Please note that this call is being recorded. I will hand over the call to Svetlana Joshi from Fractal's Investor Relations team. Thank you, and over to you, Svetlana. Thank you, Inba. Good morning, everyone, and thank you for joining us today. We'll be discussing our performance for the fourth quarter and our first full fiscal year as a listed company, both of which ended on 31st March, 2026. Our results, shareholder letter, investor presentation, and fact sheet are available on our Investor Relations website. Joining me on the call today are Srikanth Velamakanni, Co-founder and Group CEO; Pranay Agrawal, Co-founder and CEO; Ashwath Bhat, Chief Financial Officer; and Satish Raman, Chief Strategy Officer. Before we begin, please note that certain statements made during this call may be forward-looking in nature. These statements are based on our current expectations and are subject to risks and uncertainties that could cause actual results to differ materially. Such statements or comments are not guarantees of future performance, and Fractal undertakes no obligation to update them. Please refer to the cautionary statements in our investor presentation and regulatory filings. We will start with a business update from Srikanth, followed by a review of the financial performance by Ashwath, post which we'll open the call for questions. With that, let me hand over the call to Srikanth. Thank you, Svetlana. Good morning, everyone. Fractal is an enterprise AI company. We serve some of the largest, most admired Fortune 500-sized enterprises and help them make better decisions with AI. Inside a large enterprise, thousands of decisions get made every day. What price to charge, where to send a shipment, which customer to follow up with, and so on. Most of those decisions sit inside complex systems where the data is fragmented, the processes are slow, and small mistakes are expensive. This is where Fractal does its work. Fractal helps companies grow their revenue, personalize customer experiences, increase operational effectiveness, and improve the speed at which a company responds to emerging threats and opportunities. This is our first full year of results as a public company. Before I get into the results, I'd like to spend about four minutes setting the context for the moment we are in, because I believe it is pivotal one for AI. In April this year, Anthropic previewed Mythos, a model whose long horizon reasoning and agentic capability the company itself called a step change. They judged it to be too dangerous to release openly. Companies and governments responded with fear and urgency, especially regarding the ability of frontier models to launch cyberattacks. AI is becoming more capable every day. AI that can plan, reason, and act through complex enterprise network, and this frontier intelligence is becoming much more affordable to deploy. This, in no uncertain terms, means that the era of enterprise AI is here, and it is the era Fractal was built for. I believe this shift has three important implications for our industry and for Fractal. First, the size of the price. The world today runs on clunky, unintelligent software. We are entering the era of Software 2.0: responsive, agentic, and far more capable. By my estimate, the world needs thousand times more software than what exists today. AI will reduce the effort to build it roughly by 90%, a 10x compression. That still leaves a 100x net opportunity. The pie doesn't shrink. It grows by orders of magnitude. Second, the shape of the price. For the last three years, the AI economics debate has centered on cost per token. We believe cost per token is a wrong metric. The right metric is a value generated per token. Who can turn cheap, abundant intelligence into business outcomes that would not have been possible without it? Far, this has been about replacing human time with machine time at lower cost. That price is real, but it's relatively small. The bigger price is the work that was impossible before. Think of pricing every SKU in real time, discovering novel drugs, meeting every customer need with speed and precision. Third, who will capture this price? The last two years of AI were about AI infrastructure. That race was decisively won by the frontier AI labs and AI infrastructure providers. The next several years will be about AI-led transformation, and this will be won by companies that can translate AI excitement into outcomes in complex organizations. Doing this well requires three things at once. The first is deep domain expertise, an understanding of enterprise decision-making, and the ability to manage data complexity inside large enterprises. The second is the ability to build at the frontier of AI advancement. Not just use frontier models, but to improve their accuracy with enterprise data. The third, most often missed, is a contextual understanding of design and behavioral sciences. Enterprise AI transformation is at least as much a change in how people work as it is in change in software. We have built decades doing this. Let's discuss our results. Revenue for Q4 was INR 886 crores or $97 million, up 17% over last year. For the full year, revenue grew 19% to INR 3,300 crores or $374 million. Vertical-wise, healthcare and life sciences led the year at an exceptional 66% growth and is now our second-largest vertical on a quarterly run rate basis. Banking and financial services segment grew 32%. CPG and retail, still our largest vertical at 37% of revenue, grew at 12%, modest, mainly on first half weakness. Technology, Media, and Telecom declined 1% on the two specific client issues I discussed last quarter. In Q4, that decline was 19%. Geographically, Europe led the year at 34% growth. The Americas grew 20%, and APAC was down 3% on the same client-specific issues. APAC Q4 growth turned back to 7%. In Q4, our Net Promoter Score was 81, the highest we have recorded. Full year NPS was 78. Net revenue retention at 117% for the year, 112% for Q4. Our clients are not just buying more from us, they're positioning us as central to their AI transformation. On profitability, Ashwath will get deeper into this, but here are some of the headlines. Q4 adjusted EBITDA grew 28% on revenue growth of 17%. That gap is the operating leverage of our business. Q4 adjusted EBITDA reached 22%, up 189 basis points over last year. Full year adjusted EBITDA margin was 17.6% after expensing 4.1% on R&D. Net income grew 30% for the year to INR 287 crores or 43% to INR 357 crores excluding our share of associate losses. We ended the year with INR 2,052 crores of cash, including IPO proceeds of INR 957 crores. In April, we used these proceeds to repay our long-term debt, as we had committed at the time of the offering. Fractal is now debt-free. Four highlights from the quarter I want to mention. First, we were selected as a strategic execution partner by a top five U.S. life sciences company to deliver two flagship AI companions across commercial pharma marketing and field excellence. Second, we unveiled Flyfish.ai, our agentic sales platform. Most sales today suggest what a salesperson should do next. Flyfish certainly does it. 35 coordinated agents research accounts, draft outreach, and manage the pipeline. Third, our research has been moving the frontier meaningfully. Vaidya 2.0, our healthcare foundation model, is now available on all major mobile platforms with developer API access. It remains the world's first model to cross 50 on OpenAI's HealthBench Hard, one of the toughest benchmarks for clinical reasoning. PiEvolve, our agentic engine for autonomous machine learning, is now available to all our people and clients and ranks among the top-performing agents on OpenAI's MLE-bench. Asper, our AI platform for revenue growth in consumer goods, continues to compound. Annualized recurring revenue is roughly up 77% year-over-year for the last three years. Fourth, our IP-led businesses inside Fractal Alpha, that is Asper.ai and Analytics Vidhya, grew 47%, 41% for the year, with Analytics Vidhya at 49%. Segment losses have continued to narrow from INR 26 crore in FY 2025 to INR 15 crore in FY 2026, even as we kept investing. Outside of revenue, our AI for Karmayogis course on Government of India's iGOT platform crossed 25 lakh completions, a signal of how broad the appetite for AI has become. Taken together, these efforts reflect the breadth of our work from enterprise transformation and frontier AI research to platforms, products, and large-scale AI capability building. That we have looked at both these shifts underway in enterprise AI and our performance through the year, the question is: How do we prepare Fractal for this takeoff of enterprise AI? In April, we updated our structure around three pillars, one platform and three regions. The three go-to-market pillars match what clients are asking for. AI-led transformation is the process dimension, how enterprise redesign workflows, operations, and customer experiences with AI. AI foundations is the technical layer underneath, data, agents, controls. AI for work and workforce transformation is the people dimension. How we hire, train, evaluate, and coach in a world where people and agents work side by side. All of these three pillars run on Cogentiq, our agentic AI platform. Cogentiq is a platform layer that allows AI agents to plan tasks, coordinate with one another, access enterprise systems and data, and execute work reliably inside real businesses. Building on one platform every time turns a service business into something closer into a software business. The work compounds, and so do the economics. We operate through the Americas, Europe, and APAC. Three pillars, one platform, three regions. The client sees none of this. The client just sees one Fractal. With that, let me hand over to Ashwath for more details on the financials. Ashwath? Thanks, Srikanth. Good morning, everyone. Before I get into the details of our Q4 and fiscal year 2026 performance, I wanted to share a few highlights. In Q4 2026, we expanded our margins meaningfully with gross margin at 48%, adjusted EBITDA at 22%, and net income at 13%. Q4 net income grew by 109% year-over-year to INR 116 crores or $13 million. For the fiscal year 2025, 2026, year-over-year revenue grew by 19%. Of this 19% growth, 17% growth came from existing clients. Number of clients with 1 million+ revenue went up from 53 in the previous year to 59 in fiscal 2026. We continue to accelerate our investments in sales and R&D while expanding our margins. We generated INR 409 crores of cash from operations, which is a 70% conversion of adjusted EBITDA. We also repaid our long-term debt after 31st March, 2026, as planned in the objects of the offer. Coming to details of our performance. In Q4 2026, our revenue from operations grew by 17% year-over-year and 4% quarter-over-quarter to INR 886 crores or $97 million. On a constant currency basis, growth was 10% year-over-year and 1.1% quarter-over-quarter. For the fiscal year 2026, revenue grew by 19% year-over-year to INR 3,300 crores or $374 million. On a constant currency basis, growth was 13%, and it was 14% growth in dollar terms. The entire growth was organic. Our revenue per billable employee increased to INR 75 lakhs or $85,000, representing an increase of 5% in rupee terms in the fiscal year 2026. Moving on to profitability, I'll start with gross margin. We define our gross margin as revenue from operations minus direct costs, which includes both employee benefit expenses, employee expenses, and other direct expenses. Q4 2026 gross margin expanded by 47 basis points year-over-year to 48.2%. We had three drivers behind this expansion. First, 151 basis points improvement from change in mix of engagement type moving towards output-based contracts, price increases, and productivity improvements. Second, 219 basis points benefit from weaker rupee. Third, 322 basis points of negative impact from annual merit increase. Gross margin for the fiscal year 2026 expanded by 93 basis points year-over-year to 46.8%. Again, three drivers. First, 218 basis points from change in mix of engagement moving more towards output-based contracts, price increases, and productivity improvements. Second, 179 basis points from weaker rupee. Third, 304 basis points of negative impact from annual merit increase. I'll move on to EBITDA, adjusted EBITDA. In Q4 2026, gross margin expansion, along with 140 basis points of reduction in SG&A contributed to adjusted EBITDA expansion of 189 basis points year-over-year to 22.1%. We had a grant benefit in U.K. upon compliance of several employment-related conditions and variable pay adjustments to align actuals against the targets. These two items improved adjusted EBITDA by 147 basis points in Q4 2026. Some of these benefits may or may not repeat in the future. For the fiscal year 2026, adjusted EBITDA was 17.6% versus 17.4% in the previous year. While our gross margin expanded by 93 basis points, growth investments in relationship management for key clients and opening new offices led to 96 basis points uptick in SG&A as a percentage of revenue versus the previous year. In fiscal 2026, we spent INR 212 crores on R&D investments, 48% higher than the previous year. Out of these INR 212 crores, INR 134 crores or 4.1% of the revenue was expensed in the P&L. Our current margin includes necessary investments to benefit from the massive AI opportunities which lie ahead of us. Moving to Fractal Alpha, I would like to highlight the rapid growth and improving profitability. Fractal Alpha segment includes Asper.ai and Analytics Vidhya. In fiscal 2026, Fractal Alpha revenues grew by 41% year-over-year. Asper grew by 31% and Analytics Vidhya by 49%. Gross margin for Fractal Alpha expanded by 143 basis points year-over-year. Our losses in the segment for the same period has come down by 43%, while investments in sales and R&D have continued. The losses in Fractal Alpha have been coming down since fiscal 2023. Our segment loss in fiscal 2023 was INR 54 crores, which reduced to INR 44 crores in fiscal year 2024, to INR 26 crores in fiscal 2025, and to INR 15 crores in fiscal 2026. Now I will move over to net income. Net income for Q4 grew by 109% to INR 116 crores. Net income margin expanded by 575 basis points year-over-year to 13.1%. In Q4 2026, ESOP charges, including cash bonus linked to options and one-time retention bonus declined to 1.8% of revenue versus 3.3% for the same period last year. ESOP charges, including cash bonus linked to options, has come down from 9.9% of revenue in fiscal 2023 to 2.2% of the revenue in fiscal year 2026. Operating EBIT without the impact of other income, share of loss of an associate and exceptional items was INR 144 crores in Q4 2026, which was 53% higher than the same period in the previous year. Diluted EPS is at INR 6.73, which is 106% higher than the same period in the previous fiscal, and is at INR 7.14 without losses from the associate company. We have covered gross margin, EBITDA and net income. Let's talk about cash. We generated INR 409 crores of cash from operations in the fiscal year 2026, which was 3% higher than the previous fiscal. CFO has increased despite INR 86 crores increase in working capital during the year, primarily driven by increase in trade receivables, which is in line with the revenue growth during the year. DSO, which is an important indicator for cash flow generation, has improved by two days. That is from 74 days in fiscal year 2024, 2025 to 72 days in the current fiscal year. In fiscal 2025, we had 12 days of improvement in DSO from 86 - 74, which resulted in high CFO to adjusted EBITDA conversion of 82%, while the current year conversion of 70% is more representative of our steady state. As of March 31st, 2026, we had cash and cash equivalents, including mutual funds and fixed deposits of INR 2,052 crores, or $219 million, including IPO proceeds of 957 crores. In summary, great year where we delivered 19% revenue growth, 48% gross margin, 18% adjusted EBITDA and INR 287 crores of net income. For the full year, net income grew by 30% and by 43% excluding losses from associate. We can move on to your questions. Back to you, Moderator. Thank you. Ladies and gentlemen, we will now move to the Q&A segment. To ensure we provide space for as many participants as possible, we request you to limit yourselves to one question per turn. Participants are requested to click on the Raise Hand icon located at the bottom toolbar on your screen. When called upon, you will receive a prompt to unmute. We will wait for a moment while the question queue assembles. We take the first question from Ankur Jain, an individual investor. Please go ahead. Am I audible? Yes, sir. I congratulate Fractal for the wonderful performance. The opening remarks was also very much explanatory. My question to Srikanth Ji is that if we have some list of must-win clients, like, they are already some big companies, but what I believe is that AI can help smaller companies also to become very big. Means it has the potential that a company which is having a good product or good service, this AI can help them scale at large, means can become very big. This is a question as well as a suggestion. Why don't we have a list of companies that you can help to become top 100 companies or something like that? I hope I'm able to describe my question well. Yes. Yes. Absolutely. Thank you, Ankur. Really appreciate the question. Our current strategy is about serving what we called as a must-win clients. The definition of that is companies that are INR 10 billion in revenue, INR 20 billion in market cap, or have 30 million consumers or customers. If we look at the second and third criteria, INR 20 billion market cap, even a relatively smaller company, like a company with INR 1 billion revenue could have a $20 billion market cap. A company that may not have may have even INR 100 million revenue but is serving 30 million customers could also be in the target. By the way we have defined 10, 20, 30. INR 10 billion in revenue, INR 20 billion market cap, and 30 million customers. We are certainly including companies that eventually will become INR 10 billion in revenue as well, not just the ones that are currently INR 10 billion+ in revenue. The second aspect of what you may be referring to is why not address mid-sized smaller companies as well, even if they don't meet the 10, 20, 30 criteria that we currently have? It's something that we have pondered about. Today's Fractal strategy is about doing at least $2 million and as much as $100 million per year per client, and that's what really makes Fractal business become very big and sustainable. That's why we do not wish to serve much smaller companies because the path to doing a reliable $ 2 million-$3 million a year with them, where it becomes profitable for us, is harder. That's why we are currently focused on the very large companies. Having said that, it also opens up to expanding it in future. Especially as our Cogentiq platform takes shape, where there are other platforms, we expect to be able to create some self-serve versions of these platforms available to everybody, that's how we will also address the part of the market that we cannot directly serve as end clients. I hope that answers the question. Yeah. Thank you. means, I think it is in your mind, like, for future you will be implementing that. one very small question is that, you have domains like TMT, BFSI, et cetera. are you planning anything in ESG space, to serve ESG requirements of any company? And, secondly, ESG Yeah, this much. this question only. Yes. Yes, we do work with some of our largest clients on the ESG space. As part of our supply chain practice, we have pretty solid capability in the supply chain side, and that does work on matters of sustainability. We have been using data and AI to drive sustainability-related advantages and reporting for some of the most important clients. Yes, this is a new emerging area, and that is also promising, and we are working on it through the supply chain practice. Fractal also has its own very robust ESG practice that we implement internally. As you may have seen, we have a CDP rating of B, and because we're not listed by the last time, we expect to improve on that. Overall, our sustainability scores are pretty robust. We do offer these kinds of services and using AI to improve sustainability to an vast number of our clients as well. Thanks a lot. Thank you, Ankur. Thank you. We'll move to our next question. That's from Pritesh Thakkar of PL Capital. Please go ahead. Yeah. Thank you for taking my question. My question is on the, you know, on the TMT side of the vertical. If you can double-click on the, you know, client-specific issues that you're highlighting on TMT, and how should we look at TMT vertical going into FY 2027? Secondly, in a similar line, is TMT vertical a low-margin business if I compare with the other verticals? No, great questions. Thank you, Pritesh. Number one is that the client-specific issues that we had in this year resulted in. One of the client situations was where the client entered a joint venture with another provider, and in the process didn't reduce the work with us to almost zero. That's one issue. Another client situation was such that the client itself was going through massive restructuring, because of which they were unable to, you know, expand their business with us, and they had to contract with us. Secondly, to your other question about TMT margins. It is not a low-margin business. TMT is pretty robust in terms of the overall margins. It may be a couple of points below some of the other industry verticals, but it is pretty profitable. Even though, some of the, you know, gross margins may be slightly lower, the net operating margin, something that we look at internally, are pretty robust for all practices, including the TMT practice. Not a low-margin business. It is reasonably high-margin business. In TMT, just would like to add that the work we do is more input driven rather than output outcome driven in TMT. If you look at the future, we do expect that, as outcome and output-driven models take off, we expand our margins. To that extent, there is a small difference in gross margins between TMT and other practices. For the last part of your question, which is about outlook for TMT. We expect these client-specific growth issues to have worked itself out through this year, and therefore we expect to do better on the TMT vertical in the next few quarters. Thank you so much. I have other three questions. Within, you know, Fractal Alpha, which has grown notably for the full year if I look at, if you provide some metrics around, you know, number of clients or average take rate, who are, you know, actively using a subscription model there, especially in Alpha and Vidya, that you highlighted? Asper. There are two companies as part of the Alpha portfolio. One is Asper.ai and another is Analytics Vidhya. Asper.ai has about 15 CPG customers or FMCG clients, and that is continuing to grow very nicely. Analytics Vidhya works with a bunch of enterprise clients which are very similar to the clients that we currently serve as well. This include healthcare companies, banks, and other industries as well. And these companies are of the size and shape of the companies that we regularly serve, the must-win clients. Asper.ai has a slightly different go-to-market, where it can serve companies that are less than $10 billion in revenue as well. Overall, both these you know, companies serve 15-plus clients each in their business. Thank you. We'll move to our next question. That's from Gaurav Rateria of Morgan Stanley. Please go ahead. Hi. congratulations on strong execution on your margins. I have a couple of questions. first one, you talked about 100x opportunity, big, big prize. How should one think about priority for the company? Would it be to reinvest aggressively, keeping profitability where it is, and accelerate our penetration into our must-win accounts? second, to extract maximum value as a service provider, what's the best way to structure engagements with the clients? Is it more license-based for our Cogentiq product, or more like a fixed price engagement? what would be the challenges in that? Thank you. Thank you very much, Gaurav. First thing is that we expect to continue our profitability journey. We want to increase our revenue growth rate while expanding gross margins, not by sacrificing gross margins. We expect the profitability journey to continue, the operating leverage to continue to kick in. The way we are thinking is maximize revenue growth rate while making sure that we hit a certain profitability threshold. That is the kind of optimization that we are currently doing. Even though we recognize that this is the best ever time to be an enterprise AI company, we want to also make sure that we are operating at a certain level of profitability while growing at the maximum possible rate. That's really how we're thinking of it. The second part of your question is about how do we price engagements. We've seen that anything that is priced as linked to outputs, outcomes, or as license have a much stronger margin profile and also sustainable. We expect to move our engagements into more outcome-driven, more output-driven, and more license-driven. Now, between output, outcome, and license-driven revenues have the highest gross margin, though the ticket size there is smaller. That's the trade-off. The best really is if you can drive outcome or output-based deals with some license component built into them, that is the best possible way to not only create large revenue growth but also do it at a much higher gross margin overall. Thank you. All the best. Thank you, Gaurav. Thank you. Our next question is from Aditi Patil of ICICI Securities. Please go ahead. Aditi, could you please unmute your microphone and ask your question? Yes. Thank you for the opportunity, and congratulations on a very good execution. My first question is, what is our share of output-based contracts in our Fractal.ai business, and how do we see this growing? When we say that the opportunity for growth ahead of us is 100X, can you share your thoughts on how it will translate to revenue growth for us in the near term, in the next two years? Like, what kind of use cases can help us tap this growth opportunity? Thank you, Aditi. Firstly, on the fraction of revenues coming from output-based contracts in Fractal.ai, we don't separately report this number. You could say that that number is growing. We expect to get to 60% of our revenue to be output-based or license-based, output, outcome, or license-based in the next two-three years. That's the trajectory. Today, it is about 20 points lower than that. That's where it is right now, and we expect it to grow. Secondly, on the growth that's ahead of us, we expect enterprise AI to take off substantially in this year and the next few years. There is always some friction between what is possible versus what the enterprises are willing to implement right now. There is still companies understanding and implementing AI for the first time, figuring it out before they can expand dramatically. We expect that the Fractal growth rate should reflect that and should be robust in the coming year as well. We feel pretty optimistic about where the company is going to grow. We don't have specific revenue guidance at this point in time, but we expect that our historical revenue growth rates are a good indication of where we should be growing in the next year as well. Okay. Can you also share a few examples of like, new net new areas of demand because of AI which you are seeing? Yeah, sure, Aditi. Literally every aspect of a business is up for reimagination and rebuilding. That's what we are seeing. If somebody were to say, "Look, hey, why don't you just improve our, build price elasticity models that helps us price?" Now from that need, it is now moving into saying, "Do my entire revenue growth management. Run the, reimagine the entire workflows of revenue growth management, and build all kinds of AI models that inform decisions across the entire process." That's called revenue management. That's an example of how work expands, because the scope of AI has now moved from a one-off solution or a point program into something that is all-encompassing, a reimagination of the entire workflow. That's the trend you're seeing. You're seeing this in, let's say, a chief people officer, typically not our client. Chief people officers are now saying, "I want to reimagine how I hire, how I train, how people work alongside agents. How do I redesign work in the new era of AI?" That's a new stakeholder. CFO is a stakeholder because a CFO is saying, "How do I accelerate my invoices? How do I realize cash faster?" A chief transformation officer is our client, who's saying, "How can I transform any aspect of a process, any process in my organization?" We're seeing a chief marketing officer as a client who's saying, "How do I reimagine my customer engagement? How do I reimagine my marketing spends in an era of AI?" What would be a traditional agency spends is now an AI addressable spend that's addressable by Fractal. You can see that literally every CXO in an organization, a CTO, a Chief Analytics Officer, Chief AI Officer, Chief Data Officers, have always been clients that we have served. Now all other CXOs, business heads, CEOs, are also our clients because they are seeing a massive opportunity to reimagine their business or the segment of the business that they run with AI. It's not just a point solution, "Hey, predict this, and therefore help me make this decision," which has been Fractal's bread and butter. To say, "Hey, let's just go all across my business and help me reimagine this with AI." That's why we have created these three go-to-market vectors, AI-led transformation. We're going to a CXO and telling her or him that we can help you reimagine your business process, build that entire workflow, the reimagined workflow on the Cogentiq platform, and bring all sorts of models, including foundation models, in enabling that. An example of that is we're working with pharma company, a very large pharma company, and we are saying, we're telling them, and we have done this now, they used to take four days, roughly four days to answer questions coming from healthcare practitioners. Now we are saying that four-day time is now brought down to 15 seconds or a few seconds, so 15-60 seconds. We're helping them in answering the same question, answering in a, in a cited, evidence-backed way within seconds. That's an example of reimagining the process of their customer engagement, which is, which is healthcare practitioners or doctors. So, you're seeing this across the board. One is AI-led transformation. Second, we are building the AI foundations. Again, this is not a traditionally Fractal address spend, which is, how do I build all the foundations that result in AI-led transformation? What people are saying is, "Can you build an ontological knowledge layer on top of all my data and help me in therefore enabling transformation? How can I completely modernize tech spends with AI, and therefore bring down the cost relative to other service providers by 50% to even 75%?" That's the AI foundations. Third is this AI-led work and workforce transformation, where a Chief People Officer or Chief Business Officer or CEO is saying, "I want to reimagine work and workforce in the age of AI, and help me do that." This is how we have organized in order to respond to that opportunity. Thank you. Before we move to our next question, we would like to request participants to please limit your questions to one per turn. Our next question is from Kawaljeet Saluja of Kotak Securities. Please go ahead. Hey. Hi. I have a couple of questions. Srikanth, the first is on the TMT vertical. The revenue drop has been sharp. I've heard, you know, your comments, but I'm still trying to understand the magnitude of the drop seems to be quite steep. Is there a loss of relationship or wind down of an engagement? What really materialized in the verticals? Thank you, Kawaljeet. One of the clients that we were serving in the APAC region, in the TMT vertical, that relationship has not gone to zero, but it has dramatically gone down because of a joint venture that that company has entered into with another company. In the process, they moved that entire business into that, into the joint venture. That's at the CEO level of this very large TMT company. That is definitely one example. The second example is of an enterprise company, so enterprise software company that has entered into restructuring because their own business has gone through certain challenges. There again, we are seeing revenue drop. There's also a third one, where we have, again, we haven't reported about this before. We, in this quarter, we were not able to recognize some revenue because of some data-led delays. We didn't have the data in place at the right time. We could not recognize revenue. This quarter especially, that has a meaningful role to play in the decline in revenue in the TMT vertical. This is again a large Magnificent Seven company where we're doing some really interesting work. We could not start something because of some data-related delays. We recognized lower revenue from that relationship. Got that. The second question I had, Srikanth, is on reorganization of the new approach. It seems that you seem to be trying to addressing various personas in a client organization. In that case, you know, do the heads at the solution towers interface directly with the clients or is it done through a sales team? In this structure, right, how do you drive commercial discipline and risk management as you move aggressively into the agentic world? No. Thank you, Kawaljeet. Number one is that yes, most of Fractal is client-facing, in the sense that, yes, we have a commercial organization, and the commercial organization interfaces with the clients. In order to win, most of Fractal is involved in winning those client relationships. The best salespeople or commercial leaders in the Fractal organization are the ones who are able to orchestrate Fractal's expertise showing through to clients by bringing these go-to-market experts in client conversations. That's how it works. The way the commercial discipline works now is now we have unified Fractal's commercial discipline under a Chief Commercial Officer, Global Chief Commercial Officer called Matthew Jason Gennone, and he has a global remit to make sure that we are moving from the current engagement structures to more outcome, output and license-driven structures. Add pricing and all other kinds of discipline around commercial transformation he has under his belt. He also is a CEO of Cogentiq, and in that way bringing Cogentiq into every conversation and making it more platform-led than ever before. We have actually tightened our overall global commercial outreach by putting it under one person called Matt. Now, geographically, we have sales leaders who are also going to market. They will do their business, but under the overall commercial discipline led by Matt. That's really how the structure enables us to not just bring our best expertise to clients, but also organize it, also create the discipline of commercial outreach. Mr. Saluja, may we request you to return to the queue. Thank you. participants waiting. Thank you. Thank you. you. We'll take our next question from Dhanashree Jadhav of Choice International. Please go ahead. Good morning. Congrats on great set of numbers. My question to Srikanth would be, we have seen CPG showing improved growth in the current quarter. If you can share the opportunities we see there coming in for FY 2027, also in other verticals for Fractal. That is the first question. Second, I would like to get some sense on Qure.ai growth and profits. Like, if you see last nine months, the growth there and profits have been dwindling because of lower U.S. healthcare spends. I want to know how is the current picture there, as far as U.S. healthcare spends comes on. The other mix of the Qure.ai, how that is going to change coming into FY 2027. This should be the two questions. Thank you. Thank you, Dhanashree. Yes, CPG is had some initial challenges in the beginning of last year, especially because as soon as the Liberation Day-related trade regime was announced, many of the CPG companies really froze. That created its own, like, inflation expectations and also some, you know, overall slowdown in CPG for the first half of the last fiscal. Some of those things have eased. We expect CPG to do better overall, and we expect this to be across every vertical as well. This is very interesting that, A, there is a tremendous need to get enterprise AI right inside companies. We've never seen this level of excitement and need for this. It is yet tempered with all the other geopolitical situations that are happening, the trade uncertainties or whether it is related to the Middle East War, et cetera. What we are seeing is a net result of extreme excitement around enterprise AI tempered with some of the macroeconomic-related situations that exist as we all know. That's how the net result of that is. Coming to Qure.ai. Qure had last year, this was calendar last year, as soon as the new U.S. administration took office, they created a program called DOGE, as you probably know. DOGE, the first action that DOGE took was to shutter a agency called USAID, which it turns out is one of the largest funders of tuberculosis programs around the world. Not only directly through USAID, but through several other agencies actually rely on USAID. That was a significant source of revenues for the Qure.ai business, and that just dried up instantly, which created a massive headwind for Qure in the beginning of last fiscal year. That has played itself out. Today, as of April 2026, Qure is sitting on a phenomenal order pipeline. It has a very, very strong order book. They're not just going to recover from all the setbacks from the last year, but do even better than that. That's the expectation that Qure, as a business, will have phenomenal revenue growth, and therefore some of the losses that we've experienced in this fiscal year through the IPO process and, and after that also a couple of quarters, we have seen that Qure has been a huge drag on our overall profitability. That drag is getting lifted as we speak, and Qure will have a much better year, and also not contribute to additional losses to Fractal. That's the expectation in this coming year. Thank you. We'll now move to our next question. That's from Dipesh Mehta of Emkay Global. Please go ahead. Yeah. Thanks for the opportunity. two question. First is, can you quantify the impact of client-specific challenges which you highlighted, in quarter four performance, and any client-specific challenges you envisage in top 20 client? Second question is, what will be the gross margin difference between outcome output license, revenue versus traditional way of business? If you can give some sense. Thank you. Yeah. Thank you very much. Our growth rate, as you would be able to calculate, is would without PM including TMT, would be 27% for the full year in as opposed to the 19% that we've actually reported. That's the impact of the client-specific issues. Within that, if you exclude the top three client-specific issues, that growth rate would be roughly the similar number to what I just mentioned. That's the quantification of the client-specific issues. On the your question around Dipesh, your question around the margin difference between output license versus traditional way of doing business, there is a five-seven-point difference between input-driven pricing versus output-driven, outcome-driven pricing. Five-seven points of additional gross margin are there in output and outcome compared to input. There's a 25-30 point difference between that and the license-driven gross margin. License-driven gross margins are very high, but they're pretty high even for the 48%-or-so gross margin that we have today is a mix of both input plus output. That, the difference between the two is about five-seven points. With license revenue being a 30 points even higher in terms of gross margin. I hope that answers. Yeah. Thanks. Thank you. Thank you. Our next question is from Shrinath V. of Bellwether Capital. Please go ahead with your question. Shrinath, could you please unmute your microphone? Hello, sir. Am I audible? Yes, you are. Yeah, sir. One macro trend I wanted to, you know, run you by is these global analysts seem to think that, you know, putting a data platform and then running AI on top of it is the way to go. Like Databricks plus software, Snowflake plus software, Fabric plus software, and then build custom-built software as analytics tools. I wanted your view on what do you make of this trend, and how are we positioning ourself to the same? Shrinath, thanks for that question. No, no question about that, because the traditional approaches before the new platforms like Databricks and Snowflake took off were much less effective. One of the biggest challenges in any AI is being able to manage the very large and unstructured data sources that exist inside enterprises. What Databricks does is to, you know, have a very nice way of managing this unstructured data and help you run AI on top of that. One of the big beneficiaries of the AI revolution are going to be these data platforms like Databricks, and so on. These are our partners. We do work alongside Databricks and Snowflake and other big data platform companies in driving overall effectiveness with AI. We expect that they will be huge beneficiaries. Having said that is not alone. That is not sufficient. It is necessary, but not sufficient. There is a ton of work to be done in terms of driving enterprise AI transformation, and that involves a data platform, but it also involves building on top of the data platform to reimagine workflows. An agentic layer like Cogentiq is necessary, and this agentic layer will connect to the data platform layer in order to drive overall enterprise AI transformation. To start off with, what portion of our employee base is certified by, you know, all these three companies? What is our ability to take transformational projects with the help of one of these data platforms and then build forward pipelines as well as deliver, you know, deep analytics work? Shrinath, thanks for that question. No, understand our readiness to, you know, integrate with these guys. Absolutely. The great question, Shrinath. I don't have the exact numbers of certified people, though we do have certification programs on Databricks, Snowflake, and all other kinds of foundational AI as well, like Claude, et cetera. We take this seriously in terms of people being not just really good at using these platforms but also being certified by the platforms themselves so that it becomes easier for clients to also appreciate their expertise. We have I don't have the exact number, but it'll be in hundreds of people across each of these platforms that we just mentioned. That enables us to partner with these companies deeply. Today we are going to market with several of these players. One of the things that we are strengthening as part of our new overall go-to-market is our partnership muscle. We want to be much closer to these partners, because these partners are becoming quite pivotal in the overall AI journey for the big companies. We want to work closely with them, bring them opportunities, as well as work on the opportunities that they're bringing in order to accomplish the overall transformation. The size of the prize is very large. There are multiple components of that prize. That includes data platforms, the foundation model providers, and the infrastructure providers like the hyperscalers. All of these are involved in driving this. They're all means to an end. The end really is AI-led transformation. Thank you. We now move to our next question. That is from Nikhil Gupta of Vayu Capital. Please go ahead. Thank you for the opportunity. I hope I am loud and clear. Yes. My only question is, related to share of Cogentiq platform with respect to our Fractal.ai business. What I can recall is that Cogentiq is still, it has a scaling phase, and the overall share is very low, which I believe, and the most of the share of revenue is AI consulting and services. Can you comment on it, what is the current share? I believe you mentioned somewhere that we plan to increase the share to 20% by 2030. I just want to know more on it. Thank you. Nikhil, thank you very much for the question. Yes, you're absolutely right that Cogentiq, as part of our current revenue, is relatively small. We have some amazing wins though. For example, one of the largest Magnificent Seven technology company did a full evaluation, three-month evaluation of all the agentic platforms out there and decided to choose Cogentiq and work with it in their overall marketing transformation. We are working in the CMO's office of this large company, they're using that to look at their website, which gets hundreds of millions of customers every month. They're using all of this information to better serve these customers as part of the overall marketing function of the organization. They're using Cogentiq to do that. Cogentiq is seeing a lot of promise. It's not yet there in the numbers, as you, as you have rightly pointed out. The overall licensed revenue for Fractal, which includes Cogentiq but is not limited to Cogentiq, there are things like Asper.ai, PiEvolve, and other elements of Fractal which are also license-driven. That number is currently only about 3% of Fractal's revenue. We want that license-driven revenue from 3% to 20% by 2030, and which will be quite significant in terms of also margin expansion. One of the reasons, I feel pretty confident about expanding our gross margins as we expand our revenue is because the share of license revenue as well as output and outcome-based revenues are increasing of the overall pie. Each of those, moves actually expands our gross margin. Mr. Gupta, may we re-request you to return to the queue, please? There are several participants waiting. Our next question is from Randeep Singh of Randeep HUF. Please go ahead. Hello. Hi, Randeep. Sir, thanks for the opportunity, sir. [Non-English content] [Non-English content] [Non-English content] [Non-English content] [Non-English content] [Non-English content] We take that as the last question, and that concludes the question and answer session. Before I hand the conference over to Anjali Garg from Fractal's investor relations team for closing comments, request Srikanth to share his final remarks. Thank you, Inba. As I close, here's what I want to leave you with. We grew 19% at a time when growth has been challenging across most of our industry. We did it while expanding gross margins, growing adjusted EBITDA, and stepping up R&D investment to 6.4% of revenue. Our highest ever NPS, our net revenue retention, and the trust of more than 100 Fortune 500 enterprises tell us that we are central to where our clients are headed. The moment in front of us, frontier intelligence, agentic capability, and the transformation phase of enterprise AI is exactly the moment we have been spending decades preparing for. This prize is real, and it's for us to win it. Over to you, Anjali. Thank you, Srikanth. Thank you everyone for joining us today. If you have any further questions, including any that we were unable to address during the call today, feel free to reach out to us at investorrelations@fractal.ai. We look forward to seeing you again next quarter. Thank you once again. Wish you a good day. Thank you. Ladies and gentlemen, we thank you for your participation. You may now click on the leave icon to exit the meeting. Thank you.