Q2 2024 iLearningEngines Inc Earnings Call

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Operator: ILearningEngines 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.

Ladies and gentlemen, thank you for standing by welcome try learning engine second quarter 'twenty 'twenty four 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 this session you would.

Operator: After the speaker's presentation, there will be a question and answer session. To ask a question during this session, you would need to press star one on your telephone. You will then hear an automated message advising that your hand is raised.

Need to press Star one one on your telephone.

Speaker Change: I'm here in all the major message.

Speaker Change: We're hanging just raised to withdraw your question. Please press star one again, please be advised that today's conference is being recorded.

Operator: 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.

I would like now to turn the conference over to Kevin Hunt.

Kevin Hunt: Best Relations. Please go ahead.

Kevin Hunt: Thank you. Good morning, and welcome to iLearningEngine's 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 its 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.

Speaker Change: Thank you.

Kevin Hunt: Good morning, and welcome to Ireland entered the second quarter of 2024 financial results and corporate update conference call.

Speaker Change: Earlier today <unk> issued a press release announcing financial results for the second quarter ended June 32024.

Speaker Change: A copy of this press release is available on the company's website and through our SEC filings.

Speaker Change: With me today.

Speaker Change: Tom Brown, our chairman and Chief Executive Officer, Balakrishnan, our President and Chief business Officer, and Farhan Duffy, our Chief Financial Officer.

Kevin Hunt: Before we begin, please note that on today's conference call, we will be making four 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 Board-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.

Speaker Change: Before we begin please note that on today's conference call, we will be making forward looking statements, including statements relating to guidance projections forecast revenue growth and EBITDA adjusted EBITDA expected operating results the integration of our platform with our clients' existing systems.

Vacation 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 our company and the role of the company and the AI industry.

Forward looking statements are neither historical facts, nor assurances of future performance.

Speaker Change: Subject to inherent uncertainties risks and changes in circumstances that are difficult to predict and many of which are outside of our control.

Kevin Hunt: Our actual results and financial condition may differ materially from those indicated in this forward-looking statement. For a list and description of the risks and uncertainties that we face, please see the reports that we have 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.

Speaker Change: Our actual results and financial condition may differ materially from those indicated in the forward looking statements.

Speaker Change: 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. The quarter ended June 30 of 2024.

Speaker Change: This conference call contains time sensitive information that is based only on information currently available to us as of the data of this live broadcast August 13th 2024.

Speaker Change: <unk> 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.

Kevin Hunt: 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 call to Harish.

Speaker Change: 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.

Terry: 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 <unk> engines Dot Com I will now hand over the call Terry.

Harish Chidambaran: 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 adult acquisition corporations. In June, we were added to the Russell 3000 and related indices. This is a significant milestone for a newly public company, and we also secured $20 million in additional debt funding that will help fund our growth plan.

Terry: Thanks, Kevin and thank you to everyone for joining us today for our first earnings call as a public company.

Terry: It was a quarter of significant achievements and milestones for our learning engines in April we became a publicly traded company after completing our business combination with adult acquisition Corporation.

Terry: In June we were added to the Russell 3000 and related in defense.

Terry: A significant milestone for.

Terry: 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.

Harish Chidambaran: 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 Nakhwi, will provide details on the financials in a few minutes. But 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.

Terry: We generated revenue of $136 million in the quarter.

Terry: 33, 9% year over year increase as compared to the same period in 2023 and produced $4 million of adjusted EBITDA.

Terry: We added over 100, new end customers and 176000 end users.

Terry: Our CFO for Hana Cui will provide details on the financials in a few minutes, but for those of you who are not familiar with idling engines I wanted to start today's call with a high level overview of the company.

Harish Chidambaran: 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 some ways, ILearningEngines is a purely 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 Gen AI craze.

Idling engines are ideally is a leading applied AI platform for learning and work automation.

Hana Cui: <unk> enables enterprises to rapidly product type and deploy a wide range of AI applications and use cases, what we call AI engines at scale.

Speaker Change: The platform is powered by proprietary vertical specific AI models and the local AI canvas to drive rapid out of the box deployment by offering low latency and high levels of data security and compliance.

I don't think <unk> as a pure play AI company.

Speaker Change: AI platform is solving real customer problems today.

Chris: We were an AI platform at scale and solving real customer problems and use cases, even before the journey I Chris.

Harish Chidambaran: 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 power various use cases and hyper-automation 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 invested in many internally developed corporate AI projects

Chris: We have been delivering AI solutions for learning and work automation at scale for over five years now.

Chris: We built a 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.

Chris: Our platform allows enterprises to connect to all the different systems within the enterprise.

Speaker Change: I like the content and data that is there.

Chris: Put that all into an AI knowledge cloud.

Chris: That AI knowledge cloud and are noteworthy a canvas that empowers various use cases and hyper automation apps are AI engines to salt high impact customer problems within the enterprise.

These AI engines can be deployed quickly in weeks two months versus a year or longer with potentially millions of investors for many internally developed corporate AI projects.

Harish Chidambaran: And 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. Now, I will 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 iLearningEngine's technology to solve their problems.

Speaker Change: We are doing this at scale be generated $421 million in revenue in 2023.

Speaker Change: And we have generated positive adjusted EBITDA every year since 2020, we.

Speaker Change: We have a diversified customer list of over 1000 customers and $4 9 million end users that are benefiting from our AI platform today.

Speaker Change: Let me turn it over to Butler, President and Chief business Officer to walk you through a few case studies that will provide a better understanding of how companies are using learning engine technology to solve their problems.

Bala Krishnan: I'm very excited about the first example, a global manufacturing conglomerate with 22 business units and 40,000 employees that we 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 events, we weren't replacing any specific vendors, and we were not competing with any specific solution. However, 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.

Butler: Thanks Krish.

Butler: The first example of our global manufacturing conglomerate with 22% units and 40000 employers that we.

Speaker Change: 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 40000 employee base as well as their network of 5000 dealers like many of our customer wins, we werent, replacing any splits.

Speaker Change: Vic vendors and we were not competing with any specific solution. The company had no system for subject matter experts to deliver training and a scalable matter and we're struggling with siloed knowledge sources for policies and procedures across the organization. They also wanted to understand and get a good handle on.

Bala Krishnan: They also wanted to understand and get a good handle on the daily performance metrics across their employee network. ILE was brought into 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.

Speaker Change: The daily performance metrics across their employer network.

Speaker Change: <unk> was brought in to those accounts and the first step we took was to create an enterprise wide AI knowledge cloud using the company's own internal content. The Iot platform with an integrated with the company's existing systems, which included in our SAP ERP framework, Successfactors BMC remedy and a number.

Speaker Change: Of homegrown databases, as well as communication channels, which included Whatsapp mobile and Internet. One <unk> was integrated into the organization, we closed performance and cost with gaps with optimized mission critical information delivery workflows and the AI employee assets is trained to handle 18.

Bala Krishnan: 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 ratings, 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.

Speaker Change: Kpis cutting across organization structure attrition retrofit outperformance rating, but many more such kpis plant. The customer is seeing improved operational metrics across the various strategic business units. The second case is a process automation exam.

Speaker Change: Also a leading auto insurer that provides coverage to millions of acres. The organization wanted an early and accurate notification of the claim David also dealing with customer satisfaction issues with their call center around wait times accuracy responsiveness and closure by using the <unk> platform.

Bala Krishnan: They were also dealing with customer satisfaction issues with the 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, and accuracy and thereby explain how to detect fraud.

Company was able to create an AI powered klebs automation engine.

Speaker Change: First part of the platform both automation of the data collection using an image based claims intake engine, helping them to reduce fraud. The second part was in AI worker embedded into the enterprise workflow that was able to process the data for fraud duplication accuracies and thereby expedite.

Speaker Change: Claims processing using workflows built on our low code AI cameras clips that accurately about it and the <unk> platform.

Bala Krishnan: Using workflows built on our no-code AI canvas, claims were accurately routed, and the ILE platform's seamless integration with external systems ensured automatic updates to the client's claim system. The customer was able to achieve a significant improvement in the number of claims processed, as 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.

Speaker Change: <unk> integration with external systems ventured automatic updates to the claims.

Speaker Change: Claim system.

Speaker Change: Our customer was able to achieve significant improvement in the number of claims processed a centralized dashboard provided a real time overview of all claims that caters and required actions across the enterprise. The company has since been adding new cases on the Iot platform.

Speaker Change: Let me now touch on our go to market strategy.

Bala Krishnan: 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), who bring domain expertise to 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.

As Ive 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.

Speaker Change: Very closely but value added resellers.

Speaker Change: Was that bring domain expertise in each vertical that we entered into and can build a solution that addresses specific customer problems. These bars also have a lot of existing customers that we have been able to leverage we have 31 now our four largest was that accounted for roughly 52% of revenue.

Harish Chidambaran: 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 serve 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.

Harry: <unk> noted earlier, we have minimal and customer concentration as crude is whilst we service more than pattern and customer with over $4 9 million end users with that let me turn it back to Harry to talk about recent developments in the industry and at <unk>.

Harish Chidambaran: 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%, and we also play in two other very large and growing markets, the global e-learning and high What we are seeing is that the industry is evolving towards the approach that we have been taking for many years. Turning to recent industry developments in AI, open AI company CEO Sam Altman told an audience at an event held at MIT in April 2024 that progress will not come from making models bigger.

Harry: Thanks, Bala, we know that AI is a huge and growing market.

Harry: 135 billion market in 2025, with a five year growth rate of approximately 25%.

Harry: And we also play in two other very large and growing markets the global E learning.

And hyper automation markets.

Harry: There's plenty of growth opportunity for learning ahead.

Harry: What we are seeing is that the industry is evolving towards the approach that we have been taking for many years.

Harry: Turning to the recent industry developments at AI Open AI company CEO, Sam Hoffman told an audience I can even tell that it might be in April 2020 for.

Speaker Change: Progress will not come from making models bigger I think we are at the end of the era, where it's going to be this like giant giant models.

Harish Chidambaran: I think we are at the end of the era where these like giant, giant models will 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 datasets.

We will make them better in other ways.

Speaker Change: At Ione.

Speaker Change: 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.

Speaker Change: Proprietary datasets.

Harish Chidambaran: 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 Innovations conference 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 176,000 new end-user licenses.

Speaker Change: As we continue to grow as a company ideally will take an increasing leadership role in the AI industry. For example, we will be participating at two upcoming insurance conferences. The ITC vehicles in October and the insurance innovators in London in November where we will be speaking about the benefits of AI and learning.

Speaker Change: Role in advancing AI technology in the insurance industry.

Speaker Change: Turning back to specific developments at <unk> in the second quarter. We added 108, new end customers and 176000, new end user licenses.

Harish Chidambaran: 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.

Speaker Change: We are very increased by our results in the second quarter and the overall momentum we see in the business.

Speaker Change: Finally, I would like to thank the entire ILD team for their hard work and getting really to this point.

Harish Chidambaran: 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. The interest level in AI has been increasing, but doing things on your own is very expensive for end customers.

Speaker Change: I'd also like to thank all our board members advisors investors and everyone else that help make our public listing possible.

Speaker Change: We believe we are just getting started with a huge market opportunity ahead interest level in AI has been increasing.

Speaker Change: But doing things on your own is very expensive for end customers.

Farhan Naqvi: 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.

Speaker Change: 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 Change: I will now.

Hana Cui: Turn the call over to our CFO for Hana.

Speaker Change: To walk you through some more details on our financial performance in the second quarter.

Farhan Naqvi: 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. Revenue totaled 136 minutes, representing an increase of 33.9% from the year-ago quarter. Annual Reckoning Revenue increased 33% to Rs.

Hana Cui: Thanks, Rich and good morning, everyone.

Hana Cui: I will start by providing some highlights of our fiscal second quarter 2024 operating results.

Speaker Change: Question for several key balance sheet and liquidity measures.

Speaker Change: Thanks for your question regarding our guidance for the mid to longer term.

Farhan Naqvi: 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 ended June 30, 2024 was $94 million.

Speaker Change: Fiscal second quarter, ending June <unk> 2000 for any far.

Speaker Change: $136 million.

Speaker Change: Presenting an increase of $33, 9% from the year ago quarter.

Speaker Change: Annual recurring revenue increased 33% to $521 million.

Speaker Change: While net dollar retention on a trailing 12 month basis was 149, 5%.

Speaker Change: As of June 30th 24, the company had $4 9 million license and users.

Speaker Change: Gross profit for the fiscal second quarter ended June <unk> 2024 was $94 million.

Farhan Naqvi: An increase of approximately 32% from fiscal Q2 2022. ProSmartgen was 69.1%, down 160 basis points from the 70.3% recorded in Q2 2020. 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 cost. 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 2020.

Speaker Change: Increase of approximately 32% from fiscal Q2 2023.

Speaker Change: Gross margin was 69, 1%.

Speaker Change: Down 160 basis points from the 73% recorded in Q2 2023.

Speaker Change: The decrease in gross margins was due to an increase in new customer contracts.

Speaker Change: A slightly lower margins from the related onetime implementation.

Speaker Change: Operating expenses for the fiscal second quarter ended June <unk>, 2044, $179 million, an increase of $111 million.

Speaker Change: From the 68 million recorded in fiscal Q2 2023.

Farhan Naqvi: The increase is primarily due to the increase of 88 million for share-based compensation. Gap net loss for the fiscal second quarter ended June 30, 2024 was $314 million, compared to the 2 million lost in fiscal Q2 2020. The increase in net loss year over year was primarily due to the non-cash expenses associated with the business combination.

Speaker Change: The increase is primarily due to the increase of $88 million for share based compensation expenses.

Operator: Ladies and gentlemen, thank you for standing by. Welcome to ILearningEngines.

Operator: Second quarter, twenty-twenty-four 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 this session, you would need to press star 1-1 on your telephone. You will then hear an automated message about it in your hand is raised. To withdraw your question, please press star 1-1 again.

Speaker Change: The GAAP net loss for the fiscal second quarter ended June <unk>, 2024 was $314 million compared to the 2 million loss in fiscal Q2 2023.

Speaker Change: The increase in net loss year over year.

Speaker Change: It was primarily due to the noncash expenses.

Speaker Change: Shannon with the business combination.

Farhan Naqvi: The Non-Cash Expenses in QT, Change in fair market value of the convertible notes of about 170 million. ILearningEngines, Shared Based Compensation that was bested with a 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 2021. The drop in quarterly EBITDA year-over-year was primarily due to the non-cash expenses associated with the business combination, as explained above.

Speaker Change: The noncash expense in Q.

Speaker Change: Change in fair market value of the convertible notes of $170 million.

Operator: Please be advised that today's conference is being recorded.

Kevin Hunt: I would like now to turn the conference over to Kevin Hunt, investor relations. Please go ahead. Thank you.

Speaker Change: <unk>.

Speaker Change: Share based compensation divestiture with the business combination of <unk> 80.

Speaker Change: $88 million.

Jane: Jane in fair value of a make whole provision of $14 six from J J.

Kevin Hunt: Good morning and welcome to ILearningEngines. Second quarter, twenty-twenty-four financial results and corporate update conference call. Earlier today, ILE issued a press release announcing financial results for the second quarter and a June 30th, twenty-twenty-four. The copy of this press release is available on the company's website and through our SEC filings.

Jane: <unk> fair value of loan restructuring liability of $14 5 million.

Jane: And change in fair value of warrant liability of $37 million.

Jane: EBITDA for the second quarter was negative 313 million compared to the portion of <unk> 5 million in Q2 of 2023.

Kevin Hunt: With me today, our Rish Shadambaran, our chairman and chief executive officer, Bala Krishnan, our president and chief business officer, and Farhan Nakvi, 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.

Jane: The drop in quarterly EBITDA year over year was primarily due to the noncash expenses associated with the business combination and explained above.

Kevin Hunt: Our actual results and financial condition made different materially from those indicated in the forward-looking statements. For a list in the description of the risks and uncertainties that we face, please see the reports that we have filed with the SEC, including our quarterly report on form 10-fews of quarter-ended June 30th, twenty-twenty-four.

Farhan Naqvi: The fiscal second quarter ended June 30, 2024, and adjusted EBITDA was $4 million, a slight decrease from the $5 million in the fiscal second quarter of 2020.

Jane: For the fiscal second quarter ended June took gets credit for any for adjusted EBITDA was $4 million.

Jane: Slight decrease from the $5 million in the fiscal second quarter of 2023.

Farhan Naqvi: Adjusted EBITDA margin in Q2 of 2024 was 2.9% compared to the 5% in fiscal Q2 2022. The decrease in the district even 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 26th, with the increase due to share issuance for RSU and WTI debt repayment.

Jane: Adjusted EBITDA margin in Q2 of 2024 was two 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.

Jane: At quarter end, we had approximately 141 2 million shares outstanding and.

Jane: An increase of approximately $6 2 million compared to the $134 $9 million at the closing of our business combination.

Jane: <unk>.

Jane: With the increase due to shares issuance for Odyssey, and Doug MTR debt payoffs.

Farhan Naqvi: 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 the 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.

Jane: We know there are also outstanding warrants to purchase an additional 20 to one 7 million shares that will increase our share count as well as $1 3 million.

Kevin Hunt: 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 13th, 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.

Jane: <unk> restricted stock units.

Jane: Turning to balance sheet.

Jane: We ended the second quarter were $39 million in cash and long term debt consisting of $59 $3 million and a revolving line of correct.

Kevin Hunt: 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. Reconciliation of these non-GAAP financial measures to GAAP measures and certain additional information are also included in today's earnings, released in related supplemental slides, which are available in the Investor Relations section of our company website at www.iLearningsEngines.com.

Farhan Naqvi: 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 on the close of our business, Hello, and weIearning Engines. 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 the accordion provision of the original loan agreement. During the quarter, we repaid $24 million in debt owed to WTI.

Jane: Note that our cash balance compared to approximately <unk> 8 million in cash.

Jane: March 31, 'twenty ready for.

During the quarter, we announced gross proceeds of $35 3 million.

Jane: All of our business combination.

Jane: And good morning, $29 4 million from proceeds from convertible notes and $5 9 million from stock Cross proceeds.

Harish: 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 eye-learning engines.

Jane: On April 17, when Youre ready for $40 million from our commercial launch of East West Bank.

Jane: And on June 28th credit for any for <unk>.

Jane: Is an incremental $20 million in gross proceeds.

Harish: In April, we became a publicly traded company after completing our business combination with Admiralte Acquisition Corporation. In June, we were added to the Russell 3000 and 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 are 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 170,000 end users.

Jane: Accordion provision.

Jane: The loan agreement.

Jane: During the quarter, we repaid 24 million in debt <unk>.

Farhan Naqvi: Next, I would like to provide investors with a framework to help with the expectations of the mid- to long-term growth prospects for iLearningEngine, 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% rate. 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 growth margin to the mid-70s from around 70% today as our ILE engines become increasingly efficient.

Speaker Change: Next I would like to provide investors with a framework to help with the expectations of the mid to long term growth prospects are noting agents.

Speaker Change: From a top line perspective.

Speaker Change: You need to believe that we will.

Speaker Change: We'll grow our revenue above the rate of the oral.

Speaker Change: AI industry, which gartner predicts and 25% CAGR.

Speaker Change: From a margin perspective.

Speaker Change: We believe there is room to improve operating margins in the long term.

Speaker Change: We believe that unfortunate given increased gross margin to the mid seventies.

Speaker Change: DRAM, 70% per day.

Speaker Change: AI engine, becoming efficient.

Farhan Naqvi: We will continue to invest in R&D, especially in data. But we believe that the leverage would result in R&D falling to a 25 to 27 percent range of revenue over time from the low 30s today, and we expect SG&A to fall to around 30 percent 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 conferences in the coming months, including the Oppenheimer Conference and the Canaccord Growth Conference this week.

Speaker Change: We will continue to invest in R&D, especially in data.

Harish: Our CFO Farhan Nakhwi will provide details on the financials in a few minutes, but 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.

Speaker Change: But we believe that the leverage would result in R&D falling to 25.

Speaker Change: Percent range of revenue over time.

Speaker Change: Turkey's connect and.

Speaker Change: And we expect SG&A to fall to around 20% of revenue overtime.

Speaker Change: Turkeys in recent quarters.

Speaker Change: If you add up those pieces, we ultimately expect our margins to be similar to other leading.

Software companies.

Speaker Change: Finally, we hope to see investors at the conferences in coming months and joining the Oppenheimer conference and the Canaccord growth conference. This week.

Harish: 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 Gen-AI craze. We have been delivering AI solutions for learning and work automation at scale for over five years now.

Operator: During September, we will be at the City Investor Conference, the 11th Annual Benchmark TMT Conference, and the H. C. 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 on your telephone and wait for your name to be announced. To withdraw your question, please press star 11 again.

Speaker Change: In September we will be at the Citi Investor Conference the 11th annual Benchmark TMT Conference and the <unk> Tech Conference in New York.

Speaker Change: Operator, you can now open the lines for questions.

Speaker Change: Thank you as a reminder to ask a question. Please press star one on your chart look down and wait for your name to be announced.

Speaker Change: To withdraw your question. Please press star one again.

Operator: And our first question is going to come from Mike Lattimore with Northland Capital Markets. Your line is now open. All right, great. Good morning. Yeah, congratulations on the first call and stellar results here. Thank you so much. Bye.

Speaker Change: And our first question is going to come from Mike Latimore with Northland Capital markets. Your line is now open.

Harish: 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.

Mike Latimore: Hi, great. Good morning, Congrats on the.

Mike Latimore: First Colin stellar results here.

Speaker Change: Thank you very much Mike.

Harish Chidambaran: 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.

So maybe can you hurry.

Speaker Change: Talk a little bit about what youre seeing in terms of just customer demand.

Have sales cycles been.

Speaker Change: Stable improving shrinking.

Speaker Change: And maybe what verticals are particularly interesting right now.

Speaker Change: Yes so.

Speaker Change: Thanks, Mike So we're definitely seeing.

Harish Chidambaran: Thanks Mike, So we're definitely seeing, you know, a very high rate. Almost every enterprise out there is trying to figure out what AI means for their business. I feel like at the early stage during the Gen AI craze, there was a level of excitement.

Harish: These AI engines can be deployed quickly in weeks to months versus a year or longer with potentially millions invested for many internally developed corporate AI projects. And 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.

Speaker Change: Almost every.

Speaker Change: Enterprise out there is trying to figure out.

Speaker Change: What it means for their business.

Speaker Change: The early stage with.

Speaker Change: During the journey I, Chris there is a level of a type of tenders.

Harish Chidambaran: Companies have been investing a lot in POCs and trying to figure out what needs to go into production. But I think now we're really in the show-me phase, I think, with these enterprises. So, we're getting a lot of interest, 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 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.

Speaker Change: <unk> been investing a lot in poc's and trying to figure out what needs to go to production, but I think now it's really in the show me fees I think with these enterprises. So we're getting a lot of interest compared to sort of be faced with this option of.

Bala Krishnan: Let me turn it over to Bala, our president and chief business officer to walk you through a few case studies that would provide a better understanding of how companies are using eye-learning engines technology to solve their problems. Thanks, Parish. The first example is of a global manufacturer in 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.

Mike Latimore: What are the I really mean that my business at what we are really offering Mike.

Speaker Change: Most of these customers.

Speaker Change: The ability to rapidly.

Speaker Change: Deploy the platform rapidly product ties various use cases at scale.

Speaker Change: This allows them to.

Mike Latimore: The auto I am certain use cases, if it's working they can scale it up if not negative.

Harish Chidambaran: If it's working, they can scale it up, if not. [inaudible] 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 tetrothel, you know, a smaller amount.

Bob: <unk> scaled it down and so this ability of our out of the box platform and to apply product is really resonating with customers for Bob standpoint.

Speaker Change: We're seeing a lot of traction.

Speaker Change: Areas.

Keith: Education health care and really the market for enterprise hyper automation leaders Keith.

Bala Krishnan: Like many of our customer events, 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, work. ILE was brought into this account and the first step we took was to create an enterprise-wide AI knowledge cloud using the company's own internal content.

Speaker Change: Key areas that we're seeing a lot of interest.

Speaker Change: In terms of sales cycles, they've been pretty much very.

Keith: Very similar.

Keith: In terms of typically somewhere between six to nine months for the initial sales, but upsells Tetra Tech.

Keith: A smaller amount of time.

Harish Chidambaran: Yep, great, great. And your net dollar retention rate? continues to be very strong. Best in Class First, Ask Company News. Can you talk a little bit about what drives that, is this?

Speaker Change: Alright, great.

Speaker Change: Net dollar retention rate.

Speaker Change: <unk> to be very strong kind of you know.

Speaker Change: Best in class for SaaS companies can you talk a little bit about what drives that is it.

Harish Chidambaran: More users, more products, just what are you seeing in terms of driving your NDR numbers? So, the key thing here, I think one of the big drivers for our ND RS is the ability to add on new use cases and scale up existing ones. And that's really the big strength, you know; we are able to help.

Bala Krishnan: The ILE platform was then integrated with the company's existing systems which included an SAP ERP framework, success factors, BMC remedy and a number of homegrown databases as well as communication channels which included WhatsApp, mobile and intramet. 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 KPI cutting across organization structure, acquisition, recruitment and performance rating with many more such KPI's planned. The customer is saying improved operation metrics across the various strategic business units.

Speaker Change: More users more products just what are you seeing in terms of driving your NDA our number.

Speaker Change: So the key thing here I think one of the big drivers for our <unk> ability to Adam.

Speaker Change: New use cases and scale up existing use cases, and that's really the big strength.

Speaker Change: We are able to help.

Harish Chidambaran: 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 alternative chat is a custom bespoke solution. And so as a result, once they've deployed a few use cases, they're adding more and more use cases. It's a combination of scaling up these use cases and adding new use cases. That's really driving art in a dollar or ten.

Speaker Change: Company is build use cases.

Speaker Change: In a matter of weeks to months.

Speaker Change: <unk>.

Speaker Change: Months per year is that it takes.

Speaker Change: Most of our alternative to Chinese custom bespoke solutions.

Speaker Change: And so.

Speaker Change: As a result, what they've deployed a few use cases that I think more and more use cases and so.

Speaker Change: It's a combination of scaling up these use cases, and adding new use cases.

Speaker Change: That's really driving our net dollar retention.

Bala Krishnan: 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 the 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.

Harish Chidambaran: Great. And just last one for me on the channel partners. I think you said you're over 30 now.

Speaker Change: Great and then just last one for me on the Channel Partners. I think you said you are over 30 now.

Harish Chidambaran: 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 what's the plan there? And 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 Ang, the founder of Android, 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 solution. Appliance Makers for AI.

Bala Krishnan: 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 that accurately allowed it and the ILE 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 as 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 Change: 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 and then like what kind of vertical are you focused on for those.

Speaker Change: So for us.

Speaker Change: Really the value added resellers.

Speaker Change: The solution partners for Us I think.

Andrew <unk>: Andrew <unk>, the founder of Android had mentioned Becker's AI as the new electricity period.

Speaker Change: If you take that analogy.

Speaker Change: When you think of how.

Speaker Change: In the first half.

Speaker Change: Electricity being produced the choices for a producer was do I go to business with an asking me how many kilowatt hours do you want or do you go partner with the appliance makers.

Our systems et cetera people, who build solutions powered by <unk>.

Speaker Change: Electricity and so clearly these value add piece less R&D applied.

Harish Chidambaran: So they build a solution. And so for us, the more valuable resources we can add, the more high-performance applications that they can create. And these are players that have both horizontal capabilities and are also very vertical focused.

Speaker Change: Our plants make us far AI retro deep into solutions and so for us.

Speaker Change: The more.

Speaker Change: Thus, we can add the more hyper automation applications to dig in.

Speaker Change: Right.

Speaker Change: <unk>.

Speaker Change: These are players that have booked horizontal capability also very vertical focus.

Harish Chidambaran: You know, there are 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:

Bala Krishnan: 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 address retailers, wars that bring domain expertise in each vertical that we enter into and can build the solutions that addresses specific customer problems. These wars also have a lot of existing customers that we have been able to leverage.

Vehicles today.

Speaker Change: In each of these.

Speaker Change: Verticals these value added resellers or bringing a tremendous domain expertise and so everything is out of the box for a sofa every vertical we have.

Harish Chidambaran: ILearningEnginesEnginesEnginesEngines [inaudible] You know, 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.s are important to them, and so for us, this competition. Vars that can bring vertical specific expertise as well as horizontal players are very valuable as part of our strategy. Awesome. Great. I hope you will appreciate it. Thanks very much.

Speaker Change: Enterprise.

Speaker Change: Enterprise language models per vertical.

Speaker Change: Already pre program.

Speaker Change: Industry specific use cases for that vertical and so we're able to go into organizations.

Speaker Change: They pretty much no.

Bala Krishnan: We have 30 wars now. Our four largest wars have accounted for roughly 52% of revenue. As Harish noted earlier, we have minimal end-customer concentration. As through these wars, we service more than 1,000 end-customers with over 4.9 million end users.

Speaker Change: But this UK.

Speaker Change: Important to them and so for us.

Speaker Change: It's competition out of Boston.

Speaker Change: What's the complaint.

Speaker Change: Vertical specific expertise as well as the horizontal players are very valuable as part of our strategy.

Speaker Change: Awesome Great I appreciate it thanks very much best of luck this year.

Harish: 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 5-year growth rate of approximately 25%. And we also play in two other very large and growing markets, the global e-learning and high-pronommation markets. So there is plenty of growth opportunity for eye-learning ahead.

Mike Latimore: Thank you Mike.

Harish Chidambaran: Best of luck this year. Thank you. And the next question comes from Matthew Harrigan with the Benchmark Company. Your line is open, and I'm adequately. My connection is a little bit raspy.

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

Mike Latimore: Okay.

Operator: I had two capitals. Questgames and Matt, we can barely hear you. That could be the speaker for- I'm sorry, he did mention he had a very bad connection. Okay, reaction of the market to the s1 going effective, and I realize... It's delicate, but I thought the market we have been very misplaced since that was known, known, known. And I'm sucking away.

Speaker Change: EMEA adequately.

Speaker Change: All right.

Speaker Change: Capital structure questions one operating.

Speaker Change: Matt.

Speaker Change: Can barely hear you.

Speaker Change: But could you speak up.

Speaker Change: I'm sorry, he did mentioned he has a very bad connection.

Harish: What we are seeing is that the industry is evolving towards the approach that we have been taking for many years.

Okay.

Speaker Change: Wow.

Speaker Change: About.

Speaker Change: Reaction to the market.

Harish: Turning to the recent industry developments in AI, open AI company CEO Sam Alpman 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 giant, giant models. We'll make them better in other ways.

Speaker Change: One going effective in Europe.

Speaker Change: Yes.

Speaker Change: Yeah, I realize that that's a little delicate I thought the market yet.

Speaker Change: This plays into that.

Speaker Change: No no none if you will and then secondly.

Harish: At eye-learning, 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, IELI 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'll be speaking about the benefits of AI and eye-learning role in advancing AI technology in the insurance industry.

Operator: Thank you so much for joining us today at Warren Exercise to clean up the back balance sheet of the system. Thank you. Sure, Matt.

Speaker Change: Uh huh.

Speaker Change: Noncash warrant exercise.

Speaker Change: Can you help us.

Speaker Change: <unk> balance sheet.

Speaker Change: Thank you.

Harish Chidambaran: You know, to answer the second part of your question, the non-cash cleanup of the warrens, you know, those are all things that we are exploring. You know, in terms of the S-1, you know, as part of our business combination agreement with Arrow Root, we had investors come in that were post-effective, and then we had some, you know, fees that were equitized. And so as part of the agreement, we were, would have had, we had to submit a follow-on resale S-1 that went effective on Friday. And so it was something that we had to do as part of the D-SPAC itself.

Matt: Yes sure Matt.

Matt: Second.

Speaker Change: Part of your question.

Speaker Change: Noncash cleanup of the water.

Speaker Change: That we are.

Laurie.

Speaker Change: In terms of the S. One we.

Speaker Change: As part of our.

Speaker Change: Business combination agreement with idle routes we have.

Speaker Change: Mr <unk>.

Speaker Change: Came in post effective and then we have some.

Speaker Change: Fees.

Speaker Change: And so as part of the agreement.

Speaker Change: We would have had we had to submit.

Speaker Change: A follow on PCL as one.

Yes.

Speaker Change: <unk>.

Harish: Turning back to specific developments at IELI, in the second quarter, we added 108 new end customers and 170,000 new end-user licenses. We are very encouraged by our results in the second quarter and the overall momentum we see in the business.

It went effective on.

Speaker Change: Friday's and so it was something that we have to do for as part of base.

Speaker Change: The destock itself.

Harish Chidambaran: ILearningEngines, Beyond that, it's really hard for me to speculate on how the markets will react, and remember the operating side, and you've got a knife. Are you seeing anything new in the way? So, I think... What we are seeing and a very common theme when we go into enterprises is, you know, they typically, by the way, this is of interest to almost every CEO of every enterprise, AI, this is front and center for them when we go in.

Speaker Change: Okay.

Speaker Change: Beyond that.

Speaker Change: It's really hard for me to speculate on.

Speaker Change: The markets react to it.

Harish: Finally, I would like to thank the entire IELI team for their hard work in getting IELI to this point. I would also like to thank all our board members, advisors, investors, and everyone else that help 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 Change: Okay.

Speaker Change: As I anticipated.

Speaker Change: Yes.

Speaker Change: And then on the operating side I mean, you've got a nice catch algo.

Speaker Change: Certainly.

Speaker Change: How are you.

Speaker Change: Anything.

Speaker Change: Are you in the way of competition.

Speaker Change: Awesome.

Speaker Change: So I think.

Speaker Change: What we are seeing and common team, Matt when we go into enterprises.

Speaker Change: They have to.

Speaker Change: Peter.

Speaker Change: Maybe if I just look interest almost every CEO of every.

Speaker Change: Enterprise AI has become front and center for them.

Speaker Change: When we go in typically.

Farhan Nakvi: I will now turn the call over to our CFO for Hanakui to walk you through some more details on our financial performance in the second quarter. Thanks, Arish, 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 30th, 2024, revenue total 136 million, representing an increase of 33.9% from the year ago quarter.

Harish Chidambaran: They are, you know, they would have a team of a few AI engineers that are working on really, really use cases, but these are really very custom bespoke approaches, solutions built on top of, you know, an Azure or, and the language model, et cetera. And the challenge with the bespoke solutions is that they require expensive AI engineers.

Speaker Change: They are doing.

Speaker Change: Our team of a few AI engineers that are working on really.

Speaker Change: Bill It use cases, but these are really very custom bespoke approaches solutions built on purpose.

Speaker Change: And Azure are.

Speaker Change: <unk> model.

Speaker Change: Et cetera.

Speaker Change: The challenge with bespoke solutions.

Speaker Change: We had a quite expert sales.

Speaker Change: Engineers.

Harish Chidambaran: It takes a lot of time to build these use cases and test them. 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 to be solutioned or already solutioned means that they're able to deploy the platform in an enterprise very quickly and then build these use cases very rapidly, and that's a huge competitive advantage, and we are just seeing this becoming a stronger and stronger advantage because, for a lot of You must spend millions of dollars on AI, or else you'll be left behind. We don't think that's the right approach, nor do they.

Speaker Change: <unk> taken our time to build these use cases is very hard for them to.

The scale and really for us the stability of our platform since everything is out of the box and they are already ready is either a relief of solutions are already solution.

Speaker Change: It means that we're able to.

Speaker Change: Deploy the platform in an enterprise pretty quickly built it use cases very rapidly and thats a huge.

Farhan Nakvi: As of June 30th, 2024, the company had over 4.9 million license and users. Gross profit for the fiscal second quarter in June 30th, 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.

Speaker Change: Competitive advantage and.

Speaker Change: Seamless, becoming a stronger and stronger advantage because.

Speaker Change: <unk> put a lot of companies.

Speaker Change: And to figure out what we ask for their business and often they are being taught.

Speaker Change: You must spend millions of dollars on AI autos, you'll be left behind.

Speaker Change: We don't think that's the right approach not to date and so really we feel more and more of that we are at.

Harish Chidambaran: 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 offices and can reach a very, very nice first quarter as a public company. Thank you. The next question comes from Raj Sharma with Be Raleigh & Company. Your line is open. Hi. Good morning.

Speaker Change: A very important platform through which <unk>.

Speaker Change: Can be brought into the enterprise by these organizations.

Rich: Thank you rich.

Rich: Very nice first quarter as a public company.

Rich: Thank you thanks, a lot Matt.

Farhan Nakvi: Operating expenses for the fiscal second quarter ended June 30th, 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 net loss for the fiscal second quarter ended June 30th, 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.

Rich: And the next question comes from Raj Sharma with B Riley <unk> Company. Your line is open.

Raj Sharma: Hi, Good morning, Thank you for taking my question.

Operator: Thank you for taking my question. I wanted to understand your business, congratulations on becoming public in the 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?

Raj Sharma: I guess I wanted to understand.

Raj Sharma: Your business.

Congratulations.

Speaker Change: Regarding public in first call.

Speaker Change: Your business has very high gross margins.

Speaker Change: I'd like to understand if they're sustainable.

Speaker Change: And your business goes through Vars.

Speaker Change: How is how sticky are these relationships. So can you can you speak on the retention of your recurring business.

Harish Chidambaran: So, can you speak on the retention of your recurring business? Thanks, Raj. From our standpoint, one of the key things for us is that 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 us. And so that makes the platform inherently sticky. And as companies build more and more use cases, these are use cases that can be either part of one business unit or spread across business units, it becomes harder and harder to really replace.

Speaker Change: Sure.

Roger: Thanks Roger.

Farhan Nakvi: The non-cash expenses include change in fair market value of the convertible notes of about 170 million on April 16th. Share-based compensation that bested with the business combination of about 888 million change in fair value of a mature provision of 14.6 million change in fair value of loan restructuring library of 15.5 million and change in fair value of warrant library of 37 million. EBITDA for the second quarter was negative 313 million compared to the positive 0.5 million in Q2 2023.

Speaker Change #101: From our standpoint.

Speaker Change #102: Keeping scoliosis.

Speaker Change #102: We are.

Speaker Change #103: We get very deeply embedded inside an enterprise. So once we have deployed.

Speaker Change #104: It's really very hard for an organization to two disruptors and so that mix.

Speaker Change #104: The platform inherently sticky and as companies build more and more use cases.

Speaker Change #105: <unk> gives us that can be either part of one business unit are spread across business units.

Speaker Change #105: Becomes harder and harder to really reap.

Harish Chidambaran: And I think that's one of the inherent strengths of having a platform, an AI platform that embeds itself into 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 them. And so it does.

Speaker Change #105: A replay so I think that's one of the inherent strengths of Harvey.

Speaker Change #106: Our partners conduct.

Combat indexed to all the workflows.

Farhan Nakvi: The drop in quarterly EBITDA year-over-year was primarily due to the non-cash expenses associated with the business combination I'll explain above. For the fiscal second quarter ended June 30th, 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 increase operational expenses attributed to the infrastructure being put in place to support being a public company.

Speaker Change #106: Is that an organization. So that's really a big part for us whether it be.

Speaker Change #106: Go through a value added reseller or.

Speaker Change #107: We go.

Speaker Change #107: Directly.

Speaker Change #107: Makes us very sticky for us and so it does.

Harish Chidambaran: The other part to this is, since we are inherently things that are out of the box, you know, we are not caught up with implementation challenges. The big sign for us is because of our out-of-the-box capabilities. ILearningEngines is a critical, uh, continue to provide support, providing support to these enterprises is critical too. So I think that for us, given that we are stuck, it is really important that we continue to stay operational. Yeah, thank you. Thank you for that. And then this question is, perhaps, for Han.

Speaker Change #107: Sure.

Speaker Change #107: The other part of this is since.

Speaker Change #107: We are integrating some out of the box.

Speaker Change #108: We are not.

Speaker Change #108: Part of that implementation challenges so the big thing for Us is.

Because of our out of the box capabilities.

Speaker Change #108: Making it easier to implement and deploy.

Speaker Change #108: It's also easier for people to <unk>.

Speaker Change #108: On the other side the flip side is also true I think if we screw up.

Farhan Nakvi: At quarter end, we had approximately 141.2 million shares outstanding and increase of approximately 6.2 million compared to the 134.9 million at the closing of our business combination on April 16th. With the increase, new to share issuance for Odyssey and WTI debt bill. We note there are also outstanding warrants to purchase an additional 22.7 million shares that would increase the share count as well as 1.3 million in unwaisted 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.

Speaker Change #108: So the perpetual that too so I think for us.

Speaker Change #108: Turning to speed.

Speaker Change #108: <unk> focused operationally just continued to improve.

Speaker Change #108: Our operations is critical.

Speaker Change #108: <unk> continued to provide the support.

Speaker Change #108: Providing support to these enterprises.

Speaker Change #108: This is critical too so I think.

Speaker Change #108: For us given that we are sticky it's really important as we continue to see operationally focused.

Speaker Change #109: Yes. Thank you. Thank you for that and then.

This question is perhaps for Farhan.

Farhan Naqvi: You know, you mentioned the cash of $39 million, and then there's an AR of $91 million. I just wanted to understand the collectability of this AR from a working capital perspective. How should we expect to see billing and collection occur going forward? So... Can you hear me all right?

Farhan Duffy: You mentioned the cash of $39 million and then there is an EUR $91 million and I just wanted to understand the collectability of this EUR from a working capital perspective.

Farhan Nakvi: Note that our cash balance compares to approximately 0.8 million in cash as of March 31st, 24. 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 17th, 24, we raised 40 million from commercial loan from ESSA SPAC and on June 28th, 24, we raised an incremental 20 million in gross proceeds from the accordion provision of the original loan agreement.

Speaker Change #111: How do how should be expect to see billing and collection occur going forward.

Speaker Change #111: So.

Farhan Duffy: Can you hear me alright, yes.

Farhan Naqvi: 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 early. The cadence, obviously, you had a large loss this quarter, but the operating cash flow is significantly lower.

Farhan Duffy: Yes.

Farhan Duffy: So.

Speaker Change #112: We have 30, 60, and 90 day contracts and the connectivity so far has been pretty good.

Speaker Change #112: We are working towards bringing this down further so you would see more of this getting corrected.

Speaker Change #113: Got it so the cadence.

Speaker Change #114: You had a large.

Speaker Change #114: Loss this quarter, but the operating cash flow is significantly lower so.

Farhan Nakvi: During the quarter, we repaid 24 million in debt code to WTI.

Farhan Naqvi: Going forward, you should see the AR balance go down to a more reasonable negative working capital situation. As I said, we're working towards bringing it down. I wouldn't be able to give you an exact date as to when this will turn negative.

Speaker Change #114: So going forward you should see the AAR balanced go down to a more reasonable.

Farhan Nakvi: Next, I would like to provide investors with a framework to help with the expectations of the mid-to-long term growth prospects for eye-learning engines. From a top-line perspective, we continue to believe that we will grow a revenue above the rate of the overall AI industry, which got no predicts at a 25% cater. 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 margins to the mid-70s from the around 70% today, as our AI engines become increasingly efficient.

Speaker Change #114: Negative working capital situation or.

Working towards bringing it down.

Speaker Change #115: I wouldn't be able to give you an exact date as to when would this.

Speaker Change #116: Got it thanks guys.

Harish Chidambaran: Just to add to this, I think our customers, you know, like I said and Farhan mentioned, they have payment terms of 30, 60, and 90 days. We've had a very strong, the AR has generally been very good. We've had very few instances of customers not paying on time.

Speaker Change #117: Just to add to that I think.

Speaker Change #117: Our customers like I said.

Speaker Change #117: And Farhan mentioned payment of 30, 60 90 days.

We had a very.

Speaker Change #117: Strong.

Speaker Change #117: The AAR has generally been very good area and we are happy.

Harish Chidambaran: And so, as we continue to grow, I think the AR will grow, but the AR is 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-16. I just wanted to understand the cadence at which this balance likely comes down. Thank you. Thank you for the questions. I'll take this off.

Farhan Nakvi: We will continue to invest in R&D, especially in data, but we believe that the leverage will result in R&D falling to 25 to 25% range of revenue over time from the low 30s today, and we expect SGNA 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 a margins to be similar to other leading category killer software companies.

Farhan Duffy: A few instances of customers not paying on time.

Farhan Duffy: And so.

As we continue to grow I think.

Farhan Duffy: The payout will grow, but having to add as a percentage of our revenue.

Farhan Duffy: Manage that.

Speaker Change #118: Better, but that's really the reason.

Speaker Change #118: And part of it is really the terms that we have <unk> Nike with another <unk> got it.

Speaker Change #119: Yes, I just wanted to understand the cadence.

Speaker Change #120: This balance likely comes down. Thank you. Thank you for the questions.

Farhan Nakvi: Finally, we hope to see investors at the conferences in coming months, including the Open Hymn Conference and the Canacot Growth Conference this week.

Speaker Change #120: I'll take this offline. Thank you sure.

Operator: Sure. Thank you. As a reminder, to ask a question, please press star 11 on your telephone. And the next question comes from Batfleet with BTIG. Your line is open. Yeah, thanks. Matt VanVleet is on here.

Speaker Change #120: Right.

Speaker Change #121: As a reminder to ask a question. Please press star one on your telephone and then next question comes from that fleet with <unk>. Your line is open.

Farhan Nakvi: During September, we will be at the Silly Investor Conference, the 11th annual benchmark TMT conference, and the HCVN Ritec Conference in New York. I'll bet it.

Operator: You can now open the line for questions. Thank you. As a reminder to ask a question, please press star at 11 on your telephone and wait for your name to be announced. To withdraw your question, please press star 11 again.

Operator: 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? And how does that compare to, maybe, previous years?

Speaker Change #122: Yes, Thanks, Matt Van Vliet on here.

Speaker Change #123: Thanks for taking my question I guess as you look at the time that it's taking to train. The model is 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.

Mike Lattimore: And our first question is going to come from Mike Lattimore with Northland Capital Markets. Your line is now open. All right. Great. Good morning. Yeah. Congrats on the first call and stellar results here. Thank you so much, Mike.

Speaker Change #123: And then importantly kind of how where should we expect that maybe by the end of next year for instance.

Harish Chidambaran: And then importantly, kind of how, where should we expect that maybe by the end of next year? So back from our standpoint. ILearningEngines.com, you know, for us... Really, once we are inside an enterprise.

Speaker Change #124: So so back from standpoint.

Speaker Change #125: Everything we have is now out of the box.

Harish: So maybe, can you hurry talk a little bit about what you're seeing in terms of just customer demand? You know, have sales cycles been.., and 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 Gen AI craze, there's a level of excitement.

Speaker Change #125: Preprint.

Speaker Change #126: Models are perhaps that once they're deployed maybe what's happening inside the enterprises the ongoing.

Speaker Change #126: Fine tuning of these models.

Speaker Change #126: For us.

Speaker Change #126: Really once we are inside an enterprise.

Harish Chidambaran: 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 we look at this is every hyper app 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, and there is a leveling off that happens typically because we're doing much more than that. Automating simple processes, you know; we can automate complex workflows and things like that. So typically, that can go from 70, 80 to, you know, 85 or so, and then there is a leveling off.

Speaker Change #126: Like I said, each use case gets fine tune onto customers.

Speaker Change #126: Our data.

Speaker Change #126: And then as we add new use cases.

Speaker Change #127: They are also continuing that fight here so the one way how we look at this.

Every hyper apps.

Speaker Change #127: Yes.

Speaker Change #127: Our baseline intelligence. So when you start out let's say that number is.

Harish: 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 are they? I 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.

Speaker Change #127: 50, or 60 or 70.

Over time with the details we will continue to improve.

Speaker Change #127: There is a leveling off of that happens typically because we're doing much more than that.

Speaker Change #127: Automating processes.

Speaker Change #127: Automate complex workflows and things like that so typically that can go from 70 to 82.

Harish: And this allows them to test the ROI on certain use cases. If it's working, they can skate it up. If not, they can, you know, skate it down. And so this ability of our out-of-the-box platform and to rapidly productize really less needing with customers.

Speaker Change #127: 85.

Harish Chidambaran: But for us, 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 at 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, and smart risk management. And so within every, you know, most insurance companies will need any or many of these use cases or hyper- coordination apps.

Speaker Change #127: Eight.

Speaker Change #127: Leveling off but.

Speaker Change #127: Each of these use cases, we're starting out at it continues to.

Speaker Change #127: To improve and we have the benefit.

And this is really where are we.

Speaker Change #127: Particular edition of scaled really comes into play.

Speaker Change #127: Because let's say we are.

Speaker Change #127: And the insurance vertical.

Speaker Change #127: Our out of the box hyper apps could be things like claims intake claims processing loss prevention.

Harish: You know, from our standpoint, we weren't seeing a lot of traction in 3K areas, education, healthcare and really the market for enterprise, hyper automation. Or it just keep 3K 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 6 to 9 months for the initial sales, but upsells tend to take, you know, a smaller amount of time. Yep, correct, right.

Speaker Change #127: <unk> management and so.

Speaker Change #127: Most insurance companies will need in Europe many of these.

Speaker Change #127: Use cases are hyper automation apps and so they are like I said get fine tune onto customers on data.

Harish Chidambaran: 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 best prime that 85% as fast. Okay, helpful.

Speaker Change #127: We're constantly.

Speaker Change #127: Monitoring this and making sure that we can get from that.

Speaker Change #127: With that 85% as fast as possible.

Speaker Change #127: Yes.

Harish Chidambaran: 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 rest, 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, (inaudible) So, yeah, we definitely feel like these 12 verticals have tremendous opportunities within them, but we will also continue to add new verticals.

Speaker Change #128: Okay helpful.

Speaker Change #129: And then you mentioned your partners are operating in a total of 12 vertical wells in the three key ones for you internally, how should we think about sort of the other.

Harish: 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, 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 of existing use cases.

Speaker Change #130: The Delta between your top three in the 12 year 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.

Speaker Change #130: Today organically you will go after those two verticals.

Harish: And 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 alternative, which are these custom-based book solutions. And so, as a result, what they're deploying 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 our net dollar retention. Great.

Speaker Change #131: So yes, we definitely.

Speaker Change #131: These 12 verticals.

Speaker Change #131: The amount of opportunities within them, but we also will continue to add new verticals whenever we enter a new vertical we are partnering with.

Harish Chidambaran: You know, whenever we enter a new vertical, we partner with a channel partner, a value-added reseller, who is bringing that domain expertise, and we use that to really build our, you know, we call these enterprise models. Our enterprise models are both language models plus functional models for that vertical.

Speaker Change #132: <unk> channel partner, a value added reseller who's bringing back domain expertise and we use that to really build out.

Speaker Change #132: We call. This enterprise models, our enterprise markets of both language models plus functional models product vertical.

Harish Chidambaran: And then on top of that, 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. So obviously, it is easier to scale up an existing vertical, but adding a new vertical means we have to build these models, but this is an important focus for us today.

Speaker Change #132: And then on top of those you think dose we are building.

Speaker Change #132: Perhaps for that.

Speaker Change #132: Vertical selecting will continue to add.

Speaker Change #132: New verticals.

But definitely there is a huge opportunity here.

Harish: And just last one for me on the channel partner that you said you're over 30 now. How should we think about that going forward? Is that important to add, you know, sort of handful of those every quarter? Or, you know, what's the plan there? And like, what kind of verticals are you focused on for those? Anyone?

Speaker Change #132: Verticals.

Speaker Change #132: Scale, because we've already built the enterprise models and the initial setup iPad apps in the country.

Speaker Change #133: Hi, perhaps to those verticals.

Speaker Change #133: Verticals so.

Speaker Change #133: Obviously easier to scale scale up an existing vertical but.

Harish: So for us, really, the value added resellers are the solution partners for us. I think Andrew and the founder of Android 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?

Yes.

Speaker Change #133: Adding a new vertical means we'd have to build these models, but this is an important focus for us.

Speaker Change #133: Judy.

Harish Chidambaran: Education, health care, insurance, hyperautomation are big verticals, but all these other verticals also represent great opportunities, to continue to... Okay, and then just last question, you touched on the solid landed 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 [inaudible] I think there's definitely room for pricing improvement, you know, part of this is really for us.

Judy: Education Health care insurance hyper automation now.

Judy: Big verticals, but all these other verticals also.

Judy: It doesn't create opportunities for us to continue to build up.

Judy: Okay, and then just last question.

Speaker Change #135: You touched on.

Harish: Or do you go partner with the appliance makers, the engine systems, et cetera, people will build solutions powered by, you know, electricity. The ApplianceMakers for AI, right? So they build the solutions. And so for us, the more Value and Resilience we can add, the more high-pronination applications that they can create. And these are players that have both horizontal capabilities also very vertical focused, you know, very 12 verticals today. And in each of these verticals, these Value and Resilience are bringing in tremendous domain expertise.

Speaker Change #136: The solid land and expand motion you have going but curious on how an 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.

Speaker Change #137: Try to get in and sort of automate one group of workflows and then expand from there.

Speaker Change #137: I think there is.

Speaker Change #138: Definitely room for.

Speaker Change #139: Pricing improvement and a part of this has created for us.

Speaker Change #139: I think.

Harish Chidambaran: 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 prices. You know, four. The Next Set of Customers, with the net vertical. So for us, it really is. You know, that wire...

Speaker Change #139: Understanding the value, we are able to deliver to our customers. So I think this also ties.

Nice to expanding within the existing vertical because we are able to see the impact that we're creating and so allows us to better pricing.

Harish: And so everything is out of the box for us. So for every vertical, we have, you know, enterprise, our enterprise language models for vertical and, you know, 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.s are important to them. And so for us, this combination of words that can bring vertical specific expertise as well as, you know, the horizontal players are very valuable as part of our strategy.

Speaker Change #139: Sure.

The next set of customers.

Speaker Change #139: Okay.

Speaker Change #139: Vertical so for us it really is.

Speaker Change #139: Bye.

Harish Chidambaran: When we go in, we're typically, everything is out of the box, so we are able to... In our platform plus the set of use cases, we are talking about pricing. The $100,000 ish range as opposed to the million ish, but the ideas at once, uh...

Speaker Change #139: We do when we go in where typically.

Speaker Change #139: Everything is out of the box we are able to.

Our platform plus the set of use cases, we're talking about our pricing.

Speaker Change #139: No.

Speaker Change #141: 100000 screens as opposed to the 1 million ish range.

Speaker Change #142: The ideas at once.

Harish Chidambaran: 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.

Speaker Change #141: This has already been built out that think you'd be further scaled up. So if you think of an average enterprise they.

Speaker Change #141: You could have 100 200 use cases.

Mike Lattimore: Awesome. Great. I appreciate it. Thanks very much. That's the luck that's here.

Harish Chidambaran: And so... I think there's a great opportunity here to scale up those use cases, and they'll extract good value out of the engine. Thank you. I show no further questions at this time. I would now like to turn the call back to Harish for closing remarks. Thank you everyone for being here on this call. We completed our DSPAC in Q2, and, really, for us, this is our first earnings call.

Speaker Change #141: So.

Speaker Change #141: We think there's great opportunity here to scale up those use cases.

Operator: Thank you, Mike.

Matthew Harrigan: And the next question comes from Matthew Harrigan with the Benchmark Company. Your line's open. Thanks. I hope you can hear me adequately. My connection is a little bit racy. I had two capital structure questions and then one operating question. Matt, we can barely hear you. Matt, could he speak up? I'm sorry. He didn't mention he has a very bad connection. Okay. Wow.

Speaker Change #141: And extract the value out of.

Speaker Change #141: The engagement.

Speaker Change #142: Great. Thank you.

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

<unk>: Sure.

Speaker Change #143: Thank you everyone for being here on this call we completed our.

<unk>: This back in Q2.

Speaker Change #146: Really for US this is our first earnings call.

Harish: Can you talk about the reaction of the market to the S1 going effective in your business? Yeah. I realized that that's a little delicate, but I thought the market react very misplaced since that was known, known, known if you will. And then secondly, the possibility of non-cats weren't exercised to clean up the back balance sheet of the system. Thank you. Sure, Matt. You know, to add to your second first part of your question, the non-cash cleanup of the warrants, you know, those are all things that we are exploring.

Harish Chidambaran: We're really just wanted to have you out here, and we hope you found this call informative and useful. I just wanted to thank all the people with questions and really taking your time to be on our call. 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.

<unk>: We're really.

<unk>: Honored to have you all here.

<unk>: We hope you found this.

<unk>: Informative and useful in gist.

<unk>: One of the tank.

<unk>: All the people with quest integrity, taking your time to be.

<unk>: Joining us here. So thank you very much and we look forward to seeing you again in the future.

Speaker Change #146: This concludes today's conference call. Thank you for participating you may now disconnect.

Speaker Change #146: Okay.

Harish: You know, in terms of the S1, 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 acquitized. And so as part of the agreement, we were, we would have had, we had to submit a follow-on TCL S1 and that were effective on Friday. And so it was something that we had to do for as part of the, the despaque itself, you know. And beyond that, it's really hard for me to speculate on how the markets react to it.

Speaker Change #146: [music].

Speaker Change #146: Okay.

Speaker Change #146: [music].

Speaker Change #146: So.

Harish: So I think what we are seeing in a very common theme when we go into enterprises is, you know, they have typically, 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, really use cases, but these are really, really custom, these book approaches, solutions very top of, you know, an Azure or an language model, etc.

Speaker Change #146: [music].

Harish: And the challenge with the these book solutions is, they require expensive AI engineers, takes time to build these 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 solution or already solution means that they're able to deploy the platform in an enterprise very quickly and then build these use cases very rapidly.

Harish: And that's a huge company 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 they can do for their business. And often they are being told, 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, not today. And so really, we feel more and more that we are a very important platform to which AI can be brought into the enterprise by these organizations.

Matthew Harrigan: Thank you for reading very nice first quarter of the public company. Thank you. Thank you so much, Matt.

Raj Sharma: And the next question comes from Raj Sharma with Be rallying company. Your line is open. Hi. Good morning. Thank you for taking my question.

Raj Sharma: I wanted to understand your business, you know, congratulations on becoming public a first call. Your business has very high growth margins. And I'd like to understand if they're sustainable, I understand your business goes through bars. How sticky are these relationships? So can you can you speak on the retention of your recurring business?

Harish: 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're 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.

Harish: It becomes harder and harder to really replace. I think that's one of the inherent strengths of having a 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 or a 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.

Harish: So the big sign for us is, because of our out-of-the-box capabilities, making it easier to implement and deploy, it's also easier for people to see it results. On the other side, the flip side is also true. I think if we screw up, we could have the ripple effects of that too. So I think for us, continuing to stay focused operationally, just continuing to improve our operations as critical, continuing to provide support, providing support to these enterprises as 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.

Farhan Nakvi: And then, this question is perhaps for Han. 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? So can you give me a write? Yes. So we have 30, 60, and 90 day contracts, and the collectibility so far has been pretty good.

Farhan Nakvi: We are working towards bringing this down further. So you would see more of this getting collected earlier. Got it. So the cadence, 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 readable, you know, negative working capital situation or... As I said, you're working towards bringing it down.

Harish: I wouldn't be able to give you an exact date as to when would this turn negative. Just to add to this, I think our customers, you know, like I said, and for Han mentioned, they have payment of 30, 60, and 90 days. We've had a very strong... You know, the AR has generally been a 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 if the AR is a percentage of revenue, you know, will manage that matter. But that's really the reason. And part of it is really the terms that we have. Networks seen at 90. We've got it.

Raj Sharma: Yeah, I just want to understand the cadence that this balance likely comes down. Thank you. Thank you for the questions.

Operator: I'll take this offline. Thank you. Sure.

Operator: Thanks for that.

Operator: As a reminder to ask a question, please press store 11 on your telephone.

Ben Vlidon: And the next question comes from that lead with BTIG. Your line is out. Ben. Yeah, thanks Matt, Ben Vlidon here. Thanks for taking the question.

Harish: 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 is that trended over the course of this year and how is that compared 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 back from our standpoint, everything we have is out of the box. So these are pre-trained models or hyper apps that once you're deployed really what's happening inside the enterprise is the ongoing fine-tuning of these models.

Harish: 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're also continue to get fine-tuned. So the one way how we look at this is every hyper app 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 and there is a leveling off that happens typically because we're doing much more than just automating simple processes.

Harish: We can automate complex workflows and things like that. So typically that can go from 70, 80 to 85 also and then there is a leveling off. But for us so 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 in the insurance vertical our out-of-the-box hyper app could be things like claims intake, claims processing, loss prevention, smart risk 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 get fine-tuned on the customer's own data and we're constantly monitoring this and making sure that we can get from that baseline that 85% as fast as possible.

Harish: Okay, helpful. And then you mentioned your partners are operating in 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 today organically you'll go after those 12 verticals?

Harish: 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 a value add reseller who's bringing that domain expertise and we use that to really build our you know we call these enterprise models you know our enterprise models 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'll just continue to add more or more hyper apps to those verticals.

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

Harish: 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 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 trying 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.

Harish: You know, part of this is really for us. I think understanding the value we are able to deliver to a customer. 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 better pricing for the next set of customers. Within that vertical. So for us, it really is, you know, when we go in, we're typically everything is out of the box.

Harish: So we are able to, you know, our platform plus the set of use cases, we're talking about pricing in the, you know, the 100,000 is range as opposed to the million is 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 within this great opportunity here to scale up those use cases and they'll exactly value out of the engagement.

Harish: Great. Thank you.

Operator: I show no further questions at this time.

Harish: I would now like to turn the call back to Horish for closing remarks. Sure. Thank you.

Harish: Everyone for being here on this call. You know, we completed our these back in Q2 and really for us, this is our first earnings call. So 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.

Operator: 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 just connect.