Ladies and gentlemen, thank you for standing by. Welcome to iLearningEngines' Second Quarter 2024 Earnings Call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during the session, you would need to press star one one on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star one one again. Please be advised that today's conference is being recorded. I would like now to turn the conference over to Kevin Hunt, Investor Relations. Please go ahead. Thank you. Good morning, and welcome to iLearning Engines' Second Quarter 2024 Financial Results and Corporate Update Conference Call. Earlier today, ILE issued a press release announcing financial results for the second quarter ended June 30, 2024. A copy of this press release is available on the company's website and through our SEC filings. With me today are Harish Chidambaran, our Chairman and Chief Executive Officer, Bala Krishnan, our President and Chief Business Officer, and Farhan Naqvi, our Chief Financial Officer. Before we begin, please note that on today's conference call, we will be making forward-looking statements, including statements relating to guidance, projections, forecasts, revenue growth and EBITDA, Adjusted EBITDA, expected operating results, integration of our platform with our clients' existing systems, the diversification of the sources of our revenue, our expectations regarding the size and approximate growth rate of the AI market, our expectations regarding growth opportunities for the company, and the role of the company in the AI industry. Forward-looking statements are neither historical facts nor assurances of future performance, and they are subject to inherent uncertainties, risks, and changes in circumstances that are difficult to predict and many of which are outside of our control. Our actual results and financial condition may differ materially from those indicated in the forward-looking statements. For a list and description of the risks and uncertainties that we face, please see the reports that we filed with the SEC, including our quarterly report on Form 10-Q for the quarter ended June 30, 2024. This conference call contains time-sensitive information that is based only on information currently available to us as of the date of this live broadcast, August 13, 2024. The company undertakes no obligation to revise or update any forward-looking statements to reflect events or circumstances after the date of the conference call, except as may be required by applicable securities laws. During today's call, management will provide certain information that will constitute non-GAAP financial measures under SEC rules, such as EBITDA and adjusted EBITDA. Reconciliations of these non-GAAP financial measures to GAAP measures and certain additional information are also included in today's earnings release and related supplemental slides, which are available in the Investor Relations section of our company website at www.ilearningengines.com. I will now hand over the call to Harish. Thanks, Kevin, and thank you to everyone for joining us today for our first earnings call as a public company. It was a quarter of significant achievements and milestones for iLearningEngines. In April, we became a publicly traded company after completing our business combination with Arrowroot Acquisition Corporation. In June, we were added to the Russell 3000 and related indices, a significant milestone for a newly public company, and we also secured $20 million in additional debt funding that will help fund our growth plans. Today, we're pleased to report our second quarter results. We generated revenue of $136 million in the quarter, a 33.9% year-over-year increase as compared to the same period in 2023, and produced $4 million of adjusted EBITDA. We added over 100 new end customers and 176,000 end users. Our CFO, Farhan Naqvi, will provide details on the financials in a few minutes. For those of you who are not familiar with iLearningEngines, I wanted to start today's call with a high-level overview of the company. iLearningEngines, or ILE, is a leading applied AI platform for learning and work automation. ILE enables enterprises to rapidly productize and deploy a wide range of AI applications and use cases, what we call AI engines, at scale. The platform is powered by proprietary, vertical-specific AI models and a no-code AI Canvas to drive rapid out-of-the-box deployment while offering low latency and high levels of data security and compliance. In sum, iLearningEngines is a pure-play AI company. Our applied AI platform is solving real customer problems today. We were an AI platform at scale and solving real customer problems and use cases even before the GenAI craze. We have been delivering AI solutions for learning and work automation at scale for over five years now. We built our proprietary AI technology and vertical-specific small language models for dozens of use cases across 12 vertical markets, including healthcare, education, insurance, retail, energy, manufacturing, and public sector. Our platform allows enterprises to connect to all the different systems within the enterprise, collect the content and data that is there, and put that all into an AI knowledge cloud. That AI knowledge cloud and our no-code AI Canvas then powers various use cases and hyperautomation apps or AI engines to solve high-impact customer problems within the enterprise. These AI engines can be deployed quickly in weeks to months, versus a year or longer, with potentially millions dollar invested for many internally developed corporate AI projects. We are doing this at scale. We generated $421 million in revenue in 2023, and we have generated positive adjusted EBITDA every year since 2020. We have a diversified customer list of over 1,000 end customers and 4.9 million end users that are benefiting from our AI platform today. Let me turn it over to Bala, our President and Chief Business Officer, to walk you through a few case studies that will provide a better understanding of how companies are using iLearningEngines technology to solve their problems. Thanks, Harish. The first example is of a global manufacturing conglomerate with 22 business units and 40,000 employees that we had signed in 2019. The company wanted to implement a scalable platform that enabled subject matter experts to drive company-specific training for their entire 40,000 employee base, as well as their network of 5,000 dealers. Like many of our customer wins, we weren't replacing any specific vendors, and we were not competing with any specific solution. The company had no system for subject matter experts to deliver training in a scalable manner and was struggling with siloed knowledge sources for policies and procedures across the organization. They also wanted to understand and get a good handle on the daily performance metrics across their employee network. iLE was brought in to this account, and the first step we took was to create an enterprise-wide AI knowledge cloud using the company's own internal content. The ILE platform was then integrated with the company's existing systems, which included an SAP ERP framework, SuccessFactors, BMC Remedy, and a number of homegrown databases, as well as communication channels, which included WhatsApp, mobile, and intranet. Once ILE was integrated into the organization, we closed performance and process gaps with optimized mission-critical information delivery workflows, and the AI employee assistant is trained to handle 18 KPIs, cutting across organization structure, attrition, recruitment, and performance rating, with many more such KPIs planned. The customer is seeing improved operation metrics across the various strategic business units. The second case is a process automation example of a leading auto insurer that provides coverage to millions of vehicles. The organization wanted an early and accurate notification of the claim. They were also dealing with customer satisfaction issues with their call center around wait times, accuracy, responsiveness, and closure. By using the ILE platform, the company was able to create an AI-powered claims automation engine. The first part of the platform was automation of the data collection using an image-based claims intake engine, helping them to reduce fraud. The second part was an AI worker embedded into the enterprise workflow that was able to process the data for fraud, duplication, accuracy, and thereby expedite claims processing. Using workflows built on our no-code AI Canvas, claims were accurately routed, and the ILE's platform seamless integration with external systems ensured automatic updates to the client's claims system. The customer was able to achieve significant improvement in the number of claims processed. A centralized dashboard provided a real-time overview of all claims, their status, and required actions across the enterprise. The company has since been adding new cases on the ILE platform. Let me now touch on our go-to-market strategy. As I've highlighted, we are an applied AI platform company, and what we see is that enterprises are looking for solutions and applications to build on top of our platform. We work very closely with value-added resellers, VARs, that bring domain expertise in each vertical that we enter into and can build a solution that addresses specific customer problems. These VARs also have a lot of existing customers that we have been able to leverage. We have 30 VARs now. Our four largest VARs have accounted for roughly 52% of revenue. As Harish noted earlier, we have minimal end customer concentration, as through these VARs, we service more than 1,000 end customers with over 4.9 million end users. With that, let me turn it back to Harish to talk about recent developments in the industry and at ILE. Thanks, Bala. We know that AI is a huge and growing market. Gartner predicts a $135 billion market in 2025, with a five year growth rate of approximately 25%. We also play in two other very large and growing markets, the global e-learning and hyper-automation markets. There is plenty of growth opportunity for ILearning ahead. What we are seeing is that the industry is evolving towards the approach that we have been taking for many years. Turning to the recent industry developments in AI, OpenAI company CEO Sam Altman told an audience at an event held at MIT in April 2024: "Progress will not come from making models bigger. I think we are at the end of the era where it's going to be these like giant, giant models. We'll make them better in other ways." At iLearning, our enterprise language models are not one model, but rather an ensemble of models that collectively address industry and enterprise-specific problems. We deploy enterprise-level language models and industry-specific functional models that are trained on a wide range of industry-specific proprietary data sets. As we continue to grow as a company, ILE will take an increasing leadership role in the AI industry. For example, we will be participating at two upcoming insurance conferences, the ITC Vegas in October and the Insurance Innovators in London in November, where we will be speaking about the benefits of AI and iLearning's role in advancing AI technology in the insurance industry. Turning back to specific developments at ILE, in the second quarter, we added 108 new end customers and 178,000 new end-user licenses. We are very encouraged by our results in the second quarter and the overall momentum we see in the business. Finally, I would like to thank the entire ILE team for their hard work in getting ILE to this point. I would also like to thank all our board members, advisors, investors, and everyone else that helped make our public listing possible. We believe we are just getting started with a huge market opportunity ahead. Interest level in AI has been increasing, but doing things on your own is very expensive for end customers. Our out-of-the-box solutions can get customers up and running much faster and at a fraction of the cost, and they can achieve tangible, positive business outcomes. I will now turn the call over to our CFO, Farhan Naqvi, to walk you through some more details on our financial performance in the second quarter. Thanks, Harish, and good morning, everyone. I will start by providing some highlights of our fiscal second quarter 2024 operating results, then transition to several key balance sheet and liquidity measures, and finish with some things to consider regarding our guidance for the mid to longer term. For the fiscal second quarter, ending June 30, 2024, revenue totaled $136 million, representing an increase of 33.9% from the year ago quarter. Annual recurring revenue increased 33% to $521 million, while net dollar retention on a trailing 12 month basis was 129.5%. As of June 30, 2024, the company had over 4.9 million licensed end users. Gross profit for the fiscal second quarter, ending June 30, 2024, was $94 million, an increase of approximately 32% from fiscal Q2 2023. Gross margin was 69.1%, down 160 basis points from the 70.3% recorded in Q2 2023. The decrease in gross margins was due to an increase in new customer contracts, resulting in slightly low margins from the related one-time implementation costs. Operating expenses for the fiscal second quarter, ended June 30, 2024, were $179 million, an increase of $111 million year-over-year from the $68 million recorded in fiscal Q2 2023. The increase is primarily due to the increase of $88 million for share-based compensation expenses. The GAAP net loss for the fiscal second quarter, ended June 30, 2024, was $314 million, compared to the $2 million loss in fiscal Q2 2023. The increase in net loss year-over-year was primarily due to the non-cash expenses associated with the business combination. The non-cash expenses include change in fair market value of the convertible notes of about $170 million on April 16. Share-based compensation that vested with the business combination of about $88 million, change in fair value of a make-whole provision of $14.6 million, change in fair value of loan restructuring liability of $15.5 million, and change in fair value of warrant liability of $37 million. EBITDA for the second quarter was negative $313 million, compared to the positive $0.5 million in Q2 of 2023. The drop in quarterly EBITDA year-over-year was primarily due to the non-cash expenses associated with the business combination, as explained above. For the fiscal second quarter, ended June 30, 2024, Adjusted EBITDA was $4 million, a slight decrease from the $5 million in the fiscal second quarter of 2023. Adjusted EBITDA margin in Q2 of 2024 was 2.9%, compared to the 5% in fiscal Q2 2023. The decrease in Adjusted EBITDA margin was primarily due to increased operational expenses attributed to the infrastructure being put in place to support being a public company. At quarter end, we had approximately 141.2 million shares outstanding, an increase of approximately 6.2 million, compared to the 134.9 million at the closing of our business combination on April 16, with the increase due to shares issuance for RSU and WTI debt payoff. We note there are also outstanding warrants to purchase an additional 22.7 million shares that would increase our share count, as well as 1.3 million in unvested restricted stock units. Turning to balance sheet, we ended the second quarter with $39 million in cash and long-term debt consisting of $59.3 million in a revolving line of credit. Note that our cash balance compares to approximately $0.8 million in cash as of March 31st, 2024. During the quarter, we raised gross proceeds of $35.3 million upon the close of our business combination. ... including $29.4 million from proceeds from convertible notes and $5.9 million from SPAC trust proceeds. On April 17, 2024, we raised $40 million from a commercial loan from East West Bank, and on June 28, 2024, we raised an incremental $20 million in gross proceeds from an accordion provision of the original loan agreement. During the quarter, we repaid $24 million in debt owed to WTI. Next, I would like to provide investors with a framework to help with the expectations of the mid to long-term growth prospects for iLearning Engines. From a top-line perspective, we continue to believe that we will grow revenue above the rate of the overall AI industry, which Gartner predicts at a 25% CAGR. From a margin perspective, we believe there is room to improve operating margin in the long term. We believe there is an opportunity to increase gross margin to the mid-70s% from around 70% today as our AI engines become increasingly efficient. We will continue to invest in R&D, especially in data, but we believe that the leverage would result in R&D falling to 25% to 27% range of revenue over time from the low 30s% today, and we expect SG&A to fall to around 30% of revenue over time from the mid-30s% in recent quarters. If you add up those pieces, we ultimately expect our margins to be similar to other leading category killer software companies. Finally, we hope to see investors at the conferences in coming months, including the Oppenheimer Conference and the Canaccord Growth Conference this week. During September, we will be at the Citi Investor Conference, the 11th Annual Benchmark TMT Conference, and the HC Wainwright Tech Conference in New York. Operator, you can now open the line for questions. Thank you. As a reminder, to ask a question, please press star one one on your telephone and wait for your name to be announced. To withdraw your question, please press star one one again. And our first question is gonna come from Mike Latimore with Northland Capital Markets. Your line is now open. Hi, great. Good morning, yeah. Congrats on the first call and stellar results here. Thank you so much, Mike. So maybe can you, Harish, talk a little bit about what you're seeing in terms of just customer demand? You know, have sales cycles been, you know, stable, improving, shrinking, and you know, maybe what verticals are particularly interesting right now? Yeah. So, thanks, Mike. So we're definitely seeing, you know, a very high, almost every enterprise out there is trying to figure out what AI means for their business. I feel like the early stage with, during the GenAI craze, there was a level of excitement, and, you know, companies have been investing a lot in POCs and trying to figure out what needs to go to production. But I think now it's really in the show me phase, I think, with these enterprises. So we're getting a lot of interest. You know, companies are sort of being faced with this option of, you know, how do, what does AI really mean for my business? And what we are really offering, Mike, to most of these customers is this, this ability to rapidly, deploy the platform, rapidly productize various use cases at scale. This allows them to test the ROI on certain use cases. If it's working, they can scale it up, if not, they can, you know, scale it down. So this ability of our out-of-the-box platform and to rapidly productize is really resonating with customers. You know, from our standpoint, we're seeing a lot of traction in three key areas, education, healthcare, and really the market for enterprise hyper-automation are really the key three key areas that we're seeing a lot of interest. In terms of sales cycles, you know, they've been pretty much very similar in terms of typically somewhere between six to nine months for the initial sales, but upsells tend to take, you know, a smaller amount of time. Yeah. Great, great. And your net dollar retention rate continues to be very strong, kind of, you know, best in class for SaaS companies. Can you talk a little bit about what drives that? Is it, you know, more users, more products? Just what are you seeing in terms of driving your NDR number? So the key thing here, I think one of the big drivers for our NDR, is this ability to add on new use cases and scale up existing use cases. I think that's really the big strength. You know, we are able to help companies build use cases in a matter of weeks to months, as opposed to months to years that it takes, you know, most of our alternatives, which are these custom bespoke solutions. And so, as a result, once they've deployed a few use cases, you know, they're adding more and more use cases. And so it's a combination of scaling up these use cases and adding new use cases, that's really driving net dollar retention. Great. Just last one for me on the channel partners. I think you said you're over 30 now. How should we think about that going forward? Is that important to add, you know, sort of a handful of those every quarter? Or, you know, what's the plan there, and then, like, what kind of verticals are you focused on for those new ones? ...So for us, really, the value-added resellers are the solutioning partners for us. I think, Andrew Ng, the founder of DeepLearning.AI, had mentioned that AI is the new electricity, period. And, you know, if you take that analogy, you know, when you think of how, you know, when you first had electricity being produced, the choices for a producer was, you know, do I go to businesses and ask them: Hey, how many kilowatt hours do you want? Or do you go partner with the appliance makers, the actual systems, et cetera, people who build solutions powered by, you know, electricity. And so really, these value-added resellers are the appliance makers for AI, right? So they build the solutions. And so for us, the more value-added resellers we can add, the more hyperautomation applications that they can create. And these are players that have both horizontal capabilities, also very vertical focused. You know, we're in 12 verticals today, and in each of these verticals, these value-added resellers are bringing in tremendous domain expertise. And so everything is out of the box for us. So for every vertical we have, you know, our Enterprise Language Models per vertical and, you know, already preprogrammed with industry-specific use cases for that vertical. And so we're able to go into organizations, and they pretty much know that these use cases are important to them. So for us, this combination of VARs that can bring vertical-specific expertise as well as, you know, the horizontal players are very valuable as part of our strategy. Yeah. Awesome. Great. Well, appreciate it. Thanks very much. Best of luck this year. Thank you, Mike. The next question comes from Matthew Harrigan with The Benchmark Company. Your line's open. Thank you. I hope you can hear me adequately. My connection is a little bit raspy. I had two capital structure questions and then one operating question. Matt, we can barely hear you. Matt, could you speak up? I'm sorry. He did mention he has a very bad connection. Okay. I'll talk loud. Can you talk about the reaction of the market to the S-1 going effective and your... if you can. And I realize that that's a little delicate, but I thought the market reacted very misplaced, since that was known, if you will. And then secondly, any possibility of a non-cash warrant exercise to, you know, clean up the stack balance sheet... Thank you. Operator. Yeah, sure, Matt. You know, to answer your second, first part of your question, the non-cash cleanup of the warrants, you know, those are all things that we are exploring. You know, in terms of the S-1, you know, we, as part of our business combination agreement with Arrowroot, we had investors came in that were post effective, and then we had some, you know, fees that were equitized. And so as part of the agreement, we had to submit a follow-on PCL S-1 that went effective on Friday. And so it was something that we had to do for as part of the d e-SPAC itself, you know. Beyond that, it's really hard for me to speculate on how the markets react to it. Yeah. Okay. That, that's as I anticipated, but I thought somebody had to ask. And then on the operating side, I mean, you've got a nice head start with your algos and certainly your in-house data sets. But are you seeing anything new in the way of competition? So I think what we are seeing, and a very common theme, Matt, when we go into enterprises is, you know, they have, typically, they are, you know, this, by the way, this is of interest to almost every CEO of every enterprise. AI is some front and center for them. When we go in, typically, you know, they are, you know, they would have a team of a few AI engineers that are working on really building use cases, but these are really very custom, bespoke approaches, solutions built on top of, you know, an Azure or and a language model, et cetera. And the challenge with the bespoke solutions is they require expensive AI engineers takes significant amount of time to build these, you know, use cases and test it. So it's very hard for them to scale. And really for us, this ability of our platform, since everything is out of the box and they're already ready, it's either ready for solutioning or already solutioned means that they're able to deploy the platform in an enterprise pretty quickly and then build these use cases very rapidly. And that's a huge competitive advantage. And this we are just seeing this becoming a stronger and stronger advantage because you know, for a lot of companies, you know, they are trying to figure out what AI is for their business, and often they are being told, "Hey, you know, you must spend millions of dollars on AI or else you'll be left behind." And you know we don't think that's the right approach, nor do they. And so really, we feel more and more that we are a very important platform through which AI can be brought into the enterprise by these organizations. ... Thank you, Harish. Very, very nice first quarter as a public company. Thank you. Thanks so much, Matt. The next question comes from Raj Sharma with B. Riley Securities. Your line is open. Hi, good morning. Thank you for taking my question. I wanted to understand your business, you know, congratulations on becoming public, your first call. Your business has very high gross margins, and I'd like to understand if they're sustainable. I understand your business goes through VARs. Harish, how sticky are these relationships? So can you speak on the retention of your recurring business? Sure. Thanks, Raj. You know, from our standpoint, one of the key things for us is we are, you know, we get very deeply embedded inside an enterprise. So once we are deployed, it's really very hard for an organization to disrupt this. And so that makes the platform inherently sticky. And as companies build more and more use cases, these are, you know, use cases that can be either part of one business unit or spread across business units, it becomes harder and harder to really replace this. I think that's one of the inherent strengths of having a platform, an AI platform that embeds to all the workflows inside an organization. So that's really a big part for us. Whether we go through a value-added reseller or we go directly, it really makes us very sticky for us. And so it does, you know. The other part to this is, since we are inherently things are out of the box, you know, we are not caught up with implementation challenges. So the big sign for us is, because of our out-of-the-box capabilities, you know, making it easier to implement and deploy, it's also easier for people to see results. On the other side, the flip side is also true. I think if we screw up, we could have a ripple effect from that, too. So I think for us, continuing to stay focused operationally, just continue to improve our, you know, our operations is critical. Continue to provide the support, you know, providing support to these enterprises is critical, too. So I think for us, given that we are sticky, it's really important that we continue to stay operationally focused. Yeah. Thank you. Thank you for that. And then, this question is perhaps for Farhan. You know, you mentioned the cash of $39 million, and then there's an AR of $91 million, and I just wanted to understand the collectibility of this AR from a working capital perspective. How should we expect to see billing and collection occur going forward? Can you hear me all right? Yes. So we, we have 30-, 60-, and 90-day contracts, and the collectibility, so far has been pretty good. We, we are working towards bringing this down further, so you'd see, more of this getting collected earlier. Got it. So the cadence, I mean, obviously, you had a large loss this quarter, but the operating cash flow is, you know, significantly lower. So going forward, you should see the AR balance go down to a more reasonable, you know, negative working capital situation, or- As I said, we're working towards bringing it down. I wouldn't be able to give you an exact date as to when would this turn negative. Raj, just to add to this. I think our customers, you know, like I said, and Farhan mentioned, they have payment of 30, 60, and 90 days. We've had a very strong, you know, the AR has generally been very good AR. You know, we've had very few instances of customers not paying on time. And so, as we continue to grow, I think the AR will grow, but I think the AR as a percentage of our revenue, you know, we'll manage that better. But that's really the reason. And part of it is really the terms that we have, net 60, net 90, with all our customers. We got it. That's. Yeah, I just wanted to understand the cadence that this balance likely comes down. Thank you. Thank you for the questions. I'll take this offline. Thank you. Sure. Thanks, Raj. As a reminder, to ask a question, please press star one one on your telephone. The next question comes from Matthew VanVliet with BTIG. Your line is open. Yeah, thanks, Matt VanVliet on here. Thanks for taking the question. I, I guess, as you look at the time that it's taking to train the models once you're in a customer's infrastructure, how has that trended over the course of this year, and how does that compare to maybe previous years? And then importantly, kind of how, where should we expect that maybe by the end of next year, for instance? So, Matt, from our standpoint, you know, everything we have is out of the box. So these are pre-trained, you know, models or Hyperapps, that once they are deployed, really what's happening inside the enterprise is the ongoing fine-tuning of these models. You know, for us, really, once we are inside an enterprise, like I said, each use case gets fine-tuned on the customer's own data. And then as we add new use cases, they also continue to get fine-tuned. So the one way how we look at this is every Hyperapp has a baseline intelligence. So when you start out, let's say that number is 50 or 60 or 70, that over time, with the data, will continue to improve. You know, there is a leveling off that happens typically, because, you know, we're doing much more than just automating simple processes. You know, we can automate complex workflows and things like that. So typically, that can go from 70-80 to 85 or so, and then there is a leveling off. But for us, so with each of these use cases, we're starting out, and then it continues to improve, and we have the benefit. And this is really where our verticalization of scale really comes into play, because let's say we are, say, in the insurance vertical, you know, our out-of-the-box Hyperapp could be things like claims intakes, claims processing, loss prevention, smart risk management. And so within every... You know, most insurance companies will need any or many of these, use cases or, hyperautomation apps, and so they all, like I said, get fine-tuned on the customer's own data. And, you know, we're constantly, monitoring this and making sure that we can get from that baseline, that 85% as fast as possible. Okay. Helpful. And then you mentioned your partners are operating in a total of 12 verticals, and the three key ones for you internally. How should we think about sort of the other, the delta between your top three and the 12 your partners are working on? And is there an appetite to try to expand beyond that 12, or is there enough of a market in front of you that, you know, today, organically, you'll go after those 12 verticals? So, yeah, we definitely feel like these 12 verticals have tremendous opportunities within them, but we also will continue to add new verticals. You know, whenever we enter a new vertical, we are partnering with a channel partner or value-added reseller who's bringing that domain expertise, and we use that to really build our, you know. And we call these enterprise models. You know, our enterprise models are both language models plus functional models for that vertical. And then on top of those, using those, we are building Hyperapps for that vertical. So I think we'll continue to add new verticals, but definitely there's a huge opportunity here within the 12 verticals to scale, because we've already built the enterprise models and the initial set of hyperapps, and we'll just continue to add more and more hyperapps to those verticals. So obviously, easier to scale up an existing vertical, but you know, the adding a new vertical means we'll have to build these models. But this is an important focus for us. You know, today, education, healthcare, insurance, hyperautomation are big verticals, but all these other verticals also represent great opportunities to continue to build up. Okay. And then just last question. You, you touched on the, the solid land and expand motion, you have going, but curious on how initial deal sizes are looking. How have those trended so far this year, and is there an opportunity to land a little bit larger going forward, or is the strategy still, try to get in and sort of automate one group of workflows and then expand from there? I think there's definitely room for pricing improvement. You know, part of this is really for us, I think understanding the value we are able to deliver to a customer, so... And I think this also ties to expanding within an existing vertical, because we are able to see the impact that we're creating, and so allows us to build better pricing, you know, for the next set of customers within that vertical. So for us, it really is, you know, that why... When we go in, we're typically, I mean, everything is out of the box, so we are able to, you know, our platform plus the set of use cases, we're talking about our pricing in the, you know, the $100,000-ish range as opposed to the $1 million-ish range. But the idea is that once this has already been built out, that can be further scaled up. So if you think of an average enterprise, you know, they could have 100, 200 use cases. And so we think there's great opportunity here to scale up those use cases and extract good value out of the engagement. Great. Thank you. I show no further questions at this time. I would now like to turn the call back to Harish for closing remarks. Sure. Thank you, everyone, for being here on this call. You know, we completed our De-SPAC in Q2, and really, for us, this is our first earnings call. We're really just honored to have you all here, and we hope you found this call informative and useful. And I just wanted to thank all the people with questions and really taking your time to be joining us here. So thank you very much, and we look forward to seeing you again in the future. This concludes today's conference call. Thank you for participating. You may now disconnect.