IVALUE INFOSOLUTIONS LIMITED — Call Transcript 2026

Mar 10, 2026

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iValue Infosolutions Limited

(Formerly known iValue Infosolutions Private Limited) No. 903/1/1, 19th Main Road, 4th Sector, H.S.R. Layout, Bangalore – 560102, Karnataka, India CIN: L72200KA2008PLC045995|GST: 29AABCI8601B1ZW www.ivaluegroup.com | info@ivalue.co.in Tel: 080-22221143

March 10, 2026

National Stock Exchange of India Limited BSE Limited The Listing Department, Department of Corporate Services, Exchange Plaza, Phiroze Jeejeebhoy Towers, Bandra Kurla Complex, Dalal Street, Fort, Mumbai – 400051 Mumbai – 400001 Trading Symbol: IVALUE Scrip Code: 544523

Subject: Intimation under Regulation 30 of the Securities and Exchange Board of India (Listing Obligations and Disclosure Requirements) Regulations, 2015 - Group Investors Meet - Transcript

Respected Sir/ Madam,

This is further to our intimation dated February 26, 2026 the transcript of the discussion during the group investors meet held on March 04, 2026, is attached herewith and is also available on the Company’s website at https://ivaluegroup.com/en-in/investor-relations/

The group investors meet concluded at 05:30 p.m. (IST) on March 04, 2026.

You are requested to kindly take the same on record.

Thanking you,

Yours Sincerely,

For iValue Infosolutions Limited

LAKSHMA Digitally signed by LAKSHMAMMANNI MMANNI Date: 2026.03.10 18:14:34 +05'30' Lakshmammanni Company Secretary and Compliance Officer Membership No. A51625

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“iValue Infosolutions Limited Business Update Conference Call” March 04, 2026

**MANAGEMENT: **	MR. SUNILPILLAI– CHAIRMANANDMANAGINGDIRECTOR
	MR. KRISHNARAJSHARMA– EXECUTIVEDIRECTOR
	MR. SWAROOPMUVVALA– CHIEF FINANCIALOFFICER
	MR. MITISHKIRANCHITNAVIS– CHIEFTECHNOLOGICALOFFICER
MODERATOR:	MR. PRATIKJAGTAP– E&Y LLP, INVESTORRELATIONS

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Moderator:

Pratik Jagtap:

Ladies and gentlemen, good day and welcome to Business Update Call for iValue Infosolutions Limited. As a reminder, all participant lines will be in the listen-only mode and there will be an opportunity for you to ask questions after the conclusion of presentation and management remarks. I now hand the conference over to Mr. Pratik Jagtap from EY. Thank you and over to you.

Thank you, Yashasri. Welcome everyone, good day, and thanks for joining the Impact of AI on iValue Infosolutions Business Update Call. We have with us the top management of the company: Mr. Sunil Pillai, Chairman and Managing Director; Mr. Krishna Raj Sharma, Executive Director; Mr. Swaroop Muvvala, Chief Financial Officer; and Mr. Mitish Kiran Chitnavis, Chief Technology Officer. Mr. Mitish will start the call with brief update about impact on AI on the company and post that we will open the floor for Q&A session.

Before we start, I would like to remind you that anything that is said and mentioned on this call that reflects any outlook for the future or which can be construed as a forward-looking statement must be viewed in conjunction with the risks and uncertainties that we face. These risks and uncertainties are included but not limited to what we have mentioned in the prospectus filed with SEBI and subsequent annual reports that you can find on our website. Having said that, I now hand over the call to Mr. Mitish. Over to you, Mitish.

Mitish Chitnavis:

Hey, thank you, Pratik. Good afternoon, everyone. My name is Mitish Chitnavis, as Pratik said, I am Chief Technology Officer for iValue. And thanking you, thank you for joining us today. I would like to begin by welcoming all of you, our investors, analysts, partners, and everyone who has joined. We appreciate your continued confidence in iValue. And what we would be doing today is that we will talk about AI, setting the context of what is AI changing and what the market shifts are, and then we would talk about, how, iValue plays a important role in the entire ecosystem, which is now running with AI and so on and so forth.

So, that being said, we are currently witnessing one of the most significant changes of this decade. Why I say decade? Being 31 years in this industry, I have seen Y2K, I have seen many shifts in the overall journey, and I see this as a super significant shift. So, AI is now reshaping all organizations, all enterprises. Everybody is trying to adopt AI, and it's also introducing new levels of complexities. Now, these complexities are in their complete IT infrastructure, secondary, cybersecurity, and governance. These are the three areas where you would see AI now bringing that complexity in this, in the whole era. And with that being said, now organizations are also adopting, while they are adopting AI, they are also trying to address these complexities and which did not exist before. So, AI brings those complexities, but they were not existing before. So, are the organizations ready for AI and implementing that AI and addressing those complexities? The answer is where we come into picture. We help these organizations to shift from what, where they were versus where they would be in next five years. That's the shift and that's the intersection where iValue is at today.

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Now that creates us a market opportunity for us number one. Number two is that it always helps us, you know, on the enterprise level number two. Number three is trying to integrate solutions which will actually help the organizations to get those best of breed products, integrate them and giving an integrated solution for the customer to implement AI. So, we are helping them in all these enterprises and companies in their AI transformation journey. Now, that journey as we speak about, that journey is going to be super significant for the security operations of the company. That journey is going to be very significant for the business of each and every customer that we are catering to. So, that being said, let's jump into what is AI.

Very simple. In our earlier days, you give a child with 100 photos of cats and dogs and how would you select them and tell them whether it is a cat or a dog. But in today's world, AI does that with millions of photos. Every one of us is unlocking their phone using our face, like a Face ID. Whether it is Android, whether it is iOS. But how does AI learn so fast and authenticates within that user of the phone within a few seconds? So, that's the power of AI that's coming in number one.

Number two, let's look at Zomato. We all order on Zomato, Swiggy and so on and so forth. So, when we order on Zomato and Swiggy, suddenly we'll get a message after we have ordered: "Today it's raining. " that's all AI because there's not one single person sitting there and looking at the weather forecast outside and saying that, "Hey, you know what? Now it's going to rain because I see dark clouds." And then he marks it in the Zomato or Swiggy application, then that's delivered. This is all tightly integrated with the AI system and the weather management system which identifies that it's going to rain or it's raining outside and that's why your deliveries will be delayed. So, again, that's another power of AI.

The third one is where you look at this infrastructure play and the third one is where FinTechs, you take PhonePe, you take Razorpay, we all transact UPI, we take all of these. You take any example. Today, AI is powering them to ensure real-time transaction and fraud detection systems are implemented in such a way that they can detect that fraud in seconds and they can identify that, that particular transaction as an, as a problem and it can block the transaction.

Let's take an example of Mitish sitting in India, but my card transaction is happening in US. When that happens, the AI system of some of these banks and many banks are implementing this, but they will identify, "Hey, Mitish yesterday night transacted in India. How can he reach US so fast?" and do a POS transaction or any other transaction there. So, that's where the catch is and they identify these as fraudulent transactions and they block it. So, with these three examples, if you actually see, we are using AI day in, day out in our daily routine. Every one of us, every individual in some shape or form is using AI.

Now, the second question is how does AI learn? AI needs lot of training data So, it's like telling a chef to go and start cooking a recipe. Start cooking a recipe where there are ingredients. He will look at those ingredients, he will learn, he will learn many dishes, but on basis of that training, which he keeps doing it again and again, what he will do is that he will also ensure that he takes the feedback and again feeds it back to his system so that the dish is much more tastier. So, we do this day in, day out and that's how AI learns in the whole process.

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Now, moving on to the next slide. Why AI now and what's happening with India or in India? Today, almost 400 million terabytes of data is generated day in, day out. That means look at the amount of data Zomato, Swiggy, Flipkart, AWS in India, everywhere we are transacting and millions of people are transacting. And when these people transact, they generate that amount of data. Who will churn this data? How will that churn happen? And who will analyze this data? So, that's where AI comes into picture.

Now, that's where it starts. In 2017, somebody had written a simple Transformer. Transformer is a process of how AI learns and AI understands the whole data process. And over a period of time, now that we are in 2026, it has become generally available, what we call in our Tech language as, you know, availability to general population. That means it can sell, resell, it becomes commercially available.

That's where then Government of India also realized and launched India AI Mission. And this mission already has deployed about 38,000 plus GPUs with various service providers and various data center providers. They have already allocated a budget and there are about 2,300 startups plus startups already registered in the India AI Mission. The five most important pillars that the sovereign, that have actually part of India AI Mission is Sovereign Compute. That means anything that we do and our what you call citizens use AI, they all have to use within India so that the data does not go out. So, compute power has to be inside India and that's why you see explosion of data centers in India. Let it be Yotta, NextGen, and E2E and all these data centers are really ramping up the space, ramping up their data centers so that they can start catering to these GPU workloads that are coming in from AI Mission point of view.

The second part that we see is data. Data models, those models have to be built in-house because we can't borrow a model which has been written by somebody else because we are not sure of what that model can do, either good, bad, ugly, whichever way. So, data becomes super important. All this then comes is sector-wise apps. That means BFSI, Manufacturing, AI is used in different forms in different sectors. Our customers in those sectors will build apps which are relevant to them, which are helping them to measure the outcomes because in AI, outcome is more important and you are measured by outcome, which is directly proportional to the GPUs that are utilized for that particular outcome.

Now, safe AI governance is absolutely important because what's happening here is that we all use AI today in some shape or form, which is called Generative AI. That means we log into ChatGPT, we log into Claude, we ask questions, we put all our questions on that, but is it governed? Is it governed by your own organizations? This specific method is called Shadow AI. That means I'm under shadows, nobody sees me, I use ChatGPT, I upload a file, I get a response, but that file, which can have a PII data or private information of the organization which was supposed to be protected but now this individual employee has uploaded it on ChatGPT and tried to use it. So, Shadow AI becomes a bigger challenge and since it has become a bigger challenge, governance of that AI is paramount for all organizations that we speak about.

The last part is talent pipeline. And I will cover that in my subsequent slides. Talent pipeline is where we are all talking of people losing jobs because of AI, everything getting automated, will

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the jobs remain, and so on and so forth. But I will cover that talent pipeline in my subsequent slides.

So, let's look at AI impact by industry because that is very important for us as iValue. Not every industry is AI ready, number one. Number two is that it has higher impact where there is realtime data and a lot of data that's being generated. Because without data, AI can't learn anything, can't do anything. For example, ChatGPT as we all know, would be learning from billions of parameters: notebooks, PDF files, images, websites, and like that, so on and so forth. So, in BFSI, yes, it has high impact because it does real-time trading, real-time rolls approvals, everything's connected. Every bank that we are working with, they are 700 to 800 applications generating data, right from KYC to the statement of that particular individual. So, everything, there's lot of data that is being churned by BFSI. We see that's where the impact is very high of AI because you will see real outcomes.

Healthcare, another one. All the data that today what we are seeing is when we do a CT scan, when we do MRI, all that data of DICOM files will be read, immediately patient outcomes will be measured. Many of us use health bands, you know, right from Apple Watch to WHOOP to any other band. Immediately it will start showing you results. And all that data, which is your walking data, all that data, or running or exercise, or whichever data that you are using that data is fed to AI and that AI generates some kind of patient outcomes.

Similarly, IT services. CodeGen, testing, automation, high impact. In testing, the beauty is testing was automated before. Testing is not automated today just because AI has come in. Testing had testing tools where you can automate testing of that code which Infosys or Wipros of the world were writing. So, it's not today. It has been happening five, six, seven years back and they have automated complete testing. But what has happened now is that the value of testing with AI has gone up because of the level of accuracy that can come in.

Similarly, Retail e-commerce personalization. We buy, when we buy on Amazon, immediately it will tell you that, you know, this is the book that you bought and with that book, these are the other four books also were bought by customers. That's recommendation engine, personalization, all that is driven by AI and that will continue to be driven by AI. That's where we see a large potential. And that's where we believe that that's the market which has opened up for us as well.

Similarly, if you look at medium impact: Manufacturing, Transport, Logistics, route optimization, reverse logistics, Telecom. In all these industries where AI will make an impact, but with AI, as I said earlier, there'll be lot of complexities that will come in. And those complexities cannot be solved by a single product. It can only be solved by multiple products coming together and these multiple products have to be integrated, they have to work like a symphony and a like an orchestra with multiple these tools and then they will start working.

That's where we see power of iValue coming in because from day one, our story has been we have all the integrated stacks, multiple solutions to address the earlier version or earlier avatar of the ecosystem and the complexities. Today, we are still ready to support existing AI-related any complexity because of these integrated solution stacks that we have built.

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So, low impact: Construction, planning, designing. Yes, AI can be corrected, but that's low impact in terms of the outcomes. Everything in AI is outcome driven, very simple. So, our focus is BFSI, Healthcare, IT, Telecom, and Government. We do lot of work here. We have many customers, we have our partners, those who are working with these customers. For us, partner ecosystem is important. So, we do all these customers. We are providing these integrated solutions and stacks. That's where they it will help them to solve the problems and the complexities that come with AI and help them in that AI transformation journey.

Now, today there are six ways companies use AI. Obviously, their strategy is to use AI. I was talking to some of our partners yesterday and I realized that most of the organizations which fall under these verticals, they have created AI Council. And this was the first time I heard because AI implementing AI is not a CISO's job or it's not a security job, it's not a Chief Data Officer's job, it's a business job. So, what they have done in this AI Council is that they have got the business team, they have got the Chief Data Officer on it, they have got the CISO on it, they have got the CIO on it, and they have got the CTO on it as well. If I consider bank. Now, they're all sitting and transforming and creating a journey on how to adopt AI. First is the GenAI side. The second one is purely how to build our own models so that we don't use third party models and so on and so forth. So, that being said, AI Council, new version, new avatar, how do we adopt? Very collaborative way of doing it because every individual BU or every individual enabling team, which I call as CIOs, CISOs, and CTOs, all of these, they play a very important role in driving that transformation and providing that outcome to the business or to the organization.

Now, coming to one of the most important points: AI and jobs. Your World Economic Forum, yes, they very clearly talks about how they have arrived at this where 92 million jobs were lost. Yes, they were lost because of AIs, but 170 million jobs were created. Why? Because AI is also written by a human. At end of day, if somebody is losing a job because he is not upskilled, now new generation is coming up where they are skilled on AI and the jobs are getting created around AI. So, it's not about losing jobs. I think it's more of creating jobs and net new jobs are getting created. I see a Chief AI Officer coming into a bank at some point in time. That will drive complete transformation from developing the models, developing GenAI models, integrating with GenAI and so on and so forth. So, that paradox of, "No, AI is going to, take our jobs, we are going to lose jobs." Yes, you are going to lose jobs if you don't upskill, but you are not going to lose jobs if you are skilled in AI. I think every one of us needs to be skilled in AI and that's how it is going to work in our favor. The moment more jobs are created, again it adds to the complexity, which benefits iValue in any shape or form, depending on how AI is delivered.

Number two, coming to cybersecurity side of it, which is of interest to us, is 4.8 million cybersecurity positions were actually went unfilled because there's no skilled resources, there's shortage of resources. Now, will AI improve efficiency? The answer is yes. But we need now in cyber, for AI, we need more jobs will get created and we need more skilled resources than we were earlier hunting for.

So, that's where iValue's iAcademy comes into picture because now we have started our journey where we were training them traditionally on the traditional products and solutions and so on and so forth. Now, within our curriculum, we have also added AI because at end of day, the

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products and the solutions that we sell to customer, most of the products already have AI built into it.

Then, obviously, AI economy is growing. We have seen NVIDIA, we have seen Infosys talking about it, I think we have seen all large companies talking about it, how they are adopting to AI, how many projects they are running, and also how the economy around AI is changing? So, while not diving straight into number that it's a $13 trillion economy, but yes, it's going to get driven, economy is going to change, AI is going to create more jobs, and we all have to upskill.

Today, while we are working very closely with CERT-In, we are CERT-In empanelled so we get some of this data. Today, almost 3,291 weekly cyber-attacks are faced by our customers. Or India as such, all organizations in India. Whether it is under critical infra, whether it is enterprise, whether it is government, defense, and so on and so forth. We are 44% more than the global average on this.

I'll only take one case, which is three months old case, that is JLR and Tata Motors. If you look at it, JLR and Tata Motors, the attack was highly sophisticated. They had to shut down their factories. They had to incur that kind of damage plus penalties and so on and so forth. Now, imagine that such a sophisticated attack, that cannot be done by human. AI is sophisticating those attacks. AI is increasing, getting amateurs to start attacking.

Number two is when this happens, look at our industry as well. Maruti Suzuki, Hyundai, Ashok Leyland, and all these companies are all they also have to secure themselves against the AIbased attacks, which are going to be significant, the growth we see is going to be absolutely significant on that. Similarly, Star Health Insurance. Policy holders' medical data was exposed, 2024. So, some of these attacks that continue to happen are getting sophisticated and I'll cover that as well.

Plus, we have regulations. Whatever said and done, our business in terms of all verticals is somehow governed by regulations and regulations have penalties. Look at SEBI, CSCRF guidelines, DPDPA which is now enacted. Every organization, every customer is working towards DPDPA, how to comply to DPDPA and so on and so forth.

Then you have banks, they have humongous task. Basically, they have to comply with RBI, they have to comply with DPDPA, they have to comply with SEBI guidelines because they are listed and so on and so forth. So, multiple regulations are driving customers and frankly speaking, we are very happy about it because regulations drive our business as well.

So, on one side, if you see attacks are getting sophisticated, on the other side, regulations are driving companies to implement controls, that means cybersecurity solutions, to protect themselves against these attacks. And we are at the intersect of it. So, by providing these integrated solutions, we are able to help them on both sides of the story.

Now, how cyber-attacks are destroying shareholder value. If you look at JLR, 10% stocks again went down immediately the moment this news was out. It's a huge shutdown. They had to close down the factories and so on and so forth. So, over a period of time, all the companies that invest and we invest, everyone invests, they have to protect themselves against these sophisticated

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attacks and they have to implement those solutions. And those solutions are where in Indian context or iValue's context, they are right now with us, which where we can help our customer not only through their AI transformation journey, but we can also help them to protect their AI implementations or model implementations that they are doing inside their organization.

Two minutes back, I was talking about sophisticated attacks. Look at what was happening in 2021. An attack on an organization used to take 98 minutes. And now, when I see 2025, it just gone to 29 minutes. Now, because of AI, this is the shift that has happened number one.

Number two, because of AI, amateurs have also become adversaries. So, this shift is where we also see our value that we can provide those solutions again, which will ensure that any attack which is executed and which takes 29 minutes can also be stopped. So, that's how we see the market shifting, that's how we see the overall picture and we believe that this will keep coming down up to a certain extent, obviously, it will hit a plateau, but we see that this is where AI is changing the game, not only for the data, outcomes, business, but also for cybersecurity and infrastructure. To protect every organization under 29 minutes, that also means lot of data, lot of security data and lot of infrastructure is needed. So, only then you'll be able to protect any organization within that 29 minutes or so.

As I said, AI is reshaping cybersecurity, There are two parts. There's AI for Cybersecurity, that means what how AI is contributing to cybersecurity, and how a new category has got added because of this market and technology shift.

AI for Cybersecurity, that means customers already have certain products and these products now are AI driven, contributing to identifying an incident, reducing false positives, creating more Zero Trust architecture and so on and so forth. So, that's AI in cybersecurity. Most 50 of our OEMs have already AI built-in that we today sell in market. Okay? Number one.

Number two is now, let's look at Cybersecurity for AI. Everybody's implementing AI, Prompt, ChatGPT, simple example. I am putting my let's say a financial file, I download it from what you call last results from of Maruti Suzuki from their website, I upload it on ChatGPT and the challenge is that was I allowed to do that? Right? I can now start predicting. So, what has happened is people are using AI, but there's no security for it. So, organizations and employees sitting inside the organization are using ChatGPT and getting their work done. But is there a security for it? Can I have Prompt Injection, can I protect them from Data Leak Prevention and so on and so forth. So, entire new category has been added called AI Security or Cybersecurity for AI.

Which is we believe by 2031 the TAM will be massive on anything that is related to cybersecurity for AI. So, every infrastructure around AI demands a curated OEM architecture, that is multi-OEM architecture and managed security outcome. Means you have to have multiOEM defense mechanism implemented as customers would do now. We are seeing that where customers want basically multiple solutions for their AI initiatives. And there are multiple AI initiatives going on at any customer, take any vertical, any bank. There are multiple AI initiatives going on, but the challenge is that they thinking how do we secure it so that we by intent or by normal process, data which is confidential should not be leaked.

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That also means that Mitish logs in, he doesn't have access to, let's say, financial data and other engineer or a sales guy who logs in and he says, "Can I have, let's say, this, this, this, this data?" and inadvertently, unknowingly, because there is no security in place, the AI will immediately answer. So, you need security, you need role-based access control, you need all of this identity management, everything implemented for AI as well so that when AI learns, AI also learns who should have access to this. Because today, if you use ChatGPT, general purpose everyone is using and if you just type a prompt, you'll get all the answers. There are certain guardrails. Guardrails are implemented. It's not that I can say, "ChatGPT go and hack some XYZ company." So, ChatGPT will identify and it will not do that. Those are called guardrails. But within an organization, within the models that are getting created, can this be protected? Can we implement role-based access control? Can we have perfect identity management and so on and so forth? So, that's where this new category becomes super important, is coming, every one of us is using it, and we need to protect, organizations need to protect that data from AI as well. So, Cybersecurity for AI is a new category that has got added.

Now, there are four AI mega platforms that are that are in play. Everything's you look at consolidation that is happening around. Over a period of last, I would say, nine to 10 months, around USD70 billion have been invested by our signed OEMs, to acquire AI companies. So, everybody's investing, like Check Point, one of our top 10 vendors, has bought Lakera, Cyata, Cyclops, all of this. Google went and did $32 billion investments to buy Wiz, which is around cloud security. Then we have Palo Alto buying CyberArk. And then we have Data Security, where you know Forcepoint, Cohesity buying Veritas. All of this.

So, the message is that AI for to ensure that their data is secure, the AI is secure, wherever AI systems of any enterprise or our customers is implemented on cloud or on-prem is secure. And then as I said, our role-based access control, identity management becomes super important. And that's why these four pillars for AI absolutely important. Without these four pillars, you can't implement AI at all. Number two is that we already have multiple OEMs across these pillars to help customers have an integrated solution and implement that so that they are in protected in some shape or form.

Four pillars, how each one accelerates. First most important pillar is the DCI and Network pillar, which is at the bottom. Why? Because everyone needs to have data center infrastructure to run any AI. Number two, for that data to be on and to be used by AI to learn and generate those outcomes, storage has to be there. Because where otherwise you will store the data and allow AI to learn run it. Number three, data has to be streamed, it has to go into lake houses where all 750 applications of a bank will write into a single data lake house where the data can be churned. And that's how data comes into picture. And the fourth is to protect all these three layers at the bottom: DCI and Network, ALM and Storage, ALM Data Cloud, you need security. So, security, so if you look at this four pillars, all these four pillars play a major role in enabling any organization for AI. And where we come into picture is because we are already working with best-of-the-breed OEMs to ensure that the entire ecosystem, the all the solution stacks come together to protect AI. We believe that this is our opportunity, AI is bringing more opportunity to us and increasing our customer base as well.

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OEM alignment is basically if you look at it again, Data Platform, Security Operations, all that's technology jargons, but objective is we are already aligned, we are already there, we are AI relevant today. Yes, there are some solutions which customers already have and will take time for them to change, refresh, and so on and so forth. But we are actively realigning our portfolio towards AI native vendors. We are looking for new vendors also to sign up and looking at how legacy tools, who do not have any product roadmap around AI, what do we do with them? So, we are realigning our portfolio as well while we already have majority of the OEMs covered in the solution stack for AI. So, that's we are already doing that.

Why iValue is structurally positioned for AI? Same, multi-OEM curation. Not one single product can secure AI systems in an organization, whether it's yours, mine, within iValue, across BFSI, any vertical you take, not a single solution ever is going to save that organization from AI, AI-related attacks, and so on and so forth. So, multi-OEM curation is going to be working very well. We believe that all the partnerships that we have done over a period of last 18 years, that all are working together in those stacks solutioning and selling to customer because in AI; to bring those outcomes back to the customer, you need AI. For that AI, you need the integrated solution stacks to work. So, our curation model of this stack becomes our IP, it becomes more valuable for us number one. Number two is AI ready data pipelines. Today, that entire data of what we are talking of 400 TB of data being generated, it's all become pipeline. It's like very simple example can be water flowing through pipes, it's like streaming water from pipes, similarly, we are streaming data from pipes. That's why we have the best which Confluent, which was obviously recently acquired by IBM, Cloudera, Splunk, full AI stack across BFSI, Government, and Defense. So, these are the only three products that I am naming here, but there are multiple products that play a role to build data pipelines.

GPU ready infrastructure. Very so important for us. That's where we've signed with likes of Supermicro, Lenovo, so that customers understand now they need GPUs to run these AI workloads and we power their data centers by providing these GPUs to them so that they can run their models and they can get outcomes at end of day.

Third part as I said, regulatory. We are driven by regulations. Whatever we do, at end of day our customers are driven by regulations. That's the position and for driving this regulation, we need to have again those curated curated solution stacks. Why? Because a single DPDP compliance requires 13 solutions from us.

Similarly, RBI, similarly SEBI, and similarly Defense. Defense has its own complications because they are all isolated networks, they're not connected to internet and so on and so forth. So, regulatory expertise, that's where we come in and we do consulting work with customers where we can now help them to comply with all the stacks that are available with us.

Last but not the least, services overlay. Who will manage? There somebody needs to manage this complete AI, AI transformation journey, somebody needs to handhold these customers to run their AI operations and manage it. And that's where our services overlay comes into picture because first is managed security services, AI enhanced SOC, Agent-wise MDR because everybody's talking of agent and agentic. They are building agents like they are like bots, agents also have to be now secure, and they have to be managed. So, agents in AI language have to be

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protected and that's where again we help them by services layer overlay, providing these services back to the customer, which helps us to ensure that not only we sell the stack, we help them to implement, we provide them the infra, we manage their regulatory expertise and we also provide them services. So, it's a 360-degree bundle that goes in for a customer to manage their entire AI infrastructure. And that's why we call ourselves orchestrator of the AI infrastructure. That means getting all these pieces of puzzle together and helping the customer to navigate their journey.

Revenue outlook and margin sustainability. Yes, there are some revenue vectors because we are working with OEMs, we are working on AI ready data infrastructure, AI security tooling, these vectors are coming from the OEMs that we have signed. But what we see is on the product side, we at least see no compression on margins. On the AI front, actually we see better versions on product margins. Services margins, those are stable because AI advisory comes into picture, consulting comes into picture, AI we are handholding customers now through their entire journey, so multi-services opportunity comes into picture. And then renewal base is obviously locked down by compliance, so that's where we are.

That being said, it's all good picture. But there are certain risks. And the risks are also there when we're doing this business. The risks are that the customers today where they are supposed to refresh their infrastructure to support AI and getting AI into their data center and trying to build models and so on and so forth there, the risks are that everybody is going to wait and watch. They're trying to realign their existing non-AI products to AI native products. So, legacy tools are slowing down because of this realignment. Second what we see also is that deal cycle, we know customers were going to buy those and renew their legacy tools or refresh their legacy tools, we are looking at little dip in terms of deal cycle because that's getting elongated because customer wants to now evaluate an alternative product which is AI ready.

So, POCs, demos around AI ready products have gone up. That's where we see the sales cycle little increasing. The third part is talent competition. And that's where we are investing in the academy side of it. So, talent competition will always be there now because it's scarcity of talent, but over a period of time, the talent will become commodity.

Coming to our heart of our system, sales process, which is our Center of Excellence, where we are now working with data center providers to get our COE operational in terms of AI data center ready where we can showcase to customers how the AI data center looks like, in terms of air cooling, liquid cooling, sovereign AI deployments, etc.

Cybersecurity, we already have, that's fully operational. Data streaming, fully operational. Regulatory, that's also operational, we are super fine with all those stacks that are there. But AI, ML, LLM lab, now we are scaling, we are trying to build work around the OEMs that we have, create and integrate a AI LLM-based models, model red teaming, like example Checkpoints of the world, Arista of the World, they are already talking of building products around entire AI, LLM. So, that's scaling, that's ongoing process for us. And then observability is also scaling big time. Observability is scaling purely because all these workloads that already the customer has, which are CPU workloads and now you have GPU workloads and AI applications running on it, they all have to be monitored, that's where observability again is scaling up.

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So, we see at least now when we are able to showcase many of these AI use cases, there'll be some customers are interested, customers are looking at we see some deal uplift happening around that because of showcasing AI power into our Center of Excellence. We believe that the sales cycle will become faster, there'll be some cost reductions that will happen because of AI in terms of your GPU side, legacy products and so on and so forth.

And then audit prep. As I said earlier, that a bank has to ensure that they comply with multiple regulations. We believe that by having this in place, almost audit preparation, giving a file, uploading a file, whether you're comply with this particular clause of this particular DPDP and so on and so forth, all that can be automated process itself. So, there are benefits and there are obviously reductions that customer wants to see because of AI coming in.

AI for us is an accelerator. Why? Because that complexity, compliance pressure, and vendor fragmentation, that means multiple OEMs coming together, we believe that, that is providing us that structural tailwind that we need for our curated multi-OEM stacks and models that we have built. It's really helping us to have that conversation with the customer and now customer is able to understand why solution stacks are needed number two.

Then revenue outlook obviously is positive because we see in all the five growth vectors we have OEMs that we have signed up, we are partners are interested, and obviously customers are interested. So, entire value chain now is interested in moving towards AI and AI data lakes and AI related products. Margins we call sustainable. We see at least sustainable for now. We believe that with in next 3, 4 years because of AI, more complexities will come up. It's not the end of era, it's just the start, just the beginning. So, we believe that it would be sustainable. We are also strengthening our moat because of our COE and our curation model around multi-stack OEMs.

That's where we believe that we are strengthening our moat more than making it a commodity. Some of these stacks are highly what you call unique in nature because the way they work and the way the products integrate with themselves. And at end of day, fifth point where we are the orchestrator of that infra architecture application. So, top to bottom all those four ones that we spoke about, we believe that we can be AI infrastructure orchestration for any customer.

Now that we have done all our research, we have understood what AI needs, our OEMs take considering the entire lifecycle for us and the entire ecosystem, we believe that we can be the India's AI infrastructure orchestrator.

So, thank you very much. We are open probably for questions, and happy to answer any questions.

Moderator:

Deepak Poddar:

Thank you very much. We will now begin the question and answer session. Our first question is from the line of Deepak Poddar from Sapphire Capital.

Thank you very much sir, for the detailed explanation and it was wonderful actually. So, just few queries from my side. I mean there's an entire value chain, right, from OEM to Distribution to SI and end customer, right? So, where exactly we sit in I mean in this entire value chain?

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Mitish Chitnavis:

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Okay, so we are OEM to Distributor, Distributor to SI and then to the customer. Where we play a role and in a very simple terms I'll tell you. That we play at the distribution level number one.

Within distribution, there are two categories. One is pure play distribution, fulfilment, only credit financing to partners and so on and so forth. While we do that, our advantage is the second part, which is called Value-Added Distributor. Every OEM understands value-added distribution where and why that is needed and why they have carved out Value-Added Distributor. Purely because they want to see the value-added distribution partner, that is iValue, to go and make a difference when they're talking to customers and explaining these solutions to them.

So, the vertical teams that we have, we go directly to the customers, we talk about this stacks because not every partner or SI will ever speak about the all the stacks. All the products. All 100 products, very difficult. And it's practically impossible for a partner which has, let's say, 15, 20, 30 people tech team and 15 people sales team to go and talk about 100 products.

So, VAD has come in to ensure that this partner ecosystem that we build the customers via them, they are enabled number one. Number two is that they are technically qualified on certain products. But when it comes to solution selling, that is where we go, we come in, we go along with the partner to the customer, talk about all our solutions, those solution stacks the way we have curated them.

We understand customers' problems, the outcomes they want. Today, the conversation of is over when the moment you say that, sir, I have come to sell this product. The way the conversation is, sir, what is the outcome you are looking for? And then we come back, we evaluate, look at our curated stacks, we go back to the customer. That outcome can be provided by 1, 2, 3. This is how the entire solution stack will work and that's the power of a VAD in this entire ecosystem.

So, the way I would put it is that OEM, Distributor / VAD, SI, and a Customer. So, we do business via the SI, we don't bypass them ever, but solution stack selling and talking the outcome language, the which the customers understand today, can only be done by us because we have the curated models in place. Did I answer your question, Deepak?

Deepak Poddar:

Mitish Chitnavis:

Deepak Poddar:

Sunil Pillai:

Deepak Poddar:

Sunil Pillai:

Yes, actually you did. So, so just follow-up on this. So, generally we deal with SI. SI would be our customer, that would be a right understanding?

No.Could you please speak little slowly because there's some echo.

So, I was just trying to ask that would SI be our customer?

Deepak, Yes, our first level of customer is always an SI.

Okay, okay. And we also talk to end customers, I mean because you mentioned the curated stacks belongs to us, right?

Yes, we do talk to end customers. So, we take a very consultative approach and that consultative approach is always taken at the end customer level. That is the real users.

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Deepak Poddar:

Okay, understood. And coming to AI, what sort of investment we would have done currently in AI and how we are looking for investment in AI over next 1 to 2 years?

Sunil Pillai:

Deepak, right now we ourselves are gearing up, we ourselves are trained, some of them have been trained, Mitish has been trained on that. And we are learning how the models work within specific OEMs right now. Right now as we speak Mitish is right now taking this call from Bangkok, he's gone there for a checkpoint event and from there is what he is addressing.

Actually it is all about AI, he's gone there. So, there is a lot of thought leadership exchanges that is happening over there. Besides that, we are created gearing up in terms of iAcademy which Mitish mentioned, we are gearing up with next batch of people and we are trying to train them, teach them on AI and its models. So, what happens is that it doesn't sound like Greek and Latin when we in go in front of the customer.

Deepak Poddar: Okay. And any quantific number? I mean how much you're invested already and how much you are looking to invest in rupees, crores or anything on those lines?

Sunil Pillai: While our budgeting is still on Deepak, I will be able to share you in next quarter for sure, but right now as of now the plan is on. I'm just waiting for my board meeting that is anytime scheduled. Once that is approved, I'm willing to share that with you. Deepak Poddar: Okay, understood. And just one last thing on the pipeline, I mean what would be our current order book as such and what would be our pipeline that we are looking at? Sunil Pillai: Deepak, to be very frank, we are not changing our outlook for this year which we have mentioned multiple times. So we continue with that outlook, we'll retain and sustain that outlook for this financial year. Going forward, yes, we will share our outlook for the subsequent financial year after our board meeting. Deepak Poddar: Okay. Can you reiterate what is the outlook for this year? Swaroop Muvvala: We would maintain that whatever we mentioned like, in top line 18% to 20% growth and about 22% to 25% growth on PAT. Deepak Poddar: On a whole FY '26 base? Swaroop Muvvala: Yes. Deepak Poddar: Okay, I think that would be very helpful, sir. I mean wish you all the very best. Thank you, sir. Swaroop Muvvala: Thank you very much. Moderator: Thank you. Our next question is from the line of Bhavin Shah from Sameeksha Capital. Bhavin, please unmute your microphone and go ahead with your question, please. Bhavin Shah: Yes. So there's a couple of things here. One when we look at a typical project, it is fair to assume, I would say, that the value or the revenues from a typical project would have shrunk because of AI impact. That's the whole thing that is sort of in terms of your higher productivity. I'm talking

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about from your SI perspective, they're going to see significantly higher productivity and customer is going to ask for it to be sort of that benefit to be shared. So, where is that linkage. Then there are the solutions that you bundle with the SI offering, which goes as a complete package to the customer. So, what is the deflationary impact on your part of revenues for a typical project? Yes, so that is my first question?

Sunil Pillai:

Bhavin, actually that's what we have been trying to emphasize and re-emphasize. We see this as an opportunity, this we see AI opening up a new category in the market and we see it as an opportunity. So, we believe that all the SIs that you mentioned, they are also gearing up equally alongside us. It's not that we are the only ones doing it, the whole market is moving towards that, and all the SIs are also gearing up.

All those people who would be maybe on a what you call on a not relevant right now are being up-skilled and they would be redeployed for AI deployments and AI consulting services. So, all these big names that you hear and see they are into that journey alongside us. What we see is that it's an opportunity, it's a new category that opens up. And we don't want to quantify it as of now because it's just round the corner that it's just eight, nine months back that it's just pumped up suddenly and if you see it everybody's making noise around it. So, we want to wait and watch, we want to see, but then we are gearing up, we are not going to miss the bus for sure.

Bhavin Shah:

Sunil Pillai:

No, you've mentioned that it's opening up new opportunity and I understand there'll be new opportunities. But, I think on an apple-to-apple basis if you're doing one project prior to this massive improvement in productivity driven by some of the latest AI tools and doing a project in now maybe you will get to do more projects I understand that there're more things that customers want to do, but on an apple-to-apple basis, so I'm trying to figure out the difference, the value, the pricing versus the volume change. You're saying there is no pricing change? No, that's not what you were saying?

No, that's not what I say. But I'm saying apple-to-apple on a lighter note, it's like a Kashmiri apple to an American apple. So, the apple is only going to be larger in size. Please understand, for an AI to be implemented at a customer place, any LLM kind of a solution to be implemented, you need to have an underlying infrastructure to be changed.

Now, that is where obviously the customer has already spent and he's going for a tech refresh. These are all expensive infrastructure. If he wants to adopt to AI, LLM, then he needs to have those infrastructure in place. He needs to have high end processors in place. So he needs to spend those money. So I consider it to be why I am just giving an example drawing a parallel like if my estimate is that on an average billing is ₹25 lakhs per invoice, my average may go up to ₹32 lakhs or ₹33 lakhs kind of an average in the same manner.

Swaroop Muvvala:

Bhavin, just to add to what Sunil is saying. What we are trying to say is, look, yes, your point on productivity is very much valid and as AI is expanding its wings, the productivity is improving and the average time and cost which is required to complete a project is decreasing. But you have to understand us, we are what you call it as a value-added distributors wherein we recognize our revenues by selling products, not services.

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Services is a very small component. So as of now there is no what you call it as a deflation in our revenues as of now. And what we are trying to say here is in the short-term future or a midterm future, this entire AI will open up doors for newer avenues because more and more projects will come up, more and more applications will be developed, and more and more activities around it will be done. Hence, we see this as an opportunity for us.

Bhavin Shah:

Sunil Pillai:

Bhavin Shah:

Sunil Pillai:

Bhavin Shah:

So, Yes, related to that Swaroop, there's a whole thing about AI eating products versus product eating AI debate. And we talk about products in all the product in all the cybersecurity stocks had collapsed, rightly or wrongly when these announcement came around productivity gains. So, what you're saying is that at least as of now, you are not seeing a drop in pricing of those products?

So, Bhavin, there won't be any change in the pricing of the products. Where the change is going to happen and this, which is very evident that you see across is that, let's say, you take any project. If a project is going towhen it used to take conventionally about a year to roll out, now that will be on a faster pace. It may not take one year, it may take less than one year. Let's say, about, let's, I'll just cut it down by 50%, it will take about six months. If you had wanted about 300 people to get in and then the do the implementation part of it and the rollout, today you may not require 300 people, you may require only 150 or 120 people. Now, that is where and we are not into that business. That is the SI's business, which they do. We are not into that business, we don't do that business. We sell solutions, we curate those solutions, we do multi-stack of those solutions, we go and at the most one-time implementation is what we do. We do not do the sustenance part. That is done by the partner community.

Sure. Okay. And one other question, especially for some of us who have been investors in the stock since IPO and so we are sort a little bit familiar with the outlook for the company not just for FY'26, but also FY'27. I understand you don't want to talk about FY'27 right now in number terms. But from the comments you made about delay in legacy, decisions related to legacy solutions and so on and the challenges you mentioned, do we take it as sort of moderation of outlook for next year relative to what market sort of assumed at the time of IPO?

Bhavin, we have not lost the zeal or we have not lost the excitement about any financial year, because we have seen this, this is not the first time AI has just come around, we have seen many more than this worst situation and we have been growing. So, keeping our tradition and historical data that it shows, I don't think so we need to worry. We are not worried. If anything is there, we'll definitely highlight it. As of now as we speak, we don't see any situation where we need to be alert about.

Okay. And the final question from me you have certain partnerships or some alignments with specific vendors. Sometimes those vendors do well in the marketplace, sometimes a competitor does well in the marketplace. Given all this disruption that we are hearing, do you see the need to change your partner strategy a little bit somewhere here and there or how are you handling this because maybe the partner you have for the product is not as good with this AI shift versus somebody else?

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Sunil Pillai:

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Well, Bhavin, it's not about good or bad here. We anyway go through the churn every year, we look at new entities, like Mitish and my CEO is planning to be in the RSA Conference which is due in March 10th. The reason that we go for such conferences is that we identify which are the niche area, what are the niche technologies available, what is the next wave of growth coming from which technology or which what you call which sector in the market which is going to grow.

So we go for those things and the reason that we be there is that and show our presence is that to identify those marquee OEMs and sign them up and bring them into India and host them in India. So that's an ongoing thing. If you have noticed that in the last three years, we have onboarded about close to 30, 33 OEMs.

Some OEMs may not be relevant as good as last year, but if you see from the AI side of story, those OEMs are started becoming relevant. So, if you ask me, did we know about AI coming? No, we did not know. We signed up because we saw an energy with that product, energy with that technology. So, we signed them up, but now we find that it's a Eureka moment when we look back, probably our decisions were right.

It is so relevant for the AI kind of an economy right now. So, we will be signing up new products. Yes, there could be potentiality some moderation with certain traditional conventional OEMs, but that is not going to stop us from in terms of our numbers.

Bhavin Shah:

Sunil Pillai:

Moderator:

Patanjali Chinta:

Sunil Pillai:

Mitish Chitnavis:

Thank you.

Thanks.

Thank you. Our next question is from Patanjali Chinta from Nuvama.

So, just to get some sense on this AI-driven cybersecurity. Can just give us some case study kind of an example where what is the customer's requirement in terms of AI. For what was the solution which was offered by us, what was the timeline in which it was implemented? If you can just give one specific case study, it will be really helpful for understanding what is the value that iValue provides?

Yes, thanks Mr. Patanjali. I'm sure that Mitish is very tickled by this question. He would love to answer this because recently we did, probably Mitish without saying the customer name, probably you can share one of those.

Yes, so it was a financial customer, let me explain this way. They were doing their SOC transformation that is the security operation center transformation number one. Number two is what happens in a SOC transformation is that there are lot of data that gets written into a SIEM platform that is the data platform for identifying an incident in a very simple language.

So, the customer, what he wanted was that, how can your stack reduce my storage capacity, but still identify the incidents at speed? That means every incident that gets identified and churned has to be within two minutes, and then it has to be taken an investigation process.

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So, where we came into picture and we understood the customer's problem, , we said that, "Yes, fantastic, this can be done because in a security operations model, that means all the endpoint logs, security logs, data that is being written onto SIEM, there's nothing in between which was able to filter and enrich those logs." So, we integrated AI systems into every event. That event became so enriched that when the only filtered data went into the SIEM for correlation and identifying an incident, but rest all data which was not required which dropped. So, that being said, the customer understood this entire value prop that we brought to the table and we demonstrated that entire solution back to the customer and we implemented that.

So, there are lot of backend process and hard work and effort that goes into it, but in a nutshell, this is how we added direct value to the customer working with that partner.

Sunil Pillai:

Mitish Chitnavis:

Mitish, this is a little high end, to audience. So if you could just tell that OT1 that we did. Probably that could be a more relevant example

Yes, so there is a contract manufacturing company, okay, which is doing contract manufacturing for world's largest Pharma and Consumer company. And that company had a major challenge and major impact in terms of their entire operations in Ireland were actually hacked, and there was a ransomware attack and entire operations were down number one.

Number two is that, when we identified this, and we started working with customers, we started identifying this particular customer, we started identifying the entire operational technology issues that he has. He had no protection, getting the AI in between, and so on and so forth.

So, we started implementing certain products for him and that solution stack, where we were able to multiply our point product sale, which was going on, trying to help him recover from ransomware and we ended up selling him five-six products under a single solution.

We started working in an integrated fashion and we started also managing their entire infra from a service or security operations point of view. So, security operations we took over, we helped him to recover from ransomware. We sold him the solution stack, and we worked around and helping him to ensure that his other factories, that is in South Africa, China, and India, as well as were fully protected with this solution stack. But that's what we did out there as well.

Patanjali Chinta:

Mitish Chitnavis:

Got it, and one other case study on this data centers, right? How are your solutions being implemented in a data center setup?

Wow. That also I can answer, okay. for a data center to run, let me put it that way, there are 31 different types of products that are needed for a complete data center to run. One of the data centers that we are designing right now and I can speak about that. Because it's absolutely fresh in my mind as well.

So right from cooling system, cabling, network, so you take network Arista, server compute is super micro, firewall is checkpoint and they're running GPUs on top of it. So, I'm going up to application layer. So, if you look at the complete running of a data center. this one is for absolutely BFSI under RBI, I can tell you. And there are almost 31 products that go into a solution stack to run a data center. And I'm talking from observability, monitoring, data center

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should not go down, some kind of billing system, to building that entire infra. So if you look at all of those, those are like 31-odd products that go in and that's how we're working.

So, as I said, network Arista, compute, applications Confluent, Cloudera, Dynatrace WAF, Endpoint, DLP, PAM, CyberArk.

That's. Okay, Mitish.

Sunil Pillai: That's. Okay, Mitish. Mitish Chitnavis: I can go on and on, but I can just tell you that this is what is needed. Sunil Pillai: Yes. So, these abbreviations probably may not be familiar. Okay, fine. Mitish Chitnavis: That's why I said 31 products and I summarized it, you know, but that's how it's built. Krishna Raj Sharma: Yes, just to add on what Mitish said is that, I think, every organization which is planning to adopt AI has got lot of housekeeping job also. They just can't get up in the morning and saying that, "Hey, let's go for AI." I think there's lot of study which they have to do themselves before they starting, even thinking, planning, plotting, designing the requirement at least.

I think they have to summarize their ask before they really want somebody to start looking into that. I think that housekeeping job itself is an opportunity for us. I think it starts from there. Moderator: Patanjali, does that answer your question? Patanjali Chinta: Yes, that's all from my side. Moderator: Next question is from Karthikeyan Vaidyanathan from RK Investment. Karthikeyan, please unmute your microphone and go ahead with your question, please. Karthikeyan V.: Okay. Thank you. So I have a couple of questions. So one is you mentioned about this moat where you said you have 100 plus OEM curation.Which you said it cannot be replicated by hyperscalers or startups. Could you could you give some more in-depth in terms of why is it that hyperscalers cannot replicate this for the moat that you have? Mitish Chitnavis: No, I think what we are trying to say is that, not that anyone cannot replicate. Technology information is now available. Everyone can replicate it. The point we were trying to say here is that the moat that we created using the curated model of this solution stacks, that moat which we have created and strengthened it by getting AI native products into it, that's the moat which is going to be little difficult to replicate for some time at least.

So hyperscalers, they have their own products. If you look at GCP, AWS, Azure, they have their own. And we are not competing with hyperscalers. In fact, we distribute hyperscalers, we work with them very closely. But the model that a customer wants to run on these hyperscalers today, not depending on their shared security model, not depending on their shared responsibility model, is where we have already built a kind of curated OEM stack using these 100 OEMs that we have signed, because of which we believe that there is a moat that we have already created and those those OEM curated stacks are littlegoing to be very difficult to replicate for some for a certain time.

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Krishna Raj Sharma:

And it's more than a replication, I think it's the differentiator in the market today against, anybody who's trying to play that value. This is the this is the latest value which we have delivered to the complete value chain what we have, that's the value to the OEM, that's the value to the partner and that's the value to the customer.

And this is very flexible where we can actually replicate the customer scenario into this piece of moat which we have built, which could be experienced by the customer and also exhibited by the OEM. It's more of a differentiator than somebody trying to, replicate that.

Karthikeyan V.:

Thank you. So and the like going back to the previous question that the gentleman asked. So, I come from a data center domain, so I understand the products, that 31 data center products that you mentioned, which is and you gave an example of BFSI under RBI, right?

So, this is like a you know the solution that you provide, let's say these 31 data center products that you deploy, is it like a scalable solution where once you do this for one particular BFSI, whatever, you could just take the same and give it to other vendors? Is it like a straightforward I'm just trying to understand from a scaling standpoint.

Mitish Chitnavis:

Karthikeyan V.:

The answer is yes, the answer is yes because around I would say 85% to 90% would be same. Sizing may differ because stack is always same, the difference is the sizing, the depends on the data center size that you're building. How many megawatts size you're building…

So let me go back to like you mentioned about Yotta and E2E as an infrastructure, right? And I was thinking maybe if E2E is deploying this.They would probably need these 31 data center products as well. So, I guess my question was coming around that.

You know, how does it differentiate, or do you actually collaborate with such vendors, or is it that they don't use these tools? I'm just sorry, I'm and maybe it's too technical, but just trying to understand how is it that E2E is dealing with this and how are you differentiating from something like E2E?

Sunil Pillai:

Okay, so Karthik and I can partially answer that, balance Mitish can take over. Since you took the name, I'm not taking that name, you have taken the name, so I can tell you one thing about them is that they try to build a lot of solution in-house. Because they need to be, what you call, price competitive. If they go for and start deploying certain solution that is available in the market, , obviously, it's a costly affair. So what they do is that, I know them personally, so hence I'm telling, this if you go and if you talk about a solution, they try to build those solution internally because they have resources to build in and then they say that they are ready and they are protected.

So, if you ask me a question that are they your customer? Yes, they are my customer also. And are they customer for all 31, 32 solutions? No. They may be customer for only three or four solutions, rest of them they would build it, some of them they buy from some others.

Karthikeyan V.:

Excellent, Yes, that was exactly what I was looking for. So, thank you so much. Just two minor points. So, one is you talked about, not using third-party neural network models. Again, I asked this question in the previous investor's call as well. Do you have in-house neural network models

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as part of any customer deployments, or are you using open-source models and fine-tuning them anything on that?

Mitish Chitnavis:

We are also we are right now using open-source models and fine-tuning them to and building guardrails around it so that we understand the requirement and from and the outcome which is needed and then we customize it for the customer. That way. But typically, there are two flavors, just to elaborate on this, there are two flavors.

Our existing OEMs are coming with their own AI models and they're their own AI engines and neural networks. That's what gets deployed along with the products that we sell, resell and implement for the customer. In case there is an additional requirement which has to be fulfilled by any other model, that we will take up. but that has to have complete data streaming and so on and so forth. Then the entire package has to go in at end of day.

Moderator:

Thank you. Our next question is from Satish Venkatesalu from Fourstor.

Satish Venkatesulu: Yes, so my first question is on the annuity side of business. Can you just briefly highlight what percentage of our revenues are in annuity which are recurring and if there is any non-recurring annuity business which we have mentioned as part of that revenue? Swaroop Muvvala: Around 42% of our total revenues are annuity in nature and this is a metric which we have been publishing it on a quarterly basis in our earnings call. Satish Venkatesulu: Okay. And do we have any business on the CSP side, like Cloud Service Provider, where we resell or provide services from AWS, Azure, or GCP? Swaroop Muvvala: We are a value-added distributor, we are the only value-added distributor for GCP.

Satish Venkatesulu; Okay. And one last question, not related to the business. So, I see some promoter selling in recent history, so would like to know what is the future. Swaroop Muvvala: Let me clarify that, the promoters have not sold a single share and there is no intent of selling single shares at all. But you can see the holding percentage coming down because of the ESOP issue which has come up.

If you can go through our DRHP and other things, we spoke about the ESOP which has coming out and employees have got fresh shares and hence the holding of the promoters will optically come down. But if you see the number of shares promoters have held, not a single share has been sold by any promoter.

Satish Venkatesulu: Is there any plan to increase the promoter holding, because it's quite low compared to what we have?

Swaroop Muvvala: Again, this is this is a point which we have always spoken in the earnings calls and in the decks there. If you see the history of, iValue has been started by almost 20-25 people together and based on capital contributions, 10 people have become shareholders. Out of these 10, out of the roles and responsibilities they handle and many other factors which are there, three people have been categorized as promoters.

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But all these 10 people are still together, and these 10 people together hold around 53%-54% of the company. So, we look at it as the total promoter holding being around 53%-54% of the company. When I said promoters did not sell, none of these 10 people also have sold so far. So let me also add on to that.

Regarding what you call it as a buying of shares by the promoters, I think as of now, we are legally prohibited from making any purchase, because a contra trade will be recorded. After the legal window is there, based on the scenario at that point of time, the promoters might take a call. It is completely their individual choice.

Moderator:

With that, I hand over the call to management for closing comments. Over to you. Any closing comments from the management team?

Sunil Pillai:

So as we are in March, we are all busy with our closures for this March and as I said, I reaffirm that, we will keep our outlook what we shared with all of you for this year. And we will certainly share our plan for the next financial year after our board meeting.

We are not prime facie as I see it right now as it is, we don't intend to change the outlook for next financial year also, but we will confirm that to you after our board meeting. Thank you very much, I know it's a festive time. Happy Holi to all of you and thanks a ton. Thank you very much.

Moderator:

Thank you. With this, we conclude today's conference call. On behalf of iValue Infosolutions Limited, we thank you for joining us. You may now exit the meeting.

Note: This transcript has been edited for readability and does not purport to be a verbatim record of the proceedings

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