Asure Software, Inc. ($ASUR)

Good afternoon, and welcome to Asure's fireside chat on current perspective on AI. This webcast will include a presentation followed by a question-and-answer session. I would now like to hand it over to Patrick McKillop, VP of Investor Relations.

Thank you, operator, and welcome, everyone, to Asure's AI webcast. Today's call will contain forward-looking statements that refer to future events and as such, involve some risks. We use words such as expects, believes and may to indicate forward-looking statements, and we encourage you to review our filings with the SEC for additional information on factors that could cause actual results to differ materially from our current expectations. Specifically, our event today will include a discussion of AI and our use of AI in connection with Asure's business. For more information about certain risks to our business as a result of the emergence of AI and our use of artificial intelligence, please see our risk factors in Item 1A on our Form 10-K filed with the SEC on February 26, 2026. Now what I'd like to do is walk you through today's agenda. These are our forward-looking statements I just referenced. So for the agenda today, we have 4 different sections. And we have Pat Goepel, Asure's Chairman and CEO; as well as Yasmine Rodriguez, who is our Chief Technology Officer. In the first section, we plan to talk about how AI is reshaping enterprise software economics. In the second section, we talk -- want to talk about Asure's platform execution infrastructure. In the third section, we're going to talk about structural advantages of Asure's execution platform. And finally, in Section 4, we're going to talk about how AI expands Asure's operating leverage. So with that, I'd like to move on to the first section and hand it over to Pat Goepel, Chairman and CEO.

Yes. Thank you, Patrick. And hey, I'm really excited to be here today. And wow, we set up this as an adjunct investor meeting, and I got calls from employees and clients and friends that I haven't seen in a couple of years. It feels like everybody is talking about AI and wants to hear more about the topic. And with me, Patrick to introduce our CTO, Yasmine Rodriguez, but I got to tell you, I've been working closely with her for 6 years, and she is a game changer, and there's nobody I'd rather be on this panel with than Yasmine. So you'll hear plenty from her, really excited about her presenting what she's helped us do and really drove within Asure. And so we're going to get right into it, but I can't wait for the conversation. So first of all, Section 1, AI is reshaping enterprise software economics. If I go to the next slide, hey, there's no -- there's always changes in technology. And I'm old enough to think about whether it's mainframes and DOS and DOS to Windows and Windows to client server, client server to the Internet, Internet to mobile, mobile to AI, let's talk about that. And with this model, if you think about it, remember when mainframe went to the PCs, COBOL for a long time was the fastest cruncher of data. And still today, there's COBOL systems in. So it's not an either/or, but where we think it really is, is AI and software together that's ultimately going to be the answer. But before we even talk about the answer, if you think about how AI is reshaping enterprise software economics, similar to those days with COBOL, which was very narrow on a calculation engine, AI is different in that it gets data from all over the place very, very quickly and can come back with a probabilistic answer or a directionally correct answer very, very quickly. So if you think of businesses that might be at a disruption risk, consulting and labor services around consulting, where they're looking for a large amount of probable outcomes and large amount of data, they're going to be more apt to be disruptive next on the continuum feature SaaS, where capabilities are increasingly getting automated and AI can help not only in the software component of that, but also with agents as well as workflow items, et cetera. System of records has some defensible and structural advantages. And that's operational data or the data that's proprietary that really is almost system of record data, but data where action could begin where you need that data as a premise as part of moving forward. And then there's to the right where you have execution infrastructure around it where it's a regulated transaction, maybe money movement or [indiscernible] agencies or Arista legal kind of documentation compliance offering, where you really have highly regulated data and you have to be 100% right. And so very, very different on the continuum. The next slide. If we know that coming into it and we look at it, and we're going to introduce some concepts here. But on the left, you have what I talked about on the bottom left, if you have, hey, I want to be directionally correct and I want to have various levels of outcome, consulting and labor services, you can get there pretty quickly with AI with just being directionally perhaps more advantageous than when it started. Now if you want to get pretty deterministic or with a very secure outcome, system of record will help and be in the top left. In the third quadrant there with feature SaaS, where your computation is probabilistic, your execution infrastructure is high. Now that you have a lot of software tools for knowledge work, AI agents can replicate the features, you're going to have some risk. But where you have execution infrastructure. So if you think about it, the money movement, and we move roughly $20 billion today. We have 100,000 clients where we have platform of very, very secure data with social security numbers and wage, et cetera. And where it's clients cannot easily replicate, that's an execution infrastructure that will actually accelerate where software [indiscernible] AI, especially if you embed AI with the software right from the ground up. So we're going to talk a little bit about that, but we think we're in the right quadrant here. And that's why we are so excited about hitting this topic head on. Next question or next slide, I should say. So why the execution platforms behave differently? First of all, at the revenue model, we'll tell you where there might be disruption. And you see in the news or you see on CNBC where seat subscriptions or SaaS companies that have a revenue model that's all around people as opposed to agents, they got to kind of pivot to outcomes. The good news with execution infrastructure that Asure has, we already have that outcome where we get paid when people get paid. We get paid on the amount of transactions. So the good news here is our model is in place, and our model doesn't change with AI. AI will enhance some of the execution pricing on the model, which gives us really a competitive advantage here. The AI on traditional SaaS, where it replicates features, the AI automates delivery. When I think of kind of our market, you have software tools, you have software enablement, you have software workflow and you have ultimately people that execute that. software with AI opens up a huge opportunity here where we might have played in one of those areas, but now we can potentially play in all 4. And when you think about operating leverage expense, let's just take the category of people. If you have, like we do 100,000 customers where we have a payroll person potentially or an HR person, we have people within our service delivery infrastructure. If you look at the end-to-end process, and remember, end-to-end process starts at the customer wall all the way to our walls and back. Well, now you have 4 areas, whether it's people, whether it's workflow, whether it's enablement, or you also have the baseline kind of transaction where now you have the ability to open up a bigger, bigger market. Some analysts such as Sapphire and some of the work Gartner and others that have done it opens up this possibility close to 50x. So over time, we believe we're at a structural advantage that will ultimately compound and accelerate over time with software using AI. The next slide. So what is it about our platform and execution infrastructure? This is the longest 11 minutes that Yasmine has let me talk. I'm feathered. I'm going to bring in Yasmine Rodriguez, our CTO, to talk about the execution infrastructure that we really have the foundation for what we just talked about.

Thank you, Pat. And it was. It was a good time. It was good. So I'm going to start really by reframing a question. I wake up every morning, the first thing I look at what is the latest post out there about AI. So the question that the market keeps asking, is AI a threat to your business? I think in my humble opinion, it's the wrong question or maybe it's an incomplete one. The right question should be, does AI affect your specific business model positively or negatively? AI is absolutely reshaping the enterprise software economics, absolutely. That's not even a dispute today. Software features are going to continuously be automated. Cost structures are going to shift across every category. The impact. The impact is going to vary enormously by business model. For Asure, the answer is going to be a positive impact. I'm going to go through that today. It's structurally positive. Three things if I was to have you walk away with today after our presentation of that. One, AI affects business models totally differently. Second, the revenue structure is what determines the AI impact. Third, and you heard Pat talk about it, the execution platform is what Asure sits across the board. You saw it on the spectrum, you saw it on the execution, Asure sits there. What we do is gain operating leverage as AI scales, not the other way around. So let's get started on how that works. Let's go to the next slide, please. Okay. We all know that AI can reason. Most of us has used it. It can analyze, it could summarize. It could recommend, it could generate. I mean, it's impressive. What it cannot do is execute in a regulated world of work without infrastructure. Traditional SaaS has features and the system of records. Asure has all of that. But in addition to that, it has 3 additional layers that are key. Statutory system. We are, at Asure, a registered IRS bulk filer. We are a legal agent with the authority to file on behalf of thousands of employers who have been doing it for years. We are fully accountable to the outcome of our filing. That relationship is really earned through demonstrated operational history and regulatory approval. It cannot be something you just download and you get it. The tax laws that are out there are updated continuously. Sometimes agencies release detail right before the due date. One may argue AI is absolutely capable of reading an IRS update online in real time. Yes, it can. But reading a rule and being authorized to act upon it, are completely different. Asure doesn't just know the rule changed. We filed under it. We hold the power of attorney with tax agencies on behalf of our client. That is a legal trust relationship with regulators. This is not just a software integration and APIs. Let's go to the compliance systems. Every payroll calculation has to be traceable, reproducible, defensible on demand under an audit. Federal law requires those payroll records to be retained for a minimum of 3 years, in some cases, 7. Regulators are not going to accept it's because the AI said so as the documentation, a payroll tax filing error is not just a bug report on an integration. It results in IRS penalties, state interest charges, direct employer liability that is that accountability that lands on Asure, not on the model that helped us actually calculate it. Last is your financial rails. Asure participates in ACH networks. We operate under NACHA risk controls. We maintain knowing your customers, the banking laws, anti-money laundering, the BSA Act. We hold money transmitter licenses across the state that require them. Each of these require separate application. And for those that ever went through this, it required a surety bond, audited financials, regulatory approvals. Some take up to 12 to 18 months just to review your application. So in summary, AI can reason about the money movement, about all of it, but Asure is actually the one authorized to move it. So if you keep in mind, knowledge is not what gives you authorization. Being aware of something does not hold you accountable. An AI system can know every tax code in every jurisdiction and still cannot file a single return on your behalf or bear a single dollar of liability, Asure can, and we do that every single day. Now move to the next slide, please. I want you to look at the flow left to right and because this is the architectural reality that works in our favor today. On the left is AI reasoning, agents, copilots, models, excellent at that probabilistic reasoning. They analyze, they recommend, they decide. This is where the AI ecosystem lives today, your OpenAI, your Microsoft Copilot, all these models. The minute any of these need to act or take an action, they have to make a system request. They have to do it through event triggers and notice what sits at the center of that request. It's Asure Luna. You guys are going to hear me talk a lot about Asure Luna. Our AI agent, beginning of 2025 is when we actually released our AI agent Luna. So it's not just a chatbot. AI Luna is capable of actually performing actions on behalf of the employees and the customer. We've done that in beginning 2025. We launched Luna really, and we kept on growing Luna with more of more actions taken. Luna is not connecting to our payroll system from the outside. She runs within it. So within our architecture, our infrastructure, we were at the right time of a modernization journey that we embedded her into the architecture. So Luna, as you see there, is the bridge between that entire external AI and action-taking AI engine inside a live regulated payroll platform. Luna does not connect to the payroll again from the outside. She's within it. So when a system request comes in and hits the system of record, whether it's payroll, tax, employee data, it is verified. It is audited. It is legally defensible. It's not just a general database. It's a license compliance approved execution platform. On the round, that deterministic execution, the real-world outcome, money movement, as we said, tax filing, IRS. This is where AI recommendation becomes a legal transaction, real accountability behind it. So an AI agent all the way on the left cannot just skip and go right to the right side. It needs that orchestration layer. It needs our Luna. It needs our AI, the system of record, the regulated execution infrastructure that we built and all of that does exist today in Asure. [indiscernible] with anything on the left, any of the AI models, we are that infrastructure that AI model will need to reach to the right. And as AI adoption grows, that [indiscernible] only deepens. It does not shrink on our side. Next slide, please. In a world where every company has AI copilot, let's say, the future coming, what happens to the platform underneath it? The answer is right on this slide. They don't get bypassed. They become even more valuable. So external AI system, whether Microsoft Copilot, any other agent platform, connecting through the Luna orchestrator to execute payroll, perform tax actions, compliance actions securely, the AI is the interface Asure is the engine behind it. I'm going to give you an example. It's a favorite for our CFO, by the way. A CFO is budgeting in Excel. He's living in Excel, picks up Microsoft Copilot, which is embedded in Excel, the CFO would type, "Model a 3% raise effective January 1, all the way to July 1." And by the way, show me the payroll cost, the tax impact, the cash flow effect. Copilot invokes Luna, the orchestrator via secure API. Luna pulls live headcount. Why? Because Luna is in the payroll system. It pulls tax directly from Asure central, applies compliance awareness, logic across every jurisdiction. You're going to see some of that in our demo later. Returns on a real P&L and cash impact instantly. There's no separate log in. You didn't have to log out of Excel, go to an Asure central to pull the data. There is no export. There's no manual modeling. Excel stays the interface, Asure remains the system of record. Luna remains the orchestrator. Let's take another one for HR. An HR manager gets a Slack message. We're hiring 3 engineers in Texas and 2 in Pennsylvania starting next month. What's the fully loaded cost? HR asked the copilot to do agent -- the same question. Again, what does the agent do? It invokes Luna orchestrator. Luna pulls current salary benchmark calculates employer side payroll taxes by state. It includes local Pennsylvania detail, applies the benefit load and returns are fully loaded cost per head within seconds, no spreadsheet, no call to finance, no waiting, no separate login. The pattern is the same in both of those cases. AI handles the interface, Luna handles the orchestration, Asure handles the execution. The more AI copilot across our client organizations as they on take it, the more requests will come through Asure. That's not a threat. It's really a distribution advantage for us. Can I go to the next slide, please. In there, what I would like to do is I'm going to ask the operator to run our payroll demo. What you're about to see is a payroll manager running a complete payroll cycle within Luna. You're going to see proactive notification. You're going to see live data retrieval, downloading. You're going to see anomaly detection, which is key that is actually going to bring up any exception prior to processing the payroll. And then you're going to get confirmation that the payroll has processed. Operator, would you please run the payroll?

Yes. And Yasmine, just before we roll it, we're going to show you this example within our Asure Central application that we've rolled out in October. And if you really think about this demonstration and the demonstration will take about 3 minutes or so. But this is about -- it can be a full day of work. It can be a half day of work because what happens is there's a lot of coordination overtime, getting information from the time clock. Maybe somebody didn't get paid. Were they on vacation? Were they not? Maybe the person has to ask a bunch of questions. So really pay attention to the workflow and how easy it is in when AI meets software as opposed to maybe where you have to pay people 30 days ago. So with that, operator, turn on the demonstration.

Thanks, Pat.

Operator, can you play the sound with the video? [Presentation]

Pat?

Yes.

Okay. I think now that we concluded the payroll, right, demonstration, we do have one more around tax. One of the most important things in the compliance engine when it comes to tax is really knowing the rules. And I want us to see that video first, but would you like to say a couple of things, Pat, before we get started?

Yes, about a 2-minute demonstration. And if you think about the conversation, you're going to see Luna kind of look at, is it a tax ID or is it a tax jurisdiction. If you see when Yasmine talked about kind of setting up Pennsylvania in different states, you'll see some evidence of this. And then some of you have been asking questions around scale and kind of does it impact the model? I think by this demonstration, you can see it sure impacts our model and our quality as well as our cost to serve. So with that, operator, can you turn on to tax demonstration. [Presentation]

Okay. Great. If we can go to the next slide? So the structural advantages of Asure's execution platform. We talked a little bit about it, but really, if we go to the next slide, the big advantage here -- next slide.

I'm sorry. There you go.

Yes. And Yasmine, you hit it, and I'll let you talk to it. But really, if you think about our execution infrastructure, really, whether it's regulatory, money transmitter licensing, whether it's the banking, NACHA payments, et cetera, security, compliance, IRS, state agencies, local agencies. And then on the bottom here, we really talk about all the kind of things that were happening. And I know Yasmine and I both had as we go through the money transmitter licenses, we had to get fingerprinted, et cetera. I don't know if [indiscernible] has a fingerprint yet, but we'll answer that question here at some point. But Yasmine, I think this is the real advantage here.

Absolutely, Pat. You said it as well. These are the infrastructure layers that we talk about. Each one of them is defensible and all of them reinforce each other. They come together as an infrastructure. The regulatory infrastructure where the money transmitter licensing, those state regularity oversight, custodial fine handling requirement, these are legal relationships. Keep in mind, we said it before with regulators. So they actually acquire years of history and the security and the compliance, what you need to go through the audit with SOC controls and certification, data protection controls, audit, compliance framework, annual audit really against defined standards. There are standards you have to follow. Operational maturity over time. This is not an overnight thing that you do. Banking and payment, we talked about the ACH network participation. There are some NACHA risk controls. There are the KYCs, all of these have to be in place when you are actually in an HCM system where you're dealing with people's PII, tax filing infrastructure, the IRS bulk filer status, you said it. You got to get fingerprinted. There is a huge process in there. There are power of attorneys that are actually where you hold them with the customers that you are representing. There are agency notices that come through and that needs to be reconciled. Years of operational history with actual tax authorities. So each of these reinforce each other. AI without this infrastructure -- sorry, go ahead.

No, no. Yes, I'm sorry, Yas, go ahead.

I was just going to say the AI without this infrastructure in place really does not work. It becomes a liability. So the hard part is not the AI piece, it's exactly what's on this slide, those 4 pillars. Go ahead, Pat.

And if you think about on the next slide, if you think about why this infrastructure is in place, it's really all the data gravity. And what it is, if you look at the blue on this one, whether you have dental plans or health insurance or 401(k)s, local taxes, wages, social security numbers, that's why it's really legislated like it is. And that data gravity is ultimately a compounding effect when you can use software, with security, with AI to really drive transactions. And really, this infrastructure, combined with the execution infrastructure and the data, this is a competitive advantage in what we can do for our customers, what we can do is really a game changer and AI helps enable this to be really a multiple of our customers. If I go to the next slide. And the next slide -- I'm sorry, on the operating leverage, I'm going to let Yasmine talk about this because really the effect of this with the data, the legislation, et cetera, now let's show and let's talk about ways that AI embedded in our software can really help us.

Yes, absolutely, Pat. Thank you. I think if you think Luna becomes a cost reduction engine, it is one. It's that payroll query deflection. We saw it also guys in the demo. Fewer support tickets per cycle, guided compliance question and answers reduces the processor time, where today, a customer support representative have to answer those questions. The entry of a payroll keying it in, that's taken it from minutes and probably 20 to 30 minutes effort into a 2-minute and less. The automated amendment suggestion, they cut manual review, onboarding acceleration, when you're onboarding customers via conversational workflow that we have in our client onboarding module with Luna. Every payroll query that Luna resolve is a support ticket that doesn't need a human agent to handle it. Same revenue, lower delivery cost. In the near term, Luna becomes the revenue retention tool. It's that proactive compliance alert that increases the stickiness. You saw it with the EIM and the rate where a user may actually enter incorrect data and Luna will add that prevention. It alerts the user of a possibility of an error. That creates stickiness. That AI-powered anomaly detection before a payroll run reduces errors that in case most of the time turns happen because of errors. And Luna itself becomes that switching cost. A client who switches platform is no longer just losing the software, they lose the AI that already learned their business and their payroll. In the long term, Luna is that competitive differentiator, AI that only works because of a source data depth. We compete on AI intelligence, not just the features. The platform data mode compounds with every single payroll run, AI capabilities justify that premium tier pricing. So Luna, not a feature. It's the mechanism by which our margin profile improve over time without proportionally cost growth. Pat?

Yes. And if you think about it, the market expansion, given that it's not only tools, it's not only enablement, it's not only workflow, it's people, our market expands the more people we can bring awareness to or bring AI to awareness to our company and our AI-generated marketing, et cetera, can help us. It's voice activated in addition to software. So that multimedia approach is going to bring more adoption and more ask into our environment. And then from a revenue retention and sentiment analysis, we get so much learning that Yasmine says compounds and what we're finding is we can get ahead of issues before they become issues. So ultimately, that's a competitive separator for us. And it really, really is a game changer for the model. Next slide. So AI embedded across software. I talked about tools, enablement, automation and cost out at the client level. And remember, end-to-end process is not only at the 4 walls of Asure, but it's at the customer wall all the way and back. So when you think about the people and you think about tools, you might need less L&D, you might need less HR. You can really provide and use Asure Central and our software or you could say, you know what, I'd rather have you use AI Asure, you got our back office, and we can grow our business. That's a much different message. It's a bigger message ultimately by embedding both. And then I know, Yas, you had a couple of examples here on product and revenue and operational efficiency.

Absolutely, Pat. For me, more on the R&D and the product itself, right? Today, approximately 70% of any new code is generated with AI tool as a co assistant as a copilot. Our UX prototyping where you used to have to spend probably days coming up with a prototype just to share the idea of the vision of a solution. Now it's done in minutes, where we are not only able to show the prototype, we're allowing them to walk through the functionality before we write the line of code, getting alignment, legacy code translated and modernized by AI. We're only shipping faster with the same team. We are running faster through our road map because of the use of AI. And I think on the revenue itself, productivity, right, Pat, if you want to take on what our team is doing today as well.

Yes. And just in the interest of time, I'll let you read or let the investors read, but really some nice changes that we've highlighted on the earnings call. On the next slide. So when you look at kind of some of those operating leverage and AI adoption, what it really does for us, and Yasmine, I think you talked about the 3 items initially that you wanted to bring home. But maybe, Yas, if you could do that, and then I'll wrap it up for questions.

Yes. Sure, Pat. I think the most important thing that we wanted to -- those 3 items, AI affects your business model differently, it does. Not all software are equally exposed, absolutely. This is your second, whether it's a labor hour or it's an execution platform. The market has been really pricing AI risk broadly across multiple software. So what matters is the execution layer is what is important and Asure is in there. We are not -- Sorry, go ahead, Matt.

I'm sorry, Yas. There's a little bit of a delay, so I apologize. Go ahead.

No, I was just going to talk a little bit more about the execution platform, but the essence of time, I'll let you do it. I'll let you wrap this up.

So with that, all across Asure, we create structural operating leverage, and we talked about that. What that means for you as an investor over time is if you look to the next slide, what you have is the next slide, Patrick, is over time here, what we've done a nice job is building a business. And then where we build the business, we're at $200 million, roughly 30% margin. We see opportunities to accelerate growth. We see opportunities to have fixed cost absorption. We also see opportunities we can grow revenue faster than cost with really using AI embedded with software in the sense of where we can look at tools, we can look at enablement, we look at workflow. Ultimately, we look at people and where we can grow faster than revenue. And again, by looking at the end-to-end process, not only in our 4 walls, but also within the customer, that opens up huge scale advantages, not only in payroll, but if you think about our business, whether it's insurance, 401(k), money movement, tax filing, all the agencies, now you have an infrastructure that can be accelerated with AI as opposed to at risk. If we go to the next slide. So we talked about execution infrastructure. Yas talked about it, why it's a strategic advantage. We feel strongly that it is. We see where we're positioned. We want to make sure as a discerning investor, you look at the opportunity of all software and you look at our business in a different way or in a way that really is an acceleration here. And we're excited to offer that point of view today. With that, Patrick, if we have questions, Yasmine and I would be happy to answer them.

Okay. Great, Pat. Just getting some questions here on the web, and we'll check for any people that are submitting questions online. But first off, just one question is, what would it actually take for an AI company to become an IRS bulk filer and start executing payroll tax filings directly? Are the barriers primarily regulatory, operational or technical?

All 3. So Yasmine, I don't know if you want to add any color there.

No, it's absolutely. It's what you said. It's all of the above. It requires all of it, right? It's -- you've got to become a reporting agent. There is the forms that you have to fill in and there is, of course, the fingerprinting. There is a lot that needs to be in place. And plus what comes with it is really the knowledge and the authority that you have gained throughout years of experience with the IRS. It's not something that you can -- any company that just can turn on and become an IRS bulk filer.

And Patrick, I always go to the example. I go to the mailbox every day, and I have 100,000 companies that we process payroll and taxes for. And I'm always disappointed because I never get a letter from the IRS saying, "Hey, Pat, great job on those taxes." But if there's a problem, I certainly get a letter, and we work through with our clients to make sure those get resolved. And it's a negative satisfier business where perfection is expected. But if you have risk and you have a problem, it takes a little bit of know-how to work through and getting those problems solved. And that's really the key. And we think we can use AI to our advantage, and we don't think it will replace it anytime soon. Next question.

Let's see here. Where are you already seeing tangible reductions in cost to serve from AI, support operations, compliance workflows, onboarding or somewhere else?

Yes. Honestly, all of the above. And I think I thought Yasmine and Luna did a great job and Sarah on the demo that point out the payroll, the tax filing. If you think about the workflow within your own company, how quickly we went through that demonstration and how streamlined those questions got answered and the process got run. If you think about where we are as a growing business, I think you're going to see us grow more revenue faster than we're adding people, and that's by layering in those opportunities. And then we're expanding our marketplace because we're taking on more. So really excited about that opportunity, but those are the items that you've seen.

Okay. Operator, can we just check to see if we have any questions from folks that may have dialed in on the phone here?

Yes, I don't see any questions.

Okay. So we'll just continue with ones that are coming in over the web here. Next question is the Luna value proposition is very clear, but can you please talk about how you're going about monetizing the product and driving adoption?

Yas, do you want to talk about that? Do you want me to?

You could go ahead, and I'll add to it.

Yes. No, I think, first of all, monetizing adoption, we can do a lot more. So if you think about all the data that we have, what's interesting is we bring Asure Central together, we're talking about attach rates. We're talking about revenue per unit because now when we sit on this kind of data and we have the tools, the workflow, ultimately now when somebody has 50 employees and they need worksite reporting, we can automatically enroll that client. All they have to do is check Yas in worksite reporting. So that comes with a fee. It also is regulatory. 20 employees, COBRA starts. It comes with a fee. It's regulatory, and we can ask them proactively if they want us to handle it or themselves. If you think about the bigger kind of question as well, we can go to a small business and say, listen, we have all the products and services that you can use with software. But if you want Asure to help you and do it all for you or with you, we can do that. That opens up a big marketplace. And then I don't know lately, if you've seen all the work we've done with tax filing, money movement, treasury management, we have an ability to continue to grow to help customers. AsurePay is a prime example where we now have earned wage access. in addition to a default kind of opportunity to get people paid right away and get a little bit of interest on their bank account. So a number of different ways. We're going to continue to talk through all those items on the quarterly call.

Okay, Pat. It looks like we're just about out of time. I don't see any other questions popping in at the second. So I don't know if you want to take a minute and kind of wrap things up.

Yes, absolutely. And first of all, I want to thank Yasmine Rodriguez. I tell you, she's a Chief Technology Officer, second to none. She's done a great job here building the foundation with AI and Asure. She and I talked about this with our management team probably a little over 2 years ago and really excited about it. So what you're seeing, sometimes the best laid plans happen either overnight or a couple of years into making. And in this case, it was a couple of years making. We believe we're at a really good inflection point within the business. We hope you came away and you learned something today or you understand a little bit more about Asure. And we have quarterly calls. We also are available through Investor Relations. Patrick, as always makes himself available. And as if you have questions or comments, please, please reach out to us. We're excited about the opportunity and really want to thank you for taking and investing some time today.

Yes. Thank you all.

Thank you all. Operator, I think we can log off now. Thank you.

Thank you.