Cyient Limited ($532175)

Ladies and gentlemen, good day, and welcome to Cyient Limited's Conference Call for acquisition of Tao Digital. [Operator Instructions] 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. But as you know, over the last couple of years, the engineering space and what our engineering customers require has been evolving. I'll step 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 really -- 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 knocks, which were mostly mechanical. But anyway, fast forward to the vehicles of today, in most vehicles, you're able to control how the suspension feeds. 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 the settings of a suspension system. So 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. So 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 be a convergence between software, data, technology and engineering. And 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. And going forward, we believe that our customer -- 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 help 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. But 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. And 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 Power 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, and thank you again for joining at short notice. We will cover 3 quick aspects, and I will touch upon the rationale behind this deal and how it helps us in the journey. So 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 pivot towards what we have been calling life cycle engine. It simply means that we are not done in our association with our customers in just building the product. and engineering the product, but take the product and depending on the industry, these products last anywhere between 8 to 10 years in terms of its lifespan to 40, 50 and in some cases, of infrastructure like plants, even 70, 80 years. And 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. And 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 a $100 billion TAM that is ER&D 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. But 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. So essentially, that's where the next 3- to 5-year growth of our customers are, which again feeds back extremely well into this whole concept of why life cycle 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 stay limited in 1 or 2 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 multiyear, 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 engine areas that Krishna called out. And I will let Harjott also talk more about that aspect as he talks about how the 2 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 bps in terms of the mix of service line, which is in the technology area for us. So 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. And 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 engine work and more importantly, platform software engine work. And that's what again, TAO brings to the table. Just a data point, as you see over here, this is not just we talking about it. Our customers have been giving us the same feedback in our CSAT surveys for the last 2 years. In fact, this year, 79 out of 116, which is approximately 70% of our executives pull in our CA get that same feedback. Finally, as we talked about and Krishna mentioned, it creates the 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 engine and software engine, which are critical ingredients to drive this AI-driven shift. With that, let me invite Harjott Atrii, who is 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. But let me just -- while you look at the fact sheet, let me quickly explain the 3 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 managing the entire life cycle of the data and hence, getting the data ready for AI opportunities. So 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 capabilities, but 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 lake house, data warehouses is where they are already delivering work right now. And 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. So the entire life cycle of the data, whether it's dock data, unstructured data, structured data, how do you collect it, cure it, to store it, make it available for the vector databases, build the industrial grade, enterprise-grade data lake, data warehouses and then run the analytics and engineering on top of it. So that's one stream and that's one capability strength to bring on the table. The second aspect, the second strength is their ability to understand the science and the depth of new age software engineering and the platform engineering. So 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 SKU and is more towards building to retire the tech debt and then rewire the organization and the enterprise workflows. So 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. And those products will be software products, our software products embedded into the industrial products. So they understand that piece very well. And the third part is, as we look at the AI opportunity, the talent pyramid is also changing and it's evolving. So no longer just the traditional developer stack or designers which were working in the pods were required for the AI projects. So they have the scale and they have the talent, which allows us to build the new part 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 we bring in the bridge. How do we bridge the human experience and the AI models to drive a business outcome for the customers? So 3 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 new software engineering talent. So 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 in -- 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. So 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 3, 4 key changes that we see, which is happening in specific to industrial engineering customers is the concept of products is changing. So it's not just pure-play mechanical and hardware-centric products, which are stand-alone with long development cycles. The shift is more towards software-defined products, which are more connected and intelligent systems, and there is a continuous telemetry for the product to understand the consumers' experience. The second part that's changing as the customers is that the consumption and how the consumers pay to our customers is changing as well. So the customers are looking at no longer the asset ownership and just routine periodic maintenance. There is a continuous connected ecosystem. There's more predictive and proactive experience, which our customers want to deliver and they play into as a service model. So the monetization is changing, the consumption is changing and the products are changing as well in our customers' business model across industrial engineering, whether it's aviation, aerospace, whether it's energy, whether it's med devices or it's manufacturing of farm equipment and all. So 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 -- asset service life cycle for MRO and aftermarkets where we play a dominant role or it's the quality and regulatory compliance life cycle. So just to give you a brief mapping in terms of where we create synergies and as we combine the forces with Cyient and TAO 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. So we bring something which is very critical for all AI journeys and the AI road maps 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. And the other 3 blocks are more about data and software engineering, which we talked about, the Agentic layer and the AI applications. So 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, and 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, but I'll give you some additional color in terms of how the deal was negotiated. As you know, 100% of the earnout payouts which are linked to the performance, the deal is valued at $218 million. Now the upfront is 60% roughly of this value, about $130 million, which works out to 7.9x the calendar year 2025 EBITDA of the target. Now this is to be paid at closing with 100% transfer of shares. So we are acquiring the entire company. There are 2 tranches of earnouts across 2 years post closing. Now the EBITDA growth is the performance criteria for these earnouts. This is an all-cash deal and that is EPS accretive for us, also ROCE positive and the company will be wholly owned subsidiary of Cyient. I want to assure you all that the EPS accretion is after taking into account all the integration costs and the onetime transaction costs. So therefore, after the integration is concluded in about 1.5 years, this becomes substantially accretive. The acquisition will be primarily funded through debt. We use a combination of debt and equity, but primarily it will be funded by debt. And a large part of the debt will be serviced by the asset's 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 earnout performance is also value loss on the synergy. There is earnout accelerator that sort of kicks in once the synergy EBITDA are. And management incentives and retention schemes are there in place for additional support to accelerate the [indiscernible]. So that's a brief on the deal construct. I'll now hand the call back to Michelle for moderating the Q&A. And if you have any additional questions on the transaction structure itself, I'll be happy to take that during the Q&A.

[Operator Instructions] The first question is from the line of Moez Chandani from AMBIT Capital.

My first question was on the broader philosophy because of this deal. So a lot of the capabilities that you seem to have acquired with TAO Digital, seem to be what the traditional IT services companies have done. So is this a focus area for you going forward that you will use your traditional ER&D expertise? And then also -- but right now for the next 3 to 5 years, the focus is on maybe developing more traditional IT service capabilities. And then also 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 TAO Digital, who do you think will be your primary competitor for this? So that is my first question.

Yes, Moez, this is Sukamal here. I'll answer the question and if there's any follow-up, I'll request Harjott also to chime in. So 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 need 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. So 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 on time. So essentially, if I may use the common term, use the Gillette model, they make much more money by -- not by selling the original equipment, by servicing the equipment and the parts and the supply chain and the obsolescence over a period of time. So 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. So to say that this is IT and this is engineering, I think is a little too -- looking from a -- it's probably looking a little bit from a historical perspective. So while the terminology is 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 engine problem that we are solving. We are not becoming a back-end -- back-office IT company, trying to create HR applications or anything like that. We are still sticking to the product life cycle. Does that answer your question?

Sure. Yes, that was fairly clear. And 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 '25 to CY '27. So first, can I get a sense in terms of how you're expecting revenue growth and EBITDA margins to shape up over the past -- over the next couple of years? And then also, I mean, the company seems to have a strong history of acquisitions, TAO Digital. So how has organic growth been like for the company? I know it's just a 4-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 will be helpful.

Sure. Shrini, would you like to take that?

Yes, absolutely. So I think from a growth perspective, what we have considered is fairly conservative compared to the history in past. I think the target has been growing at more than 100%, as you can see from the numbers in the last 3 years. And even the organic growth is a very big growth, right? The past 3 years growth is very high for organic as well. What we have considered going forward is actually conservative in that sense compared to the 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? So 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. But these are the numbers I can provide. If you have any questions -- further questions on this, I can clarify.

Sure. Any number 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, 4 years startup. It started from a small base. So on that smaller base, it has been 100% CAGR for the organic growth.

Understood. Understood.

And organic by definition is obviously inorganic after 12 months becoming organic.

Okay. Understood. And just the last question on my side. Management continuity for this company post the acquisition, do you expect the CEO of TAO Digital and the broader 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. So as you see in the deal structure itself, there's a 2-year earn-out, and it is substantially incentivizing the management team for that 2-year earnout. So that is a clear locking 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 2 years. Our intent is to make them -- is to have them continue to be part of the journey because we see substantial growth in this area. And 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 2 years, and that is the plan.

Am I audible, sir?

You are audible now.

We'll take the next question from the line of Sandeep Shah from Equirus Securities.

Congrats on this target. Just the first question, I think if I look at the revenue size and the employees, the average revenue per employee comes out to be $33,000 to $34,000, which is much lower than what we generally look at in the engineering R&D company. So how do you explain this? And is it largely offshore? And what is the current EBITDA margin of this company? Because if I look at the upfront payment and EBITDA which we are assigning, we are targeting 16% to 17% EBITDA margin in CY '27. So how does it look like in CY '25?

Shrini would you like to go ahead?

Sandeep, there is a component of EBITDA of the work which [indiscernible].

Sir, I'm sorry to interrupt you. Mr. Shah, please mute your line. There is a follow-up from your line. Mr. Sandeep Shah, please mute your line. Sir, please continue.

Sandeep, can you hear me now?

Yes, I can hear you.

So I was saying the billing rate appears low because there's a heavy offshore component. And within offshore, about 40% of the workforce is also data digitization work for the overall deliverable. But that business is about 20% of the overall business, right? And therefore, the billing rate appears a little bit low when you look at it on a consolidated. But 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, right? So that was the question you had on the overall revenue per employee. What are the other questions, Sandeep? I missed it.

I think the EBITDA to EBITDA multiple of 9.5x has assigned for upfront payment of $160 million. So in that scenario, the target EBITDA margin is 16.8%, 17% by CY '27, which for a high offshore company looks lower. So what is the current EBITDA margin and where we are looking in the coming years?

So for upfront payment, Sandeep, we have to clarify that. Then the EBITDA margin are closer to actually 20% for the company. We have taken some haircut number at 2 years from a modeling perspective with [Technical Difficulty] come in. Otherwise, the target is growing at 20% [Technical Difficulty]. If you see the SEBI disclosure we had made [Technical Difficulty].

Sir, I am sorry to interrupt you. Can you repeat your last sentence because the audio was not clear.

I was telling that if you look at our SEBI disclosure, we've 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 -- overall multiple revenue we are paying for it.

Okay. Just a clarification of previous reply to my question on billing rate. You said 20% of the business is low delivery business. What is that? What is the nature of this?

That is data digitization business.

Okay, okay. So where you convert the physical image into digital image.

I think, Harjott, if you may want to explain what it is and how it is critical for what we are trying to do?

Yes. So the data 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, unstructured data. And then once the data inflow data pipeline is there, how do you define the data hierarchy in the model, cleaning the data as per the business rules, allocating the data and then getting the data ready for storage into databases. This entire pipeline, which -- through which the data flows from pure play collection, curation, compilation and annotation, whether it's video, whether it's unstructured social media data, whether it's unstructured engineering drawings, e-mails, 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. So the data, getting the data ready for AI and collecting as much data as we can is the low-hanging opportunity in the market, which has a 2- to 3-year runway for us. And that's where they are very important because not only they manage it, the digitization of it, but they're also bringing in tools to automate it and scale it up as the data volumes grow. So 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 2 to 3 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 the -- it's not as simple sort of physical to digital, which 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 25 to 30x in the last even 10 years. So 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. And it will also help us with our existing business because they've developed tools, et cetera, which actually can add a lot of value. So 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.

Here, if I may just add to be clear what the value that we are -- 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.

Yes. Just a follow-up on that 80% of the business, is it predominantly focused on maintenance of the industrial products as an after service? Or is it also in terms of the production phase? So where do we play the role through this target company with more presales in the production phase or after sales services in terms of AI agent led maintenance model?

So there are 2 parts to this answer. One is what is TAO bringing to the table, which is of value to Cyient. And the second is what do they do? So Harjott, if you want to explain on both sides and answer this question?

Sure, Sukamal. So 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. So that part of -- is one big part in terms of next-gen open interoperable platforms, making sure they are modular, they're composable. So 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 part of their large part of the business. And the second is a big component of the 80% of the business is high-end data engineering. Data digitization is just a wedge play entry point where we see a short-term opportunity in terms of volume scale and complexity. But what comes back, 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' road map strategy in aerospace, energy, that data, the 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. So they will build their own private AI infrastructure. So they will need new architecture. So 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 lake house, data warehouses, how to understand the customers' business and identify what kind of performance is required, which workloads will consume the data and as they consume the data, what inferencing decision they will take. So this is all part of the Agentic AI operating system that will -- that is coming into a customer's organization. So that is high-value, complex design engineering of data along with software and platform. That -- those are the 3 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.

Last bookkeeping. I think, Krishna, you in the Q4 call have highlighted the nonrecurring charge on the M&A-related transaction may have some future acquisition. So this is the one you were talking about or there are more in the pipeline?

The Q4 one was not related to this. There are some in the pipeline. But for now, I think we will have a nonrecurring charge this quarter as well coming in from this transaction, but not -- obviously not to the extent of what we mentioned last quarter.

Okay. So that means some others are also in evaluation stage.

Yes, but they are in the early stages, right? But if I just clarify the one that I talked about in Q4, we put a pause on it. It was a different transaction, but there's others, but not related to what we talked about in Q4.

We'll move on to the next question from the line of Bhavik Mehta from JPMorgan.

So 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 to 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?

Yes. So 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, 7, 8 years. There have been other areas also which have evolved -- roles which have evolved like Chief Data Officer in many organizations. And so firstly, to give a perspective on the lay of the land, there's obviously a variation depending on which enterprise you are talking about, about how these things are divided. So 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. And 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. So there are all kinds of these scenarios that Cyient has direct access to where services platforms, like I was talking about earlier, becomes very, very important. So the answer is there are multiple buying centers. There are increasingly these new buying centers, which are emerging. But yes, still the CIO will remain important. And in some cases, we do have to work with the CIO. And we have successfully done that in some of our key accounts already in terms of winning businesses in the CIO organization or in -- with the blessing of the CIO organization, a combination of that. And so 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 1, 1.5 years, is this after the amortization cost and the interest cost the debt? Just to clarify that.

Yes. It is after the amortization costs, after the integration cost, after the interest cost for the debt as well, yes.

The next question is from the line of Dipesh Mehta from Emkay Global.

A couple of questions. First, I just want to get sense on the cost of management incentive and retention scheme 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? And lastly is about cross-sell, upsell opportunities, considering the buying center differences and other things. How do you expect cross-sell upsell to play out in the acquired entity as well as client core business?

Maybe I can take the first question, Sukamal, and then I'll hand it over to you. So the management incentive is a function of many things in terms of performance, the synergy aspects of it as well. But it's not a big number. It's less than 5% of the overall deal value that we are talking about. And that gets paid only if those outcomes are achieved.

And if I understand your other part of the question was how will -- what you talked about will get executed in terms of cross-selling?

Yes, cross-sell, upsell across, let's say, our client as well as their client, how do you expect some of the synergy benefit to play out? Or you expect it to be very limited considering the buying center? And second was client concentration.

Sure. So to talk about cross-sell and upsell first, a key part of what we want to achieve, especially on signed customers is to achieve that cross-sell and upsell into the signed customer base. We do believe it's going to take time. So when we talk about the business plan as well as some of the earnout, 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 for year 2, year 3 in terms of synergy of cross-sell and upsell into our accounts. So 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. but definitely something that we look into because they do have significant enterprise accounts as well, some of the leaders in the respective industries that they operate in. But the business model that we are presenting and the valuation we are presenting is built on cross-selling to client 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 we have built up some significant accounts, which are in high single-digit million dollars amongst their top accounts, which makes up the constitution of the revenue for CY '25.

Any number for top 5, top 10 you want to say?

I don't have the number handy on top of my mind, but it's probably in the 60% range, top 10.

The next question is from the line of Abhishek Shindadkar from InCred Equities.

Congrats on the acquisition. A quick question on the business model. Recently, one of the Investors Day, 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?

Sure. So the answer to your question is all of them is yes. Harjott, do you want to talk in terms of explaining the journey that we have in mind?

Yes. So 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 our engineering function vis-a-vis aftermarket function or a service plan function and how do those functions consume the data. Is there -- and how do they kind of use that data to build inferencing models to take their autonomous decisions, which the agents will be executing and with the human in the loop. That's the architecture we're looking at. Did I answer your question?

Yes, partially. But just to follow up on that, if this data cleansing or a lot of data works gets automated, how do we see playing in that market? Or what would be our right to win in that market?

Yes. So data collection is a massive opportunity with a very limited runway for next 2 to 3 years. So there is -- I mean 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 build in the products and across the board. So there is a massive data that's being created. So it has to be collected. There is -- and we are already playing in that place in that space. We also have TAO who is 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. So 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 customers' overall data life cycle, which kind of the next phase is data storage, data engineering, reference architectures and then building analytics on top of it, training the agents. Those are high-value, higher-margin work streams. They're also sticky and long term. That's where we lock ourselves into a 3- to 5-year strategic road map for the customers. So 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. And as TAO brings the tools, we can define the business tools, whether it's a mining customer, whether it's aerospace customer, it's an energy customer, we understand their life cycle. So as you merge the domain and into the rules into the tools, our proposition just to make an entry and win the data collection, data dispersion work is much more compelling than the rest of the competition.

Yes. So 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. But 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. And 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. So 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 Har articulated.

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 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 customers' organization, the lines between engineering and IT and technology have been blurred. So 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. So 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. So we are very, very confident of what's happening and what lies ahead with the integration of TAO Digital into Cyient. So 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.