Good afternoon everybody, hope you're all having a great ignite and you know welcome to this session. This is a session on building an AI first enterprise with TCS industry Co pilots and Microsoft Co pilots. So welcome to this session. So hopefully, you know, this helps you round out a lot of things that you've heard over the last three or so days. So today, so I'm my name is Hemant, I'm part of Microsoft. So I have the great pleasure of introducing the TCS team that we work with very closely. So I lead the solutions and go to market for financial services for Microsoft globally. And you know, this session, I will open this session with a few context setting remarks and then I'll hand it over to Girish who leads the TCS Microsoft Azure practice. And he will talk a little bit about how, you know, we are doing work in the financial services space.  And then Naresh will come close it out. He is the CTO for TCS's manufacturing business group. So the three of us will hopefully give you a deep immersion into how we can apply everything that you heard to date into industry specific outcomes and how we can enable value for our joint and customers by using the Copilot technology. So that's the plan for today. So let me just jump in. So as I, as I said, as context setting, essentially when, you know, end customers talk to us, you know, the things that are on the minds of their leaders, the CEO's are essentially these three things, which I'm sure are no surprise to all of you, You know, driving growth, capturing new markets, capturing new customers, selling more to existing customers, basically all the drivers of business growth is a priority. And right next to it is the priority of how do you do it efficiently such that you're constantly taking out costs so that the investments that you're making actually add value to your bottom line. And we are a knowledge and people intensive business. So attracting talent and retaining talent is a key priority. So a lot of our conversations we have with end customers is around these priorities and therefore how technology can help you to actually meet these priorities. AI as we know is, is really making a huge difference in how we work. AI is something that will affect and impact all of us as individuals. Let me just go back real quick. So it will in impact all of us as individuals. It will impact how we run the business processes at our, you know, place of work, how we work. 
And it's also going to impact all the organizations, you know, as a whole. In fact, this graphic is very interesting where 87% of the organizations believe, I think these slides are got some animation in them. So 87% of organizations believe that AI will give them a competitive edge.  This, this start, to me is extremely important because it also signals to all of us the promise of this technology and how we think this will have a profound impact. In fact, 20 to 30% of potential improvement in productivity at a bank using generative AI, you know there is a strong belief that it will drive a 6% increase in revenue. 
So the point of these two statistics is to really show that there is a strong belief that this technology will help us do things better, faster, but more importantly it will actually help us capture revenue that we are really trying to do. For every $1.00 of investments that financial services companies invest in AI, there is a belief that it will actually yield a three times benefit. And this is this basically helps you scale the two statistics that that just were on the previous two slides. And there is a belief that in 14 months an organization can get a return on investment. Now historically 14 months as we all know is actually not a very long time frame. It takes nine months to sometimes just launch a new, you know, new product innovation and then build the effort around it to to actually make it work. So this is going to significantly increase our ability to roll out new changes and be able to go after customers that we wouldn't have in the past because our long cycles would have actually LED us to, you know, innovate at a much slower pace. This is the Copilot stack. You all saw this from, you know, the keynote on Monday, so I borrowed the slide from there and essentially how this entire stack can help you actually in a business setting, in a specific industry. That's the thrust of the content that Girish and Naresh will cover. They'll talk a little bit about how this concept that you heard and all the various breakout sessions that actually went deeper into this, how can they be applied and what's the real value that we get we can get out of it. So before I pass it on to Girish, the one thing I wanted to actually also call out is, you know, we are as Microsoft partner LED and you know, TCS is one of our, you know, top partners.  They have helped actually land technology and help our customers get benefit out of it. So we've done a lot of collaborative work. So one of the things you will see in the slides and the discussion ahead is how TCS has actually helped take this as a technology platform, personalize it to the industries, you know that are out there in the market and how they have extracted value. So with that, without further ado, I will pass the floor on to Girish. And once again, thank you for spending time at Ignite and you know, listening to all of us. 
So look forward to more discussions. Yeah. Thank you, Hemant and good afternoon everyone. Before I delve into, you know AI first business architecture, what is TCS point of view on that and how we are helping our customers become truly AI first enterprises, I wanted to spend a moment on the partnership that we have with Microsoft. So we started this partnership way back in year 2002 when.net was launched in the market. And already this partnership has seen multiple technology transformation waves including.net and SQL wave, Azure Cloud Wave, the data platform wave and now we are into AI and Genii wave, where we already delivered more than 300 engagements, you know, customer facing engagements on Azure Open AI, M365 Copilot, GitHub Copilot and almost 75 of these engagements have actually gone into production, right?  So, so we are there in the forefront of bringing AI infused offerings for our customers on Microsoft Cloud. And in terms of specializations, we are again number one in terms of largest number of, you know, advanced specializations. We also partnered very significantly when it comes to Microsoft's industry clouds. In fact, we were the launch partners for several of their industry clouds, including cloud for financial services, cloud for retail, manufacturing, sustainability. Not only launch partners, we also do a lot of work with them in terms of giving them advice in terms of where these products should go as part of the Partner Advisory Council. Again, in terms of building world class capabilities, we are investing significantly in building world class capabilities. We are #1 globally on advanced Azure certifications. We are also number one in terms of GitHub certifications across the globe. And again, we are ranked not only, you know, the partnership is strong, we are perceived as top players on Microsoft Cloud by analyst. So ISD released implementation survey way back sometime back in April 2004 where we are among the top two partners globally in terms of delivering, you know, Microsoft platform services successfully to our customers. So now moving on to, you know, how are we taking this whole concept of, you know, AI first business architecture. So we believe that it requires a multi tiered approach basically, right. If you look at the bottom tier, you need raw compute power. Basically you need your IT to deliver AI as a service with the necessary guard rails with the necessary velocity coming in from, you know your operations and largely it is driven through cloud platforms. So we are seeing customers, you know, leveraging all the you know, LLMS which are available on Azure and then building guard rails around that and then providing that AI, you know as a service. So this is the foundation, this is the starting point, but this is not good enough. You also need to invest in change management and that is where we have an offering which can where we work with our customers in setting up their AI offices. Because the GENI adoption or becoming an AI first enterprise is not just technology play. It requires significant change management, significant thought process in terms of not only how you adopt technology, but how do you change your legal processes, your finance processes. All of that is very, very critical for an enterprise to be really successful. The second layer is about data. You cannot do AI without data. And this when you, when it comes to Gen. AI, is all about how do you intelligently combine your unstructured data which is residing into your file shares, SharePoint document repositories, with structured data which is residing into your, you know, warehouses, databases, lake houses. How do you bring that, you know, and essentially tag your unstructured data with the right metadata so it becomes searchable?  90% of the success of a Genii engagement essentially depends on how well you do the data engineering. We have done some very, very complex data engineering where we were dealing with highly variable unstructured documents and we actually had to leverage LLM's to interpret the structure of the documents. It is not that LLM's come when you start, you know, doing your prompts and sending those prompts to the models. Even LLM's play a very, very critical role in data engineering as well. So data engineering is very, very important. And then comes building very, very purposeful and contextual agents which do one single job very well, right? 
And that is where you know the whole AI for business. I mean these agents can deliver, right, very, very business centric functionality. We call it AI for business, looking at the entire value chains of say manufacturing or life sciences or health care industries, identifying the value streams within those value chains where there is a very, very strong ROI with Genia infusion and then identifying the personas right, which are going to get transformed, whose day-to-day work is going to get transformed with significant Genia. And then also it, it also the entire service direct desk, contact center, SDLC is going to get reimagined. And, and beyond that, once you have these three layers, then how do you infuse Gen. AI in the entire set of business processes end to end? That is where the agentic AI comes into pictures. You have basically AI augmented work systems which come into reality. And that is the true transformation where you have these army of agents working in a orchestration or choreography and delivering, delivering you real business results actually, right, AI infuse business results. So all of it is highly industry LED on the cloud ecosystem because no one can crack it on its own, right. So there are always gaps in technology platform you need to bring right ecosystem partners. And it is all about how do you bring in the enterprise flavor, right? So how do you make this Gen. AI, which is largely world wise, how do you make it enterprise wise?  So what we have done is we have actually done as I said 300 plus engagements and some of the things that we have seen. So initially lot of adoption happened at the back end. So it was one of the first ones in terms of service desk use cases, contact centre transformation, SDLC augmentation, AI OPS back office then started coming in, in terms of customer servicing, contracts analysis, claims processing. Then came the mid office basically right. So the people who are supporting the your sales staff is directly supporting the customer. So we have done some very interesting work, for example, credit research for one of the insurance companies in Canada. We have built a credit research copilot which allows them to analyze lot of information on the companies that they want to invest and then take the right decision as part of their certain wealth management, you know business unit risk and compliance. Again we have seen as a very, very prime use case. We have also gone and worked with you know plants, actually the manufacturing plants. We have created plant operator assistant for some of our customers. Procurement again the traditional way of forecasting commodity prices, right? Because you need to take decisions what is the right price point to buy based on what demand you have demand forecast and what are the right trigger points, right, so that you buy it at the right time basically. So here we have actually augmented traditional commodity forecasting. You know, we did it for copper for one of the transformation transformer manufacturing company. And there we brought in Gen. AI along with traditional AI in terms of not only looking at those commodity prices which are obviously changing on a day-to-day basis, but also looking at what are the disruptions in supply chain are happening, right. Is there a factor that is shut down in China or there is a new plant that has opened up or there is a blockage somewhere from a transportation perspective, what kind of impact will it have? And you are able to get almost 15% uplift in accuracy and with which essentially 15% might look small, but when it comes you are doing bulk purchases, that's a huge, huge amount of you know, benefits in terms of billions of dollars. Then finally, we have front office as well where customers are actually unleashing Genii to their customers. So product search augmentation, we have done this one of our customers in UK where not only, you know, from moving away from just keyword based search, customers are today doing national language search. So they sell beauty products, but now customers can ask questions in terms of what are the right products if I have a oily skin. So we are now seeing technology actually moving to the front office and and customer facing and then data talk to data is again I would say a horizontal kind of A use case across you know multiple tiers. So this is an example I want to talk about how we are bringing Jenai to industries. So on left hand side, if you see this is a value chain in a retail banking all the way from sales and marketing to customer on boarding, KYC, regulatory compliances, then obviously enabling seamless frictionless transactions for them to do deposits, withdrawals. And then finally, you know how do you make that customer as a very, very loyal customer right for your bank. And there each of these value strings you identified the right use cases which are which will give you positive ROI. And then you identified on the right hand side you have personas in terms of here you have an example of a personal banker in terms of how the day-to-day life, you know, in terms of financial data analysis or loan and credit data, how the day-to-day job is going to get transformed. And then with that we identify the solution areas all the way from sales to personalized banking to finance. So that's how and Naresh will give a very good examples, multiple examples actually from a manufacturing industry, how we are applying this approach of you know, industry driven, you know, identification of use cases, personas to drive transformation at scale actually. And then essentially our value proposition is we have, you know, end to end play. I mean we help our customers, you know, set up enterprise AI offices and then if you see the bottom three, you know, responsible AI, we have an offering on that. Data engineering is very, very fundamental testing for Genii is not your traditional testing that you do and it needs to be approached with a very, very different perspective actually. And then you you have you know enterprise, we have our enterprise playgrounds for ideation exploration. We are setting up enterprise Genii platforms for our customers bringing all of this under one umbrella. And on top of that we are delivering industry and cross industry copilot. So with that I will invite Naresh to take us through, you know how what we are doing from a manufacturing you know perspective on generative.  
Yeah, OT Naresh. Thanks Girish, am I audible? Yeah, yes. Naresh, I think Hemant alluded to the larger view of how we are bringing Jenny to life. Girish did mention about industry wise, if you take a nuance, what are the specific themes around it?  The next 45, sorry the next 30 minutes I want to spend giving you a view about manufacturing industry. See particularly when you talk about generative AI and the use of generative AI in the last 18 months, there is so much of interest, there is so much of exploration, experimentation that every single manufacturing customers. And when I say manufacturing, it's basically cut across 5 different sub sectors, right. There is automotive, there is aerospace and defense, there is a Heavy Industries, there is food, paper, pulp, Agri and then there is of course chemicals. Each one of these sub industries have gone through a major, major interest exploration and experimentation phase with generative AI. And and if you look at largely the way it has evolved in the last 18 months, largely it is all copilot driven transformation. And what you are seeing on the screen is an actual examples of projects that have been implemented and outcomes that have been benefited with right, each one of our customers and the implementations that you have seen are transforming the day in a life of a specific persona like what Girish was already. And there is a quantified outcome that it has been able to put on the board. For example, if you take a building materials from the first example that you see on the board, this particular firm manufacturers technical insulations that go beyond the inner sheet rocks and that the sales reps actually used to take a lot of time, about four to six weeks to generate the RFQ responses that they used to get right. They have to go to the spec sheets, different configurations and try to build the actual response. With the power of generative AI and large language models, we were able to quickly put up a solution where in which all these thousands of combinations of multiple product lines, they had about roughly 80 plus product lines. We ingested all of the data and the ability to get the sales Rep a very targeted outcome and recommendation on what would be the right configuration to be able to create those code. Now the same thing that took about four to six weeks, now they have been able to do it in less than two weeks, two to three weeks Max. And that transformation of getting that code faster is the actual outcome. Often times when we talk about Co pilots and generative AI, we always speak about that in the context of productivity benefits. I will be the first one to raise the hand and say no, it is just one of the starting steps. Co pilots and generative AI is not about not just about productivity. They go way beyond in what business outcomes they do. In this particular case because of their ability to go faster with the court in the market. Their ability to capture a larger wallet share and mindshare and hence enable business growth is the actual benefit. Likewise, there are many examples in repair service and repair technicians. We are actually building Co pilots where we are doing text to text. Of course, you are ingesting the documentation in a multi model setup and we are able to give targeted recommendations for different error codes for a luxury auto OEM who is basically addressing a particular service or a repair within a car to that complexity. Let's say, for example, the third example that you see is an aircraft engine manufacturer and for them, but actually looking at eliminating the complete technical variance documentation process itself, which the designers have to do on the ground in order to basically make shifts and give a pass to the design. So that way you're able to accelerate the NPI cycle time, you're able to, you know, provide a better service experience to your customers and that's how the outcomes are seen. So one of the interesting use case that you see on the slide is for a food and packaging company in Europe, where in which we actually work with the legal department. You know of all the people in the world, legal adopting Gen. AI is a big transformation and legal people actually adopting Gen. AI and saying that OK to do litigation assistance, to do draft memo writing that actually using generative air to transform those spaces. That's the power that we are seeing in the manufacturing and mobility space for different customers. But while all this is good, if I have to largely give you a broad context of how each one of these different implementations are cut across three large categories, majority of our work is assist, right? And that's where industry has started. Of all the work that you saw there and Girish mentioned number of about 300 plus projects, majority about roughly I would say 70 to 75% of those projects are assist where in which you're automating A repetitive task, whether it is going through a set of documentation, making some insight out of it and basically doing an action. That part largely is getting automated. And that's the step #1 lot of rag based approaches that you know, forms are enterprises are adopting. It's all the first part assist. 
Then there is a little bit of maturity with respect to assist, but also augment the work where human is there. But AI is also playing an equal role in generating the enriched insight, right? So you are augmenting the human work with an additional piece of insight which we were not able to do earlier in that space. We have been able to do about roughly 2020 to 20 to 25% of the work. And finally, there is transform where in which you're not just assisting, you're not just augmenting with an additional insight, but you're taking that value chain to the next step where you're also using agentic workflows to initiate a level of autonomy and transformation, right. So that's the third step of transform where we are seeing very minimal work now, but that's the future, right? And how and what level of autonomy can be induced into the system. So if you bundle up all this, that's how the work is. But I wanted to leave with one more site of data. Data point that I want to mention is the best work that have happened in the industry and particularly manufacturing industry is at the intersection of traditional AI and generative AI. All the great work that manufacturing our customers are irrespective of manufacturing as an industry, even other customers, the ML insights that they have or you know, the ability to use traditional computer vision with generative AI and you basically merge both of these together. Then the power of the use case becomes multi fold. So the best work is at the intersection of traditional AI and generative AI. Now, while all this is great, how do you create a differentiated strategy for every enterprise, right? I'll give you a little view and the journey and the parlance so that you can get a little visually kind of imagine this industries and enterprises started with an approach called as rag to build their solutions. It's retrieval augmentation generate all of us know right now it's great for some standalone use cases. But as the complexity of the use cases increases, you'll start seeing that the model starts into with little or no context or prompt engineering. You can still get a little bit of accuracy, but largely on an average, the accuracy starts dropping. It's a great start, but as your people on the floor start using it, they'll demand for accuracy. And that's where RAG eventually will need to be rethought. And the rethinking needs to happen with another strategy called as a raft, which is RAG, but not on foundational models, but on fine-tuned models, right? The middle layer that you see here on this particular slide is fine-tuned models, right? How can you get to an approach of starting to build fine-tuned large language models or create SLM's of your own with your enterprise data so that when you are building rack solutions on top of that, the accuracy of those solutions are really good. It's a very simple math, better the accuracy, better the adoption, lower the accuracy, lower the adoption. If you really want to see Gen.  AI come to life on the ground, accuracy is an extremely important thing. And that's why we believe RAFT is an extremely important strategy. And what we have been doing is cut across all of our sub sectors, whether it is building fine-tuned large language model for mobility of the future or building fine-tuned LLM for chemicals, which we call it as chemically aware AI or aero AI or industrial for Heavy Industries. That's the future strategy, right? And we'll give you some demos today. While I showed you little bit of customer implemented case studies, I gave you a little glimpse of the strategy of rag versus Raft. Using those techniques, we have been able to build a suite of multiple Co pilots into our digital store for the manufacturing industry. Now the question could be, are these ready to be plug and play? 
No, absolutely not. When rags and models itself cannot be plugged and played solutions are and even the next step. But are they a good half baked asset or starting step to be get get an accelerated pace or an accelerated head start? Absolutely yes. And that's where all these solutions and catalogs come into picture, right. Let me start giving you examples of some of these in our catalog. I give you examples of sales, service, repair, legal, different personnel.  
Now let me take a advanced step of giving you an example of a supply chain scenario. In a traditionally supply chain has always been seen within the walls. 
But if you explore the boundaries of the wall or extend the boundaries of the wall beyond it and bring in a lot of other external data, like an unstructured data, you know, think about a factory strike, think about the Suez Canal issue, think about labor issue, or think about a supplier, a bridge collapse. 
When each one of these events happens. And let's say if there is a market news or a news associated with it, which is there, which is an unstructured data sitting in the pomp of news or an article or whatever. Your ability to bring in that unstructured data married with your enterprise data and generating that insight, helping your supply chain analyst make appropriate recommendation. Whether it could be, let us say, if a particular supplier is bringing a raw material through a particular route and the bridge is collapsed. What are the other alternate suppliers who are in your system who could basically take that load of fulfilling that particular material? Or for that matter, you know, if you look at a labor strike, you know, what are the different impacts? Can you basically mitigate what level of production capacity needs to be shifted to different other regions? Those level of supply chain early warning indications needs to happen and that's why generative AI can play a very significant role. For one of our aerospace customers, they realized that their Tier 3, Tier 4 and Tier 5 suppliers were deeply impacted because of a particular issue that they realized only six months later on. Such a solution is extremely important to be available so that they can figure out if there is an impact on their production line because of a Tier 3 or a Tier 4 supplier. That's why it helps when you do that, it helps you get that resiliency in your supply chain network and hence avoid delays and what not. I'll shift my focus from supply chain to shop floor. And you know there is a whole suite of different use cases on the shop floor, right? Whether you take a final assembly line, whether you take a standalone machine with a connected machine and connected workers, no matter what the use case for a shop floor plant operator. If you look at the ability to keep the overall equipment effectiveness, we call OEE up at all the times that is the most important or a single most important metric when it comes to plant and shop floor. Now with the power of generative, IBM been able to give targeted nudges and recommendations to the plant operator in order to basically see whether it has to be maintenance schedules of particular equipments or giving recommendations on the optimizations of the equipments within the shop floor lines or basically giving recommendations on how to service a particular equipment. No matter what the use case, but plant and plant operator scenarios is to retrieve and validate all the maintenance activities recently performed on the coding conveyor belt with the help of Copilot. He observes that the conveyor belt was installed 3 1/2 years back and there's a maintenance record for the realignment that was performed on the 23rd of October. Bob wants to verify if there is any future maintenance scheduled for this unit and he quickly finds that there is one scheduled on February 2nd. Use cases are many. I'm just giving you snippets of how you can do it. Here is another interesting piece of use case with so much of capabilities and compute and ability to twin a lot of stuff. You know with Nvidia's Omniverse, we are also looking at an ecosystem of NVIDIA and Microsoft stack and open AI to basically bring about the power of what we call making the assembly lines more flexible than rigid. Now think of a scenario when design engineers or layout planners used to basically go through months of efforts trying to determine what will be the most optimized layout for your warehouses. Now with the power of digital twins and you know, the power of generative AI today we have been able to simulate the whole environment much ahead in advance and reduce the overall cost of rework and reduce the overall defects. Here in this an example on the right hand side, you're seeing a design layout planner actually is giving targeted prompts to basically add newer components and figure out, you know, how you how he or she can simulate and figure out what is the most important part, whether it's adding racks, adding capacities, adding boxes, or for that matter, just, you know, adding different, you know, let's say if I say make this whole warehouse OSHA compliant, right?  Can I get then, then you'll see, you know, multiple stoppers getting added and you'll see multiple fire extinguishers getting added. So that way our ability to use generative AI to automate a lot of work and assist the layout planners or the design engineers to build this at a much faster pace and simulate it to the power of Omniverse. Let's say that is the power. You know, we can we are actually seeing and actually building this as pilots for different branch with few of our customers today. Again, an example in the service space, service space where you actually are looking at warranty and you know, looking at how you can give targeted recommendations on the availability of the part or the right level of service technician instructions to transform the overall field services life cycle, right. And of course, productivity as a byproduct. Here is an example of how all of these and the fine tune models come to life. And I'm taking an example of mobility because this is something that all of you will resonate and you know, because all of us drive cars. So here's an example of what it could mean for our day-to-day lives in the new age of mobility. Hyper personalization is the new luxury. Through data-driven innovation, we've unlocked the secrets to hyper personalization, tailoring every journey to your unique preferences. The TCS Mobility Suite is the future of in vehicle intelligence.  Take me to work on our way to the office grind. Buckle up for a productive journey. Can we actually stop by the nearest coffee shop on my way to work? Let's brew up some adventure on our way to get caffeinated together. 
We need to rush there.  Hold on tight and brace yourself because we're about to hit maximum speed. Buckle up, we're on a fast and furious journey to your destination. What cafe are you currently taking me to? I'm currently taking you to Esta Restaurant Cafe Bara de Kinga St. What's the rating there? The rating at Esta Restaurant Cafe bar is 4.3. What can I expect to spend there? You can expect to spend 5 to €15 per item at Esta Restaurant. I'm on my way to the cafe. Can you play me a song to put me in the mood for some coffee? That sounds like a nice trip. How about a song to get you in that perfect coffee house vibe? Here's a suggestion, One coffee by Sylvan Esso. I don't understand this sign at all.  Sure, that sign means you're in a residential area where kids might be playing, so be extra careful and drive slowly. Am I able to park here at the moment? Nope. You can't park here right now as it's outside the allowed hours for parking with a ticket. When can I park here then? You can park here after 5:00 PM on weekdays or anytime on weekends. Was that a monument we just passed? Yes, we just passed the Berlin Victory Column, an iconic landmark here in Berlin, Germany. What's up with this warning light I see on my dash? Could you describe the warning light for me? Yeah, it's red.  Kind of looks like a steering wheel. That's the power steering warning light. It usually means there's an issue with your power steering system. Powered by generative AI, it will revolutionize the way you experience mobility. Immerse yourself in a seamless, connected ecosystem where every aspect of your journey is optimized. Monetize your mobility data by opting into personalized services that enhance your travel experience, transforming your vehicle into a software defined powerhouse. Embrace the future of mobility with TCS Mobility Suite. Redefine the way you experience mobility. So as you saw in that particular example, the use cases started off with something that we all normally do, right? 
But as you kept on increasing the complexity of the use cases, like for example, parking sign, right? You know, cities like Chicago, New York, you know where in which you have parking signs which says Monday to Thursday, 8:00 AM to 5:00 PM.  And then there are set of exclusions. At any point in time, any new person may not be able to comprehend that and instantaneously make a decision. So the use of power technologies like generative way, I have now made it easier so that you can put that as an unstructured data ingested and immediately give a recommendation whether you'll be able to park that or not. Or just passing a monument and just you know, getting a view of you know where you are and what it is about and getting some additional piece of insight around your surroundings. I think all these capabilities or just the old ambient mood and lighting in your car by itself, right? The cars are an extension of your living space today. It is no more just a car, right? And you need a copilot which can seamlessly understand, be empathetic about your needs, your context, your journey at that particular micro moment. And that's what the generative AI solutions here, what you saw brings to life. Actually we are working on this particular solutions with two auto OEMs today and we are actually integrating this solution as a fabric on top of their product engineering suite. So that's it. So while you saw all of these different solutions, I just wanted to leave it one thought, whether it is assist, augment or transform or no matter what persona you are solving for the world of manufacturing and mobility and largely by and all for all the industries Co pilots are not just about productivity. 
They have far reaching more profound business outcomes at the end. Thank you for listening.