Good day, everyone. My name is Abigail, and I will be your conference operator today. At this time, I would like to welcome you to the ExlService Holdings, Inc. June announcement conference call. We ask that you please hold all questions until the completion of the formal remarks, at which time you will be given instructions for the question and answer session. Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time. I will now turn the call over to Andrew Thut, Head of Investor Relations and Capital Mark ets. Thanks, Abigail. Hello, and thank you for joining us to discuss this morning's announcement of EXL's proposed acquisition of iMerit. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer of EXL, Radha Basu, Chief Executive Officer of iMerit, and Maurizio Nicolelli, Chief Financial Officer of EXL. We hope you've had a chance to review the press release we issued this morning that is also posted to our company website. As a reminder, some of the matters we'll discuss this afternoon are forward-looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, those factors set forth in today's press release and in EXL's filings with the Securities and Exchange Commission from time to time. EXL assumes no obligation to update the information presented on this conference call today. With that, I'll turn the call over to Rohit. Rohit? Good morning, and thank you all for joining us today. I'm pleased to announce that EXL has entered into a definitive agreement to acquire iMerit, a recognized leader in AI model training, evaluation, and reinforcement learning. iMerit is focused on helping its clients train large language and multimodal models for accuracy, precision, and effectiveness. We expect to close in the third quarter of this year, subject to satisfaction of customary closing conditions. This deal marks a significant milestone in EXL's data and AI strategy, further strengthening our position as a strategic partner for enterprise AI clients and now foundation model companies. This acquisition will bolster EXL's ability to help enterprises achieve important business outcomes fr om AI by combining EXL's deep data, context, and AI capabilities with iMerit's technology platform, expert-led solutions, and generative AI experience. As I shared at Investor Day last month, what is becoming abundantly clear is that every model deployed in an enterprise environment must be fine-tuned, evaluated, and continuously improved for the specific use case it serves, and that only happens through model evaluation, reinforcement learning, and expert human feedback. It is now a critical capability for any forward-leaning organization serving enterprise clients. iMerit and EXL together deliver exactly that, helping clients build AI systems that are trusted, accountable, and built to perform in the enterprise. iMerit has a proven track record of delivering strong growth across multiple long-tenured clients and innovating new AI technologies. We will connect their capabilities to our enterprise client base and broader data and AI platform to expand these solutions across larger, more complex client environments. We look forward to welcoming iMerit's CEO, Radha Basu, and her talented team to EXL. Let me now highlight three important reasons why this acquisition matters and why the timing is particularly compelling. Number one, iMerit's relationships with leading foundation model companies. Number two, it deepens EXL's vertically specialized AI capabilities. Number three, it expands EXL's total addressable market into high-growth AI tech sectors. First, iMerit's relationship with leading foundation model companies will place EXL at the center of how next-generation AI is being built. The next wave of models will need expert-curated multimodal data that does not exist in the public domain. This growing demand has fueled iMerit's strong momentum and is expected to drive growth in the years ahead. Additionally, foundation models are moving deeper into domain-specific applications. EXL and its clients will benefit from early insight into how AI is trained and improved. As the focus shifts towards token efficiency, latency, and accuracy, purpose-built small language models, as well as fine-tuned models will become the standard, particularly in regulated industries like healthcare, insurance, banking, and capital markets, where EXL is a trusted partner. Second, EXL's vertically integrated end-to-end AI capabilities, when combined with iMerit's expertise, deepens our ability to deliver trusted AI at enterprise scale. As enterprises increasingly demand agentic AI systems that are reliable, explainable, and continually improving, iMerit's capabilities become critical. EXL is already a leader in helping clients reimagine workflows, powering enterprise data infrastructures, and applying domain guardrails to enable AI-driven operating models. With iMerit, we will further strengthen resilience, reliability, and trust in agentic systems. iMerit enhances EXL's platform and human intelligence capabilities through its Ango platform and Scholars network. Ango powers sophisticated data interactions with GenAI models, enabling chain-of-thought reasoning, red teaming, and multimodal evaluations. Scholars network expands EXL's domain expertise through iMerit's global network of specialists, including physicians, scientists, engineers, linguists, and other subject matter experts. We will integrate Ango with EXL's agentic technologies, creating an end-to-end AI platform that helps enterprises accelerate AI adoption. Lastly, this acquisition broadens EXL's total addressable market into high-growth AI sectors, including high tech, mobility, and physical AI. iMerit's expertise across complex data types gives EXL the capabilities to build and scale specialized AI solutions for these adjacent markets now and into the future. We believe this combination strengthens our position in the fastest-growing areas of AI and creates a powerful platform for long-term growth. I will now turn it over to Radha to say a few words, after which our CFO, Maurizio Nicolelli, will give a brief overview of the deal terms. Radha? Thanks, Rohit, and thank you all for attending. It's a great moment for iMerit, and I'm excited about joining hands with EXL to realize our joint vision around data and AI. iMerit has been lucky to have a ringside view of the AI high-tech industry over several years. The markets we are in, high tech, autonomous mobility, healthcare, and physical AI, are evolving fast. Certainly, the fastest that I've ever seen in my career. We have learned to be agile, responsive, and tech-driven in our approach. This has leveled up even higher in our work with the foundation and frontier AI companies. The big AI firms that want to sell their foundation models into the enterprise need domain knowledge to build specialized AI systems and trusted relationships to sell them. The frontier labs are largely product companies. The question is: how do you move these models into the enterprise where trust, culture, and long-term customer relationships matter to embed AI into the business workflows? EXL plus iMerit provides that answer. What is exciting about joining EXL is the opportunity to take iMerit's experience and capabilities into a much broader enterprise market. EXL's relationships with large enterprises gives us a pathway into regulated, data-driven industries. To ach ieve high-quality data for model training, clients need both domain experts and a technology platform that captures it, evaluates it, and governs it. This is even more impactful as the models become more powerful and the business workflows become more demanding. That's where the differentiation comes in for us. Putting together solutions like prompting, chain-of-thought reasoning. Multimodal evals has taught us a completely new language of technology. EXL and iMerit, we envision a whole ecosystem that connects the fantastic work being done by our clients and foundation models to trusted AI deployments in large companies, and extends that into long-term production oversight and improvement. I'm very proud of the iMerit team, which has led innovation and established trust and leadership with customers over many years, and I look forward to continuing this energy as part of EXL on a much larger scale. I will now turn the call over to Maurizio. Maurizio? Thanks, Radha. I'd like to give you a few highlights of the deal and how it fits into our capital allocation strategy. The $310 million purchase price includes $170 million of upfront consideration and up to $140 million in incentives and earn-outs over two years, contingent on the achievement of specified milestones. Preliminary unaudited revenues for iMerit were approximately $59 million for the 12 months ended March 31st, 2026, driven by high growth in GenAI. We expect the acquisition to accelerate our growth rate and to not have a material impact on near-term profitability. We will update our guidance in the customary manner on our second quarter earnings call at the end of July. We enter this acquisition from a position of strength. The robust business momentum we started the year with has continued through the first half of 2026. The transaction is fully consistent with the capital allocation strategy we articulated at our May Investor Day, deploying our flexible balance sheet and strong cash flow generation to build capabilities and extend our competitive advantage. That means continuing to execute on our $500 million buyback program while also investing in intellectual property and capabilities that best serve our clients' evolving nee ds. We look forward to upd ating you on all aspects of our business during the second quarter earnings call. Finally, I would like to join Rohit in welcoming Radha and the entire iMerit team to EXL. We are incredibly excited about the opportunities ahead. With that, I'll turn the call back to Rohit. Thank you, Maurizio. In summary, iMerit will supercharge EXL's AI offerings and deepen our differentiation in the market. EXL is setting the standard for AI that is reliable, accountable, and built to perform at scale in the enterprise. We'll now open the call for questions. We will now move to our question and answer session. If you've joined via the webinar, please use the raise hand icon, which can be found at the bottom of your webinar application. When you are called on, plea se unmute your line and ask your question. We kindly ask that you limit yourself to one question and one follow-up. We will now pause a moment to assemble the queue. Our first question comes from Bryan Bergin with TD Cowen. Please unm ute your line and ask your question. Hi, guys. Good afternoon. Thank you for taking the question. I think the strategic rationale is very clear here. Maybe my first question will be on the financial implications, maybe digging in a little bit more here just so I understand. Can you just comment on the growth trajectory that iMerit has shown, maybe over more than a one-year timeframe? Just trying to understand if this has been a consistent type of an expansion or accelerating. Just as it relates to maybe the EPS implications, accretion, dilution in year one, anything like that, or synergies, anything more that just help us with the model a bit. Hey, Bryan, it's Maurizio. When you look at iMerit's business and the way they've built up the business, there's been very good growth, particularly over the last, I would say, three to five years. They've done a very great job in really pushing forward into the GenAI foundational model work that's really started to propel the business, and that'll be the big synergistic capability that we're go ing to embed into our enterprise workflows going forward. Overall, you've seen a very good trajectory over the last three to five years, and now it's really the opportunity for us to really embed it into our business and get that full synergy out. Okay. Bryan, let me just add to what Maurizio said. The financial trajectory of iMerit, number one, it's going to be a high growth business for us, and it's a profitable business. It's a business which is set up on good, solid financial fundamentals. The opportunity set of the combination of iMerit and EXL, we believe that is tremendous because it allows us to be able to serve foundation model companies, which are obviously growing very, very rapidly, work with some of the large high tech companies that are deploying AI in a very aggressive manner, and it opens up the TAM for us very, very meaningfully. Frankly, the opportunity set going forward into the future is very significant, and you can see the way in which this deal has been structured with a very strong earn-out component. It allows for the right kind of incentives and the right kind of alignment to be created between the two organizations. Okay. Very good. My follow-up is just on the nature of iMerit's contracting. Can you comment on, is this broadly kind of a T&M FTE model or a non-FTE model? I'm trying to think about the forward business visibility as it relates to kind of annuity type versus kind of episodic swings, depending on how kind of model training ramps and ramp downs go. Yeah. The business model is pretty much got both elements to it. It's got an ongoing nature of work, which acts like an annuity business model. There are elements which will be a lot more project-based as such. I think when we looked at iMerit, and we looked at the tenure of their customer relationships and the trajectory of growth with the clients that they've had for several years, that was very appealing because, keep in mind that the work that iMerit does, it's all focused in high growth companies, which need these capabilities in a pretty meaningful way. We are actually quite comfortable with the kind of revenue model that they have in place and how this will evolve over a period of time. Okay. Very good. Thank you. Our next question comes from Puneet Jain with JPMorgan. Please unmute your line and ask your question. Hey, thanks for taking my question. It seems like a nice asset, so congratulations there. Can you share if there is any customer concentration? Seems like healthcare is one of the large verticals. Maybe if you can talk about synergy opportunities you see with EXL's healthcare business. Sure, Puneet. Thanks for that comment. Like Maurizio said, the revenue of iMerit on an unaudited basis for the period ending March 31st, 2026, was $59 million. They work with several clients across the foundation model companies, across the Magnificent Seven, across the autonomous mobility companies, in healthcare, and in a number of different areas. They don't have any one single client, which is the predominant part of their business. Actually, the business is well distributed, and the growth of the business is coming in from a number of newer frontier model companies that iMerit is signing up independently, as well as from some of the existing clients that have relied on the quality of work and the kind of value that iMerit is delivering to them already. We think it's very broad-based as such. In terms of the synergies, yes, there are significant synergies in terms of the revenue growth across our industry verticals. Healthcare is clearly an industry vertical that is likely to leverage AI in a much more meaningful manner going forward. iMerit already does work with a number of clients in the healthcare area, and with our healthcare payers and with our clients, we think that there will be a fairly meaningful synergy there. No, that's great. Can you also quickly talk about the deal structure, like $170 and $140? Now portion seems high for typical deals. Can you talk about the goals, the targets that will drive that earn-out over in future? Yeah, Puneet. At this point of time, we just have a definitive agreement, and we've shared the broad structure of the deal. As we get to closing and as we get to providing you with updated guidance on EXL and iMerit together, we will provide you with additional color on those details going forward. The part that you referenced, which is the earn-out component being pretty strong, it's directly tied to the kind of growth and the value creation that we think that the asset can generate. It basically aligns both sides to be able to realize that revenue synergy and profit synergy as such. It's tied to both revenue and profit metrics, and it's over the next two-year period, and we believe that in that two-year period, the integration will be complete. Got it. No, that's great. Thanks. Our next question comes from Matt Dezort with William Blair. Please unmute your line and ask your question. Hi, team. This is Matt on for Maggie. Thanks for taking our questions and congrats on the acquisition. I guess can you double-click on iMerit's headcount and pyramid model? It seems pretty intriguing. Across full-time employees and active resources and contractors, I guess, and also how many fully deployed engineers does iMerit have on staff? Sure. Matt, the total number of full-time employees that iMerit has that will transition over to EXL upon closing is going to be approximately 3,600. In addition to that, there is a very strong network of experts that are part of the Scholars network and the Scholars program that iMerit has built and curated over the last couple of years. Those experts which are on the Scholars network, they are independent contractors, and they are not full-time employees of iMerit. iMerit uses them to get expert data and model validation and model evaluations done for being able to do a number of things to improve the accuracy, precision and effectiveness of large language models. In terms of the engineering headcount and in terms of the forward deployed engineers, iMerit has been building up that capability over the last couple of years. We've been actually very impressed with the kind of talent base that iMerit has put together, and we have been spending time with their teams. We think that their knowledge, their expertise in this area of model evaluation, red teaming, chain-of-thought reasoning, putting together solutions for both foundation model companies and enterprise clients, is actually very strong and very well established. Therefore, we are excited to partner with them and take this forward. We don't have a number to share with you on the forward deployed engineers, because that's a team that kind of keeps increasing. We'll be adding some of our staff alongside with iMerit to take this capability forward to the enterprise clients. Appreciate that color. I guess as a follow-up, how concerned are you about co-opetition versus competition with the frontier model providers? Obviously, they're expanding their offerings and opportunity sets that they're going after. I'm just wondering how insulated iMerit is from competition with the Anthropic and the OpenAI of the world. What's keeping the model providers from doing more of this upstream data work? Actually, this is a very strong partnership that aligns the goals of some of these foundation model companies and iMerit. iMerit helps these model companies become better. These foundation companies use iMerit's skills and capabilities in terms of improving on their models, going into more vertically specialized models and going into more domain-specific models. Frankly, there is no real element of competition that should be there. It's much more of a strategic partnerships. Now, Matt, one thing we should keep in mind is all the foundation model companies at this point of time seem to be devoting a lot of their energy, effort, and resources to taking these foundation models to the enterprise. That's a skill set and a capability where actually EXL can help these foundation models succeed in effectively deploying these foundation models in the enterprise. That's the opportunity set that we see in terms of this combination. The enterprises, on the other hand, some of them want to build their own small language models or specialized language models and be able to take the contextual knowledge and data that they have sitting within their organizations and be able to fine-tune and train existing models. Frankly, we see this as a n activity that can work both ways, which is helping the foundation model companies go into the enterprise and get adopted there effectively, as well as for the enterprises to be able to deploy their own models effectively within the enterprise and own that intellectual property. Thank you. Our next question comes from Surinder Thind with Jefferies. Please unmute your line and ask your question. Thank you. Rohit, just following up on the last question, this is just kind of a broader question about this idea of the success of the foundation models versus enterprises moving towards the more small language models. Does the shift in market share between large models, maybe if we move towards one or two large models, large foundational models, does that impact the growth rate of the business or the strategy for iMerit? Then maybe how much of this is also a strategic bet on what I would call the proliferation of small language models at this point? I'm just trying to understand the scenario or the conditions under which iMerit is successful and maybe where the risks are in the transaction. That's a great question, Surinder. Let me try and address that comprehensively. First off, if you just step back and you think about AI, it really at the very core boils down to data and the model that being in place to be able to make AI effective. You know that EXL has been investing in helping clients on their data readiness, and we've got a very strong capability on helping clients get their data ready for AI, and that component is in place. The AI model piece is what we are now investing in alongside with iMerit. To your question as to whether this is going to be something which the foundation model companies, how will they think about it and how might this evolve? How would the enterprises think about it and how that might evolve? Our view is that this is actually going to happen in a bidirectional way, it's not going to be one model versus the other model, rather using the right combination for the right use case, therefore there'd be some level of orchestration that's needed, where you can use a base level model for base level reasoning. You can use a specialized model for specialized work that needs to happen. It can be a contextually driven model that the enterprise owns for their own proprietary data sets and for their own proprietary work that they undertake. Just like you have an ability to manage infrastructure on the cloud or on-premise, whether it's a private cloud or a hybrid cloud, you're going to see model development take place in a similar pattern, which is going to be large language models, open source models, open weight models. There are going to be some specialized models, and then there are going to be some proprietary models that are going to get created. There's going to be a proliferation of these models and these capabilities across the board, and the right combination and the orchestration of the right combination is going to be critical. One other piece that I'd add to this, the entire world in AI is now moving towards agentic AI. The same elements that are needed for reasoning and improving the accuracy and efficiency of a large language model or an AI model, that same reasoning capability and evaluation capability is also going to be applied to agentic AI. Frankly, we are at the very early stages of how these models are being developed and how these will evolve and how they will progress, and then how it might be used for agentic AI as well. Our sense is, as this thing kind of evolves over the next one year, two years, and three years, you'll have a proliferation of models, and it's going to be using the right set of models for the right use case that's going to be critical. That's again, something which we would have expert knowledge because we would be helping some of these leading-edge foundation model companies deploy this capability. We'd be helping the enterprises deploy this capability. We are in the best position to be able to advise these clients as to how they can create these models and use these models. That's actually very helpful. Then just to make sure I fully appreciate the revenue model here for iMerit, is this primarily services at the end of the day, or do you also are building IP in the process that you can monetize on a non-FTE basis here? Yeah. First off, iMerit owns two platforms. One is the Ango platform, and the Ango platform makes model evaluation much easier, quicker, and cheaper to execute. That's a capability that continues to be developed, and that allows for iMerit to be much more effective when it partners with a foundation model company or an enterprise. The second is the Scholars network that's been created and the platform that's been created because the ability to curate experts, to be able to compensate them, to be able to evaluate them for their quality of work, and to be able to choose the right set of experts, that's very well-established, and therefore, the ability to scale up and scale down, that's very meaningful. In terms of the work and the revenue model for iMerit, that's something which we think there's going to be an enormous need for this to be deployed. As you know, so far, most of the model training has largely been on public data sets and things that have been available on the internet. Now, as you go into much more unstructured data, as you go into much more of images, video, and contextual data sets that reside inside of client organizations or inside the heads of experts. Also, as you go into the physical world and you go into physical AI, all of those data sets need to be brought together and be used to train models. Frankly, the need for this is really, really high and really strong. We think that there's a tremendous amount of work that needs to be done here. The question is, can you do that work smartly, cheaply, and at high quality? That's the foundation that iMerit has built, and that's what EXL plans to leverage, and scale that up in a very meaningful way. Thank you. Does that help address your question, Surinder? Yes, that was actually very helpful. Thank you. As a reminder, if you would like to ask a question, please use the raise hand function that can be found on the black bar at the bottom of your Zoom screen. Our next question comes from Jacob Haggarty with Baird. Please unmute your line and ask your question. Hey, guys. Thanks. Yeah, this is pretty interesting here. Just one question. It seems like it's going to be maybe dilutive to your revenue for employee numbers in the first year. How quickly can you improve that? Should we expect that to get back to growth pretty quickly, or is this going to be a headwind for a little bit? Yeah. Look, I think, first of all, we are focused in on the strategic capability that we are building out here and the potential opportunity set for us, which we think is enormous. Second to your immediate question, yes, this will be dilutive to the revenue per head count initially, keep in mind that this is a small piece of EXL's overall revenue base and overall employee head count. As we move towards more complex work associated with this model reasoning, as we do more work with data experts, the revenue per head count actually will be higher than the average revenue per head count that we have at EXL. The ability to use this for the dilution of the revenue per head count, that's going to be a very short-term phenomenon, and I think this will fall in line with the overall EXL revenue per head count metrics very, very quickly. Frankly, as we go into more complex work and as we go into more work that requires more expertise, this should be accretive to the revenue per head count numbers. No, that's helpful. Just as a quick follow-up here, just thinking through these two sides of helping the enterprises with the contextual models versus helping the foundational models. Is there a side of that that's larger now? Is there a side of that that has a larger opportunity going forward? I guess within that, is either of those two options more at risk or less at risk of being automated through further use of AI? Will the foundational models be able to use AI to do this in the future, or could the companies do it in the future? Just if you guys could touch on that a little bit. Yeah. Our sense is, over the next few years, the demand set for this kind of work is frankly unlimited. The enterprises are just getting started on thinking about their small language models or their specialized language models, but neither do they have the capability of creating this, nor have they built anything which is meaningful in this area. It's really being at ground zero with the enterprises. The foundational model folks are obviously ahead on this game, and they've been building up this capability very rapidly over the last year or so. Our sense is just by the volume of data sets that exist outside, which haven't yet been used to train their models, this is an enormous opportunity that's likely to continue for a while. The use of AI for model reasoning, model evaluation, and model testing and red teaming, I think that's bound to happen, and that's likely to play out. At the same time, you're going to have more edge cases and more places where you'll be looking at how do you triage this and how do you get to a space where it can be much more effective. It's going to require a combination of both reinforcement learning through human feedback as well as it's going to require some level of automation that can be built in as well. That's helpful. Thanks, guys, and congrats. We have no further questions at this time. This concludes our call. Thank you, and have a good day.