would mention is the way we also approach demand generation and the way we speak to the market. I would say our enterprise demand business has been invested in. The way we go and approach the top of the funnel on the enterprise side is very different from how we did it in the past. When you now look at these three levers alone, demand, partnering, so surrounding the accounts with the right partners, and the upgraded execution in people, those are three key ingredients that come to mind. Mark, Brandon, Scott, anything else? Sorry. Actually, no. Very important fourth lever. We were very honest with ourselves about what we had to do in the product to be truly respected from enterprise organizations. We identified over the past two years, errors of the product that were not acceptable in an enterprise environment, that maybe were not sexy. All right? They were not AI forward, but it needed to be done to satisfy the likes of really large banks, of really large insurance companies, really large healthcare organizations that are running businesses that are so sensitive to regulatory environments where you can't mess up. We put a lot of work into that as well. Thank you. Yeah. I'll comment on one thing as well. It's almost like we have two directions of innovation, right? There's innovation in the direction of agentic and forward-facing and in the news and all this kind of stuff. There's a reason that the legacy providers still exist and are still huge. We're kind of growing in two directions, where we're closing some of these boring gaps that are absolutely mission-critical at the same time that we're growing in the other direction. It's a good point, but I see them as both innovative, just in opposite directions. Thank you, Scott. Hi, everyone. Ken Wong from Oppenheimer. I don't want to steal your thunder from earnings, but I think one of the big headlines today, you guys pre-announced more positively than expected. In fact, you guys raised above the beat. Brandon, you touched on this a little bit, but would love Alessio, you and Brandon maybe to tag team. What else under the covers in the quarter were better beyond enterprise? Was it new? Was it the NRR side finally picking up? With the macro backdrop, kind of the way it is, again, surprised that you guys were able to raise by more than the beat. What are you seeing in the business that gives you the confidence to go ahead and lift those numbers? That's a great question. Certainly, when we looked at our guidance for 2026, we already embedded assumptions that mid-market was going to continue to improve. They already had three quarters of great quarters in a row, and we took that. In Q1, mid-market had a good quarter, but it wasn't the reason we raised our guidance. It was really the enterprise motion, and within enterprise, we actually had $2 million+ expansions. One was with what I mentioned, the regulatory body for brokers. We initially had the internal use case won in Q4. We did such a great job during the pre-sale motion and during the implementation that we self-sourced the external use case opportunity. At that point in time, when we're working the internal use case, we're actually working two different use cases with two different departments at the same time. The second expansion was in the healthcare space where we owned a smaller subsidiary of that company, and we ended up getting the top dog, the top entity. Obviously, when we talk about our guidance, we initially put out, we assumed no million-dollar-plus new wins or expansions. We had two this quarter. We not only saw a great Q1, we saw a great Q1 pipeline performance in the enterprise space. We're not only taking the Q1 and raising guide just for the Q1 beat. We're raising guidance throughout the year due to improved visibility in the enterprise space. As far as the latter part of the question, Ken, I'd say there are a few things that make us realistically optimistic. The first one is the realization that we have matured a leadership team across the organization that is ready, ramped, understands the market, and has brought in the right leaders below them, and it's creating the osmotic conditions for performance to persist. The second one is more backed in the numbers. Brandon stole my thunder a bit, but I will be more emphatic. We continue to see sustained pipeline performance, not only in the enterprise space, but also in other segments where we've historically been present, like mid-market, our bread and butter, where we continue to perform at very strong levels. We've been working to improve the performance of our international business as well, that in the past couple of years has been choppy, to say the least. We are seeing very strong momentum even there. Connects back to the comment I made about our leadership. Finally, I think the momentum on top of the signals of pipeline that exists in product, our ability to ship at a very accelerated pace, our ability to be here announcing something like 20+ meaningful evolutions of our product between core and highly innovative AI forward. There is a lot of material to go back to our existing customers and build these account plans and expand upon them. Fifth, competitive landscape. We are seeing a significant influx in sheer head-to-head wins with the usual suspects that have a large market share. You know the companies that I'm referring to. We at Docebo have had always very clear ideas about how we plan to grow the business, doing things that benefit our customers. One of those things is not getting distracted by, for example, acquiring competing assets. We could have done that a number of times. We have chosen not to do it. Why? Because we believe that creating that magic closed loop that I showed earlier, where the convergence of learning, knowledge, and skills get together in a unique solution, that's what is going to make us win the long game. We're seeing that playing out right now. The companies that we compete with are either in the learning segment that we saw or in the skill segment that we saw, and we win on sheer capability. I'm referring even to the more modern ones that have been acquired by larger assets like Workday. We're very excited. Across all fronts, the signals are green, and so we're bullish. Thanks, Mike. Hey, Ryan MacDonald with Needham. As we think about the 10%-15% sort of target model growth rate over the next few years here, how should we think about what the products are that are driving those growth rates? Because it was interesting to see in the keynote today, it feels like the market's sort of in two different spots. Customers very excited about blocking and tackling core improvements and maybe more skeptical or inquisitive about brand-new agentic workflows and ways to use the platform. Where do you think that growth is driven from, sort of the core versus some of the new, and how do you bridge the base of customers to go from core to some of the new function? When we go into an enterprise, Ryan, everyone, we, like Brandon described this very well earlier, we have a number of use cases that we address, whether they are one of the few internal use cases or one of the few external use cases. All these use cases relate to different capabilities and products and pains, frankly, that the customers have. Today, the biggest contributor to our ARR, without a shadow of a doubt, is our core LMS platform that ships with, roughly if I recall correctly, there's about 20 modules or so that are attached to it. That supposedly is going to continue to be our major driver of growth for the time being. The acquisition of the skills intelligence platform gives us an opportunity to differentiate ourselves and compete in deals where, despite having such a rich platform feature, we would have not been able to play. There is a very large organization today at Inspire. It is a prospect, and it's a big investment group, which I will not name for privacy purposes. When we started talking to them, everybody in this room would know their brand name. They were very interested in our capabilities, but when they identified the fact that we were light on the skills element, I would say we were about to lose that race. We were about to be dismissed. We're talking about the potential of a very significant deal, certainly top 20 ARR in the company. More than top contributors to growth, the way we think about it is how do we create the right product mix to serve these organizations that are going to have multiple use cases and needs that are very articulated. If the LMS is saying a $2 million deal, $1.6 million, but there's $200,000 of skills, not having the possibility to position skills would make you lose the possibility to win the $1.6 million of LMS. That's more how we think about it. It's less about having a product that wins the race and more about having a platform that gets together and really positions us uniquely against anybody in the competition in the target market, because it's also a matter of choices. While we're choosing to do all of the above, we're also choosing and telling ourselves that the organizations that are looking to spend $10,000, $20,000, $30,000 over time are not going to be our ideal customer profile anymore because we're designing the product, the company, the strategy, the PS, the partners around a different type of organization. That's why we have great partners that take care of them. I'll just add quickly from an AI perspective, with prospects, they're coming to Docebo, and they're asking what we're doing with AI without really knowing what they want to do. They want to know we're thinking about MCP. They want to know it's coming. When we ask them, "How are you planning on using Docebo through MCP?" They don't actually know the answer. They just want to make sure we're thinking about it. They want to make sure we're building agents, even though they don't really know how to use it yet. They want to be a platform that's thinking about the new generation, and we've been able to demo, we've been able to show, and that's going to result in more LMS revenues. But we're not just thinking about LMS revenues. We're thinking about the whole product suite. I'll wrap it up because it's a product question. Yeah, I'll go last, though. The interesting thing to think about is the same way that the organizations that we work with are typically mixed in between internal, external, hybrid. The customers that are happy with us and stickiest are using us for the most use cases. Similarly, we want to give our customers the opportunity to grow into their own ambitions. It's easy to think of ourselves as we're innovating, we're growing, but they want to innovate and grow, too. Some of them can't innovate and grow in the direction of AI right now. Guess what they're doing? They're becoming skills-based organizations. They're innovating and growing at a human level. It's almost like a dichotomy. Some of them say, "I'm going AI." Some of them say, "Invest in our people." We want to be there in the situation where an organization says, "No, we are really the all-in company. We're going to do all of this." Yes, we have that offering. It is important to note that we'll happily sell our product to an organization that's doing compliance, to an organization that's doing skills and learning, and of course, to one that's doing an AI-forward agentic future. It's a bit of we want to make sure that we have what's necessary on the table, but also respect our own customers' goals and ambitions because they sometimes go in skills versus AI in different directions. Hi. Erin Kyle, CIBC. Just on the guidance, how can we think about some of the other factors that are impacting the guide this year? If I think about 365Talents, Brandon, you talked about the fact that you cross-sold a solution about 71 days into the quarter. Maybe if you can speak to. Is it not on? No, we can hear. Keep going. We can hear. We're good. If you can speak to whether that acquisition has been performing in line with your original expectations and kind of the ARR that you expected to be added when you acquired it back in January? Maybe on the flip side of that as well, for Dayforce, you noted in the press release this morning, you're expecting the ARR percentage to be around 3%, 3.2%, I think was the exact number. I think in past quarters, we had talked about Dayforce maybe being anywhere between 3%-4.5% by the end of 2026. It seems like it's now kind of below those old expectations. Maybe you can speak to where you expect it to be at the end of 2026. If it does churn faster than expected, what the base business needs to look like to continue to offset that. Yeah. From 365Talents, we continue to expect the revenues to be exactly as we previously announced when we acquired the acquisition. So that's roughly $9 million of revenues. We acquired this asset with the expectation to sell it right away. Did we expect 71 days versus 90 days? Maybe we sold it a little faster than we expected to an enterprise customer.