Ladies and gentlemen, good day and welcome to Cyient Limited's conference call for acquisition of TAO Digital. As a reminder all participant lines will be listen only mode, and there will be an opportunity to ask questions after the presentation. Should you need assistance during the conference call, please signal an operator by pressing star then zero on your touchtone phone, please note that this conference is being recorded. I now hand the conference over to Mr. Krishna Bodanapu. Thank you, and over to you, sir. Thank you very much, Michelle. Good morning, ladies and gentlemen, and welcome to Cyient Limited's investor call to discuss the acquisition of TAO Digital. I'm Krishna Bodanapu, Executive Vice Chairman and Managing Director, and present with me on this call are Sukamal Banerjee, Executive Director and CEO, Shrinivas Kulkarni, CFO, and Harjott Atrii, Chief Business Officer. I would like to mention that some of the statements made in today's discussions may be forward-looking in nature and may involve risks and uncertainties. This call will be accompanied by a short presentation, the details of which have been already shared with you. Firstly, I would like to thank you for joining us at a short notice. The acquisition of TAO Digital marks a pivotal moment in how our business is evolving. As you know, over the last 35 years, we have evolved with the markets, and we have evolved quite significantly with the markets. We started off as a digitization company, basically creating digital drawings from paper drawings. Over a period of time, we are very proud to say we've evolved, and we've taken worldwide leadership in being a pure play engineering services company, a leadership position that we continue to hold and that makes us quite unique in the market. As you know, over the last couple of years, the engineering space and what our engineering customers require has been evolving. I'll reflect back and give you a quick example. If for those of us who are fortunate to have driven cars in the '90s, or my daughter describes as being old, we remember that we had very few choices in terms of how a car worked. You were lucky to have shock absorbers, much less being able to adjust the suspension system. Whatever was controlled was controlled through some dials and knobs, which were mostly mechanical. Anyway, fast-forward to the vehicles of today. In most vehicles, you're able to control how the suspension feels. You're able to set an off-road setting, you're able to set a fuel efficiency setting, and many, many other modes. How does this happen? Though the ultimate system is a very mechanical or an electromechanical system driven by electronics and electricals, how we control the system is a software imperative or a software solution. We use the in-car device where we set, it's all touchscreen now for most part, but we use that to set these settings of a suspension system. What is happening, if I may quickly summarize, is what is happening is in engineering, we're seeing a lot more interplay of both software platforms and digital, and of course, as an extension of digital data and as an extension of all of that, the prevalence of AI and some of the AI applications that are coming in. My point is the need for our customers has evolved from being a pure play engineering solution, which is based on mechanical engineering, electronics, et cetera, to being a convergence between software, data, technology, and engineering. I think this gives us the opportunity to really strengthen our offerings in that interplay. I'm also very proud to say that we've already put in a lot of effort in this area. Already today, 8% almost of our revenue comes from the technology aspect, which again, I will describe as software, digital data, AI, the technology aspect, rather than the pure engineering aspect. Going forward, we believe that not just we believe. We are seeing and our customers are asking us to do more from this interplay and create capabilities around the technology and software that helps them do engineering better and control their products better or manage their products better or manage their aftermarkets better. Obviously, Sukamal and Harjott will take you through what this means and how the market is evolving. I just wanted to introduce the fact that this is a great opportunity for us to really lead this evolution in the market like we have done in the past transitions. As we transition from being a pure play engineering provider to being a leader in this technology convergence, again, I'm very proud to say that we've built some great capabilities, and TAO Digital will only enhance these capabilities. With that, I'll hand it over to Sukamal to talk about the deal and some of the details of the deal. Sukamal, over to you. Thank you, Krishna. Good morning to all participants on this call. Thank you again for joining at short notice. We will cover three quick aspects. I will touch upon the rationale behind this deal and how it helps us in the journey. As Krishna mentioned, what we are trying to achieve in this is first and foremost, it helps us position ourselves for a larger market and for larger deals. It aligns Cyient's pivot towards what we have been calling lifecycle engine. It simply means that we are not done in our association with our customers in just building the product and engineering the product. Take the product and depending on the industry, these products last anywhere between 8-10 years, in terms of its lifespan, to 40, 50, and in some cases, of infrastructure like plants, even 70-80 years. As time passes, what becomes more and more important is the data around engineering, the data of the product, the data of the processes, and the software platform, which harnesses all of this. Obviously, with the digitalization journey, this has become far more important, especially over the last 10 years. With the advent of AI, the ROI on harnessing this data is far more significant than it ever was. What it does for us also, it drives a shift from a narrow view of $100 billion TAM that we are in the outsourcing, to an estimated $2 trillion market in just the industries we play in without expanding into any additional industries. It aligns also very well with what our customers have been asking for, and also, more importantly, what is their growth journey. In many industries, like aerospace, as you are well aware, there are no significant new designs which are happening. What is happening in aerospace, in energy, in med tech, is a phenomenal growth in volumes in terms of number of assets and number of products that need to be simply delivered by them. What also needs to be done along with that is a significant amount of services, because that is where our customers make money. Essentially that's where the next three to five- year growth of our customers are, which again feeds back extremely well into this whole concept of why lifecycle engineering is so important and how it drives growth for us. We are already playing in this space, in certain sectors like aerospace, and in limited ways in others. We definitely, with this, want to harness and make sure that we can take it across all of our verticals and not just say limited in one or two areas only. Of course, it delivers the second point, annuity deals. It creates more of a longer-term predictable growth, moves away from project-based work to more multi-year, multi-tower deals. It also helps us get ready for what's coming up, and actively under consideration and discussions with several customers, which are outcome-based commercial models. It definitely positions ourselves for making sure that we can address customer needs beyond traditional engineering areas that Krishna called out, and I will let Harjott also talk more about that aspect, as he talks about how the two organizations fit together. The third point quickly, obviously just in terms of numbers, in terms of the portfolio mix that we have and something that I have discussed with several of you before. As Krishna mentioned, from around high single-digit areas, we are adding more than 10 basis points in terms of the mix of service line within the technology area for us. From high single digits, we are going to high teens in terms of the mix of business that is in a higher growth area. Fourth, it obviously brings in critical or fills critical gaps for us in an AI-led market shift. Again, I'll let Harjott go into more details, but this is directly relevant and we are seeing already a good amount of activity in the market where the advent of AI and the potential what AI can do to many of our existing business as well as new business areas where customers' backlogs are significant, need to be harnessed through more data engineering work and more importantly, platform software engineering work. That's what again, TAO brings to the table. Just a data point as you see over here, this is not just me talking about it. Our customers have been giving us the same feedback in our CSAT surveys for the last two years. In fact, this year, 79 out of 116, which is approximately 70% of our executives polled in our CSAT, gave that same feedback. Finally, as we talked about, and Krishna mentioned, it creates a capability scale. While we have been building it up on an organic level, this helps us create a more robust critical mass, in areas like data engineering and software engineering, which are critical engineering areas to drive this AI-driven shift. With that, let me invite Harjott Atrii, who's our Chief Business Officer for Strategic Initiatives, and over to you, Harjott, to talk about TAO and how it fits into our journey. Sure. Thanks, Sukamal. Good morning. Let me just, while you look at the fact sheet, let me quickly explain the three key capabilities which TAO Digital brings and how that impacts and brings about a step function change into our growth strategy as Cyient and TAO joint capabilities. Three key strengths which TAO brings on the table, is one, their understanding and their successful track record in the managing the entire lifecycle of the data, and hence getting the data ready for AI opportunities. Starting from data collection, data curation, annotation, but using tools, automated tools to do that cheaper, faster, better. Is one pillar of their data strategy and data capability. They go into data engineering, identifying what in terms of the AI models, what is required in terms of the storage architecture, the data performance, the data security, data gravity, data velocity, all those aspects in terms which go towards building the enterprise-grade data lakehouse, data warehouses, where they're already delivering work right now. Then eventually, in terms of using that data to build the ontologies and knowledge graphs to train the agents, and then deliver the outcome-based services to our customers, in industrial engineering. The entire lifecycle of the data, whether it's dark data, unstructured data, structured data, how do you collect it, curate, to store it, make it available for the vector databases, build the industrial-grade, enterprise-grade data lakehouse, data warehouses, and then run the analytics and engineering on top of it. That's one stream and that's one capability strand they bring on the table. The second aspect, the second strand is their ability to understand the science and the depth of new age software engineering and the platform engineering. We all know that the AI will drive more software and more code to be generated. Our customers are looking at in terms of their build versus buy decision, the skew is to move towards building to retire the tech debt and then rewire the organization and the enterprise workflows. Cyient understands how to build next gen platforms, how to engineer the next gen platforms which are composable, scalable, reusable, and are secure for AI products. Those products will be software products, our software products embedded into the industrial products. They understand that piece very well. The third part is, as we look at the AI opportunity, the talent pyramid is also changing, and it's evolving. No longer just the traditional developer stack or designers which were working in the pods are required for the AI projects. They have the scale and they have the talent, which allows us to build the new pod structures, new pyramids, which will bring in the concept of forward deployed engineering, which will bring in the concept of AI leads driving the customer ROI in business cases, and will bring in how do we bridge the human experience and the AI models to drive a business outcome for the customers. Three key pillars, the life cycle of the data, the life cycle of the platform and the software product engineering, and the life cycle of the talent and how we pivot from what was traditional talent management for software to AI age, new software engineering talent. With this, when we look at the market opportunity, if I just double-click on terms of what Krishna shared in his opening, is that across the board, what we are seeing, especially in the industrial enterprise, engineering customers, is that they're all being rewired. The competitive advantage is shifting from just pure play physical assets to how do you extract intelligence from the life cycle of the product. The change we are seeing across the board, and that's getting reflected in our customer conversations, that's getting reflected in the demand signals we have from the market in terms of the RFPs, and some of the deals we are solutioning and we're already delivering as well. The three, four key changes that we see which is happening in specific to industrial engineering customers is the concept of products is changing. It's not just pure play mechanical and hardware-centric products which are standalone with long development cycles. The shift is more towards software-defined products which are more connected intelligent systems, and there is a continuous telemetry for the products to understand the consumers and experience. The second part that's changing as the customers is that the consumption and how the consumers pay to our customer is changing as well. The customers are looking at no longer the asset ownership and just routine periodic maintenance. There's a continuous connected ecosystem. There's more predictive and proactive experience which our customers want to deliver, and they pay into as-a-service model. The monetization is changing, the consumption is changing, and the products are changing as well in our customer's business model across industrial engineering, whether it's aviation, aerospace, whether it's energy, or whether it's med devices or it's manufacturing equipment and all. What this really means is that the data and the software becomes the key across the board, whether it's engineering life cycle or whether it's service, active service life cycle for MRO and aftermarkets where we play a dominant role, or it's the quality and regulatory compliance life cycle. Just to give you a brief mapping in terms of where we create synergies and as we combine the forces with Cyient and how digital is, we understand because of our legacy in engineering, we understand not just the data, but the domain and the context behind the data. We bring something which is very critical for all AI journeys and AI roadmaps is how well we can bring the context and domain in the data, how well we can write the ontologies and knowledge graphs to train the agents and build the new AI enterprise agentic operating model for our customers. The other three blocks are more about data and software engineering, which we talked about, the agentic layer and the AI applications. We believe that this acquisition strengthens Cyient across both the upstream engineering foundations, where we understand the business case, the ROI, the intent engineering, as well as the downstream AI application delivery, where we create outcomes for our customers in the engineering life cycle. Whether it's product engineering or whether it's quality engineering, regulatory compliance, and the asset and service life cycle engineering. I will now hand over to Shrini. Thank you, Harjott, good morning, ladies and gentlemen. Thank you for joining the call early in the morning. I'll quickly touch upon the deal construct. I think this has already been published, I'll give you some additional color in terms of how the deal was negotiated. As you know, that 100% of the earn-out payouts, which are linked to the performance, the deal is valued at $218 million. The upfront is 60% of this value, about $130 million, which works out to 7.9 x the calendar year 2025 EBITDA of the target. This is to be paid at closing with 100% transfer of shares. We are acquiring the entire company. There are two tranches of earn-outs across two years post-closing. The EBITDA growth is a performance criteria for these earn-outs. This is an all-cash deal that is EPS accretive for us. It's also ROCE positive. The company will be a wholly owned subsidiary of Cyient Inc. I want to assure you all that the EPS accretion is after taking into account all the integration costs and the one-time transaction costs. Now, so therefore, after the integration is concluded in about one and a half years, this becomes substantially accretive. Now, the acquisition will be primarily funded through debt. We will use a combination of debt and equity, but primarily it'll be funded by debt. A large part of the debt will be serviced by the assets' free cash flow itself. The asset is a very healthy cash-generating asset, and they have very good conversion despite their growth. The other aspect of the earn-out performance is also the value lock on the synergy. There is an earn-out accelerator that sort of kicks in once the synergy EBITDA management incentive and retention schemes are there in place as additional support to accelerate the ACP. That's based on the deal construct. I'll now hand the call back to Michelle for moderating the Q&A. If you have any additional questions on the transaction structure itself, I'll be happy to take that during that. Thank you. Thank you very much, sir. Ladies and gentlemen, we will now begin with the question and answer session. Anyone who wishes to ask questions may please press star and one on their touch-tone phone. If you wish to withdraw yourself from the question queue, you may press star and two. Participants are requested to use only handsets while asking a question. Ladies and gentlemen, we will wait for a moment while the question queue assembles. You may please press star and one to ask questions. The first question is from the line of Moez Chandani from Ambit Capital. Please go ahead. Yeah, hi. Good morning, and thank you for taking my question. My first question was on the product philosophy for Cyient because of this deal. One of the capabilities that you seem to have acquired with TAO Digital seem to be what the traditional IT services companies have done. Is this a focus area for you going forward that you will use the traditional R&D expertise But right now, for the next three to five years, the focus is on maybe developing more traditional IT service capability? In a related note, in some of the large deals that you expect to win, the annuity deals, et cetera, that you expect to win as Cyient plus TAO Digital, who do you think will be your primary competitor for this? That is my first question. Yeah, Moez. Hi, this is Sukamal here. I'll answer the question and if there's any follow-up, I'll request Harjott also to chime in. Two things. One is, while technology level wise it may sound like traditional IT skills, I think, I will repeat what Krishna started with, that the boundary between IT and engineering, as in what product needs to succeed in its life cycle from a technology needs perspective are blurring. It has been blurring for quite some time. I think AI and advent of AI is accelerating that boundary to be blurred. As our customers build out more and more about how they service their products from a services perspective, and by services as in how they provide support services, maintenance services, how they do spare parts over and above just selling the equipment one time. Essentially, if I may use the common term used, the Gillette model. They make much more money not by selling the original equipment, by servicing the equipment and the parts and the supply chain and the after-sales over a period of time. That is what is creating a significant shift towards, from a business perspective, need for platforms which are integrated with the product, like Harjott explained. The telemetry of the product actually creates a rich source of data. To say that this is IT and this is engineering, I think is probably looking a little bit from a historical perspective. While the terminologies used in terms of technology skills may be similar, but the need and the domain experience that is critical to make that successful is entirely an engineering problem statement we are solving. We are not becoming a back-office IT company, trying to create HR applications or anything like that. We are still sticking to the product lifecycle. Does that answer your question? Sure. Yeah, that was fairly clear. Thanks so much, Sukamal, for that. Then just secondly, if I'm looking at the numbers that you have provided, it seems that you're expecting a fairly sharp improvement in EBITDA from CY 2025 to CY 2027. First, can I get a sense in terms of how you're expecting revenue growth and EBITDA margins to shape up over the next couple of years? Then also, the company seems to have a strong history of acquisitions, TAO Digital. What does organic growth mean for the company? I know it's just a four-year startup, so the time period is fairly limited, but any sense of organic versus inorganic growth for the past as well as for the future may be fairly helpful. Sure. Shrini, would you like to take that? Absolutely. I think from a growth perspective, what we have considered is fairly conservative compared to their history and past. I think the target has been growing at more than 100%, as you can see from the numbers in the last three years. Even the organic growth is a very big growth, right? The past three years' growth is very high for organic as well. What we have considered going forward is actually conservative in that sense, compared to their historical perspective. Even on EBITDA, I think we have made an assumption on the historical EBITDA, taking into account some of the integration costs that might come up during the course of the next few years, right? We have not assumed any growth in the EBITDA percentage per se. I think obviously there will be volume growth and therefore EBITDA will also come in at a substantial growth. These are the numbers I can provide. If you have any further questions on this, I can clarify. Sure. Any numbers in terms of what the organic growth has been for the past few years for the company? Harjott, do you have that number with you? Sukamal here, I can answer. It has been over 100% CAGR, year-over-year. Obviously, it has started, as you said, four years startup. Yeah. It started from a small base. On that smaller base, it has been 100% CAGR for the organic growth. Understood. Okay, clear. Organic, by definition, is obviously inorganic after 12 months becoming organic. Yeah. Okay. Understood. Thank you for that. Just the last question on my side, management continuity for this company post the acquisition. Do you expect the CEO for TAO Digital and the product management team to continue to be a part of Cyient, even after the acquisition has been completed? Sure. I'll take that. The answer is yes. As you see in the deal structure itself, there's a two-year earn-out, and it is substantially incentivizing the management team for that two-year earn-out. That is a clear lock-in which is there. Having said that, in terms of intent and the journey they are on and what they see ahead with us, there has been extensive discussion, and the intent is for the key management personnel to continue for sure, even beyond the two years. Our intent is to have them continue to be part of the journey because we see substantial growth in this area. Given this not being a bolt-on acquisition for us, this being almost a platform-level acquisition for us, we would like to see the management team continue beyond two years, and that is the plan. Okay, understood. Thank you for taking the question. Thank you, sir. Sure. Am I audible, sir? Audible now, yes. Yes. Maybe you're audible now. Okay, sir. We'll take the next question from the line of Sandeep Shah from Equirus Securities. Please go ahead. Yeah, thanks. Thanks and congrats on this target. Just the first question. I think if I look at the revenue size and the employee, the average revenue per employee comes out to be $33,000-$34,000, which is much lower than what we generally look at in the engineering R&D companies. How do you explain this? Is it largely offshore? What is your current EBITDA margin for this company? If I look at the upfront payment and the EBITDA we are targeting a 16%-17% EBITDA margin in CY 2027. How does it look like in CY 2025? Anybody like to go ahead? Yeah, sorry, I was speaking on mute actually. Sandeep, there is a component of the work which is in. Shrini, you are not audible. Sorry for this. Sir, I'm sorry to interrupt you. Mr. Shah, please mute your line. There is feedback from your line. Mr. Sandeep Shah, please mute your line. Sir, please continue. Sandeep, can you hear me now? Yeah, I can hear you. Thanks. Yeah. Yeah. Okay. All right. No, I was saying the billing rate appears low because there's a heavy offshore component. within offshore, about 40% of the workforce is also data digitization work. It is critical for the overall deliverable. that business is about 20% of the overall business. therefore, the billing rate appears a little bit low when you look at it on a consolidated basis. the other part of the business, the billing rates are very competitive. In fact, they are quite high and therefore, it sort of reflects in the margin as well. that was the question you had on the overall revenue per employee. What are the other questions, Sandeep, I missed? Yeah. I think the EBITDA multiple of 9.5x has been assigned for a upfront payment of $160 million. In that scenario, the target EBITDA margin will be 16.87% by CY 2027, which for a highly offshore company is lower. What is the current EBITDA margin and where we are looking in the coming years? Yeah. The upfront payment, Sandeep, clarify that. The EBITDA margins are closer to actually 20% for the. We have taken some haircut on the next two years from a modeling perspective because of some depreciation and other costs that might come in. Otherwise, the target is growing at 20 as well as pull down to that EBITDA. If you see the SE disclosure, we had given historical for the last three years. On the- Sir, I'm sorry to interrupt you. Can you repeat your last line because the audio was not clear for some reason? Okay. No, I was saying that if you look at our SE disclosure, we have given historical revenue for the company. You apply a 20% growth on those numbers and take a 20% EBITDA, we get the sort of target, the overall multiple that we are paying for that company. Okay. Shrini, just a clarification in the previous reply to my question on billing rate, you said 20% of the business is a low billing rate business. What is that? What is the nature of the same? That is a data digitization business process. Okay, where you convert the physical image into digital image. I think Harjott, you may want to explain what it is and how it is critical for what we are trying to do. Yeah. The data digitization business is primarily physical image to digital image is just one part of it. It's also about collecting the data, both the structured data, the unstructured data. Once the data and flow data pipeline is there, how do you define the data hierarchy in the model, cleaning the data as per the business rules, annotating the data, and then getting the data ready for storage into vector databases. This entire pipeline through which the data flows from pure create collection, curation, compilation, and annotation, whether it's video, whether it's unstructured social media data, whether it's unstructured engineering drawings, emails, legacy data sources, everything has to be made ready before any SLMs or LLMs can work and consume that data to drive any outcomes for the customer. Getting the data ready for AI and collecting as much as data as we can is the low-hanging opportunity in the market, which has a two to three-year runway for us. That's where they are very important because not only they manage it, the digitalization of it, but they're also bringing in tools to automate it and scale it up as the data volumes grow. Multiple sources of data, volume scaling up, and then getting the data into the target platforms from where it gets picked up by the agents for the training and inferencing is where the maximum value is where the biggest opportunity is for the next two to three years. If I may just add to what Harjott said, having seen this business for the last almost 30 years, from a data conversion perspective, I think it's very important to also realize that it's not a simple physical to digital. This is what we'd originally do. I'll say that the amount of annotations, the amount of associated information that goes with a piece of data, a point of interest, or what have you, has gone up at least 25x to 30 x in the last even 10 years. It's just not a simple conversion. The amount of data that's associated with the conversion and how the conversion happens, the automation, et cetera, I think is what TAO does very well. It will also help us with our existing business because they've developed tools, et cetera, which actually can add a lot of value. I think it's not just a simple conversion. My point is, it's not just a simple conversion, but the associated information that goes with that conversion and how that happens has changed quite dramatically, and therefore it's also important not to lose sight of that piece of the business. Thanks. Sukamal here, if I may just add, to be clear, while this is an explanation of your question, the value we are focused on is the other 80% of the business, and that's the driver for this acquisition. Yeah. Thanks. Just a follow-up. On that 80% of the business, is it predominantly focused on maintenance of the industrial products as an after-sale service, or is it also in terms of the production phase? Where do we play the role through this target company? Is it more pre-sales in the production phase or after-sale services in terms of an AI agent-led maintenance model? There are two parts to this answer. One is, what does TAO bring to the table which is of value to Cyient? The second is, what do they do? Harjott, if you want to explain it on both sides and answer this question. Sure, Sukamal. The 80% of the business which Sukamal referred to is primarily complex platform engineering. How do you build platforms for next-gen on which the software products could be developed? That part is one big part in terms of next-gen open interoperable platforms, making sure they're modular, they're composable. The platform engineering as a science and understanding that how do you build the platform business on which the software products get developed is one large part of the business. The second is, this component of the 80% of the business is high-end data engineering. Data digitization is just a wedge play, an entry point where we see a short-term opportunity in terms of volume scaling complexity. What comes as downstream business from that point is, how do you decide data velocity, data gravity? How will that data be consumed by the industrial workloads? Where will that data be stored? As we look at our customers' roadmap strategy in aerospace, energy, that data, that ton volumes of data, petabytes of data, will not go to public clouds because that's the secret sauce in terms of hundreds of years of product engineering data, which will be made available in the vector databases. They will build their own private AI infrastructure. They will need new architecture. That's what TAO understands very well. TAO understands the technical design and the architecture part of how to build next-gen enterprise-grade data lakehouse, data warehouses, how to understand the customer's business and identify what kind of performance is required, which workloads will consume the data, and as they consume the data, what inferencing decisions they will take. This is all part of the agentic AI operating system that is coming into a customer's organization. That is high-value, complex design engineering of data along with software and platform. Those are the three pillars which form the remaining 80% of the business. Let me take a pause before I go into how do we monetize this from our go-to-market strategy. I hope I answered the 80% question clearly. The last bookkeeping, I think Krishna, you in the Q4 call has highlighted the non-recurring charge on the M&A-related transaction may have some future acquisition. This is the one you were talking about, or there are more in the pipeline? Sandeep, the Q4 one was not related to this. There are some in the pipeline, for now, I think we will have a non-recurring charge this quarter as well coming in from this transaction, but obviously not to the extent of what we mentioned last quarter. Okay. That means some others are also in evaluation stage? Yes. They are in the early stages right now. Okay. Thanks, and all the best. Maybe if I just clarify the one that I talked about in Q4, we've put a pause on it. It was a different transaction. There's others, but not related to what we talked about in Q4. Thank you, sir, for answering those questions. The participant has left the queue. We'll move on to the next question from the line of Bhavik Mehta from JP Morgan. Please go ahead. Hi. Thank you. A couple of questions. Firstly, traditionally we have seen the buying center for engineering services and IT services being different. Now, the kind of services TAO Digital provides, and if you're taking those services for existing engineering clients, are we going to still deal with the R&D organization and the buying center, or do we need to start dealing with the CIO or the IT services organization from a client perspective? Yeah. Bhavik, Sukamal here. Your point is absolutely correct, that traditionally they have been different. Obviously, there has been some evolution with roles like chief digital officer and in many enterprises over the last, I would say seven, eight years. There have been other areas also which have evolved, roles which have evolved, like chief data officer, in many organizations. Firstly, to give a perspective on the lay of the land, there's obviously a variation depending on which enterprise we are talking about how these things are divided. There is a lot of decisions which is taken in the R&D organization. There are decisions which are taken in the chief data officer organization, in the chief digital officer organization, and in the CIO. Depending on the organization, the boundaries are different. Second is, a lot of what Cyient does is not just R&D. We work with the Chief Supply Chain Officers. We work with the Chief Services Officer or the aftermarket business leaders. We do a lot of work with the business units themselves, which may be regional business units or they may be market-specific business units, which may be focused on specific market verticals, where they put together multiple different products for a specific market. Like for example, what's happening with the AI data center, where suddenly energy OEMs have to bring together a lot of capabilities from different business units for one specific customer in a very rapid space. There are all kinds of these scenarios that Cyient has direct access to, where services platforms, like I was talking about earlier, becomes very important. The answer is, there are multiple buying centers. There are increasingly these new buying centers which are emerging. Yes, still the CIO will remain important, and in some cases, we do have to work with the CIO. We have successfully done that in some of our key accounts already in terms of winning businesses in the CIO organization or with the blessing of the CIO organization, a combination of them. It is a little bit of all of these aspects that we have to navigate. Okay. Got it. The second question is, when you say this deal is going to be EPS accretive, let's say in one and a half years, is this after the amortization cost and the interest cost with the debt? Just to verify that. Yeah. Yes, Bhavik. It is after the amortization cost, after the integration cost, after the interest cost for the debt as well. Yes. Okay. Thank you. Thank you. The next question is from the line of Dipesh Mehta from Emkay Global. Please go ahead. Yeah. Thank you for the opportunity. A couple of questions. First, just want to get a sense on the cost of management incentive and retentions. Since you indicated, what would be the likely implication on P&L because of that? Second question is the client concentration. Can you provide some sense what is the client concentration in the business? Lastly, it's about cross-sell, upsell opportunities concerning the buying center differences and other thing. How do you expect cross-sell, upsell to play out in the acquired entity as well as Cyient core business? Thanks. Maybe I can take the first question, Sukamal, and then I'll hand it over to you. The management incentive is a function of many things in terms of performance, the synergy aspects of it as well. It's not a big number. It's less than five percent of the overall deal value that we are talking about. That gets paid only if those outcomes are achieved. Sure. If I understand, your other part of the question was, how will what we talked about will get executed in terms of cross-selling? Yeah, cross-sell, upsell across, let's say, our client as well as their client. How do you expect some of these synergy benefit to play out, or you expect it to be very limited considering the buying center differences? Second was client concentration. Sure. To talk about cross-sell and upsell first, a key part of what we want to achieve, especially on Cyient customers, is to achieve that cross-sell and upsell into the Cyient customer base. We do believe it's going to take time. When we talk about the business plan as well as some of the earn-out, while there is a synergy target that the acquired management company will have, it is not a significant portion of the EBITDA because it takes time to convert to EBITDA. But we do expect that conversion itself will create significant momentum from year two, year three in terms of synergy of cross-sell and upsell into our accounts. That is definitely a part of the plan. In terms of the aspect of the accounts or cross-sell into their accounts, that is not something that we are prioritizing right now. Definitely something that we'll look into, because they do have significant enterprise accounts as well. Some of the leaders in the respective industries that they operate in. The business model that we are presenting and the valuation we are presenting is built on cross-sell into Cyient accounts. In terms of concentration of customers, it's fairly distributed. We don't have any single customer concentration risk. They work with enterprise customers and I would say they have built up some significant accounts which are in high, single-digit million INR amongst their top accounts, which makes up the constitution of the revenue for CY 2025. Any numbers for top five, top 10 you want to share? I don't have the number handy on top of my mind, but it's probably in the 60% range. Top 10. Thank you. Thank you. The next question is from the line of Abhishek Shindadkar from InCred Equities. Please go ahead. Hi. Thank you for the opportunity. Congrats on the acquisition. A quick question on the business model. Recently, one of the investors, we heard that in an agency model, a lot of data work, rather than being dependent on scripts and SQL rules, can be deployed in an automated format and autonomously. First, just wanted to understand if this is correct. Second, if actually this is correct, how does this change this current business model and also impact the future growth? Thank you. Sure. The answer to your question is all of them is yes. Harjott, you want to talk in terms of explaining the journey that we have in mind? Yeah. Yes, it's across the board in terms of how data will be consumed and deployed. The more important part is understanding of the business workflows and the workloads, which are supporting a certain function, which will consume that data. That's where the value gets created. In terms of how engineering functions vis-a-vis a aftermarket function or a service plan function, how do those functions consume the data. How do they use that data to build inferencing models to take their autonomous decisions, which the agents will be executing and even the human in the loop. That's the architecture we're looking at. Did I answer your question? Yes, partially. Just to follow up on that, if this data cleansing or a lot of data work gets automated, how do we see playing in that market or what would be our right to win in that market? Yeah. Data collection is a massive opportunity with a very limited runway for next two to three years. Across the enterprise, we are doubling the data we create every quarter. Every customer is doubling the data because of all the way to IoT edge, and the sensors built in the products and across the board. There is a massive data that's being created, so it has to be collected. We're already playing in that space. We also have TAO who's playing in the space. TAO brings in tools to quickly collect it, wrap it, and build the correlation models, build annotation models, define the hierarchies. We do have the right to win in this space, and we do intend to play to win that because that becomes a wedge play that's also an entry into the customer's overall data lifecycle. The next phase is data storage, data engineering, reference architectures, then building analytics on top of it, training the agents. Those are high value, high margin work streams. They are also sticky and long-term. That's where we have three and a half years into a three to five-year strategic roadmap for the customers. Data digitization, data collection, wrapped up with automated tools, and understanding of the context. We understand unstructured data very well because we have been within the customer journey for the last 25 years, managing their product engineering, their plant engineering. We understand that data very well. As TAO brings the tools, we can define the business rules, whether it's a mining customer, whether it's the aerospace customer, whether it's the energy customer, we understand their lifecycle. As you merge the domain and into the tools. Our proposition just to make an entry and win the data collection, data discussion work is much more compelling than the rest of the competition. And if I may just- Yes. Yeah. Harjott, thanks. I'll add just a couple of quick additional points. In terms, if you are trying to assess business model, yes, business model can shift. It has not happened yet, but it can shift from number of people and hours to amount of data being digitized. It's a very possible reality in the future, number one. As Harjott mentioned, the amount of data to be digitized is exploding. Also, a lot of this was earlier not done and left as is in terms of scattered data, even in Excel sheets, in some cases, in terms of plant data, is even in notebooks, as in physical notebooks. Those now have ROI because it can be done in an automated way, and you don't need hundreds and thousands of people to transcribe it manually. One is the explosion of the volume of data. Second is a lot of things which were left alone and not touched simply because it was not worth it from an ROI perspective now becomes interesting to do, apart from the journey downstream that Harjott articulated. Super helpful, sir. Thank you for taking my question, and best wishes for the rest of the year. Thank you. Thank you. Ladies and gentlemen, due to time constraints, that was the last question for today. I would now like to hand the conference over to Mr. Krishna Bodanapu for closing comments. Thank you, and over to you, sir. I'm sorry, sir. If you're speaking right now, you're not audible. Sorry, I was in mute. Thank you, Michelle, and thanks, everybody, for joining on a Monday morning. As we articulated, it's a very exciting phase of our evolution from being pure play engineering to managing or addressing the life cycle. I think the key point is, even in our customer's organization, the lines between engineering and IT and technology have been blurred. I think that's the key message I want to leave with you is it's not that linear anymore. The opportunity set is really in that convergence, and our customers are also organizing themselves for that convergence. We see this as a great opportunity. We see TAO as a great company. Though relatively early in their life cycle, they've achieved some fantastic results, which we believe that we can leverage. We are very, very confident of what's happening and what lies ahead with the integration of TAO Digital into Cyient. Thank you once again for listening to us. Thank you, of course, for your support, and we'll keep you posted on how things go. Thank you. Thank you, members of the management. Ladies and gentlemen, on behalf of Cyient Limited, that concludes this conference. We thank you for joining us, and you may now disconnect your lines. Thank you.