iLearningEngines Q2 2024 Earnings Call Transcript

Quartr
Provided by Quartr
August 13, 2024

There are 9 speakers on the call.

Operator

Ladies and gentlemen, thank you for standing by. Welcome to I Learning Engine Second Quarter 2024 Earnings Call. At this time, all participants are in a listen only mode. After the speakers' presentation, there will be a question and answer Please be advised that today's conference is being recorded. I would like now to turn the conference over to Kevin Hunt, Investor Relations.

Operator

Please go ahead.

Speaker 1

Thank you. Good morning and welcome to iLearning Engine's Q2 2024 Financial Results and Corporate Update Conference Call. Earlier today, ILE issued a press release announcing financial results for the Q2 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 Chaddambaran, our Chairman and Chief Executive Officer Balakrishnan, our President and Chief Business Officer and Farhan Nakhvi, our Chief Financial Officer.

Speaker 1

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, the 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 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 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 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.

Speaker 1

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.ilearningsengines.com. I will now hand over the

Speaker 2

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 milestone for I Learning Engines. 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 3,000 and related indices, a significant milestone for a New Republic company and we also secured $20,000,000 in additional debt funding that will help fund our growth plans.

Speaker 2

Today, we're pleased to report our 2nd quarter results. We generated revenue of $136,000,000 in the quarter, a 33.9% year over year increase as compared to the same period in 2023 and produced $4,000,000 of adjusted EBITDA. We added over 100 new end customers and 176,000 end users. Our CFO, Farhan Nakhui will provide details on the financials in a few minutes. But for those of you who are not familiar with iLearning engines, I wanted to start today's call with a high level overview of the company.

Speaker 2

ILearning Engines 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 the 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, iLearning Engines is a pure play AI company. Our applied AI platform is solving real customer problems today.

Speaker 2

We were an AI platform at scale and solving real customer problems and use cases even before the Gen AI craze. We have been delivering AI solutions for learning and work automation at scale for over 5 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 hyper automation apps or AI engines to solve high impact customer problems within the enterprise.

Speaker 2

These AI engines can be deployed quickly in weeks to months versus a year or longer with potentially 1,000,000 invested for many internally developed corporate AI projects. And we are doing this at scale. We generated $421,000,000 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 4,900,000 end users that are benefiting from our AI platform today. Let me turn it over to Bhalla, 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 iLearning Engine's technology to solve their problems.

Speaker 3

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.

Speaker 3

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 assist 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 operational metrics across the various strategic business units.

Speaker 3

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.

Speaker 3

The second part was an AI worker embedded into the enterprise workflow that was able to process the data for fraud, duplication, accuracies 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 claim 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, the status and required actions across the enterprise. The company has since been adding new cases on the ILE platform.

Speaker 3

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 added resellers, VARs that bring domain expertise in each vertical that we entered 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 4 largest walls have accounted for roughly 52% of revenue.

Speaker 3

As Harish noted earlier, we have minimal end customer concentration as through these walls, we service more than 1,000 end customers with over 4,900,000 end users. With that, let me turn it back to Harish to talk about recent developments in the industry and at ILE.

Speaker 2

Thanks, Bala. We know that AI is a huge and growing market. Gartner produced a $135,000,000,000 market in 2025 with a 5 year growth rate of approximately 25%. And we also play in 2 other very large and growing markets, the global e learning and hyper automation markets. So there is plenty of growth opportunity for I learning ahead.

Speaker 2

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 at 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 I learning, our enterprise language models are not one model, but rather an ensemble of models that collectively address industry and enterprise specific problems.

Speaker 2

We deploy enterprise level language models and industry specific functional models that are trained on a wide range of industry specific proprietary datasets. 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 2 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 iLearnings role in advancing AI technology in the insurance industry. Turning back to specific developments at ILE, in the Q2, we added 108 new end customers and 176,000 new end user licenses. We are very encouraged by our results in the Q2 and the overall momentum we see in the business.

Speaker 2

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.

Speaker 2

I will now turn the call over to our CFO, Farhan Nakhri to walk you through some more details on our financial performance in the Q2.

Speaker 4

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 Q2 ending June 30, 2024, revenue totaled 136,000,000 dollars representing an increase of 33.9 percent from the year ago quarter. Annual reckoning revenue increased 33% to $521,000,000 while net dollar retention on a trailing 12 month basis was 129.5%. As of June 30, 2024, the company had over 4,900,000 licensed end users.

Speaker 4

Gross profit for the fiscal Q2 ended June 30, 2024 was 94,000,000 an increase of approximately 32% from fiscal Q2 2023. Gross margin was 69.1 percent, 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 lower margins from the related one time implementation costs. Operating expenses for the fiscal Q2 ended June 30, 2024 were $179,000,000 an increase of $111,000,000 year over year from the $68,000,000 recorded in fiscal Q2 2023. The increase is primarily due to the increase of $88,000,000 for share based compensation expenses.

Speaker 4

The GAAP net loss for the fiscal Q2 ended June 30, 2024 was 314,000,000 dollars compared to the $2,000,000 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 $117,000,000 on April 16th Share based compensation divested with the business combination of about $88,000,000 change in fair value of a make hold provision of $14,600,000 change in fair value of loan restructuring liability of $15,500,000 and change in fair value of warrant liability of $37,000,000 EBITDA for the 2nd quarter was negative $313,000,000 compared to the positive $500,000 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 Q2 ended June 30, 2024, adjusted EBITDA was 4,000,000 dollars a slight decrease from the $5,000,000 in the fiscal Q2 of 2023.

Speaker 4

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 141,200,000 shares outstanding, an increase of approximately 6,200,000 compared to the 134,900,000 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 22,700,000 shares that would increase our share count as well as 1,300,000 in unvested restricted stock units.

Speaker 4

Turning to balance sheet. We ended the 2nd quarter with $39,000,000 in cash and long term debt consisting of $59,300,000 in a revolving line of credit. Note that our cash balance compares to approximately $800,000 in cash as of March 31, 2024. During the quarter, we raised gross proceeds of $35,300,000 upon the close of our business combination, including $29,400,000 from proceeds from convertible notes and $5,900,000 from SPAC trust proceeds. On April 17, 2024, we raised $40,000,000 from a commercial loan from East West Bank And on June 28, 2024, we raised an incremental $20,000,000 in gross proceeds from the accordion provision of the original loan agreement.

Speaker 4

During the quarter, we repaid $24,000,000 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 I learning 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 margins in the long term. We believe there is an opportunity to increase gross margin to the mid-70s from the around 70% today as our AI engines become increasingly efficient.

Speaker 4

We will continue to invest in R and D, especially in data, but we believe that the leverage will result in R and D falling to 25% to 27% range of revenue over time from the low 30s today, and we expect SG and 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 H. C.

Speaker 4

Wainwright Tech Conference in New York. Operator, you can now open the line for questions.

Operator

Thank And our first question is going to come from Mike Latimore with Northland Capital Markets. Your line is now open.

Speaker 5

Hi, great. Good morning. Congrats on the first call and stellar results here.

Speaker 2

Thank you so much, Mike.

Speaker 5

So maybe can you, Harish, talk a little bit about what you're seeing in terms of just customer demand? Have sales cycles been stable, improving, shrinking? And maybe what verticals are particularly interesting right now?

Speaker 2

Yes. So thanks, Mike. So we're definitely seeing a very 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 Gen AI craze, there's a level of excitement and 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.

Speaker 2

So we're getting a lot of interest. Companies are sort of being faced with this option of how do what do AI really mean for my business and what we are really offering, Mike, to most of these customers is this ability to rapidly deploy the platform, rapidly productize various use cases at scale. And this allows them to test the ROI on certain use cases. If it's working, they can scale it up. If not, they scale it down.

Speaker 2

And so this ability of our out of the box platform and to rapidly prioritize really resonating with customers. From our standpoint, we're seeing a lot of traction in 3 ks areas, education, health care and really the market for enterprise hyper automation. These are key three key areas that we're seeing a lot of interest. In terms of sales cycles, they've been pretty much very similar in terms of typically somewhere between 6 to 9 months for the initial sales, but up sells tend to take a smaller amount of time.

Speaker 5

Yes, great. And your net dollar retention rate continues to be very strong kind of best in class for SaaS companies. Can you talk a little bit about what drives that? Is it more users, more products? Just what are you seeing in terms of driving your NDR number?

Speaker 2

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 mean that's really the big strength. 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 most of our alternatives, which are these custom bespoke solutions. And so as a result, once they've deployed a few use cases, 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 our net dollar retention.

Speaker 5

Great. And 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 sort of a handful of those every quarter? Or what's the plan there?

Speaker 5

And then like what kind of verticals are you

Speaker 2

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 Android had mentioned that AI is the new electricity period. And if you take that analogy, when you think of how when you first had electricity being produced, the choices for a producer was do I go to business and then ask them, hey, how many kilowatt hours do you want? Or do you go partner with the appliance makers, the systems, etcetera, people who build solutions powered by electricity.

Speaker 2

And so really these value entities are the appliance makers for AI, right? So they build the solutions. And so for us, the more value added resources we can add, the more hyperautomation applications that they can create. And these are players that have both horizontal capabilities also very vertical focused. We're in 12 verticals today.

Speaker 2

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 enterprise language our enterprise language models per vertical and already pre programmed with industry specific use cases for that vertical. And so we're able to go into organizations and they pretty much know that these U. K.

Speaker 2

Are important to them. And so for us, this combination of bars that can bring vertical specific expertise as well as the horizontal players are very valuable as part of our strategy.

Speaker 5

Yes. Awesome. Great. Well, appreciate it. Thanks very much.

Speaker 5

Best of luck this year.

Speaker 2

Thank you, Mike.

Operator

And the next question comes from Matthew Harrigan with The Benchmark Company. Your line is open.

Speaker 6

Thanks. I hope you can hear me adequately. My connection is a little bit raspy. I have 2 capital structure questions and then I have one operating question.

Speaker 4

Matt, we can barely hear you.

Speaker 2

Matt, could you speak up?

Operator

I'm sorry. He did mention he has a very bad connection.

Speaker 6

Okay. Can you talk about the reaction of the market to the S1 going effective in

Speaker 2

Europe?

Speaker 6

I realize that that's a little delicate, but I thought the market react very misused place since that was known, if you will. And then secondly, any possibility of non cash warrant exercise to clean up the STACK balance sheet?

Speaker 2

Thank you. Sure. Sure, Matt. To answer your second first part of your question, the non cash cleanup of the warrants, those are all things that we are exploring. In terms of the S-one, we as part of our business combination agreement with Arrowroot, we had investors came in that were post effective and then we have some fees that were equitized.

Speaker 2

And so as part of the agreement, we were we would have had we had to submit a follow on presale less one that went effective on Friday. And so it was something that we had to do for as part of the de SPAC itself. And beyond that, it's really hard for me to speculate on how the markets react to it.

Speaker 3

Okay. That's as I

Speaker 6

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?

Speaker 2

So I think what we are seeing in a very common theme, Matt, when we go into enterprises is they have typically they are 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, they are 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 an Azure or and the language model, etcetera. And the challenge with bespoke solutions is they require expect to sell AI engineers, takes a significant amount of time to build these use cases and test it. So it's very hard for them to scale.

Speaker 2

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 for a lot of companies, they are trying to figure out what AI is for their business and often they are being told you must spend 1,000,000 of dollars on AI or else you'll be left behind. And we don't think that's the right approach, not 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.

Speaker 6

Thank you, Harish. Very nice first quarter as a public company.

Speaker 2

Thank you. Thank you so much, Matt.

Operator

And the next question comes from Raj Sharma with B. Riley and Company. Your line is open.

Speaker 7

Hi, good morning. Thank you for taking my question. I wanted to understand your business congratulations on becoming public in your first call. Your business has very high gross margins. And I'd like to understand if they're sustainable.

Speaker 7

I understand your business goes through VARs. Harish, how sticky are these relationships? So can you speak on the retention of your recurring business?

Speaker 2

Sure. Thanks, Raj. From our standpoint, one of the key things for us is we are 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.

Speaker 2

And as companies build more and more use cases, these are use cases that can be either part of 1 business unit or spread across business units, it becomes harder and harder to really replace it. 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 the other part to this is since we are inherently things are out of the box, we are not caught up with implementation challenges.

Speaker 2

So the big sign for us is because of our out of the box capabilities, the 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 of that too. So I think for us continuing to stay focused operationally, just continue to improve our operations as critical, continue to provide the support, 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.

Speaker 7

Yes. Thank you. Thank you for that. And then this question is perhaps for Han. You mentioned the cash of $39,000,000 and then there's an AR of $91,000,000 And I just wanted to understand the collectability of this AR from a working capital perspective.

Speaker 7

How should we expect to see billing and collection occur going forward?

Speaker 4

So can you hear me all right?

Speaker 7

Yes.

Speaker 4

So we have 30, 60 and 90 day contracts and the collectability so far has been pretty good. We are working towards bringing this down further. So you'd see more of this getting corrected earlier.

Speaker 7

Got it. So the cadence, I mean, obviously, you had a large loss this quarter, but the operating cash flow is significantly lower. So going forward, you should see the AR balance go down to a more reasonable negative working capital situation or?

Speaker 4

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.

Speaker 2

Raj, just to add to this. I think our customers, like I said, and Farhan mentioned, they have payment of 30, 60 90 days. We've had a very strong the AR has generally been very good AR. 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 in the AR as a percentage of our revenue, we'll manage that better.

Speaker 2

But that's really the reason. And part of it is really the terms that we have net 16, net 90 with the dollar customers.

Speaker 7

Got it. Yes, 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.

Speaker 7

Thank you.

Speaker 2

Sure. Thanks, Raj.

Operator

And the next question comes from Matt Vliet with BTIG. Your line is open.

Speaker 8

Yes. Thanks. Matt Van Vliet on here. Thanks for taking the question. 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?

Speaker 8

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?

Speaker 2

So back from our standpoint, everything we have is out of the box. So these are pre trained models or hyper apps that once we are deployed, really what's happening inside the enterprise is the ongoing fine tuning of these models. 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 hyper app has a baseline intelligence.

Speaker 2

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. And there is a leveling off that happens typically because we're doing much more than just automating simple processes. 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.

Speaker 2

But for us, so it's in each of these use cases we're starting out and that 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, our out of the box hyper apps could be things like claims intakes, claims processing, loss prevention, SmartRx management. And so within every most insurance companies will need any or many of these use cases or hyper automation apps. And so they all, like I said, get fine tuned on the customers' own data. And we're constantly monitoring this and making sure that we can get from that baseline that 85% as fast as possible.

Speaker 8

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 3 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 today organically you'll go after those 12 verticals?

Speaker 2

So yes, we definitely feel like these 12 verticals have tremendous opportunities within them, but we also will continue to add new verticals. Whenever we enter a new vertical, we are partnering with a channel partner or value add reseller who's bringing that domain expertise and we use that to really build our we call these enterprise models. Our enterprise models are both language models plus functional models for that vertical. And then on top of those, using those, we are building hyper apps 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 hyper apps and they will just continue to add more or more hyper apps to those verticals.

Speaker 2

So obviously easier to scale up an existing vertical, but adding a new vertical means we'll have to build these models. But this is an important focus for us. Today, education, health care, insurance, hyper automation are big verticals, but all these other verticals also represent great opportunities to continue to build up.

Speaker 8

Okay. And then just last question, you touched on the solid land and expand motion you have going, but curious on how initial deal sizes are looking, how 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?

Speaker 2

I think there's definitely room for pricing improvement. 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 it allows us to build better pricing for the next set of customers within that vertical. So for us, it really is that when we go in, we're typically everything is out of the box.

Speaker 2

So we are able to our platform plus the set of use cases we're talking about are pricing in the 100,000 ish range as opposed to the 1000000 ish range. But the idea is that once this has already been built out, that thing will be further scaled up. So if you think of an average enterprise, they could have 100, 200 use cases. And so I think there's great opportunity here to scale up those use cases and extract good value out of the engagement.

Speaker 8

Great. Thank you.

Operator

I show no further questions at this time. I would now like to turn the call back to Harish for closing remarks.

Speaker 2

Sure. Thank you everyone for being here on this call. We completed our DS back 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.

Speaker 2

So thank you very much and we look forward to seeing you again in the future.

Operator

This concludes today's conference call. Thank you for participating. You may now disconnect.

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