All right, excellent. Thank you, everyone, for joining us. Keith Weiss, I run the U.S. software franchise here at Morgan Stanley, filling in for Elizabeth Porter, who had a beautiful baby girl. So excuse the substitution, but really pleased to be able to talk about a really exciting story, one that's been putting up really good fundamentals, not getting appreciated enough for it, I'll just tell you guys, in Klaviyo. And we have the full cast. We have Andrew Bielecki, co-founder and CEO. We have Amanda Whalen, CFO, and Chano Fernandez, co-CEO. So thank you all for joining us. So two and a half years since going public, and I think thus far Klaviyo has disproven some of the initial bear cases in terms of too limited of a scope, too limited of a market opportunity. and it made me to phrase it as a question rather than just a statement um how have you done that like how have you guys been able to really sustain a durability of growth through an environment
that's been mixed at best yeah uh all right thanks for everything um look we since we're clearly now 14 years old i think uh and since we started we just had this thesis that if you think about businesses and organizations on one side and then end consumers, customers on the other, we're just going through this massive shift of, you know, hey, it's not humans in the middle, sales reps, customer service reps, other folks. It's going to be all software and increasingly, we think, just, you know, powered by intelligent software, thus, you know, AI and agents. And yeah, we've been just following that trajectory for the last 14 years. And so that's what's driven us, You know, over 190,000 customers, $1.2 billion in revenue last year, growing really nicely. But I think what's exciting for us now is we think about what, if you look at, like, you know, AI and LLMs and what those capabilities have unlocked, we've kind of thought about, clearly, the first 14 years. We built all this infrastructure to basically allow businesses to program the rules, store all of their customer data, you know, communicate with customers across a wide variety of channels for marketing use cases for service use cases all of it but the big unlock was like making that not just things that humans could program the rules but that ai could you know write the rules and then optimize it that's what we wanted to get to um and this is like i think now i think it's i don't think it's another 14 years i think these next couple of couple of years maybe the next 14 quarters are like the most interesting because I think every business now is going to say, hey, when it comes to how I treat my customers across all services, you know, marketing, sales service, et cetera, and we're focused on consumer businesses, I think everybody's looking at what is the agentic way, or we say the autonomous way, to deliver those customer experiences, to have AI that can define the rules of how I treat folks and then deliver those experiences and then learn from them and optimize. So that's what we've been after, and obviously we've increased the breadth of our products. And we think about the infrastructure that's underneath it. And then back in September, we launched our two agents for marketing and for customer service. We've seen really good adoption there. And yeah, we're just excited. The future is autonomous customer experiences. And we're excited to usher that into hundreds of thousands of businesses today and hopefully millions in the near future. Excellent.
One of the things that's always been impressive and compelling about this story from the get-go is that closed-loop nature, right, of that it's not just automating a process for customers, it's automating a process for customers, letting them understand the results, feeding that back into the system to further automate the process. And when we think about that in context of AI and these learning systems, it seems to be a really ripe opportunity for exactly the system that you put together. This is actually a question for Chano. So, Chano, you've had a really impressive career. You're co-CEO at Workday, leading 8-fold AI through a lot of AI-driven growth. You're on the Klaviyo board. I'm sure there's something that you saw of how you could make this story better. You're not going to join the company unless you see an opportunity to make it better. What was it that you saw on Klaviyo, and how are you going to take what's already a good story that's operating really well, how are you going to make it better?
Yeah, thank you, Keith, and thank you for having us and everyone for joining. I mean, well, first and foremost, I would say because I had the opportunity to be a part of the board, and I clearly saw that there is a shift coming. I think I don't need to tell all of you what is that shift about. I really thought that Clavillo was really built for that future and for that shift, particularly for that infrastructure and that real-time customer engagement opportunities that it provides. And then that was the time to do it, for sure, right? Obviously, it's about people, and I think the world of Andrew has really a great vision for the future and quite of a unique founder, and I think we can do amazing things together, so it's very exciting about that. And last but not least, Keith, you know, I spent my career mostly working on large enterprise companies. We are ready. We know how the infrastructure will have the foundation to move towards the enterprise and building that market opportunity, which is certainly even larger than the one we've been navigating on. So that's really exciting. Got it.
So it's really the intersection of, one, an expansion in sort of the solution portfolio and what you're focusing on. So going from more of just a focus on marketing lens to being the autonomous B2C CRM, and at the same time expanding out the customer focus of it's not a S&B solution, It's one that's now ready to move up-market into the mid-market, into the enterprise. And those two axes enable a very nice scaling function. Maybe to start on the TAM and the opportunity side of the equation, what does autonomous B to CCRM mean? Can you walk us through kind of the vision and how the agentic layer is going to further that vision and enable it to really work for the end customer?
Yeah, well, so here's how we look at our product portfolio. I think there's, you know, we think about, like, traditionally, you know, pre this wave of AI, how we thought about software. It's either, like, infrastructure, and we thought about that as primarily stuff that you sell to developers and developers use to build applications. And there's applications, and then applications kind of get muddled because we're like, well, some of it's kind of, like, domain-specific infrastructure, and some of it's just, like, a nice, you know, polished UI. um what's great about lms is i think they've expanded the scope of what software can be and so now it's like hey look all these like you know human users that are running around using software as tools like that that now that can be done by artificial intelligence um and that's just a huge new opportunity so for so for us we look at that and say hey for every dollar that's spent on klavio there's like between five and ten dollars businesses are spending on like human capital to go run it. Um, and, and not, I mean, frankly, particularly efficiently, right? We talked to a lot of folks and like, Hey, do you think you really know, you know, do you have a good sense of like what's your customer experience? Like whether it's on customer service or in marketing should be, no, I don't. You guys have best practices. Can you help coach me up on that? Hey, do you feel like you're getting to all of your ideas? And the answer is like, well, I'm getting more done, but no, I mean, still there's stuff that I don't know where I can't get to. So we look at that and think, man, so you've got a whole bunch of people that are doing things. It's actually not optimized. They actually could be driving more customer engagement, well, great, we're going to build agents to go do that. So that's one big opportunity. So we have a whole product group that affects what we call our autonomy group that is just focused on building these agents that will sit on top of, you know, software as infrastructure. And then underneath that, we think about, like, hey, what is the best, you know, infrastructure, the best platforms, the best software that agents are going to prefer and use when it comes to storing all of their customer data, indexing it, allowing it queriable in real time than actually using that to communicate, so messaging, but messaging not just for marketing, but having conversations with customers, whether it's over web chat or voice or through text messaging, and then where are they going to define the rules and run the experiments? One of the really cool things we've seen now with our customer agent products is now that people have this always-on agent that is good at adhering to the rules of the business, the policies of the business, we're seeing these CX teams and operational teams. They're starting to experiment with the policies they use to run their business. Like, how do they treat customers? For instance, who gets a refund and when? And now, all of a sudden, that used to be something that's like, man, it's so hard to get experience with that, you know, across, like, a customer experience organization. Well, agents don't have that problem. And now we can start to experiment and say, hey, 10% of the time, why don't we try something different? And let's see how that impacts, you know, things like NPS and retention. So, we look at these two different layers. It's this autonomy layer, and we have a whole product group that just focuses on agents that will play the game of marketing and service and really, you know, design and deliver, you know, customer experiences for businesses. And then we think about the infrastructure it needs to run on. And, you know, since that's the thing we were founded on, you know, we built, we started Klaviyo as a database company that has, you know, is very good at storing and indexing consumer data at scale, but making it available in real time for, you know, whether it's an agent chatting with a customer or rendering a web page or sending a message. Yeah, those are the two big opportunities. And we think about that, you know, that agent layer, I mean, that we think is going to be a bigger business than our current, you know, really, like, software infrastructure business. And then we also think that agents, because they're better users, you know, one of the things we talk about is, like, a typical software user base has this distribution where you, like, have a couple of power users that are really advanced. And then you have a bunch of intermediate users and you have a long tail of kind of more novice users. And so you do a lot of work to try to move people up the curve. But the nice part about agents is they just go straight to advanced mode. In fact, they actually beat our advanced users. They're kind of like PhDs at whatever the discipline is once we train them up. So we also find that they push the infrastructure and they use more of it. So just as an example, like a typical business for us, we don't have a seat-based model. We charge based on the size of the business in terms of the number of profiles or contacts or customers it has. We find that our agents, because they're better at delivering customer experiences to those individual consumers, they actually retain more customers. So you end up, the infrastructure gets up getting used more. So we look at both of those opportunities. One, we have a whole business that's going to be even larger than our business today, and the acceleration you're going to get from agentic usage. It's the most exciting I've been since we started Klaviyo, because I think both those things are very big opportunities.
So we've expanded into service, right? and expanded the scope, a really competitive environment out there. There's a lot of people who are focusing on the customer service opportunity. There's some really big incumbents, whether it's a sales force or the like. And there's a lot of startups who see this agentic opportunity as well. How do you look to differentiate the Klaviyo solution from both the incumbents and the startups coming into the marketplace?
Yeah, well, we're very good builders. So it's, I mean, no surprise, we kind of put our heads together and think like, what does it take to build agents and build a good agent building platform? I think that's become table stakes. How do you train up an agent, evaluate it, deploy it, et cetera, across all the various channels? There's two things that we're adding to our customer agent that we think are quite differentiated. The first is we built a set of algorithms. You can think of agents on top of the agents that are good at training up customer agents. Let me explain why this matters so much. We've got about 193,000 businesses. I'll tell you, there's probably a thousand that know what it means to build an agent. If you gave them the tools, they would know how to train that thing up. And one of the patterns we've seen is there's a lot of this like, well, we'll deploy engineer, we'll forward deploy engineers, or we'll sell services around training these agents up. The problem is, for a lot of businesses, that just immediately prices them out of the market. I mean, they can't afford hundreds of thousands of dollars to train this thing up once, let alone ongoing cost, right? They're just not at that scale. We actually think there's an enormous amount of latent demand amongst SMBs for customer agents because those are also the businesses that are the least optimized when it comes to customer service. Because who is it? It's the founder of the business, the owner of the business, or somebody on the team that's handling these conversations one by one. These are not organizations that are like, yeah, we've employed a lot of automation. We've done a bunch of BPO. So we need to help them train up these agents. And so what we started was we started this program where we're building agents, they act like a coach or trainer, right? You can imagine like a fitness trainer that like basically gets that customer agent for that business into shape. So what does it do? It goes and takes all the information we can find about that business, right? On the internet, you know, on the publicly available internet, plus all the connectors that people already have embedded in Klaviyo. It then goes, creates basically like, oh yeah, you can think, I'll just send the fitness example here for a bit. A workout routine to put it through its paces and figure out how like strong it is, right? How good is it at delivering customer experience? Grades it, figures out where it's weak and then tells either either goes and fixes those problems on its own or goes back to the user and says hey you know what you're a restaurant i can't handle all your reservation requests because you haven't hooked me into your back end here so go connect you know toast or whatever it is that you're using to manage reservations and like and now i can answer those and so literally almost we have this like video game style interface where you're training up this customer agent that represents your business and it just gets smarter and better like as you give it more information as it learns on its own and what's what's really important about this is we think this is not important. This is matters just for SMBs. It's also really important for enterprise because enterprises need the agents that are going to be sitting on top of their agent, kind of like the boxing trainer that's get whipping it into shape as the business evolves, right? A large enterprise might launch a new product line, new set of services. They might acquire a company, something changes and they need to like constantly evolve that agent. That's not a job for human developers to do the way we're doing it today. So we're running, I mean, we're sort of, it's a little bit existential for us. We want to serve all 190,000 businesses. So therefore, we have to build these agents that can train up, you know, these customer agents. And we think this technology is going to become table stakes and the differentiator in enterprise as well. So that's one. The second thing is, because we already have our customer database, right, which is, you can think of it as like a data warehouse that's just optimized, not just for analytical queries, but also real-time usage, right? You can connect it to a customer agent. It's not going to go think for 10 minutes it can respond in milliseconds because we already have that every time you talk to our customer agent if we have context on you you can think about you know we talk about like memory files when it comes to like chat gpt or clod we have basically have a memory file on that consumer and we can inject it into the context into that conversation and optimize it and just like you see like if you play with these personal ais like man it starts to feel like it knows you yeah we can do the exact same thing so now you can imagine you're getting these signals from you know, our marketing use cases, you know, what are you browsing on a website, looking at on a mobile app? How are you using those products and services, you know, interaction with other marketing events? We then plumb all of that into the customer agent and it produces a way better experience. Simple example, like we have folks that will come back, you know, come to websites, you know, where we've enabled, you know, web chat through our customer agent and people will just stop browsing the site. They'll just ask for recommendations and get spit out things based on their past purchase history because they're already authed through our customer agent. Our customer agent can handle that. It's instantly a better experience. I think we're going to find personalization on the customer agent side, not just generic handling, but personalization is going to be another big difference here.
Got it. And Chano, in terms of taking this platform, and one of the things that has always been impressive about Klaviyo is it brings the customer a solution. It doesn't bring them a set of technologies. And that's, I think, a necessity for operating an SMB company customers they don't care about technology they don't want to deal with technology they just want a solution to their problem does that resonate in the same way as you go into the enterprise or does the go-to-market have to change the presentation of the solution have to change to make this a meanable and make it attractive to a larger enterprise customer yeah great question
Keith what we're seeing with the enterprise customers is they're looking for one single unified platform that really powers that entire customer relationship because otherwise they have too many different flows and architectures and you know that don't provide that much efficiency and they are costly let me give you a couple of examples right when they have that kind of retrofitting into general this flywheel effect and understanding of the market of the customers as a whole with that kind of memory file and you know understanding the consumer that Andrew was talking about and we help them out provides real-time information with some of our automated flows those produce ten times more revenue per customer that basically static customer campaigns will produce, right? So that talks a little bit to the power of the infrastructure, right? It resonates, obviously, in the enterprise. I think we commented that we doubled the number of customers to 1 million ARR plus in Q4 last year, and we have our largest enterprise pipeline ever. So really we are confident that we can solve for that problem.
Excellent. And then, Amanda, to bring you into the conversation, in early 2025, you guys has implemented some pricing changes, billing based on total active profiles, an auto downgrade feature, flexible sending options. As we go more into the Gentic opportunity, are there more pricing changes that need to take place, or are you comfortable that you're well sort of positioning to accrue the value of what Andrew's bringing to the equation here?
The beauty of our business model is that we do not price based on seats. We have never price based on seats. We price based on the value that we provide to our customers. So our mission has always been that we are going to help you take your most valuable asset, which is your customer relationship, and make that even more valuable. Because we're going to help you with technology make it feel like every single customer is the only customer that that business has. And by personalizing those experiences, by, as Chano said, making them more automated, we increase the value of those profiles, and by making each messaging and each communication and every experience more valuable, we increase the demand for it. So the pricing going forward will continue to align to that point of view, which is as we help customers generate more revenue and more value, we'll align to that. So in the case of marketing agent, which Andrew spoke about. It'll be priced based on the number, roughly the usage that you have, and how are we able to drive really great outcomes with that usage. So it'll be, think about it as roughly equivalent to tokens. But the most important thing is that, again, if that usage is driving better consumer experiences, customers are very, very willing to pay for it. Got it. And then if
we think about that and take it up one level into the 2026 revenue guidance, you talked about a a minimal contribution from the newest slate of the AI products and the service products. Can you help us better understand the adoption curve? Like, what should be our expectation for the adoption curve? I mean, Klaviyo Service was only going to the GA back in September 2025. The agents now coming into the marketplace. How should we think about that adoption curve?
The way to think about that adoption curve is it's off to an incredibly strong start. So service, for instance, is our fastest-growing product in our company history, even going back to looking at text, which has been a really significant product for us over time. And today, almost 30% of our S&B Plus customers are using that text product. Service is off to an even faster start. What's driving customers to adopt it is that as they get exposed to the product, they're seeing the revenue that it drives, and they're seeing the benefit and really ingraining it in its workflow. So one of the things we're really excited about on the marketing agent side is many of the customers who are adopting marketing agent, it's become their default. It's become the primary way that they're building campaigns because they see that it drives back to better outcomes. One of our customers, Adelson, is seeing 50% higher revenue per campaign from the campaigns that they're generating with marketing agent. And then similarly on the customer agent side, the customers who are really adopting it into their workflow, it's becoming the majority of the way that they interact with their consumers. And so as it becomes more embedded and customers are seeing this revenue from them, we're really seeing adoption pick up.
So we started to already see kind of the benefits of multiproduct adoption. I think you guys talk about 60% of ARR now coming from multi-product customers, 15% from customers adopting at least three products. Any kind of view on where that's going to go over the next two to three years? And, Chano, how could you kind of further accelerate that sort of platformization, if you will, of getting these customers to buy into the broader capabilities of what Klaviyo is able to bring?
Yeah. Obviously, as I said before, I believe in the power of that unified platform. And we're going to be setting up things that will be much more ad hoc in terms of really focusing on, you know, upfront on those problems for those largest customers in terms of the upselling and cross-selling motions, clearly thinking much more in advance in terms of pipeline and qualification on those opportunities and where are the ones that we should be participating on, right? As Andrew said, you know, the opportunity for some of these products is potentially 5 to 10x, you know, what we have today in terms of the current opportunity. so you know typical customers may start more on email and text but we're seeing much more adoption moving more rapidly and we're going to have some cross specialist things that are going to be supporting some of those motions going forward and we're closing the loop much better between the value proposition that is coming from product marketing and sales that we were doing before as the things are getting more ad hoc into you know how do we drive dynamics that
work in the enterprise market and a big part of what makes that sale so possible is the value that our customers see from it. Just a couple of examples to help bring that to life. We have one brand we work with who's in the D2C space who unified their email, their text messaging, and their analytics on Klaviyo. And they saw their time to generate campaigns decrease by 60%, and they saw their total cost of ownership decrease by 30%. So they're really seeing the benefit of having everything on one platform. But even more importantly, it's the uplift that it helps to drive from them. Customers who have more than one product with us are seeing much higher revenue coming from it, even higher than just adding one plus one. It's almost a one plus one equals three situation here. And a great example of that is a makeup brand who we work with, who again unified email, text messaging, analytics, and in their case, service on it. In service, they're seeing high 70s resolution rate, and they saw a doubling in their AI-assisted revenue. So we love it when unifying the platform drives higher revenue for our customers.
Got it. So given the environment that we're in, I mean, software has been under pressure through most of 2025, 2026. It probably even accelerated. And a lot of it is on concerns about Claude, Claude Cowork, in terms of tool use, is about the ability to DIY. The Amazon CTO tweets that he developed a CRM system over the weekend. I'd like to ask him if he wants to support that CRM system every weekend for the rest of his life, but that's another question. But all of this has weighed on multiples in software and multiples for Klaviyo as well. despite no degradation in terms of the fundamentals, which is frustrating for us, and I'm sure even more frustrating for you guys. But, Andrew, where are investors getting it wrong? Like, why is DIY not a risk? Why should we not heed more signal from what the Amazon CTO is tweeting out about what he can do with these Vibe coding tools?
Well, look, first of all, we've always taken a very long-term view of, like, hey, what do customers care about? like where's technology really going so i go back to two things like two things we think are like very persistent themes one is hey we now have the technology that you can build agents that can do the work that humans were doing before like that that's just a whole new category software that is going to be you know one or defined right uh literally i mean if not certainly years but like certainly probably the next couple months right and i think we're going to find out also you know what like one of the things that's tough is hey what software is actually like differentiated infrastructure and what's like a nice, you know, a nice coat of paint on top of an Excel spreadsheet. And, uh, you know, I think we're, that's, I don't envy folks. You gotta go kind of figure that out. But like, I think that's something that we feel very confident. It's like, Oh no, no, what we've built in terms of our customer database, what we do around messaging is like, Hey, you couldn't just go replace that. So do your comments on like, do yourself actually an experiment that we run internally to kind of prove this out. Cause actually I think in the future is very near future, you're going to get a lot of, uh, agents that are actually making the decisions about what software to use. It's not going to be humans doing the eval. It's going to be, you know, hey, agents are going to go tell me what you think is the right stack. So one thing that we do is we actually have set up, you know, some agents, some coding agents and others to, like, hey, you're a business. And we do this different size. You're just starting out or you're a larger enterprise. And, hey, you want to set up, like, you want to build your software stack so that software defines the entire customer experience. So you're going to want something that is this always-on agent, API, voice, text, et cetera, that is our customer agent or something that looks like that. You want something that's going to handle marketing, proactive messaging, something that personalizes and customizes mobile applications, the website, even maybe in-store experiences. And you want some underlying database that kind of makes all that consumer data accessible and stores it so you have sort of a central source of truth. We tell it to go out and go build that. And we say, like, and by the way, I want you to use as much open source as you can, right? I mean, do the best, do a high-quality job, but, you know, dumb down stack as much as you possibly can. And what we find consistently when we send out, I mean, now these coding agents that can code for hours and hours, what they go off and do is they basically try to go replicate, you know, our data platform, and they quickly recognize, they're like, oh, shoot, well, I can't use a data warehouse or a data lake. It's actually too slow. I can't use an off-the-shelf database. Like, it's not flexible enough in terms of the querying. Okay, you know what I'll do is I'll build this hybrid stack that basically rebuilds what we've tried to do, right? Now, it doesn't have the insights and, like, the query patterns that we've seen, right, and that we have these, like, routing systems that can optimize for that. So I think there's a little bit of an experiment that folks need to run like that of, you know, hey, how really, like, trivial or non-trivial is that underlying infrastructure? And so we feel really good that, you know, what we've done on the database side in terms of real-time access to customer data and this attribution loop built in, plus then what we've done with messaging around identity and compliance really matters. So on top of that, we're actually doing a lot of work to expose that to agents so they can pick us from the start, or I think a lot of RFPs now will be done agentically. And then also we have our agents that we're running that will do marketing, right? So our marketing agent will just go define the marketing strategy for a new business or audit the marketing strategy of some of our largest customers. And again, it kind of reasons at a PhD level because we expose it to all the marketing data we have of here's what works best. And then we tell it, pick whatever underlying technology you want in terms of how would you access customer data, how would you build marketing in these customer experiences. Tell us what requirements you need for that system. We're actually using those agents, not only plumbing back into obviously our infrastructure, but we're telling it, where are there gaps in what we built that you would want as an agent if you could have it? And it's actually pressing hard. So I'll give you an example. Just email as a medium actually allows for a lot of interactivity. We're not used to this because very few companies actually expose it, but you might have bumped into a Google spreadsheet or Google Doc that allows for commenting in line and things like this. Actually, any business can do that. Just most people don't have the time to be able to do it. Our marketing agents will happily build interactive messages, and they convert way better, right? But don't call back to our system and say, hey, it'd be really nice if you could add support for these different content types and maybe some components like this so the user can do some last-mile editing. And so that's now become part of our roadmap for our infrastructure. So we're actually using agents almost as like a customer advisory panel to feed back into the roadmap for our core, you know, marketing and now customer service infrastructure teams. So I think I would look for companies that have these kinds of loops of, like, they're trying to build both the infrastructure layer and the agent layer and getting them to, you know, kind of work together to make both parts better. Got it.
So, Amanda, it sounds really cool, but it sounds like a lot of tokens. And in FY25, we saw operating margins come under, gross margins come under some pressure. How should we think about that gross margin line on a go-forward basis? Is this a fundamentally kind of different COGS equation going forward as we get more and more of an agentic layer on top of the infrastructure layer?
Sure. I would think about two things when it comes to gross margin. The first is that we have a lot of levers to play with. We have different products in the portfolio. As we expand the product portfolio and we're offering new products, that enables us to mix out the customer relationship in a way that overall is profitable. And the second is that as we scale, we're getting increasing benefits from our own scale and our own infrastructure, which helps a lot. But if you take a step back on gross margin, the way that we really think about it within our business is we think about almost miniature unit economics for each product line. and each one that we're offering. So it's not just about the gross margin, it's also about what's the right R&D model to support that, what's the right customer acquisition model to support that, and does each product that we're selling have strong unit economics. So in certain ones, you may have lower gross margin, but they also come with lower customer acquisition costs because, as Chano said, they're being sold into the existing customer base, and they come with huge expansion potential because they're driving those better outcomes and that higher revenue. And our customers almost see our business as a revenue optimization engine for them. So if we can show them those better outcomes, they continue to expand to the point where they're seeing great ROI. So we think about that gross margin not in isolation, but really for each product, making sure that it's delivering the right economics for the long term.
So then if we abstract back, we see the results of that. operating margins up 170 basis points, you're getting to another 100 basis points of margin expansion in the year ahead. So we're running short on time here. There's a lot more to talk about. So maybe just as a sort of wrap-up question, there's so much innovation taking place at Klaviyo right now. There's expansion of the kind of market opportunity. As a fundamental analyst, I always want to focus on the fundamentals and the stock price will take care of itself. If we're looking for the key indicators, the key performance indicators that is working, that this is really taking off, what should we be looking for in your results? What should we be looking for in terms of the KPIs to show us that, hey, listen, the fundamentals are
following through on the opportunity? Yeah. Well, it's interesting. I'll give you a couple that we look at internally, and we'll start to share more of this as we go. One is we think this agent opportunity is very real. And what we've seen in some disciplines, coding, et cetera, is coming to all parts of human labor, but let's say digital human work. So you mentioned like, hey, multiple products. I mean, we actually think about like, yeah, multiple products, but like today it's all multiple products or primarily multiple products through our infrastructure, right? I mean, we have a lot of adopters of our customer agent. We actually think it's like by this year, this is the year that every business, small business and enterprise, will adopt a marketing agent that helps augment what they're doing, It's the PhD that sits in the room and helps the whole team be better. And then two is this is the year that everybody adopts a customer agent for their business. So last year was the year of personal AI, this is the year that every business doesn't just have a website, it actually needs an agent that represents them that's always on. I think both of those are going to grow quite quickly and so we actually look at the multi-product adoption but really focusing on how many agents of ours have customers adopted. So yeah, stay tuned. We've seen some good growth so far and I think this is going to be the year that that really
Great story going on at Clayview. Congratulations on the success, and thank you for coming and sharing with us.
Thank you. Thank you.