Intellect Design Arena Ltd

The Transcript of the Analyst/ Institutional Investor Call is also available on the website of the Company. Kindly take the above information on record and confirm compliance. Yours truly, for Intellect Design Arena Limited, V V Naresh Company Secretary and Compliance Officer Encl: As above VUDALI VENKATA NARESH Digitally signed by VUDALI VENKATA NARESH Date: 2024.12.27 15:02:00 +05'30'

Investor Meeting Transcript Praveen Malik  Greetings and welcome everyone. Thank you for joining us today. The exclusive event for the investors, Transform Operations with our enterprise AI platform, that is, Purple Fabric. We have with us today, Mr. Arun Jain, Chairman and Managing Director, Mr. Banesh Prabhu, CEO of IntellectAI. Then we have Ms Vasudha, she's the CFO and we have Mr. Deepak Dastrala, he’s the CTO of IntellectAI. So now I hand over to Arun for his briefing first. But before that, one safe harbour, I would like to remind you that anything which we say, which refers to our outlook for the future is a forward looking statement, which must be read in conjunction with the risk the company faces. With that, we hand over the mic to Arun. Over to you, Arun. Arun Jain Good evening everyone. Thank you very much for joining this special session on Purple Fabric. This was requested during the investor presentation where all of you asked us to present what the Purple Fabric means. We know it's a multi-agent AI platform, but what does it do? And that was a curiosity you demonstrated in your investor conference. So just to recap what Intellect Design Arena stands for. It addresses the kind of wastages which is happening in the way the IT industry works. I was addressing a bank conference two weeks back and some of the CEOs are saying that we have a technology budget, need to increase, somebody is spending more than double digit billion dollar technology budgets and they think we have a technology manpower of more than 50,000. Is it a right thing to do that you need to have 50,000 people, you need to have spent more than 10 billion dollars, 15 billion dollars for technology? Something is wrong somewhere that we are solving the problem and creating a forest and jungles of the knowledge or data. And if you apply first principle thinking, which I was reading a book of “What is life?” from Paul Nurse, he simplified the life - which is so complex to me - into just five words. There's a cell, there's a gene, there's an evolution, there’s chemistry and there’s information. So six words, five words, we simplified life. And similarly, when we talk about Intellect Design Arena, what we had achieved in last 10 years, yesterday was our 10th anniversary of getting listed in Bombay Stock Exchange. On 18th of December, we got listed in 2014. In 10 year journey, what we have achieved is. We ask this fundamental question of why banks should exist, why financial institutions should exist. Financial institutions should exist because there is a financial event happening in the life of a customer. And customers are consumer, SME and corporate. There are three categories of customers a bank has. And a bank provides just six or seven services, core events they handle. First event is opening a SAVING ACCOUNT when I get a job first. Then maybe some LENDING ACCOUNG to buy my scooter or a car, or I have surplus money to do the DEPOSIT , or I have to invest the money INVESTMENT , and I have need a CREDIT CARD and I need to make some payments so PAYMENTS These are the role of a consumer banking. When it comes to SME, then I may add SUPPLY CHAIN FINANCE to it or working capital to it. And if it is a corporate, I need a GUARENTEES or some instrument of LC and guarantees, which is important for me to provide my guarantee to some of the

Investor Meeting Transcript imports or some of the export I'm doing. So these are the seven, eight services, bank events happen in the life of the customer and bank provides the services. And these services, when you package into technology, is called micro services. So the events are finite. The services which is required to support is also finite if it is revolved around client. But today, if you look at it, many banks say, I have 50 products, I have 60 products, I have 150 products. and when we go to Europe and America, they say we have 400 products. Now, these 400 products, when they have to support with 400 different databases, you are creating a huge data waste and we are challenging that initiative. And then similarly, when there are 400 products, they get connected to each other there will be 10,000 APIs. We simplify this entire architecture to make it finite thinking possible. There are only 386 microservices. They are close to 750 events, even micro events. And there are 2,000 plus APIs. That's all.  So now, by making everything finite, we are able to go deeper and deeper and providing the configurability to compose a solution and to contextualize the solution. This is what we call Black Book for us when we go to customer; we just show this black book that this is the map of the banking. There are 13 product lines bank operate on, on the top we have the channels and bank office operations here. Then we say each product line, if it is G1, how this product line map to the detailed processes of the bank and each process over here is numbered, is zip coded. And for each line of business, lending, to Credit card, to Wealth, to Transaction banking, Liquidity, all our Wealth management is put into eMACH.ai. So this is my core collateral for selling my banking. Our propositions to the banks are stop buying products, start buying customer solutions and you can do the composing yourself. And this is available in the next level. Each micro service has been codified into a coding zip code number. Each API is written over here with the API number. Each technology is in a Blue book such as a micro services security for getting enterprise grade solutions. So we invested close to 20 million hours of the time in 10 years since we went for public. Two million hours per year, you had the international cost of $100 per hour. It's a $2 billion cost. At the cost of India, 20 million hours, it still cost $400 million of the investment which we made in building this platform together. Today, I want to spend some time on what came out. We have a team of close to 150 research engineers since 2016. They were operating from US, UK and India. And these people, since they started in 2017, they knew what works in AI, what didn't work in AI. We failed in 2018, we failed in 2020. We did some work, we succeeded to bring back with accuracy, 87%, sometimes bring 89%, sometimes you bring 91%, but we are not able to make it usable AI. The difference is the promise of AI and usability of AI. And that's the biggest gap we find in our journey. But since we had more chances of exploration and experimentation between 2017 to 2021- 22, when generative AI landed us in 2022, we could identify the right places where generative AI can create a larger value for the customer. And we, over the period of time, when we built up these use cases of magic submission in America for insurance underwriting space, or we built up this technology for ESG for the largest investment bank, Norwegian Investment Bank, or we use it for magic invoice processing. All were build on the same technology. And this technology revolves around principally eight technologies. First is how you get the data from the various sources, whether it's a structured data sources, which is a data source DS 1, you get a data sources from second data source, which is a document which are present, which customer is submitting it, or document you are crawling and retrieving back from a website of a customer, that is a DS 2 data stream 2. There's a data stream 3 which you pick up from , from,

Investor Meeting Transcript not websites, the paid data, you buy out from Dun & Bradstreet or ROC data. Then there could be a regulation related data and there could be operation related data. These data, we convert into knowledge by using four technologies. The technology number one is ingestion technology. Technology number two is, Deepak will highlight to you what these technologies are. Technology number two is classification technology. Technology number three is extraction technology and technology four is trusted data technology. Now these four technologies, once it is done, we call it ‘Document “Intelligence Management System”. And this is the four technology constitute to create DIMS system, which is parallel to DBMS what Oracle has promoted in 1985. So today, with the AI coming into picture, banks needs a document intelligent management system of their own. It's not a part of LLM, it's a part of the bank, their own LLMs, which should be sitting in the bank system, or whether we call bank LLM or bank SLM. But these documents must be tagged so that we can do a continuous KYC and continuous risk monitoring can be done on the same documents. From this, we move to technology number five, which is how do you create agent, which will be shown to you Deepak will show you how we create agent by feeding in the information by creating almost like a digital expert. So there's a digital expert and there's a human expert. So how do we replicate human expert to digital expert is the technology number five. Once you get a human expert, we simulate an operation room where five or six different category of experts are there, which we call role holder. They could be sales manager role holder. They may be ops manager, they may be compliance manager, they may be a collection manager who is responsible for collection. They could be a risk manager. So all these roles, we create five human expert in operation room. Now technology number six is how the six digital experts are talking in a same operation room is technology number six. Now, once the six people come together in an operation room, you can understand how much noise they can create because everybody has their own perception and contradictions. To manage the contradiction among the six people is the biggest job management deal within a bank. But in AI world, it's much better.  We developed a very, very highly sophisticated algorithm called ‘Socrates Dialogue algorithm’. We trained this digital expert to do the prompt engineering among themselves and ask the questions which make the difference to their life. And that is Socrates Dialogue is technology number seven. And once these contradictions are there, we record all the conversation behind it with the traceability. So if the regulator wants why you come on the decision or business head wants why operations has decided in particular lending or not, he can look at the actual Use Case and traceability of it. And all throughout the journey, then this the jury of six agents will give a final file or final recommendation and that recommendation go for the human advisor who is there. So I am converting your operation room of 100 people to just few recommendation advisors or who can take decisions on it. And that's recommendation, the technology number eight, which should come in the shape of file or it can come in the shape of recommendation letter. So this 8 technologies, we wired together and all are wired into a single box with all the security and entitlement embedded into it. And all these technologies are LLM agnostic. So this is our first platform, which is LLM agnostic. It's not that Microsoft is selling Open AI, which is better than Bedrock. We say for each different use case, we do the benchmarking of the LLMs. And this benchmarking of LLMs

Investor Meeting Transcript capability is on three AI constraints, which is a speed, cost and accuracy, we benchmark which is the best LLM there. So this purple fabric platform can consume the best and optimized LLM model for solving a specific problem, not one problem. Within one problem, we may use multiple expert agents.  Deepak will show you how we use for solving one problem more than 13 experts, digital experts to solve that problem. Now you look at it, what is the market size as an investor? How will it translate into the business scenarios? I'll come to you after Deepak present to you Purple Fabric, that’s how we are translating this into the business case through three different business models, how Purple Fabric is leveraged for creating a shareholder value. So at this point in time, I want to give the stage to Deepak.  Deepak Dastrala  Thank you, Arun. Good evening, everyone. Let me know if you all can see my slides .   See, one of the biggest opportunity, I mean, historically, all the technology companies spent in terms of helping the banks. But if you look into that typical area opportunity, which at this moment we targeted, was IT costs of a bank is anywhere between the 11% to 17%. And at least 40% to 46% of the cost goes in terms of an operations. And this is a completely new space is actually up for the opportunity for purple fabric. Because right now, if you see the nature of the work that can be done in the middle and back office , so far the technology could not penetrate into that because it requires a human level of capability in terms of intelligence to do that. Now, with where the AI is, where the purple fabric is, I think this is the completely new opportunity space, I think where we are working on the various use cases, and I will walk through some of the use cases. So the value of the Purple Fabric is not only to bring in the operational efficiency or reduced the cost, but also in terms of helping the banks to improve their revenue through h improved customer satisfaction, enhance risk and compliance and all. So pretty much we help both in terms of banks improving their top line as well as their bottom line. And just to give a context, when we say AI agents, this is very important for me to set the context. Because a lot of people, historically, even automation, they might have a view about the bots. But why agents are different and how different they are from bots? At a very high level, the expert for some particular digital agent to become an expert agent, one, they should have a capability to leverage the foundation models which pretty much trained on the universal data and process it. That's what first brings the human level of an intelligence, number one. Number two, it should have a memory, just like a human, right? How we have a short-term memory, long-term memory, it should have the memory. Plus, it should have an access to the tools because with the kind of a digital transformation that happened over the past decade and so a lot of the digital information is available through the APIs or the website and everything. So it should have an access to tool for it to act. But to a little bit further context, right? To go right at a very high level, you also can look into that, a combination of IQ, EQ and AQ. Why I'm trying to highlight this is because at this moment, what we are trying to do is build a digital twin of a human. It means at a very high level, it should have an understanding about a

Investor Meeting Transcript language it should be able to synthesize the knowledge. And more importantly, it could reason and reflect because a lot of people who are playing with the chat GPT, they might be using it to create a, using a text, they can create a text or audio or video. But I think what we are talking right now is about actually further where like you can do a human level of reasoning and reflect on certain things before making a decision.  Another thing is EQ, which is empathy. And because whenever we are talking about one of the biggest use cases around the customer service, you should really have a right level of understanding what the customer is talking about, whether his customer is sad, angry, happy, any of those things, and also adapt based on that. Then the AQ, and this is the most important thing, because what people haven't leveraged the AI for so far is, once you reason, once you understand, once you contextualize what actions that you can take, which typically so far only humans can do. Now, as long as anything is digitally available, actually these agents can take an action leveraging the tools and all. And this is just to give you the context about what qualifies when we say expert agent, what actually qualifies in terms of the cognitive ability. Now at a very high level, Purple Fabric as Arun highlighted, these are the eight proprietary technologies that we have built in the last 10 years with a lot of research work that we have done and all. So if you see a lot of people, I know at this moment, they just plug into the LLM and they call it as an AI platform. But what is the fundamental challenge is still the age-old problem of garbage in garbage out exists with the data. Unless you really get the data at the right quality, this models cannot really make sense.. So what we use this LLMs is largely for purely for thinking like a brain. Actually the doing part is what we have the unique these capabilities. And what we also we do is, for example, while we have this capability, the biggest opportunity what we have is how we can leverage the knowledge that exists in enterprise. That knowledge could be enterprise structured data. And here, right, a lot of critical information from a decision perspective, it helps in such data. So we have ability to really tap into structured data. Then the unstructured data. And this is where we have spent lot of time in last 10 years. And because everyone who focuses on the data quality, all the AI program, they have a challenge. And when they say challenge, they still they're struggling with structured data. And the 10 years’ work that we have done in unstructured data, which is what gives us a significant differentiation. And we see the unstructured data, it could be a text data, or it could be an image data. And right now everyone's comfortable with multimodality, but that's what we spent last 10 years, how we can get this data right at a scale. And also we can tap into the knowledge of enterprise policies and practices because most of the banks have their own, let's say from lending perspective, what are the guidelines, what are the process, policies that they need to apply for a different line of business so we can tap into all of the data. And along with the market data, it could be from website, it could be the third party providers, it could be anything at any frequency of any format. And then also the regulators kind of a thing. So these are the five different knowledge’s that we can tap into, whether they're in a structured data form or unstructured data form. Now, a little bit going into the eight technologies that we have developed.  The first is Data Ingestion technology. Here we can actually ingest the data at a huge volume, and basically at any scale that is possible, whether that is structured or unstructured and all. So, and also some for most of the use cases that we need to do, probably we have to do it at a near real time across the various sources. That's what we can do. And then especially in

Investor Meeting Transcript enterprise, people will just leave the data wherever it is. And it's very, very important to have an ability to classify the data, whether they're structured or semi-structured or an unstructured form. Because the classification is so important in terms of getting the context right and all. And normally you might see right in the landing, someone just submit all the documents, all of that be under one PDF. So how do you know actually which one is a PAN card, which one is a passport, or which one is anything else, right? And sometimes they do it within a one page itself. So we'll be able to classify the data at a various level, at a page level, the document level, it could be anything and all. Then how do we contextually extract them? Because it's not just an extraction. This is where the major difference is. And we need to really understand what I meant by contextual extraction is which Use Case we are looking into that. Within that Use Case also, what sub-Use Case that you are looking into and how do you really get, right, and all. Because the word policy might be very different depending upon when you're looking into the insurance versus some other context and all. So how do you understand the context? This is where our deep domain knowledge in the BFSI will come in and that's what differentiates us from anyone else and all. Because it is not just about just applying in AI, right? Unless you understand the domain, unless you understand the data, you really can't get that last 2% right. And for us, this contextual extraction, where our deep domain knowledge helps in terms of how do we apply to the various banking, financial services, and the insurance use case. And ultimately, it is about trusted data. So because especially when you're taking the data from so many sources, how do you triangulate it? And how do you know which one is trustworthy, especially when you're combining your in-house enterprise data versus external market data? Even external market data, you don't depend upon just one source. You get from multiple sources. Each of them may be best at some particular entities and might not be good at other entities. Some might be good for MSME, some might be good for the large enterprises. So how do you bring all of together in contextualize where actually you can make a critical decision in your bank on the particular data? So that's what this trustful data helps us.  Then the expert agent, as I mentioned the expert agent what qualifies, right? Now there are the three major user journeys what we focus on while you might see so many co-pilots out there, so many agent related platforms out there, and we see it as three major journeys. It is basically assist, augment and autonomous. Assist this could be any help that will help you. It will give you only the 10 to 15% incremental productivity. Augment is we are talking about a very deep domain expertise persona within the bank. It could be an operations person, it could be compliance person and all. So that's where we focus a lot of our use case because that's where our domain expertise helps us to really make a difference and all. So how we build deep expert agents and all that is the augment. Then autonomous could be typically any critical decisions never happened by the one person within the division or sometimes even one division itself. So how do you have this expert agents within the divisions and across the divisions can work together to actually make the business decisions happen. So a lot of our focus is around the augment and autonomous any that's where our domain knowledge and data understanding that we worked on for last 10 years makes a difference. And finally, the decision and action. So even the decision, right? How do you actually make these agents collaborate each other? Because a lot of people are talking about agents there. Our differentiation is how these agents collaborate each other and how they actually come together to the decision and all. And then finally, what actions and all. When probably they

Investor Meeting Transcript are happy for someone to bring into the loop and when they are comfortable to make a decision. These are the critical capabilities of Purple Fabric provide. Again, we are completely cloud provider agnostic and the model provider agnostic, and our deep knowledge has really helped us to really solve the last 2% better than anyone else. So what I will do is just to contextualize it further. I'll focus on the one major use case. By the way, I think to the best of our understanding we have with talking to many firm, we are the first one to put out there the multi-agent use case in the world. While everyone is talking about the platforms and I think we already went live in a highly-legated financial services form and I'll walk through that particular use case now.  So if you look into the use case, Right now, the complaints are claims. While they're a universal problem, right now if you just take UK alone at this moment, this actually, it's costing around $9.24 billion a month and handling the complaints across the various businesses . This is the data from the Institute of Customer Service in UK. So if I just take the Use Case that we have operationalized, especially after the consumer duty came into the picture last year, a lot of people are complaining around the various financial services. So what we are talking about a Use Case with a one of the largest financial services firm where they're dealing with so many complaints and these complaints are actually sometimes cost them anywhere few thousand pounds to the sometimes a millions of a dollars kind of a thing. And they're not that easy for someone within their department to really investigate in a short span of a time. So the reality out there is at this moment while they have almost more than 150 people they're able to actually meet only less than 30% of the SLA. Because of the complexity of the case, it is taking so much time. And the number of complaints are just increasing, especially because some of the legal firms are just recruiting the customers who have a concern. they are doing everything on behalf of the customer. Why it is so complex is typically this compliance could be anywhere between last year to the last 14 years. And each of the time, the policies or the regulations might be very different. And there is no single source. The data might be across the various sources. And also because there is no standardised rubric for the nature of a complaint, it will be extremely difficult for people to really investigate and do that within SLA of less than 50 days. So if just for you to understand what it looks like, this is the typical operations team that looks like. If you look into the, if you have to deal with the capacity of anywhere between the 200 to 400 complaints per week, at this moment they need anywhere between five to 10 FTE just to look into the complaints, log those complaints, and they need five to 10 FTE just to classify and understand whether the complaint is right or not. And they actually need a 50 to 100 FTE if they have to meet a capacity of 700 per week to just do the evidence gathering and then finally to the case handling, they need 100 FTE. So if you look into the whole thing, right, everything is manual in nature because of the complexity and the nature of the challenge. Only thing that this is, they have a complaint system where they log in the user to a system of record. That's all. That is what it is. Now what we have done is this is where we build this multi-agents who actually replicate this particular persona for us and pretty much each agent replicate this persona. You have a compliance manager, just like how in a, any division you have a manager or supervisor, and then you will have the complaints checkers who does the various roles. Someone focused on extracting the complaints, someone goes and creates, not reports, someone focused on verification and all.

Investor Meeting Transcript So what we have done, how these agents will work together is they work together in terms of tracking the incoming complaints, understanding and classifying them, creating the whole dossier, which shortly I'll do a demo, and finally actually recommending the decision. So the most important thing that you need to understand here is these sources could be both structured and semi-structured and could be unstructured, number one. Number two, we never just use the one LLM to do that. And this is where Arun is referring, right? We benchmark what is the right LLM for the right agent for that particular job. So for this particular Use Case that I'm referring, we use more than six LLMs from the different providers. And 13 plus agents actually came together to really deliver the outcome. So the outcome that we have provided took five weeks. We are able to deliver the outcome in 20 minutes for 80% of the plus cases which has brought in the significant operational efficiency and customer satisfaction. The beauty of this work is now what we have delivered to the customer actually doing a very reactive work of doing the complaints because we have built agents in a modular and reusable form. Now the customer is using this to proactively look into who are the other vulnerable clients who might complain and what are the risks they have. So this is where our first principle thinking actually helped us to really build the whole thing in a very modern way. So what I will do is I'll quickly get into the demo and then I will probably walk through where exactly it is headed. So this is our Purple Fabric platform. And what you can see is so many agents that we have here. So let me go through the agent who's actually the complaints manager who pretty much orchestrates all the work that across the various agents. So what I will do is, because this takes some time, I'll just probably fire the case in front of you. And then I'll go through the previous case in the interest of time. So before I go through that, what you can see right now is just, again, for the interest of the demo, we're showing it more as a conversation, but the whole thing works as an API at the back end. So what you can see here is just I asked, I clear this particular complaints of a case reference, something. And what you can see is there are complaint manager immediately kind of called the complaint logging agent. And in fact, this is where you can see live what is happening and all. So the Complaint Manager, I gave a very natural language, investigate the complaint for case reference. So this is where the decision logic, because this way the explainability and everything will come into the picture. What the Complaint Manager has done is first, I need to extract log and verify the complaint using the Complaint Logging Agent. And then it gave the, what is the action. This is where I referred to as a, what is the decision, what is the action? You can see we made it completely, which will be available for the people to actually understand what happened. Because tomorrow, Regulator want to come and see okay, how these agents had made a decision? What exactly happened? Can you prove me? We have a complete audit trail at any point of a time how this happened. Okay, now if you see, right? I mean, this is just what happened is that it has kind of got that particular complaint. This is actually the letter which it has read and it got completely that information, which is a very unstructured here in nature. And then it has logged this particular complaint in the complaint system. You can see the input for that. What I will also do is just to see, it is also verifying whether the complaint has a mandatory details or not. And it is also clearing, giving the output. It is actually successful. For example, it is saying that all the required, if we can see what I highlight, and all the required information present, which is the first name, last name,

Investor Meeting Transcript date of birth, address, and everything. And also, it has the letter of authority and all. So just like how the human will do, the agents are doing it. Now what I will also show is it is actually it took that particular text that is classifying the complaint. This is where like a human, right when we read it, we can always see there might be more than one category might be qualified for a complaint. Sometimes when a human who is not trained, they might miss this. This is where agent has really read the whole thing, which is a text. And if you can see it has kind of understood the complaint category. For example, if you see here it has two categories. One is inappropriate product transfer and the partner service. And it also kind of validated according to the FCI category, what are those particular categories they come under. This is a pure human intelligence, which is actually replicated by the agents. Now, if you can see, as I said, right, complaints gathering. This is where you can see just these agents. It is actually gathering the data from the various client systems. And it is gathering the data now even from the data warehouse, which is, as I said, we have a capability to really get the data from the structured data. And all these things it is doing in doing parallel. Now, what it also doing is once it gathered all the data, it is saying that the next step is, for example, in this case the next step is to the side it started investigating that and all. So basically it has identified that I need to investigate two complaint reasons in a product transfer and partner service. First I will handle just like human, first I will handle it in a product. So it is doing all this particular investigation going through the various sources, which could be structured, semi-structured and everything. And then what it is also doing is now, now I have an outcome of an inappropriate product transfer complaint it will proceed to investigate the partner service. So it has completed one investigation and it is going through that. And because it is happening live, some of the work is in progress, but let me show what's already done just before this meeting I had done demo. What it will do, I'll go through the traces. What it will do is once the investigation is completed, it say, I have all the necessary details to generate a final report. Now I will use the Complaints Report Agent to generate the report. And what you can see is Complaint Agent has taken all the information that it has received so far and it has created a simple, easily understandable report. So let me show that in the actual, this one. If you look into this particular report, that you can see this, just like how the human at the end of the investigation prepares. Dear case handler, I have completed the investigation, so on and so forth. What is the client information? What are the case details? And what are the complaint categories? And what are the reasons? And how these are against the FCA? And what is the final verification status? The status is successful. And it got whole complaint summary, and it got all the, in this case, this is the context to say a wealth advisor. So these are the various products. It has all the particular product details the codes and everything, the client name, and all the advice details. And then finally, what is the investigation? So in case the first thing is, first is an inappropriate product transfer. The outcome is the complaint is rejected. The reason is it has identified actually within this particular record, where there's an evidence that client has agreed to the recommended portfolio and also accepted the higher ongoing advice fee. So I'll show you where it is. And then similarly, the second one is a partner service. Basically, for something to be qualified under the advice service, it has to prove that actually within the 12 months, the advisor has met the clients at least once. So from all the kind of a meeting details, it got all

Investor Meeting Transcript the dates and it has calculated that actually the advisor has met the clients multiple times in less than a year and it has rejected And it has given evidence for that. Now people may still, don't believe it. Just go for that. This is where that information is available in the document. What we got is only like one reference. And similarly, this is like some 28 pages document, the information available on the 26th page. We show exactly where that information came from. For example, if someone want to see, here there is a note like agreed client has a medium attitude or risk and is given. SRA willing to play high to high equity content. So all this is recorded. So everyone knows exactly where that information in and just leave it to the humans. How many people for how many hours they need to coordinate across them, across the various systems to achieve that. And this is, we're able to do that in less than 20 minutes. Now, going back to the, what was the outcome look like after we implemented the system. This is the difference that I showed you, right? This was the state before it was everything is manual. Now, everything that is manual, now we replace that with an AI agent. And in fact, we have an agent, which is also kind of recommending the decision, but we are leaving the decision to the human expert to look into it. And what was where they have some number of an FTE for each of this task. Now, they really don't need any FTE for that and actually the capacity could be infinite. It's up to them because we can scale the compute. Similarly for try-outs, they don't need anyone and actually the capacity could be actually scaled to the infinite. And then for the case handling, this is where we left the defining decision with the human. Actually with the same capacity, they can do the 10X of the work.  So this is the kind of an impact that we have provided to our customer. And this is just a one Use Case and we are already many use cases as Arun has mentioned in the insurance underwriting space and the ESG are non-financial risk. In fact, what we have done for ESG now in governance risk and the compliance space can be applied for any company. It is basically a corporate intelligence, then account payable, the trade finance. These are the various use cases where we are already making a huge difference in terms of an operational efficiency and the newer opportunities.  This is just a start. We are applying it across various intellect products. We have identified hundreds of agents that can be implemented within our intellect products. Over to you, Arun.  Arun Jain Thank you. Perfect timings, I think 4:42 and exactly less than 25 minutes to explain such a complex topic was quite interesting Deepak for looking at it. People may have many questions. I'll put on the chat box if you can ask that question on it. In the meantime, I'll ask Banesh how areyou trying to monetise. What is the business model through which Purple Fabric can be giving value to the shareholders? Banesh Prabhu Arun, I think you know I'm just going to touch on a couple different thoughts and also the areas that we are focusing on for our clients. So firstly, I think as we have already seen the platform capability, the operations area of a financial institution spends a lot of money. And the work we've been doing and investing over the last many years is actually helped us identify certain specific ways in which AI can be implemented for our customers. Clearly, we focused a lot on the UK, US and the developed markets because their cost income ratios are

Investor Meeting Transcript very high in operations over there. And the other important thing about the platform, I just wanted to state one more thing is that we bring in a lot of experience in BFSI. I think it's very critical because when we use AI we have to ensure an lot of focus on trusted data, the audit trail of what we do from an AI perspective, all of which Deepak has covered and something we have considered very carefully when we built the platform.  So, I think it's very important to understand because a lot of people are doing investments in AI in very generic cases. But I think our capability very much focused on ensuring that services are well managed from an AI perspective. I think is very critical. Now to Arun's question. We have 3 broad categories of how we are monetising the system as such Purple Fabric. So Purple Fabric has one where we basically offered the whole platform that you saw so that the client who's already been working on existing various kind of use cases and AI initiatives can actually bring those initiatives can take some of our use cases, can build their own use cases for which we actually train them through an Academy of Purple Fabric where they can get certified to actually build use cases, we provide them the support required. They can take various capabilities and tools that you saw on the platform that we have built. These tools are continuously evolving as AI is evolving in the market. So, we'll keep looking at new capabilities and enhancing existing capabilities. Deepak gave an example of how we use multiple LLMs we benchmark which LLMs is most ideal and also various sort of AI ethical focus around data because data is at the heart of it. And I think all the technologies the first 4 or 5 of the technologies focus a lot on data, and I think that's very important for us to consider. So, we actually offer the platform for them so that they can govern multiple AI initiatives. So if you are an CIO or you are an business manager or an CEO, when you have multiple use cases across financial services and across the various platforms that Arun mentioned as part of our eMACH.ai frame, the books that you was talking about, whether it's wealth, whether it's insurance, whether it's banking, whether it's lending or any of those areas.  As a CEO, there's a lot of pressure by organisations to capture AI capabilities which are very generic today. What we bring is BFSI focus platform that they can actually govern multiple AI initiatives, ensuring they use the best LLMs. They ensure that the data is trusted. It is very, very well trailed from an audit trail perspective so that we can follow all the risk, compliance and regulatory requirements for the client. So, this is what we basically offer as a platform is one construct. The second construct is the example Deepak gave of use cases. We have multiple use cases in different functional areas. We take those use cases, and we offer those use cases for a customer to be able to actually implement it in their technology environment or their technology ecosystem in the bank. And we therefore have these use cases which we normally charge per Use Case for a customer. And the third is where we actually embed some of the AI capabilities that you heard today into various services. For example, you know in wealth, so from a relationship manager perspective, you could have actually embedded certain capabilities for the financial advisor to be more efficient in the way they would deal with customers. So, there is a lot of embedded AI in various solutions for our various platforms as eMACH.ai. So, there are these 3 constructs. Most of our customers come and offer this as a subscription type of pricing rather than the traditional license type of pricing that we have had in some of our products. So, this is what we actually I think right now we are focused on and this is evolving very rapidly. As both Arun and Deepak mentioned, lot of people are doing stuff in GenAI asking assistance questions of data. But our ability to replicate and human operation setup through multi agents

Investor Meeting Transcript from an AI perspective. I think is a unique positioning and I think it's effectively going to grow into sort of a replication of a different workforce within the bank that will complement the human and change the future of way work is done. Arun Jain Thank you, Banesh. In summary you are saying there are 3 business model to monetise. BM 1 model is around Purple Fabric as a platform. Like Palantir, they offer a platform. They charge $3 Mn per annum per customer subscription. We are looking this to be in the range of $1Mn a year. Customer can purchase it. So that's the first model. Second model is built up an application specific application like invoice processing, application, AI based invoice processing application, which is APX, underwriting application like Magic Submission which you have launched in the US. We have more than 20 clients using the complete underwriting platform which is AI driven platform where we are charging from them on basis point for the insurance premium they are underwriting on our platform or we are using claim management which is per transaction per application file or per complaint which could be even if it is close to 100 pound per complaint. It's a kind of a if 10,000 complaints or 100000 complaints are there. Looking at the market complaints in the marketplace of mis-selling. So, these are the cases within business Model 2. There are 4 business cases of 5 business cases we have already put in the market. Each Use Case we are looking at it that each use case, it should be minimum Rs.100 crores revenue index 3 to 5 years. Each Use Case can bring. And because model canvas model one which is around Purple Fabric as an platform, if we can reach out to 50 to 100 clients in next 4 years, that's the kind of an objective that we are looking at it and 3rd with the case which is there which is embedding our eMACH.ai in all our products.  So, our product value which we are selling, we can sell at least 20% at higher premium to other market players using the Model Canvas 3. So, there are 3 models – BM 1, BM 2, and BM 3 to capitalise on it. With this we are quite bullish about this entire Intellect AI platform investments are quite high from the the R&D perspective and POC perspective. So, there are 2 kinds of investments we are making. One is on research. Second, on doing the proof of concept for our clients. So as of now, that investments are going in proof of concepts because whenever we want to implement certain thing as Deepak puts it, the claim investigation system is the first of its time which has gone live in the world where it is using enterprise structured data from multiple databases and unstructured data from the market perspective. Nobody has currently used the 2 systems of structured data and unstructured data and multiple system for multiple system to solve a particular problem what he's showing using 13 digital experts. I don't call it digital agent because they are experts, so they are 13 digital experts plus 6 LLM models. So interesting thing is 6 LLM model. So, for one claim management system we are using 6 LLM model and system is going smoothly there. It's traversing and saying what is the billing for Open AI? What is the billing for bedrock? What is the billing for different LLM? And these are the important things which we are very uniquely defining and getting a good traction from our partners. So, we have one partner who started doing the Academy. He wants to train many people in his organisation, so we have a line from January, February, March will be training our partners who can sell to their customers to reach extend our outcome. So, at this point in time, maybe Iwill reply the questions what you have.

Investor Meeting Transcript Praveen Malik Now you open further questions. Please click on raise your hand so that we can ask your question. Arun, some questions are there in the chat box. Also, let us take that first. Nachu please read the question. Nachu Nagappan Thanks, Praveen. The first question is, what is the unique value proposition of multi agent platform against any other GenAI platform? Arun Jain That's what we explained. So, I think this has been explained very well that the multi agent is 13 agents solve one problem . You go to next question. Nachu Nagappan How can manage trust and explainability in platform? Arun Jain Deepak Deepak Dastrala I think there are multiple levels, not just an one dimensional thing, because throughout that what is required typically for data privacy perspective, we will ensure that from an either there is a masking capability for any PII data and there is a toxicity capability to ensure that the questions are the right and sensible kind of a thing actually adhering to the standards of an GDPR and everything is one part. But explainability you have seen right? I mean every single thing what the agent does is absolutely available to verify - we ensured that actually the trust is visually felt also. We spend a lot of time to really understand what the human AI interaction should look like and how they should be able to interpret what agents are doing. That's the reason we have decision logic and action. So, this helps in in terms of having the right traceability right explainability not only in the interest of an organisation, but also an auditor and the regulator. So, this is what we have put in place. Nachu Nagappan Ok. So, the next question is are the target plans even ready to adapt to AI agents actively? A lot of current adoption is let's do a small pilot and we will see later. Arun Jain Exactly, that's what we have demonstrated. In US, there are 20 clients are already using it for full-fledged underwriting submission. These agents are being used. We started this journey in2021 and today we have pipeline of more than 100 leads where we are doing the PoCs with them so that they can on board. Inn claim one customer is fully live ESG customer is fully live. So that's what our uniqueness is. We are in action mode not only in PoC mode.

Investor Meeting Transcript Banesh Prabhu No, just to add right. Most of the clients go through a PoC, but once a particular Use Casein the BM 2 sort of model is Available, then they can actually start consuming this much faster than before. But what really happens is the client is still going through the stage of accepting AI into their environment. And I think that takes an POC which we do. We are doing multiple PoCs across product lines today and I think that's the way the model is still a point where everybody will accept it and then you can scale it a lot faster thereafter. Arun Jain Deepak, can regulatory requirement on how AI is implemented in BFSI sector detailed some of the? Solution you have built. Deepak Dastrala I think one of the things is we are very actively working with the various regulators through the sandboxes in the UK and other places and also trying to ensure that the ISO standards like 42001 are applied. So that it is completely in the context of regulatory. For example, in both UK and US, lot of regulators are helping with innovators like us in terms of how to operationalise. So at least where we are operating, what we are doing, we are kind of seeing some help and we are also constantly focusing on what are the new frameworks they're releasing, new regulators, regulations, they're calling it out for us to adopt. Banesh Prabhu Arun, just to add to what Deepak is saying, right. I think what we also do, not just all the discussions with the regulators. But we are actually finding ways in which we supplement the human beings. Sometimes people feel believe AI is completely autonomous. Not only do we ensure that the data of the bank is something that we keep secure within all the security regulations of data that exist, but also the work that we do actually enhances the human's capability to do processing. And therefore it supplements or augments the human’s decision a lot faster, and it's not something that from an AI perspective is completely doing things which eliminate the control of the human in this case or the operations process where it will land up doing the wrong things, you know the wrong financial transactions for the customer. Deepak Dastrala And also, Banesh just adds to you right the use cases that we operationalize the biggest challenge for the companies is they don't have enough expertise available to deal with the demand that is coming on their way. And if you look into the use case, , the biggest challenge they have is they're not able to meet SLA. They were adding more and more people that is not helping them. So, if we especially look into the risk and compliance space, there is a huge shortage of an expertise, and this is where the agents help in terms of giving the right coverage and also reducing the cost or optimising the cost. Arun Jain Yeah, there's a question. How is it to some of your leading competitor to replicate this kind of solute? I think we have an almost 10 years experience since when we are working on it.

Investor Meeting Transcript Replication is possible and that nothing can be said that we have a unique solution which cannot be replicated. We have applied few patents on it which can protect us from the replication. But more than that, I call it basement 1, basement 2, basement 3, basement 4. If our solutions well, as we find as 97% accuracy or 98% accuracy, somebody has to reach to 98% accuracy takes almost months and months of jobs. At least it is a two year to three-year moat available to us on an applying in banking financial sector which I have talking about black book. This black book gives us a uniqueness because it's within each area process, we know the difference, which is there. What are the cumulative investments? Cumulative investments in this business over few 100 crores were made.. Something is available in our balance sheet capitalisation. Something is available in UK subsidiaries. Few 100 crores of the investment are already made in this area. With platform potentially replacing human operations, are we facing resistance from the employee from our customer? Deepak highlighted in very beautifully that we are not able to solve the problem fully. The regulator questions we are not able to solve. Today, it's not a question of employee, it's that people are paying fine in US and UK. They are paying fine to the government because they are not able to solve problems. This is a problem solving they’re using both hands, so there will be some situation where employees may feel constrained, or they may feel threatened with it. The question number two, Rahul Bhansali, you are asking what the biggest risks seen to monitize the Purple Fabric. I think the risk or opportunity, if we look at opportunity, whether this opportunity there 1000 crore opportunity or 5000 crore opportunity, that's the dilemma we have. 1000 crore we are sure of it in next 4 years we will be able to capitalise on it. Can 1,000 crores be 5000 crore opportunity for us at a margin of 50%? That's what is current challenge right now, which Banesh, Deepak or me spending a lot of time in the flight to look at it, how do we make it 1000 crore to 5000 crores journey anywhere in between. In terms of complexity of implementation, I think this is where the beauty of the platform Deepak has done a phenomenal job under R&D team. WeYou can do it so sometime if you want to take it. We can give a session to you. You can build on it. Praveen Malik Rajeev Sharma.: Has any customer that does not use any of the company product s signed for this platform? Banesh Prabhu We have one which is right now doing a PoC with us. I think we have got a big pipeline of PoC is going on with clients. Many of them are not clients of ours at all. Arun Jain Manish’s question, there is one beautiful question. How well does Purple Fabric integrate with legacy infrastructure of the banks in US? This is a beautiful question because that’s exactly the structured data access which we are able to provide, which generative AI of many players not able to do it, therefore he explained.

Investor Meeting Transcript Banesh Prabhu Yeah. And, you know, there was this question the other day also somebody asked me all the platforms that we have built and clients have our platforms this platform actually is an operations transformation platform that connects to those platforms to pull data along with the unstructured data from documents, puts it together through the agents and the multi agents to do the actual decisioning involved in supplementing the human to achieve an outcome. So, it is actually complementing existing platforms by pulling data just like a human would log into those systems, pull the information and take decisions and AI agent is helping the human doing it more efficiently and faster. Arun Jain There is another interesting question is that can it reduce the cost of building software. So how the software engineering cost has come down? I think we are doing it. We have stop hiring the people from October onwards, our headcount is constant, and we are now in the phase of using the Purple Fabric for our internal automation substantially. How much is incremental investment is needed? I think we need to continue the investments s every week changes there. Our GTM Investments will be much higher. We are we are creating a good marketing department in US and UK. These are 2 countries we picked up as a first phase of going there. Does Purple Fabric improve conversion ratios? I think it's just a question of each lead generation. It's improving a lot. I think each quarter we are signing around 8 to 10 deals. There may be small POC, they may big POC which may not reflect on the revenue as much in each quarter. But each signup customer has a potential to grow the revenue based on testing our products. So, some of the customer can become almost like ARR of an $1,Mn ARR to $3Mn ARR, those are the possibilities of this large customer who are under pressure for regulator to solve their problems.$3Mn for them is a peanut for the kind of a penalty they're being charged by the regulator. Any other question? Praveen, you can look at it, even raising the hands if somebody wants to ask question. Praveen Malik Yes, there's one from Neil Chabra. Please unmute yourself. Nagesh please unmute him. Moderator Neil, you may ask a question. You are unmuted. I can see in it is not mute from your side, so you can ask the question. Praveen ji, you are maybe some technical issues you might go on to the next one. Praveen Malik Vimal Kumar has already put it in the chat also. There are no other hands that are raised.

Investor Meeting Transcript Arun Jain Okay. Two participants raised hands. Moderator Vipul, you may ask the question. Vipul Yeah. Am I audible? Moderator Yes. Praveen Malik Yeah. Please go on. Vipul So sorry, you said that the revenue potential is INR 1000 to INR 5000 crores. So that is spread over how many years? Arun Jain 5-year period. Vipul 5-year period. So, can we say that from next quarter onward will be clocking revenues will in hundreds of crores for this product? Arun Jain Hundreds of crores we are saying it's an accelerating revenue not a quarter-to-quarter revenue. So please don't expect anything significantly change in quarterly revenue because when you are doing an POC, we are doing first 100 complaints to be handled, not 1000 complaints. But as soon as it's accepted, it can become 1000 complaints a month. So that is the time the revenue build up will start. Vipul And what will be the margin profile for this product as compared to our other products? Arun Jain This is a completely different lines of business than other products in enter transform enterprise space. This is in the transform operation space. They are not contradicting to each other.

Investor Meeting Transcript Vipul Margin will be higher or lower. Arun Jain Margin will definitely be higher. This is a very distinctively different AI product. Today, AI margins are still on higher side. So that will be higher than the existing products. Vipul Okay. Thank you, Sir. Praveen Malik There is no other hand raise. Arun Jain Time is also over. We have a one-hour session. So, we can close the call. Praveen Malik Any other question out there, please click on raise your hand so that we can unmute you. Moderator There is 2 question Praveen from Pratap and Nishanth. 3 now. Arun Jain Go ahead Pratap. Moderator You can speak, Pratap. Pratap Yeah, Am I audible? Moderator Yes. Pratap Yeah. So, can you explain few other use cases that that can be like one is that customer resolution you have explained like what are the other areas or what are the other use cases you have currently developed or in future what are the other areas where you will be you think you can have developed application basically?

Investor Meeting Transcript Arun Jain There are 68 use cases we identified in banking. We are going step by step to look at like trade finance business. The entire use case is in trade finance business. The entire use case in lending underwriting solutions, the entire use case in regulatory management of the reporting which is there. So, there is a 68 list of experts. Digital expert use cases. We have a book available which we are using for selling in entire black book space. Pratap If out of the 68 like for all of these are developed or like how confident we are with the how many cases and like what is our plan to add up basically? Arun Jain Yeah. So that's what our depend on our capacity to go to market. So, we are doing at least 2 use cases per quarter to go live. So, we are just picking 2 at a time and then making it perfect and then go to market on that. We need to set up a full team to sell this product like claim which is for UK market. Now, it can go to the US. So now we are setting up a US team for claim investigation, so one quarter it will take to set up an US. Team UK team is ready. US team will be set up in next quarter. So, it's a 3-step process. First is building the agent, second is do a first live sites and then 3rd cycle is for go to market. Banesh Prabhu And just to add to that, this is not like a coding platform. We actually configuring this so the third option is to get the partner's trained so that they can actually create the use cases for their customers or cases that the customer already has enabled them on this platform. So, it's not a traditional coding type platform as the earlier plan. Pratap And so, what is your plan for go to market like we want to have some partners, or you have mainly we want to have our own? Arun Jain So, this we will be doing with the partners. So, some other use cases are taken by consulting companies. The consulting companies of big 4, which I mentioned big 5 we have mentioned not big 4. We added another company also along with the 4 partners. So, 5 companies which are there, we are going, they are taking us to the customers. Pratap OK. So just to understand like traditional IT software companies like, can they be our partner or there are some conflicts of interest?

Investor Meeting Transcript Arun Jain They are our partners. Traditional IT companies are definitely partners and there are two IT companies which are definitely training their workforce using Purple Fabric. Pratap So, there is not much conflict of interest in with them, basically? Arun Jain Initially we felt the conflict of interest when we launched it, and they also launched it. But when they saw our platform, they themselves backed out that they don't have capacity to do such a deep R&D on these kinds of products. Banesh Prabhu Particularly in the multi agent side. Pratap Thank you. Nachu Nagappan Next, we have a question from Nishant Gupta. Nishant, you can ask. Nishant Gupta. Nishant Hello. Am I audible? Nachu Nagappan Yes, Nishant. We can, we can hear you. You can ask your questions. Nishant Thank you for the for this interactive and deep dive session. So, I had actually 2 questions. One is this product definitely has a lot of use cases, right? But and you are targeting the back office in the operations team. But will this actually lead to, you know, termination of the workforce on that front, I mean you briefly touched on that, but I just wanted to get an more realistic sense that will people lose out on jobs when you start implementing because eventually you replacing the agents with technology, so more from of fairness point of view, right? If you can comment more? Arun Jain Yeah. So, I'm not to comment on this fairness point of view, but I think AI will definitely take away a lot of routine jobs. People have to reengineer themself. Within a software engineering business, I believe that 30% of our engineering service industry will lose their jobs in 2026. A software engineering industry from India has a serious risk of losing the business as well as

Investor Meeting Transcript jobs, so it's a fair question. There is no answer to it. By not doing it, it doesn't solve the problem. If technology has come in, those technologies have to create a new kind of a job. More fear jobs, more sustainability element. So, if the job can be put on ESG compliance, more sustainable thing, I think world will be a better place using AI. Banesh Prabhu And this is a bigger moment than the Internet when it started. And new kind of jobs will arrive, but there is going to be a transition of skills and changes and different kind of jobs. But this is not like Arun said, this is not something you can stop. This is already in motion. Its changes are going to happen, and it is going to change the way we operate. Nishant Got it, sir. So, my next question is that Google Gemini, they are also innovating a lot. They are also conducting sessions where they are telling about what all capabilities they are developing. You mentioned that there are 4 levels of basements, and anyone would take 2 to 3 years kind of a timeline. But considering Google is there, you know perplexity has recently received and fundraise of $500M valued at 9 billion dollars. Is it fair to comment in light of these events that you still have a 2-to-3-year kind of a moat in this? Arun Jain The difference is we are in applied research. We are not in foundational research for Google and Gemini spending money is in foundational research. We are leveraging them to solve the specific business use case, which is not their competency. What did work on is a technology competency. We work on applied banking competency. That's what Banesh tried to highlight in the beginning of the session that our differentiation is which particular smaller space it when you are issuing an LC, where is the in LC which is the particular context of LC and which business will need to be applied for that is not known to Gemini or any of these products. So that's where the moat of 2 years is there. Deepak Dastrala Just to add to what Arun said right. If you see pretty much from the level of intelligence, this narrowing down between the top players. Now, even if you see OpenAI, most of their recent work, they're all trying to figure out how to verticalise their intelligence. And what kind of a user journeys? What kind of problems they want to solve? So that's where being in an BFSI space for more than 3 decades gives us say that very unique insights on how this can be applied and how it can be solved to the last mile better than anyone else. That's where, that's where our differentiation is. Nishant Got it. One final question from my side. This is more like from a long-term view you are targeting to capture, let's say 1000 to 5000 crores for market, right? But let's say for a bigger player comes in, offers to buy this particular product segment from you. Would you be open to selling it at a particularly higher value or you want to keep it in-house and keep kind of getting a recurring revenue?

Investor Meeting Transcript Arun Jain Boss, you have seen the Intellect. These investors do come with the always some purchase options we must do some action on this company, some corporate action on this business. We can think about something of that nature where we can leverage it better, subsidise it something we don't know as of now. We are just discussing it how do you really capture the value of 1000 crore to 5000 crore journeys? That's what we are discussing in board meetings that how do we capitalise. 1000 crore we are sure of. But 5000 crores, if you have to take some action, where we need to, we are exploring. Nishant Got it. And you are targeting not the Indian market right now, only the US and the UK and all market right now. Arun Jain Yeah, that's right. Nishant And any particular setting for that? Arun Jain Pardon, the question. Nishant So why not also target the Indian market where you know the BFSI sector and the Backoffice… Banesh Prabhu There are some Intellect tactical clients, which are critical to us, where we are talking in other geographies to those clients. But our main focus of scaling the business is going to be in the US and in the UK to begin with. Arun Jain We are doing an APX model like Magic Invoice, some small way, Indian market. We are using it. Some big customers for India, we want to look at its Central Bank and as of now, India rules are not clear where they can use where they cannot use Central Bank has not issued any notification. How can they use AI or not. We tried doing in India. I think we will take it up when some clarity will emerge from the Central Bank on the how AI and which use cases can be there. So as of now, we are waiting for the AI policy to confirm India and then we can use in India.

Investor Meeting Transcript Nishant Got it, sir. Thank you and all the best. Nachu Nagappan Thanks Nishant. Next, we have a question from Puranik. Puranik, can ask your question please. You can unmute yourself and ask the question. Puranik I have a question. See this year, human and agent is a wonderful collaboration. So, this produces tremendous output. Can you hear me? Arun Jain Yeah. We can hear you. Puranik Yeah. So, it it's been it pretty, it's delivered fantastic optimisation. It's an optimisation tool. It’s an enhancement tool. It's done a fantastic job. So how do you measure this going forward? From use case to use case you know from application to application, the productivity of this in combination when they work, how do they work and how do they get technology and domain specificity together? Then how they will get the productivity of the platform, because I don't see much of a productivity in this application. Banesh Prabhu No. So yeah, I'll just take that question Arun. So, I think you got the example from Deepak on actually showing you the outcome from 50 days to 20 minutes, 30 minutes for an investigation. You got the accuracy; you got the cost benefit of that speed. So, all the quality dimensions that would be there in Gen in any kind of quality management on accuracy, speed, cost and efficiency is all there to actually measure use case by use case. So, the client actually can see that and that's what we work with the client as part of the PoCs to do. Puranik My question is seeing if you what you're saying is right. If let's say your spread, it across the 10 use cases. So, is there an improve productivity improvement? Is there an output efficiency coming up in each and every I'm trying to understand, let's say it does 10 application, 10 projects, at every stage because of the agent involvement, the combined productivity should get better. The output could get. More importantly, the output get better the result that you're seeking gets better. So, does that happen? Arun Jain So, Deepak what is the benchmark you are looking at? He is asking the question that you have a benchmark metrics now which you're not showing it. Otherwise, they were the accuracy.

Investor Meeting Transcript Deepak Dastrala And yeah, I think if you see right, we are in a very interesting phase where we are actually initially the people see as more of an automation journey. That is the reason a lot of metrics are around the operational efficiency and the productivity that was the first part. But if you see right, this is where also we have working with our customers to also see the different dimension because automation is a largely the FD saving and those kinds of metrics. But what we are also doing is like the use case that we developed for the complaints right now we are using it for a compliance means what is the cost of getting the decision right and how we are reducing it means. For example, if you take the banks, , the real value is how you can reduce your NPA? How you can deliver the loans so that your chances of you're paying and all? So that is where what we said is we are not only helping in terms of improving the bottom line, but we are also helping in terms of improving the top line. So that the mis- selling can be stopped So, it's so, because that is the reason we are also helping and educating our clients to not to see it as an automation way, but also see it an AI way. That was the number one second thing is in fact we are working with some Advanced Research universities to how we can benchmark the competency of an agent across the human. Because right now, from a human perspective, right when you on board someone, this is the competency, this is the salary and all. Now we will be continuously increasing the competency and that also will give a differentiation in terms of a price point and also the quality of decisions. So, this is the various ways we are helping the banks also kind of a creating a new way to benchmark on this. Arun Jain So, I'm using this, Puranik, for my software testing. And for software compliance testing of security, I am using the Purple Fabric for doing it, which used to take 4 weeks’ time to have a security leakage in the software. Today I'm using Purple Fabric to check gates of entire software quality. So, we are going to leverage almost 20% headcount reduction for the same job in next 6 months. Puranik Wonderful, Banesh, can you give us some use cases on underwriting? Because underwriting is everybody's concerned that a well-earned loan is well recovered. So initially you give it the right lender, you don't have to put in too much effort. The borrower, you don't have to put in too much effort in collection. So how will you help in that process? Arun Jain Banesh has run Citibank operations. Banesh Prabhu Underwriting, whether its loan underwriting or insurance underwriting, you know for underwriting, you normally get unstructured documents that are that we have a product called Magic Submission where people submit documents, and this can be very complicated

Investor Meeting Transcript documents with great deal of details. So, we actually through our technology that you just heard today, you can take that data, you can ingest it whether it's unstructured or structured. We can also have a data platform of our own for underwriting where you can triangulate this combination of data. Now, what are you doing in this model? You're actually going up to BPOs, right? BPOs do these jobs operationally. Document hand to them, they data enter few fields in the document and then that underwriter uses those fields. But what we are actually doing is through AI, not just collecting all that information into knowledge database of information for the underwriter to ask questions, but we will also be creating underwriter agents to help the underwriter become more efficient as we go forward. With those examples on underwriting and this can apply across the board for underwriting. Puranik Can you give some examples of that? So, what are the fields that you have eliminated? What fields you got it more efficient than what it is? Banesh Prabhu So, if you, I mean we don't need to go to the field level, but there are lot of data that you do not capture from submissions, right? Because data emtered into a few things, but actually the data is kept on document folders and then the operations people actually look at document folders and look log into systems and then do the decisioning. Here actually by extracting all the information from these unstructured documents along with the transaction systems, you can actually get the AI agent to help the underwriter become faster, taking complex underwriting decisions. For example, in the US you wanted to insurance on 50 Marriott properties or something like that at one time. That is very difficult to do as a human underwriter today. Puranik But how does predictive underwriting engine work? Banesh Prabhu So, it's not about predictive underwriting only. This is actually about helping the human with information to help them manage underwriting and give them things that they will have missed as humans with more information to triangulate and take decisions faster. Deepak Dastrala Just to add to what Banesh said, right? What we do is extracting the data is one thing, but what we do is we really enrich the data also with the multiple sources and then triangulate it. So that way, because as a human, the challenge is most of the time, with all due respect, depending upon the time in a day they end up taking an different decisions and most of the time they're biased with their historical view of that particular entity or that particular organisation or whatever it is. So, what we do is we completely irrespective of the size of the data or volume of the data will triangulate the whole thing and then we will identify the blind spots at every single thing. So, if you see what is the value that we are creating? Because if you see the one that I showed, we also kind of recommended the decision on the various

Investor Meeting Transcript things. So that we kind of do the red flags. At this moment, it all depends upon the underwriter, he or she, what they view about. The biggest challenge in underwriting is standardisation. Every underwriter has a particular view, irrespective of the clear guidelines within the organisation. So now what we do is we actually ensure that those guidelines are properly applied irrespective of the volume of the data and also when there are gaps, we also have an ability to enrich that by connecting to the public or private sources and fill the gaps also. The coverage also will increase. So, the biggest value that we bring in is ensuring that we are able to aggregate, triangulate and then also recommend. Arun Jain Enrich and one more process is continuous monitoring. Yeah. Every 3 months I can go to website and check it up that status as the loan - continuous KYC and continuous loan monitoring. So, I can I go every month to the website of the customer and see whether they are, or any new results have come in embed and along with-it triangulate it again. Deepak Dastrala Exactly as Arun beautifully said, right, the biggest challenges people do this only when they disburse the loan. They never have a capability to do it on a continuous basis. So that way a predictive part what you are saying, right? This is what we can bring in terms of predicting the chances of a default at a frequency that you can configure for that particular corporate. Puranik One another question I have is how do you measure an agent's performance with reference to its augmenting the human skill? Deepak Dastrala So, for example, the use case that we have done initially, that was the exact dilemma. So, we kind of actually led the same use cases. Because in an model office, that's how they benchmarked and what is the beauty is, it kind of opened up an lot of gaps because typically when humans make an decision, very rarely people will check now we have a way to compare on the performance. The way they got the acceptance is actually when they ran on the use cases where where already the decisions were made, we identified how many cases actually they wrongly paid back for the wrong reason because the human hasn't really validated them. So, the sign off on this happened against the human benchmarks Puranik. Puranik Interesting. Arun Jain Thank you, Puranik. I think we need to just close the call. Puranik I think we need a lot of chat on this. Thank you. Very interesting.

Investor Meeting Transcript Praveen Malik Thank you, Puranik ji. Arun, we have a last question from Mr Vivek Kumar from BestPal. This is the last one. Vivek ji, please unmute yourself. Vivek Kumar Can you hear me now? Arun Jain Yeah, Vivek. Go ahead. Vivek Kumar Thanks for making us understand how AI agents can bring big opportunity for Intellect, sir. But in terms of changing of profit polls of the industry, like what happened in the content creation has moved to aggregators like that. Are you seeing any trends in IT, Arun ji? I understand how AI is helping agencies and Intellect, but in general? Arun Jain It will happen. It will happen if you're going first principle thinking a lot of aggregation will happen. I think India IT industry is still sleeping. And too much disruption will happen on the service companies. It's going to happen. It's bound to happen. As of now, we are still not acknowledging the capacity of what AI has done. I am pursuing it from last 10 years, so I know what the power is and how it accelerates. Completely it's a titanic shift. It's a tsunami. It's no less than tsunami right now. But tsunami takes three hours to come to surface when it happened from the where the attack happened. It will take 3 years for India to understand. Vivek Kumar Got it. Thank you, Arun ji. Arun Jain Thank you. Praveen Malik Thank you, Vivek. Thank you everybody for joining the call today and still in case you have any more questions, please do write to us. Thank you. Now you can log off.