Greetings. Welcome to Innodata's second quarter 2024 results conference call. At this time, all participants are in a listen-only mode. A question-and-answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. Please note this conference is being recorded. I will now turn the conference over to your host, Amy Agress. You may begin. Thank you, Mike. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata, and Mariz Espineli, Interim CFO. Also on the call today is Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. We'll hear from Jack first, who will provide perspective about the business, and then Mariz will follow with a review of our results for the second quarter. We'll then take your questions. Before we get started, I'd like to remind everyone that during this call, we will be making forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, assumptions, and estimates and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release in the Risk Factors section of our Form 10-K, Forms 10-Q, and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward-looking information. In addition, during this call, we may discuss certain non-GAAP financial measures. In our SEC filings, which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliations of these measures with comparable GAAP measures. Thank you. I'll now turn the call over to Jack. Thank you, Amy. Good afternoon. It's great to be with you here today. We have several exciting updates to share on the financial and operational fronts. Innodata delivered another outstanding quarter, highlighted by the record revenue growth of 66% year-over-year. During the quarter, we significantly expanded our partnership with a big tech customer, while also gaining traction with others. We take great pride in the foundation we have built to establish Innodata as a leading partner of choice to deliver reliable, complex generative AI training data. We believe we are uniquely positioned to capture what we believe to be an enormous market opportunity and to drive value for our shareholders. Given the strong organic growth we are seeing, we are raising our 2024 full year guidance to 60% or more revenue growth, compared to the 40% growth we guided to last quarter. Also, as we indicated in the last quarter, in order to ramp up for our recent wins and anticipated growth, we invested in scaling our organization. Most significantly, we incurred $3.6 million in recruiting agency fees for a significant ramp in our workforce. As recruiting costs come down to a normalized level next quarter, we expect our third quarter margins to reflect strong unit economics and operating leverage from substantial revenue growth. Consequently, we expect third quarter Adjusted EBITDA to be approximately triple the $2.8 million Adjusted EBITDA reported this quarter. In a minute, I will describe some of the business we have won and some of the new business opportunities we are pursuing. Suffice it to say, we are seeing an increase in both the number and the magnitude of potential customer requirements, with which we reflect in our increased guidance. Therefore, we're taking steps to ensure that we have sufficient liquidity to accommodate working capital as our already substantial growth potentially accelerates. First, we have increased our receivables-based credit facility with Wells Fargo from $10 to 30 million, subject to a borrowing base limitation, with an accordion feature that enables it to expand up to $50 million, subject to the approval of Wells Fargo. Mariz, in her remarks, will give more color on the terms of this facility. I believe the Wells Fargo facility, as now extended, will be sufficient to fund our working capital requirements for our anticipated growth. That said, we want to be prepared to react quickly to customer demand that could result in us significantly exceeding our anticipated rate of growth and therefore having additional working capital needs. Toward that end, this afternoon, we filed a universal shelf registration statement on Form S-3 with the SEC. Once the registration statement is declared effective by the SEC, we will have the flexibility to sell up to an aggregate of $50 million worth of our securities in registered offerings pursuant to the effective registration statement. We believe it is prudent and good corporate governance to have an effective shelf registration statement on file with the SEC to preserve the flexibility to raise capital from time to time, if needed. As disclosed in the registration statement, we have no specific plans to raise money at this time. The intended uses for the net proceeds from any such offering would be set forth in a prospectus supplement. Now I'll give you an overview of the success we're experiencing in the marketplace with both existing and new customers. On 3 June 2024, we announced one of our existing Magnificent Seven Big Tech customers had awarded us two significant new LLM development programs. These programs are expected to deliver approximately $44 million of annualized run rate revenue and represents the single largest customer win in Innodata's history. These awards are in addition to the new programs and program expansion with this customer, announced on 24 April 2024, and 7 May 2024. In the one year that Innodata has been working with this customer, Innodata has landed new programs and program expansions that bring the total value of the account to approximately $110.5 million of expected annual run rate revenue. Innodata aspires to replicate this success across the six other Big Tech customers already contracted for generative AI development and to land additional Big Tech accounts. We won several other new assignments in the quarter as well, and we expect to land several others in the near future. Some notables include a Big Tech company that would be a new customer for us. It is one of the most valuable companies in the world and one of the companies most often talked about in connection with generative AI. Another is with an existing Big Tech customer. In connection with this opportunity, we would aim to become certified to work on their premises. We believe being co-located with their engineering and operations teams may potentially enable us to access new, attractive opportunities. We also expect to shortly sign a prominent social media platform that is building its own generative AI models and would be a new customer for Innodata. Another noteworthy win was with a clinical provider in the healthcare market. Up until now, we've been focused on the use of the Synodex platform as a tool for supporting insurance underwriting. This new engagement is the first time that we will be applying the platform in a clinical use case. We believe that the Synodex technology roadmap may enable us to expand to support additional clinical use cases in the future. We have also been awarded a deal to provide news briefs and media monitoring to a federal government agency that will be leveraging the new generative AI capabilities built into our Synodex platform. We are seeking to expand into the public sector, so we consider this a strategic win. We have started to integrate Agility with what we call PR CoPilot, our purpose-built generative AI layer that enables PR professionals to get more done in less time and at lower costs. While we're only about 30% into our roadmap for PR CoPilot, it is already delivering tremendous business value. This quarter, Agility revenue crossed $5 million mark for the first time. Our Agility demo-to-deal win rate in the quarter was 36%, significantly higher than the sub-20% win rates we were achieving prior to starting this integration. And we doubled our new business bookings in second quarter compared to the prior year period, even though we're operating with a leaner sales force. Now, before I turn the call over to Mariz, I want to share our perspectives on the generative AI market opportunity and how we have shaped our strategy to capitalize on where we see the market going. In our view, the big tech companies are clearly bullish about how generative AI technology will support their core products and services and enable exciting new opportunities. For the Mag Seven, capital expenditures in the latest quarter were up 63% year-over-year, with the bulk of these expenditures tied to generative AI spending. It is clear that the market sees under-investing as a greater risk than over-investing. In the not-distant future, we believe the technology will enable computers to reason and plan, to solve hard problems, and to self-organize in complex ways that help people accomplish their goals. Our belief is that generative AI technologies will soon sit deeply and ubiquitously in every tech stack. That's why none of the big tech companies can sit this one out. The shift in experience is destined to be too significant, making the risk of being left behind untenable. Just as the California Gold Rush began on 24 January 1848, the GenAI Gold Rush began on 30 November 2022, when OpenAI demonstrated to the world the power of training a deep neural net on enormous quantities of data and utilizing massive compute for inferencing. As a result, the world's largest tech companies went on the offense, committing to massive GenAI programs, solving for the next big market opportunity while simultaneously defending their hegemony. One analyst has forecasted $1 trillion of GenAI CapEx over the next several years. We subscribe wholeheartedly to the notion that in a gold rush, you want to be the person selling the shovels. The shovels required by the big tech companies in the GenAI gold rush take the form of compute and data. Compute is expensive and hard to come by, which is why we believe NVIDIA's market cap has skyrocketed over 7x to $2.6 trillion since the beginning of 2023. Data is also expensive and hard to come by. Once more, we believe the data that is likely to be required to train tomorrow's GenAI is going to become even more expensive and even harder to come by, and we believe that is Innodata's opportunity. The next generation of LLMs will be trained to handle more complex tasks and to be more agent-like. The complexity will take the form of models that handle difficult multi-turn tasks. For example, asking an LLM to find out how much vacation I have left and book me a trip. Complexity will also take the form of deep domain-specific tasks, like helping doctors diagnose disease or helping banks sort out complex regulation. Complexity will also take the form of models that enable users to work with audio, video, and text interchangeably. You'll hear this referred to as multimodal capabilities. Training data will be required to build models that can handle this complexity. Unlike web data that gets users halfway there for today's LLMs, these more complex LLMs are going to require a high quantity of high-quality data to be specifically developed to show the models how they're supposed to function. Right now, this data does not exist anywhere. It isn't on the web, it isn't on the cloud, it isn't on premises of enterprises. Because it is neither input nor output, it exists only in a transitory, unpreserved state. Its impermanence perhaps justified by its nature as byproduct. In other words, when we solve hard problems, we don't save our work. When we began building our own AI models and applying them to our managed services work in legal data and medical data, we had to build new workflow platforms to capture and preserve this interim knowledge in an organized way to be used to train our models. This was our eureka moment, when we realized that our breakout opportunity would be in creating this byproduct of human thought in order to train other people's models. To doing this as a science and a way that is repeatable and scalable is a huge opportunity, and we are still in the early days. We intend to be the preferred provider of complex demonstration data at scale, required to train models for complex reasoning, multimodal use cases, agentic Retrieval-A ugmented Generation, or RAG, and for domain specificity across all languages. Our competitive advantage is that for decades, we've been providing high-quality data across domains such as medical, law, regulatory, science, and finance. We are encouraged by the feedback from our customers, who already recognize that no single factor has as much influence on LLM performance as the quality of customized data for supervised fine-tuning. We will always be looking for ways to drive continuous improvement in how we operate, ensuring that our training data is both the best quality and the most economical. Now, on the enterprise side, we believe that in 18 to 24 months from now, enterprises will dramatically accelerate their generative AI adoption. We believe the catalyst for this will be generative AI that can tackle multi-phase tasks without losing its way, now often referred to as Agentic RAG, in combination with advanced open-source models, which significantly lower the bar for experimentation. These smaller but highly trained language models will likely prove ideal for enterprise applications that require high accuracy for specific tasks. Like the big techs, we believe enterprises will drive both offensive and defensive strategies to support their investments. The offensive play will be defining new product experiences, while the defensive play will be keeping pace with competitors, who we anticipate will work to enable their current products and reengineer their operations to be AI-first. Just as with the big techs, we believe enterprises will come to recognize that you've got to be all in, even with uncertain near-term ROI. A few years from now, we envision enterprises will face a shortage of experienced talent and may struggle to manage their internal data. Thus, the shovels for enterprise will be the people with the experience to help them choose the right architectures, the right approaches, and the right models, and to help them manage and deploy their internal data. Innodata's enterprise strategy is focused on this. Specifically, we see the opportunity to respond to these anticipated emerging ways, needs in three ways. First, for enterprises building their own capabilities, we will be ready to assist across the entire continuum of integration types and levels... from fine-tuning custom models to building Agentic RAG applications. As enterprises move GenAI services from development to production, they will need to know: How are the models working? Are they performing as intended? Are they, as they were intended, helpful, harmless, and honest? We see a big opportunity in helping them monitor their LLMs for alignment and safety. We're developing both services and platforms to respond to this need, driven by high-quality custom data. Second, for enterprises that prefer to outsource, we will make available managed services that are engineered to leverage the technologies. And third, for enterprises that prefer generative AI encapsulated in industry platforms, we will provide platforms specifically designed for industry-specific, knowledge-intensive workflows. In this way, we intend to serve enterprises at their highest point of value. I'll now turn the call over to Mariz to go over the numbers, and then Mariz, Aneesh, and I will be available to take your questions. Thank you, Jack, and good afternoon, everyone. Let me briefly share with you our 2024 second quarter financial results. Revenue for second quarter 2024 reached $32.6 million, reflecting a year-over-year increase of 66%. On a sequential basis, we observed a 23% of the increase of $6 million from first quarter of 2024 revenue of $26.5 million. Adjusted gross margin for second quarter 2024 was 32%, reflecting a sequential decrease from 41% we achieved in first quarter of 2024. This reduction is attributable to the $3.6 million of recruiting costs we incurred in the second quarter to support a substantial expansion for our organization to prepare for a significantly larger revenue base. When you separate this unusually high recruiting cost, adjusted gross margin in the quarter would have been approximately 44%. Similarly, adjusted EBITDA for the quarter was $2.8 million, a reduction from $3.8 million in first quarter of 2024. But without the $3.6 million of recruiting costs, adjusted EBITDA would have been $6.4 million or 20% of revenue. There are three things noteworthy. First, as Jack mentioned, we expect our adjusted EBITDA next quarter to be approximately triple the $2.8 million of adjusted EBITDA reported this quarter. Second, we have since enhanced our captive recruiting engine to enable us to reduce the cost of future larger-scale recruiting. And third, we expect quick payback on recruiting costs, typically within just few months, and strong ROI. Our cash position at the end of second quarter was approximately $16.5 million, up from $13.8 million at year-end 2023. Let me also elaborate a bit on credit facility extension that Jack mentioned earlier. We are indeed very pleased to announce that Wells Fargo has increased our receivable-backed credit facility from $10 to 30 million, with an accordion feature that enable it to scale up to $50 million, subject to Wells Fargo's approval. The amount drawable under the facility at any point in time is determined based on the borrowing base formula. The facility has an attractive cost of capital for amounts drawn under the line, set at SOFR plus 2.25%. We greatly appreciate Wells Fargo's confidence in our business and believe the extended facility will be sufficient to fund our working capital requirements for our anticipated growth. That said, we want to be prepared to react quickly to customer demand, which could result in Innodata significantly exceeding the rate of growth we have guided to today. With this in mind, this afternoon, we filed a universal registration statement on Form S-3 with the SEC. Once the registration statement is declared effective by the SEC, we will have the flexibility to sell up to aggregate of $50 million worth of our securities in registered offering pursuant an effective registration statement. We believe it is prudent and good corporate governance to have an effective shelf registration statement on file with the Securities and Exchange Commission to preserve the flexibility to raise capital from time to time, if needed. We have no specific plan to raise money at this time. The intended uses for the net proceeds from any such offering would be set forth in a prospectus supplement. In terms of preparing for accelerated growth, our expanded Wells Fargo line of credit and the flexibility provided by the shelf registration statement are expected to allow us to finance our short cycle, growth-driven working capital need for a revenue base significantly higher than our current projection. Thank you, everyone, for joining us today. Mike, we're open. We're open for questions. At this time, we will be conducting a question-and-answer session. If you would like to ask a question, please press star one on your telephone keypad. A confirmation tone will indicate that your line is in the question queue. You may press star two if you would like to remove your question from the queue. For participants using speaker equipment, it may be necessary to pick up your handset before pressing the star key. One moment please, while we poll for questions. Okay, we do have our first questioner, Allen Klee from Maxim Group. Yes, hi. Great job. This is just a clarification question. In your press release, one of the first things you say is, you won large language model development programs and expansion with a big tech customer, valued at approximately $87.5 million annualized run rate. Is that the same, is that a new contract, or is that, as you talk about below, the customer that you expanded with, that you say has a total of $110.5 million? Thank you. Correct, Allen. Those were the contracts that were announced during second quarter. So that was a recap of what was won and announced in second quarter. Okay, great. And then, some of these other new contracts, could maybe you talk a little bit about of the contracts you've announced, which ones are, just generally, how many are fully ramped up? Or maybe what percent you expect to get a greater contribution in the future? In second quarter, or generally speaking, going forward in the year? Generally speaking, going forward, you know, of... Sure. ...the announcements you've made, are there certain ones that could you give us a sense of like? Yeah, yeah. Yes, so there are seven big tech customers that we're contracted to do now to perform generative AI work, and that includes the one that scaled very, very nicely. Of the seven, I don't believe we're fully ramped up with any of them. I believe that they all hold tremendous opportunity for us to expand into, and I think we're going to be making significant progress along that path over the next several quarters. Okay. The other thing I would add to that is we also anticipate, as I mentioned in my remarks, landing another couple of big tech customers, which similarly will have the opportunity offer us the opportunity for significant potential expansion. Just following up on that, the guidance you give today, does that incorporate any contracts that haven't been announced yet that you may expect to win? So, I think the revenue from contracts that we haven't yet announced but expect to win can certainly accrue to third quarter and fourth quarter. When we provision our guidance, you know, there are a lot of puts and takes. We're baking in all sorts of factors into that, including new contracts. But again, if you take that in the aggregate, you know, we're comfortable that our guidance is conservative, and we think there's opportunity to exceed it. Okay, thank you. And then you mentioned that you're bringing the recruiting in-house, and that's gonna save money. So do you feel that, or is it that you still have to recruit a lot more, or that you can just do it more efficiently now that you're saving the money, and you feel comfortable about getting enough people to ramp up? Yeah, we're very comfortable in our ability to recruit. It's not that we won't use external agencies anymore. I think we still will, especially for, you know, particular kinds of recruiting. But we're very excited to have built an internal recruiting engine. You know, had we had that in place, we probably could have avoided the several million dollars that we had to spend in this quarter. But recruiting costs is, you know, it's an elegant problem to have. You know, we recruit primarily, reactively, pursuant to demand from our customers, and we get a very fast payback on those investments with a very high ROI. So, it's good that we're, you know, we've got a strategy and now facility to lower those costs prospectively. But even without lowering them, the ROI and the payback is very fast and very compelling. That's great. You talk about a bunch of contracts that you've won recently. In terms of what you're doing, is there anything different with them, of what part of the adaptation and training and monitoring? Are they kind of everything, or is there a certain focus that customers are looking for? So, they're a little bit of everything, but our strategy is very much focused on, you know, what I think of as three tiers, all of which are growth factors. You know, at the foundational layer, you have kind of the bottom tier. You've got the big techs and the ISVs who are developing generative AI foundation models. In the middle tier, you have enterprises who we're helping leverage generative AI, and then at the top tier, you have, you know, us building generative AI-enabled platforms for kind of niche industry use cases. So, in the things that I mentioned, there's a bit of a sampling of all three tiers. The tremendous growth that we're seizing on today is at that bottom layer. It's the enablement layer, working with the big techs. But we're aggressively planting seeds and earning referenceability in the other tiers as well, and especially long term, we see those as, you know, if we do things right, and we're planting the seeds properly today, we see those as things that enable our growth, you know, three or four or five years out from now. Got it. Thank you. You talked about Agility and adding Copilot and the benefits from that. Could you, I don't know if I caught everything, could you expand a little about what the value add of Copilot is and the opportunity you see of that growing Agility? Sure. So for, you know, in Agility, just for a little bit of additional context, we have about 1,500 customers, $20 million of ARR, about 17%, you know, 18% growth, I think year-over-year, 70% adjusted gross margin. So, you know, lots of operating leverage. We were performing very, very well. I mentioned that we doubled our bookings in the quarter with a sales force that I believe is about 15% smaller than it was last year. So very high performing, you know, very, very operated very efficiently. The idea behind PR CoPilot was that you could disassemble the workflow of PR professionals and enable them to use generative AI at multiple points in that workflow to enable them to do more with less resources for their customers, to be able to do more for less money. And as I mentioned, we're only 30% into the integration, meaning there are, you know, we'll call it, you know, eight different points within the PR workflow that we feel we can creatively leverage these technologies. And we've only gotten through a couple of them. We're planning on making an important announcement, probably within the next few weeks, about another, you know, element of that PR CoPilot integration. We're excited about that, and we've got every reason to believe that the improvement in the results that we're now seeing will be further accelerated as we further integrate that roadmap. Thank you. For Synodex, you mentioned that this is the first time you have a clinical application. Could you go into a little bit of what that means? Sure. So, what we're doing in Synodex is we're extracting at a very granular, very detailed level, medical information from patient healthcare records. And the use case that we've been working with up until now has been primarily life insurance underwriting and related insurance underwriting to property and casualty, and things like this. What we haven't done is had the technology layer that's sufficient to support clinical use cases. So, for example, analyzing, you know, patient medical records in order to make determinations about treatments, or make determinations about things that would occur and decisions that would be needing to be taken in a clinical use case, meaning, you know, hospitals and doctors and live patients. With this new win, and in conjunction with the development we've been doing in our technology, we now see the opportunities now and down the road to increasingly target clinical use cases. You know, we're very excited about that, obviously, because that would represent an expanded market. Got it. Last question. So you've mentioned that, the there's around $300,000 of recruiting costs that will be less in the second quarter— I'm sorry, the third quarter. If I was just thinking about expenses overall, is there any way to think about, like, like, do you, do you try to, like, think about, like, the operating leverage and to what degree maybe, operating expenses will grow at a lower rate than the top line? Yeah. So, I think the, the way to think about it is, you know, even if you, if you just look at this quarter, you know, our sequential revenue is up about $6 million, and our adjusted EBITDA net of that $3.6 million of recruiting costs was up about $2.6 million. So, that would be, you know, 2.6 over 6 is about a 43% flow through to contribution. Now, you know, obviously, that won't hold up for, you know, every quarter. There are puts and takes in, in any quarter, but I think it's indicative. Now, if you look at operating costs, one of the benefits to executing as aggressively as we are in the big tech market is that it's very concentrated. You don't need a lot of sales and marketing in order to work these accounts. You know, what you need primarily is great execution, and that's what we've been bringing to the table. So, you know, as you think about that contribution margin, which in this quarter would have been, you know, 43% or so absent the recruiting costs, you're not gonna consume a lot of that in SG&A. Now, you're not gonna. It's not all gonna show up as, you know, operating profit, but a lot of it will. That's great. Thank you, and really fantastic quarter. Thank you very much. Thank you, Allen. Appreciate it. We now hear from Hamed Khorsand with BWS. Hello, everyone. Thank you so much for taking my questions. This is Sarah calling in for Hamed at BWS. My first question is regarding the addition of the large tech company win that you announced today. Does this indicate that you're at all seven Magnificent Seven companies? So Sarah, first, thank you for being on the call. Welcome. Say hello to Hamed for me. What we've talked about is that we're in seven big tech companies, and of those seven, five of them are Mag Seven companies. Okay. Thank you. I hope... And then my... I hope I said this... Yes, yes. I know we need to be... Very clear. Thank you. Under NDAs, we can't use customer names, but it's, you know, five of the seven, Mag Seven, plus two others who are, you know, very important to notable customers in the GenAI market, but they're not Mag Seven customers. Right. Got it. Thank you so much. My next question is, other than the one large tech company that has given you the $110 million worth of work, where are you in the revenue recognition process with the other six large tech companies? I don't have a number for you, handy right now, but what I would say is we're, we're at an early stage. You know, we're seeing that accelerate with, especially with a few of them pretty rapidly now. And, you know, I think we're gonna see that acceleration through the end of the year on several. We believe, frankly, that, you know, all of the seven are gonna grow with us this year. So, you know, very early days, but very exciting. Our goal, of course, is to replicate the success that we've had with this big one, which was earliest out of the gate, with as many of the others as we possibly can. No guarantees, obviously, but, you know, we're competing against the same people, and we're bringing the same execution that has so differentiated us in this large account. Great. Thank you for that. My next question is regarding the previously mentioned, I guess, increase in recruiting costs. Do you find that you're still needing to hire people, or are you at a good headcount figure? And I guess, what is the timing of revenue to help offset the hiring and recruiting OpEx? Sure, great question. So the timing is pretty fast. It's really within a matter of a couple of months, and there's training that needs to go on. There's you know, other things that need to take place. But generally speaking, you know, it comes on very quickly, and that's why, you know, as an investment, it's as compelling as it is. I think you should think about the recruiting spend, you know, over time as you know, one that will be reactive. There'll still be a baseline spend that's you know, kind of always going on and in a normalized growth mode, and then when we get big lands like we did in the quarter, you know, $44 million win, our largest ever, you know, single win, then there's going to be concentrated spend. Now I'm hopeful that concentrated spend will be lower than it was this quarter by virtue of our captive recruiting capability that we've now put in place. But frankly, even if it weren't, you're not gonna ever find a better investment opportunity than that. Right. Thank you so much, and this will be my last question. So you are raising liquidity needs. Are you seeing extended net terms from customers? No, we're not. We're paid very quickly. So, you know, nothing, nobody's stretching us out. You know, all is good there. Now, when we do our modeling to determine our needs, we take a very conservative approach. If someone's paying us in 30 days, we'll model it at 60 days. If someone's paying us in 60 days, we'll model it at, you know, 120 days, just to provide that conservatism in our forecasts. But no, we're paid well and Maria makes sure of that. Okay. Thank you, Sarah. Thank you so much. Our next question is Tim Clarkson with Van Clemens. Hey, Jack. Obviously, I'm thrilled with the results. Great work. I'm sure you and Mrs. Abuhoff are very happy with all the hard work over the many years, and maybe I'm the third happiest person in the world about Innodata. But anyhow, getting into the business side. In terms of the, you know, these big contracts, you know, what's the magic elixir that's allowing you to get a contract of that magnitude versus the competition? So, I think there are several things. You know, I think, you know, if there's one thought that I'd ask you to, you know, kinda hold on to, it's that data and AI are inextricably linked. That's true with the big techs when you're training the models. It's true in the enterprises when you're, you know, fine-tuning or customizing or implementing, you know, RAG-based solutions. Now, you know, as you know, we've been in the data business for a long time. We've worked with the most demanding customers who are the most error intolerant in the world for years, and we learned how to keep them happy, and we were working across domains: tax, regulatory, legal, medical, healthcare, technical, financial. All of that is repurposable into this opportunity. All of that is what makes us, and we've been told, the number one provider for this largest account that we now have. Now, the good thing is that, you know, what we've learned and what we've developed as a platform is transferable. We're now engaged by these other accounts. We're bringing those same capabilities to the field and these other, the, these other competitions, and, and we have every reason to believe we'll be successful. Right. Now, I suppose a corollary of this is that, you know, when your large customers work with companies that don't have as accurate of annotation, they don't get the successful results. So, from their point of view, it's very risky to do business with someone where the data isn't as good as it should be. Well, yes, I, that's absolutely right. Garbage in, garbage out, bad data, it trains, you know, poor performing models, of course. I think there are two aspects to it. One is the data quality, and as I mentioned, the challenge of creating high quality data is only going to get more significant. As we move into Agentic RAG and these other applications and think about domain-specific models and more complex models, the challenge of finding training data is going to be more significant, and then the challenge of getting it right is going to be harder still. So, that's one of the differentiators that we bring. The other thing that we bring is just very reliable, very tuned execution. So, when someone's training a model, they're depending on getting that data payload on time when expected, 'cause they've reserved the data center, they've reserved the GPUs for that, those training cycles. If the data's late, if the data needs to be reworked, those GPUs are sitting, not being used, costing a ton of money. So, you know, we think of that performance on essentially those two vectors, data quality and data timeliness. And if we can execute well on both of those, we, as we have now, we become a very important partner to very important customers. Sure. Hey, just kinda give a profile, what would be the typical background of an employee in the Philippines or India that's doing this kind of work for you in terms of, you know, college degree, speaking English, how many years of experience? What would that look like? Yeah, so a couple of things there, Tim. Firstly, you know, as you, you well know, our, our legacy has been in hiring people offshore, you know, Philippines, Sri Lanka, India, have been locations of choice historically for us. Now, for, for this set of opportunities, we are hiring people in those locations, but we're also hiring a ton of people in other locations. We're hiring a lot of people here in the US, many hundreds of people here in the US. So, you know, our footprint and, and our profile relative to, you know, who we're hiring is changing dramatically from what you recall. Right. That's one. The other thing I'd say is, when we're building these teams, we're hiring kind of a pyramid of different skills and capabilities. At the base of the pyramid are people with very fine-tuned language capabilities, people who are linguists or you know, we have Ph.D.s, and we have master's and bachelor's in linguistics, computational linguistics, journalism, English. If the language that we're working within is English. People need to pass significant batteries of tests in order to be qualified. We then measure their aptitude for this kind of work, and then, of course, we put them through, you know, pretty extensive training and design in partnership with our customers, the kinds of workflows that we can parse out to people and have them, you know, be effective very quickly after being trained. So, the good thing about that is we believe we can keep on scaling and not face a, an impediment in terms of being able to recruit and staff. We believe the platform is extensible and can keep growing to support the growing customer base and the expansions that we're anticipating. Great. Great. Well, look, I'm speechless with how well things are going. Appreciate it. Let someone else ask some questions. Thank you. Thank you, Tim. We have reached the end of our question and answer session. We now turn the call back to Jack for any closing remarks. Thank you, operator, and thank you everybody who joined the call. You know, we've really never felt more bullish about our business and more enthusiastic about a market opportunity. Creating large-scale near perfect data is a hard technical problem. It took us years of development and a ton of trial and error, but that's why we've emerged as the preferred data engineering partner at our biggest big tech customer and why we're getting solid traction with other big tech customers. We believe large language models will not be built without training data. It's a critical must-have. We also believe that our differentiating capabilities will be even more highly valued as models become more complex and require more complex training data. If you remember, back in the middle of 2023, we said we were executing on a transformational strategy. We also said that our training data would be the foundation of making LLMs valuable. You're now seeing that with your own eyes in our results. Today, we are focused on growing Innodata to be a larger and more valuable company. We believe there are exciting days ahead, and we're really thrilled that you've chosen to be part of our journey. Thank you. This concludes today's conference, and you may disconnect your lines at this time. Thank you for your participation.