Accelerate 2025 - Energizing & Decarbonizing the Future: A Look into GE Vernova’s APM Roadmap

Accelerate 2025 - Energizing & Decarbonizing the Future: A Look into GE Vernova’s APM Roadma

The power and energy resources industries are under pressure to deliver more — more efficiency, more uptime, more sustainability — all while a navigating complex infrastructure, fluctuating demand, and stricter emissions targets.

Unlock this exclusive webinar where we’ll lay out a clear, actionable roadmap for energy operations, focused on AI-powered Asset Performance Management (APM), DataOps strategies, cloud technologies, emissions management software, and robotics.




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TRANSCRIPT

Hello. Welcome to today's webinar. My name is Christy Pooler, and I, lead product marketing for GE Vernova’s electrification software, primarily focused on power and energy resources. This is our third annual Accelerate 2025, where we will focus on energizing and decarbonizing the future. Really going into a deep dive on our GE. Our portfolio is for APM as well as CERius. So updates as well as, a sneak peek into what is coming. So a few housekeeping items. This webinar is being recorded. There is no live voice activated for this webinar, so submit your comments as well as questions via the chat box. You'll see the the interface tool to your right which you're able to and ask questions and chat. Also, there's additional content that's in the resource center. This is where you'll if you guys have any questions you want to get Ahold of us, please fill out the contact Us information or the Contact Us page. We have white papers, data sheets, as well as you'll have access to the Demo Hub where you can have a full live experience with the software that's being presented today. As always, we have a Safe Harbor slide, which basically where this information that we're presenting is intended to be a general product direction.  
 
This is for roadmap for information purposes, to give you insights of where we're heading. And key areas that we're focusing on from a strategic perspective. And also the any kind of development or release timing of any of the new capabilities that you see. These are, again, purely function and subject to change. As we move forward. All right. Let's dive into today's speaker.  
 
I've already introduced myself, but we have Luke Smaul, who's our chief product and strategy officer. We have Martha Saker, which I'm sure a number of you guys have joined her previous webinars. She's a principal product manager focusing on APM health as well as our connected worker solution. We have Janet Webb who focuses on APM reliability, Smart Signal, and also Performance intelligence. Vipin Nair. It was a director of Asset Performance Management. Again, these names and faces may sound very familiar. We have Mark Sinozich, who is focused on APM strategy. Mazen Younes, who is really focusing on our platform, essentials and foundation. Laura Robertson, who is a product manager that's, working with our CERius emissions management solution. And then finally, we have Neha Joshi, who is going to talk about our autonomous inspection and the really cool, activities and cool devices that we've added online to that solution.  
 
I'm not going to go through the agenda, but you guys, have seen a lot of this. We're going to talk about our strategy. What is our emissions, reliability and safety. We'll talk about asset health and health and how to use the asset data, to improve your maintenance programs and also what's what's new for what's new and what is the frontier of SmartSignal? We'll go into some newer capabilities for defect elimination and then, go into strategy, which we'll talk about, using a risk based approach. Mechanical integrity going into, other devices like autonomous inspection. And then we'll talk about the platform and we'll wrap it up with, emissions monitoring and management. And with that, I'm going to pass it over to Luke Smaul. Well, Christy, thank you very much. What an exciting day. To be talking to our our users, our customers, and really have an opportunity to talk about where we're going with the portfolio and get ready market feedback and hopefully a level of excitement around the vision for where we want to take our business in the future. And you're to hear we talk a lot today about balance. For me, APM and our portfolio is really all about balancing performance, emissions, reliability and safety. So, as some of you may know, some I'm Luke Smaul, I'm the chief product officer for for our business. I took over probably a little over seven months ago. And one of the reasons, I decided to come back, I'd actually been at GE for about 13 years, and I lived through the, the really that kickoff of the disruption in of this landscape with the now infamous Predix platform. And that really kind of was the catalyst, again, for the initial phases of disruption in the APM markets, where we could sit down over a wine or beer and and discuss the pros and cons of how the platform went.  
 
But I will tell you that the, the vision and, where what we saw, industrial, IoT cloud, big data and AI going was spot on to a point that when we had our challenges internally with GE digital and Predix, I decided to, go out on my own and create a start up in the predictive maintenance market, leveraging AI as a way to deliver new outcomes, new values, to the industrial market. I wasn't the only company or person at the time who saw that opportunity. This is kicking off the 2014, 2015, 2016. A lot of companies saw the ability for taking a new approach to APM, specifically predictive maintenance. And unfortunately, it was not my business that became the first unicorn of a unicorn being the business that reaches $1 billion in valuation, but we did get in 2021 the first unicorn in the APM or industrial IoT and predictive maintenance space. So so that to me, first of all, if you look at disruption, you start with net-new technology and being able to deliver value and new ways into a particular segment.  
 
Then you typically see someone capitalize on that. On that approach, we get this first unicorn. But we do kind of learn, that industrial IoT, and on a pure, deep and skinny, IoT approach is great for a focused use case. And I think we all, we're excited about now being able to collect data from the edge, pull it up into the cloud, and do magic with that data. In reality, what happened when you just pulled all of that pure, fresh industrial IoT data into the cloud was it lost context. And this gave rise to really the next phase of disruption in our market, which was the introduction of or creation not even introduction, creation of the industrial data ops segment. So these are companies that didn't exist before a market segment that didn't exist before. And now you've got, again, companies driving billion dollar valuations because of the need coming out of this rich new data driven approach to to the world we all work in.  
 
And then look at patterns of disruption, what you typically see once this white space is opened up, once this is created and businesses see the opportunity on the table, you got entrance into your markets that are taking a or come with pure play tools. So we've seen businesses that take a pure AI, but even coming from our sector, but pure AI companies deciding, you know what the this this industrial IoT see this data driven approach. The impact of cloud, the impact of AI is changing the APM market to a point where I think I was actually going to enter that market and compete and again, offer a completely different, approach to to solving other performance management challenges. And typically, once you start to see external entrants enter a market and you're you're watching for the patterns of disruption. And I was following this disruption myself as my own businesses, as we were growing and finding new customers in a AI based space for for APM, there's then typically a level of market consolidation. And then we've seen that a lot of the big players around us who have gone through either being acquired or acquiring businesses and consolidating kind of the traditional incumbent players, in our space, I will say that for, our business, we started that M&A very, very early on.  
 
Again, a little bit. I hate us. The work we did with with Predix and GE digital gave us this first mover advantage, and we were smart enough to make investments and acquire businesses like Meridian. And even way before that, our SmartSignal business. And that means that we've been through all of the pain that comes with with M&A. If you've ever worked in that space, you know, we can take five, six, seven years Post-acquisition to to really integrate those businesses and deliver value in a consolidated platform for your customers. So that's exciting for us to, again, that first mover advantage in this disrupted space. I know the exciting part after this period of disruption, after really, data, offering an alternative approach to classic APM, we have an opportunity to to reimagine the space. And we're hearing this from our customers. We're hearing it from some of our partners in the academic space, maintenance and reliability professionals have been trained in a new ways and been trained on how to use data and take alternative approaches. So what I wanted to present today was if you agree that this market has shifted, and the kind of things we're hearing from our customers is that APM has always been around herding cats, right?  
 
To try and get through a full scale enterprise APM project has always been an I love that analogy. Herding cats and and now what we're seeing is every stakeholder, every cat is coming to the table with their own tool or startup of choice, which is slowing down the overall adoption. So we wanted today to present, our response to this. Again, we we were at the forefront of this disruption. We we definitely took a couple of missteps, but we're now at a point to come into the market with a refined vision, a holistic platform, and show you how we want to take advantage of and to really help you as our users and customers take a new approach. A more holistic approach to your APM journey. So if we take a step back and say, okay, so we've got this, this technology disruption happening in the background, but as a, as a user, how was my world changed and how can I take advantage of some of this newer technology and these new ways of working? And most of you will be very familiar with what I call the the APM trifecta.  
 
So managing risk cost and balancing that with availability and reliability. And when I talk about disruption, when I talk about new approaches to APM, really what I'm talking about is balancing, a strategic approach with a data and AI based approach. So imagine starting top down with your asset strategy and going through the work that's required to get that set up, and then marrying that with more of a bottoms up data and AI based approach. So the reason you need to do that is that the trifecta risk, cost, availability, reliability, balancing act that we all live every day as APM customers and users is coming under pressure across the board. So we're all living with capacity and workforce challenges, right? If you're in the energy or in the power generation market where we're capacity constrained for us to be doing a lot, lot more as we track the energy super cycle, often with the same amount of resources. We've all been talking about workforce challenges for many, many years. They're very becoming true now and a absolute challenge for us to balance, as the the owners and drivers of these platforms, the economic dynamics. Right. We're in a very interesting time. Let's just say globally, from a geopolitical and economic perspective, that's putting pressure on the decisions we need to make as as users of this, of this platform.  
 
And then as I mentioned, the disruption while it while it brings an opportunity to us to take fresh ways or fresh approaches to the APM, we also need to manage all of that data and be responsive in the age of data and AI. And then we see as well new stakeholders coming to the table, putting demands on this balance, you know, so for example, sustainability is something that we're very close to. GE Vernova’s have other work, including in the portfolio. So we think about as I looked at this, as you think about this and you think about disruption as happened in the APM markets, and you say, okay, we've got to take a new approach to managing risk cost and improving availability, reliability and when I was looking for my next challenge after selling my predictive maintenance, AI start-up, I looked across the market and said, okay, who has the most holistic platform? that if you wanted to reorientate a set of capabilities to compete and drive value in a market that's gone under a level of disruption, who has, the strongest platform? And again, I'll say the now infamous P word that the Predix journey gave us first mover advantage to deeply understand what it takes to compete in this landscape.  
 
So that gave us the the acquisitions we made. So with SmartSignal, you've got 20 years of delivering AI outcomes in the industrial market. Meridium gives us all the core capabilities, domain expertise and confidence that as you look at taking a strategy and compliance based approach and balancing it with a data driven approach, we have that pedigree. So that gives us all of the pillars that you know and love in our portfolio strategy, health, reliability, integrity, safety, balanced with again, 20 years of AI and SmartSignal. We're investing in emissions management technology. Again like we can talk about the energy transition. Driving to net zero is absolutely the mission of GE Vernova. We know that as a APM users, balancing emissions is something that you're under pressure to do as well. So that really gives us this, this holistic set of capabilities to deliver asset performance management and keep you on the right journey when it comes to net zero and your emissions. And the exciting part, again, of having that first mover advantage in the disrupted market is it now sits on our essentials platform from our our foundational platform that is born out of the lessons learned of trying to do a full scale industrial IoT platform.  
 
Almost ten years ago, which is which was unique at the time in the market that all those lessons learned, all the battle scars we have, all the domain expertise we kept, all the great technology we kept from from going through that journey is now baked in to, to our platform. And that gives us, to me, really, the three big elements of this, which is, okay, can I balance strategy, compliance, take care of integrity and safety with more of an AI based approach, and then feed all of that with rich data and context. And I'd argue there's no better portfolio out there set up in a way that's going to allow us do that. So what does that mean and what does that look like in the future? So this is the exciting slide I wanted to share with everybody today. We're looking at combining all of those capabilities right. The ability to impact your performance, the ability to take care of your safety and integrity, the ability to drive enhanced reliability. Excuse me with with things like SmartSignal and also balance that with an emissions focused approach. We're going to combine all of that with the rich, rich data and AI based approach to deliver what we're calling asset intelligence. Asset intelligence to me, is is the next evolution of asset performance management. Asset performance management to me, kind of conjures up images of a static, long, you know, kind of approach to really getting to where you need to be.  
 
But it doesn't conjure up to me images of the refreshed, data driven, agile, iterative, holistic approach that we need to get to. So that's what we're we're, focusing on asset intelligence. Anybody that has used our asset health accelerator, or what we call AHMA, really has gotten a preview of how we're thinking about this. So putting initially your asset health on steroids, making that the center of the chessboard for everything we want to do and, then wrapping that around or wrapping around that areas such as operational risk. So again, as you take more of a data and AI based approach, that's only half the equation. You've got to balance that with the pedigree, credibility, safety and confidence that our existing portfolio gives you. And the way we're thinking about that is wrapping a full operational risk workflow around, everything we do from an asset intelligence, perspective. And then likewise, all of those those pillars I showed on the previous slide, combining those through another workflow we're going to call defect elimination, which gives you that much more holistic view. And imagine being able to start a journey with a lower, let's say, degree of confidence around asset intelligence based on some limited data. But it's enough to get you started and then iterating around the circle, delivering more data, more confidence, more context, and continuing to drive enhanced asset intelligence. So for us, that means this is going to be the future vision for where we're taking the portfolio. That's going to give me the opportunity to drive governance and common themes across all of the products and product leaders that you're gonna hear from today.  
 
Like I mentioned, if you're an asset health accelerator user, this is really going to be the next generation and putting that on steroids, enhancing it with operational risk. We're going to invest heavily in defect elimination. We're going to enhance our analytics offering, give you the chance not only to purchase our analytics in this space or bring your own analytics, but also the ability to build your own analytics with us, we're going to enhance our industrial managed service offerings. We see a role for generative and agentic AI to support us with IMS. And then we're going to really heavily, focus on a SaaS first approach or the reality is the exciting things we're doing now with our platform are really, really possible when we do them in partnership with our big hyperscaler technology partners. And of course, like I mentioned, CERius is our mission is management product. That's a key, part of this. So with that, I want to thank everybody again for your time this morning and hand over to Martha. Thank you Luke. Well, it's and one of the best things about being part of the digital age, to have all these massive amounts of data available to you for decision making. And it's also the most challenging part of being in this industrial revolution.  
 
Data revolution, having all these massive amounts of data to bring complexity into your decision making. Hello, I'm Martha Saker, the product line manager for health and frontline productivity. And thank you for your time today. I would like to walk you in the next few minutes, through how to transform all of these massive amounts of data into very tangible maintenance knowledge with our APM health, application. Sorry, little, technical issue. Let's begin by understanding where what's all this data and where it comes from. And in order to give you the best possible, KPIs and outcomes, we believe that every little bit of asset data contains a potential insight. And for that reason, we gather all this. So your system, your IT, your assets, your feasibility, all of it can be combined into our very cyber secure, robust and scalable GE Vernova APM platform. From there, our asset health product has the ability to collect all that data and contextualize it for you with the objective of giving you an asset health maintenance assessment, which is a series of KPIs, that very clearly allow you to prioritize what needs to be done in order to increase your availability, your reliability, in your assets while reducing your cost and overhead. Within APM health, we have a series of modules, fully dedicated to enhancing the fidelity of this asset health index. These modules work in conjunction to deliver, again, the most granular and up to date data, starting with your health manager, which is your mothership, your main, main application for you to manage your data is your administrator dashboard.  
 
Then we have our Rounds and Rounds Pro applications, which are our mobile apps. And this mobile app allows you to capture the most up to date information, again, calibrating all the other data sets that you have in there with the latest and greatest on status and maintenance stats, and then a couple other supporting data modules which are ENOCs, which capture those things. They are not numeric in nature. This is for observations and for shift reports and so forth and calibration, which of course tell you if the data that you're getting from your instruments is fully up to date. As a takeaway, this, all lead to a consolidated single pane of glass view where you can make, the best maintenance decisions with clear priorities around where the criticality resides and where would you have the most impact. Why are you doing all this? Of course, you guys know it a lot better than we do. We need to reduce operational risk. Enterprises have many objectives. None of those have to do with spending countless hours figuring it out how to do maintenance. If you are targeting, having a robust, condition based maintenance program, this is the way to go about it.  
 
We simplified the data for you for your consumption, giving you exactly what you need to know, when you need to know it, and that by default, accelerate your target value, bringing productivity to your operators and your workforce, reducing downtime, increasing availability and reliability. And of course, none of this really matters if those who need the data to do their day to day activities do not have it, in their hands. So mobility is a huge factor enabling the whole organization with exactly what they need when they need it. So it looks presentation and a little bit through, the past two slides, you guys have heard me talk about this asset health index and how we give you this information in a contextualized, fully digested, approach where you can quickly make decisions and direct your maintenance resources activities. Here we have a little sampler of what that dashboard. Dashboard from our health maintenance, assessment looks like. And you can tell that is, pretty much the data that you need to drive your day to day activities. First, we start with the fleet summary, which it doesn't have to be a fleet. This can be a group of asset summary.  
 
This could be a full facility. This can be your full enterprise. There's flexibility here that captures exactly the KPIs that you would like to see. Then we had like your worst performing assets which again immediately direct your attention to those assets that need highest priority for risk avoidance for operational excellence as it assets monitor. And this dashboard at the bottom is basically where you would find those key KPIs in order to run and manage your condition based maintenance program. Here is where you would find that numeric calculation, which is your asset health index which aggregates all this data, giving you a sense of where you are in the in the specific health of that asset. Then in order to marry your condition based maintenance, with your time based maintenance and your OEM recommendations, you also have visibility to your estimated remaining life.  
 
Again, one more critical data point for your decision making. Then you will also find a third KPI, which is the probability of failure driving that decision process. Just a little bit further in regards to who needs to be first on maintenance activities. And lastly, we provide, recommendations, digesting all these data together and giving you a one single view around what the application believes should be your next steps. Before we dive into the roadmap, and I'm very excited to tell you guys what we're going to be working here on the, for the remaining of the year. I'd like to spend a little bit of time talking to you about Rounds Pro. As you heard me previously mentioned, mobility is a big deal. Nothing that we do in here really matters if the user does not have the information when they need it. And that's where mobility comes in. Our Rounds Pro application, is fully in sync'd with your with your SAPs of the world, with your enterprise asset management systems, making sure that all that data and effort that you have collected over the years on those systems can be fully utilized in combination with our applications. We are agnostic, so you can pretty much use any platform that you like in order to collect and manage this data in the field. So again, bring your own device. We have, no particular technical preference. And lastly, we also are working on other integrations with APM modules and enterprise asset management systems. You will see some of that in the roadmap. Very exciting integrations with for example, autonomous inspections.  
 
So again, incorporating the product even further into how we deliver this data to you. And now we are at the health roadmap. So just for benefit of time I'm going to concentrate on future direction. And here is what you see a little bit of a transition in regards to our investment allocation. Our purpose here for the next six months of the year, eight, six months of the year would be to migrate or move our attention to asset health. We have historically, at the beginning of this year, concentrated heavily on Rounds Pro. Now that that solution is robust and we are ready to proceed with obsoleting our legacy Rounds application and have all the functionality in Rounds that we would like to give you and that you needed, from, a transition point of view from legacy to pro.  
 
We are ready to start investing on health manager and some of the most important, developments that you will see coming have to do with integrations again, I'd like to highlight is specifically the integration with autonomous inspections, where you will see in in a few, in a few presentations here. We're introducing other digital technologies such as robotics to capture these data. And the integration will allow us to again capture, manage that data and utilize it to enhance the fidelity of that asset health index. The same is true about, APM interconnectivity. So you guys are going to hear about, defect elimination, for example, combining these modules together. Again, comes to augment, the value that you can derive from our advice and from the succinct data set that we provide you with. We would also increase the capability to add documents and, reports that boards, and even videos. So full videos of, inspection processes can also be, will also, be within the capabilities of the application in the future. And lastly, again, we understand that our application needs to be in lockstep with your enterprise asset management system.  
 
So we looked and introducing the ability to move your work packages into rounds and use one single application for all your field needs. That's also coming up second half of this year. And, lastly, other ways to enhance the operators experience in the field with Rounds Pro, such as 3D visualization for complex assets and maintenance tasks that maybe are not intuitive. This will allow you to visualize different ways to approach that work. And with that said, I had outlined to you guys the process which health gets you to a preventive state, but that's not all we have in the portfolio. Again, as I mentioned, things here work together. And this preventive state, when coupled with your next presenters, information will get you to predictive. So with that said, I'll pass you guys on to Janet Webb. Thank you. Martha. Hello and welcome.  
 
I'm Janet Webb. I'm a product manager for advanced reliability, including APM SmartSignal. As Luke mentioned, SmartSignal has been a proven and mature technology for over 20 years. And so I want to bring to you what's next. What's the next frontier for APM SmartSignal. Where will we take this technology to continue to lead in this space and give you the capabilities that make your jobs easier. First, I want to connect the dots here to what you just heard from Martha looking at this comprehensive maintenance support. So as you can see from the table here, many of your assets are going to be covered from a maintenance planning perspective through time based and condition based maintenance. SmartSignal is going to the next step for your higher criticality assets, where reliability and availability are really critical to your ability to run your production. And that's getting predictive and prescriptive, leveraging time series data and leveraging advanced pattern recognition.  
 
So for those who don't know SmartSignal, I'll quickly describe, how it helps you reduce unplanned downtime. So this is a probability of failure curve. And the goal in terms of maintenance planning is to go from right on the curve towards the left. So where you have equipment that has operational alarms, if you if that's your only ability to detect when there's an issue, you can see that that's happening from the orange dot just before a functional failure or a shutdown that would prevent that functional failure. And in a worst case scenario, this could be happening on a weekend or a holiday when you have even fewer resources. They're available at the site to manage these issues. Leveraging SmartSignal and predictive analytics takes you to the visibility of those early signs of failure that exist in the data, and it gives you a lot more time to determine could you run to your next planned outage or change operation in order to get there?  
 
Or if you have to do maintenance in the interim to be able to plan people and parts and do it in a way that's much lower cost as well as a shorter duration of outage. So I want to talk first about some of the exciting deliveries that we have coming in the first half of this year, and then I'll get into that longer, three year roadmap where we talk about, really, what's the vision for this technology, to continue to lead in this space. So, as you know, we started with SmartSignal on prem and in recent years have built this capability into the cloud and then integrated that into APM, as Martha and Luke described. And then additionally across APM, we've we've modernized this platform in a way that makes it highly scalable and, enables you to connect to other applications and maintain APM as your single pane of glass. The single place where you want to see all of these insights together. So all of that work has been, by and large, completed. We'll continue, of course, to enhance the user experience. But but with all of that being done, the future of SmartSignal is now to really focus again on that, technology where we can advance the product in ways that are going to be significantly improving your experience of using SmartSignal.  
 
So in the cloud, a couple of features that have kind of continued to bring us to the parity of of what functionality was on premises, including that time to action forecasting where you can project into the future at using linear or nonlinear regression to see when you would anticipate, a failure or wanting to take action. A very exciting new feature in the alerting capabilities. It's it's being beta tested internally by our monitoring center currently, but we will be inviting our users to do some beta testing in this area in the third quarter of this year. And this shows you the likelihood of a false positive, as a percentage likelihood. And depending on how you confirm or deny whether that was a false positive. It leverages machine learning to continue to improve those analytics as well. We're enhancing the sensor health monitor capability. So this is again around false positives. But in this case reducing those false positives that are coming in as a result of a sensor issue.  
 
So it's taking that sensor that's maybe flatlined or returning not a number. Some other criteria pulling it out of the analytic and into a single sensor health alert. So you're really able to continue to run those analytics without that bad sensor, and get insights and not get those false positives. And then looking at, the maintenance analytic history notes, this is again, one final kind of enhancement, bringing some capabilities that that were already on prem. And this is tied to the ability and SmartSignal to do machine learning operations or MLOps, where you can see when an analytic was updated, who made changes, all those things in order to, have that MLOps capability. That kind of leads into something that I'm really excited to share with you. A new offering that's going to be coming to our essentials platform in the space of predictive analytics. In the first half of the year, we're working with our partner, AWS, to build a prototype that we'll be, inviting our users to beta test in the second half of the year. We do still have a couple of slots available to do that. If you'd like to join that beta program, please do reach out to myself or to Mazen or others on the team. And we can get you involved. This is the ability to build your own analytics, including that MLOps capability. I described. And it's highlighting a couple of use cases that we see.  
 
The first being someone who understands the equipment that they want to monitor or already monitoring in some capacity, but doesn't have a machine learning or a, data science background. So this would be the ability through a gen AI interface and a chat bot to describe the analytic and the inputs and sensors that you want to include, where it will ask pertinent questions, and then actually develop that analytic for you to then be run and integrated through our essentials platform in the Common Analytics framework. The second capability is a similar situation, but someone who is a data scientist or has that background and wants to either write the code themselves or to use more of a drag and drop capability to create that analytic and then again integrate it back into APM, maintaining that single pane of glass. I'll touch quickly on one of the recent features for our on premises customers, where we actually develop this first on prem and plan to bring it into the cloud offering later this year. But we started on prem because this was a more significant pain point. Due to when you when you perform maintenance on a model, checking the model out, making the changes, checking the model back in. That happens a lot faster in the cloud.  
 
But on prem it's it's a slower process. And so in order to address that and reduce that time significantly, we've created the ability to dismiss an alert while adapting the model with the new data. So when I say false positives, when I describe an alert, in many, many cases, that is a healthy mode of operation for the equipment that the model just hasn't seen yet. It hasn't seen that pattern. And so the user would like to train that data in that pattern back into the model. This makes that very quick and simple. Next I want to jump into the longer term roadmap because I think this is this is probably what you're all, here for in terms of, the SmartSignal piece of the presentation. So we've got five themes of technology that we're that we're building on. Not shown here, but as I noted, is is also weaving throughout all of this is the user experience and making sure that we're continuing to improve that user experience with each of these, five themes and technology areas. So you're looking at kind of a three year projection. This wouldn't be doing, a large body of work over 2 or 3 years, and then delivering something at the end.  
 
This is continuous improvement and continuous enhancements throughout that time. And so in the 2025 to 2026 space, I've included some of those more significant features that you can expect to see. And I'll talk through each of these, quickly. So the first being growing transient analysis capability. This is becoming more and more important in power generation where power generators are operating more flexibly, starting up, shutting down more frequently. But we're also getting requests for this capability in the oil and gas space. So applicable across multiple industries where really you're asking to be able to detect things during those transient modes of operation. So we're starting by adding some equipment as well as adding failure modes, and then looking further into the future, potentially adding other modes of, of transient operation besides, you know, in addition to startup and shutdown. Next is approving, alerting accuracy and prioritization. So I mentioned the alerts beta feature as well as sensor health monitor enhancements building on the sensor health monitor. The next step, once we've taken out a bad sensor, will be to add back in a virtual sensor, so that that analytics continues to run optimally. We’ll be able to replace those sensors with a virtual input in what we call VSR, the virtual signal repair.  
 
Next is around prescriptive recommendation enhancements. So I think everybody's really excited about the gen AI space. We know that this is a technology that's not totally mature yet. So we're continuing to build so that as that technology matures, we're ready to to release it in the product when we feel like it will add a lot of value for you and not a lot of uncertainty. So last year, we had a proof of concept that we performed with our partner AWS. We'll be taking that this year into some pilots with some of our users to have them starting to test that out in the software. Again, not not looking to release that technology until we feel that it is at a maturity, that will bring you the most value. In the interim, we'll be adding some static recommendations, which are recommendations that come from our our wealth and depth of knowledge from our own monitoring team.  
 
The IMS team, who have a many, many years of experience saying what recommendation would go with a particular alert. For, additional AI capabilities, we're looking at leveraging AI in faster implementation. So starting with tag mapping enhancements and building on that and hit, you know, enhancing and improving implementation in other areas as well. But recognizing that as you add assets or add sites, that being able to do implementation quickly and easily is very important. And lastly, looking at faster model maintenance. So I mentioned the adapt from alert or adapt from chart feature this year. That again will be we'll be building into the cloud offering as well. This is an area that's becoming more and more important to our users. As Luke noted, workforces are not growing, but what those workforces are being asked to do is increasing and so improving that productivity is going to be very significant here. And so from here, I will, tie this into our next segment. So you can see SmartSignal was a, a very significant component in the reliability piece of, your asset intelligence started with the next step in reliability is really once I'm able to be predictive here and and see these issues, to know when I'm getting repeat issues or bad actors. And then to be able to ultimately look at, eliminating those defects. So with that I will hand it to Mark Sinozich and Vipin Nair to talk about defect elimination. Thank you. Thank you Janet. Hello, everyone. And thank you again for joining today's session.  
 
Mark and I would be covering this section, and we are absolutely thrilled to talk our vision around defect elimination. As Luke pointed out in his opening slide, that defect elimination would be a core component as we envision our next journey of APM, which is asset intelligence. But is defect elimination a new word? Absolutely not. Right. It is a widely accepted terminology, and it's been in the industry over several decades. So what's different? And that's where the word reimagining becomes so important. With that said, let's get into the next slide. So we have been talking to our customers over the last Right. We have been talking to our customers. We have been interacting with analysts. We have been seeing trends, both macro and the technology trends and what we have understood that defect elimination is definitely a core component of asset performance management, but that definitely some big problems that we can solve and opportunities that we can help improve in this space.  
 
The first one being the workflow itself. Workflows are a core component of any offerings, right? As a core component of any digital offerings. And the question is how we can provide you a seamless guided work process connecting multiple modules. We have seen few customers who just use just use RCA for carrying out defect elimination. RCA is definitely a core component, but is that the only module which enables defect elimination? No. There's definitely much more steps like bad actor identification. So we would like to give you a holistic, seamless work process connecting multiple modules to enable this work process. the the other segment is is defect elimination only to identify bad actors? No. We really want to enhance this one step ahead. We wanted to feed into your strategy optimization. Martha talked about how we are envisioning health monitoring.  
 
So we would like to combine that data with the defect elimination to help you continuously improve your strategies over a period of time. And finally, we have been deploying the solution for defect elimination for the last two decades. But it has been custom solutions, you know, varying from one customer to the other. And with this new offering, we would like to move away from those custom solutions and deliver you a baseline workflow. The second design tenant that we have really honing in on with this new offering would be the work history data and the quality around it. As you all know, the core component of any defect elimination is identifying bad actors. And that is completely dependent on the quality of work history and interacting with our customers. One thing has been very clear that they find it really difficult to maintain the quality of it. So we would like to really, deploy the best technologies to give you a data confidence calculation, which is we are calling it as a smart data confidence calculation. Figuring it out, the breakdown indicator is right.  
 
Whether it's the right PPM type, all of those to flag it so that you can be confident around the data quality, which is driving your bad actor identification, and also some of the key SMR metrics that we will be delivering out of the box. So that is your second most important tenant that we would be focusing as a part of this offering. The third tenant which we'll be working on is obviously tying it back to value. One of the biggest challenges that we see across our customer base is to tie it how they are able to tie back their programs to a tangible value. And one place where we can definitely help that is with defect elimination. So we intend to provide you with out-of-the-box metrics to quantify your brand reliability. As I mentioned, we will be working on on a seamless defect elimination process. But eventually, what do you do with that data is tracking improvement program right. We would be highly focusing on the improvement programs, which is coming out of those defect elimination and linking it back to your overall reliability at the end of the day.  
 
We all want to quantify the value of these programs in our organization. So all these three would be some of the key elements that will be focusing on as we get into the development of this new offering. So that's why you will see at the tag line is we want to shift from a traditional APM program to a very smart, data driven reliability improvement approach. We are also excited to say that we would be soon starting, a, pilot program, and we would be really interested for customers to reach out to us and see if you would like to be a part of this journey. With that, I'll hand it over to Mark, to get into the details and some of the preview of the user experience here. Thanks Vipin, that’s great. So I, wanted to talk a little bit more about the details around the what we envision for this defect elimination workflow. Some of the fundamental elements to expand on, what Vipin had described philosophically, that we want to try to attack in terms of being able to deliver additional value through this workflow. The workflow itself is very straightforward. Being able to measure how you're performing can often be one of the most difficult things that you have, to do. We've talked a lot about ways to prevent failures and to, utilize the tools in APM to prevent things from happening and, and have a more proactive stance. But we know that failures do occur. And so when things do slip through and we do have issues, we want to be able to identify those things, leverage the data that you do have in terms of work order history as well as potentially production loss data. Anything that's contributing to the negative impact on your bottom line are things that we want to be able to measure.  
 
And so we know that work history is an incredibly rich source of data that customers aren't fully taking advantage of. And part of what we want to do here is allow them to, use that data to, of course, one assess how they're performing, but also take a look at the quality of that data, assess what they're capturing, what they're not capturing, rendering that data, giving it a home directly in the APM product where you can go and visualize that data, drill through that data and identify areas for opportunity. So identifying defects is the second part of this workflow that certainly, you know, there are a number of ways to do this. Oftentimes, you know, those things are very apparent where we've had defects, especially if they've had big numbers associated with them in terms of impact on the business. But there are also a lot of chronic things that we want to investigate to ensure that they don't occur. So those types of, defects are both catastrophic and potentially chronic. So we want to be able to identify those in drive into investigation and determining what the root cause of that, failure was. And of course, then establishing what, what the, corrective measures would be to eliminate that defect. And so just a couple of, screenshots to give you a sense of what that might look like in the product. We do have a working prototype that we've shared not only at the conference this year for those who were there. We appreciate your attendance on that and your opportunity to get feedback, but we, are working with, several customers very closely.  
 
We'd love to work with you as well to understand what you envision and how the prototype that we've got currently and our plans, work for what you want to do and how they could be improved. So being able to, again, leverage your work history and other information that you have out of the box and compare that not only against yourself, but against industry best standards like the SMRP metrics. As part of that baseline offering to be able to take advantage of the data that you have and see what you've got. But there's also what we're hearing a lot from customers is that the quality of that data varies significantly. And so we've had opportunities to address this issue in the past. And we have done that through products like asset answers and IDD and assessing the quality, of the data as well as the completeness of that data in terms of what you're capturing and what gaps you might have in capturing that data. And that's going to help you continually improve your ability to collect meaningful and useful data. That is going and, and higher quality data have confidence in the tool that we're providing to be able to assess how you're performing. So that's a big piece of what we want to do. The other big piece of of what we want to provide here, of course, is to be able to, utilize the data to develop a list of bad actors.  
 
And that could be a, a simple list of equipment that's not performing well, and not measuring up to what you, prescribe in, the initial strategy that you've created to maintain, mitigate risk and monitor that piece of equipment. So this is an indication of where your strategy is not performing well. And we've experienced a number of failures or, catastrophic failures, or things that, again, we want to identify. There might be a lens through which you might want to identify specific events that have occurred, for instance, leveraging PLA data production loss data to identify specific events that have really been, impactful in a negative way on your bottom line and, investigating those specific events. So it could be through, and equipment or, taxonomy related lens. It could be through an event lens. To be able to serve that data up to you, provide you information on, on the day that you do have. And, as well as being able to render out things like the dominant failure modes that are being experienced as you drill through and look at through date, range, equipment type, various levels of the, the hierarchy, and be able to slice and dice that information and hone in on opportunities to improve the reliability of your assets.  
 
And then, of course, we want to drive into creating, investigations. Primarily root cause is one of the fundamental accepted principal aspects of doing that and generate the corrective actions to improve over time. And so while we want to prevent as many failures as we can, we know that they occur and we want to learn from them. We want to leverage the data that we have about them, and we want to have a structured workflow and process that that is supported by the baseline product to enable you to take advantage of that and get the value from it. So I'm not going to spend too much more time on the overall APM reliability roadmap, but I'm going to talk a little bit about strategy here momentarily as well. But as you can see, many of the things that we're working on are aligned specifically around mostly three major initiatives. One would be the defect elimination workflow. And as a rule, really companion to that would be our focus on improving our existing root cause analysis, offering as well. So we've got a lot of feedback over the last, you know, 6 to 8 months on this product. We've got a plan and you can see we've already been executing on that plan on how to make, the root cause analysis application as simple, intuitive and easy to use. As well as leveraging automatically the data and importing the data that you need to conduct the analyzes. And so you'll see that represented here under those two initiatives. The third one is work that we're currently doing exploring the opportunity around spares analysis.  
 
And this is one that bridges both the reliability and strategy spaces. So it would be a nice transition into strategy as well. But the spares opportunity that customers have to optimize their spares, we've got a lot of tools to optimize strategies and the maintenance and monitoring activities that you have in place, but as a part of that, you also need to be optimizing the spare parts that are used for both planning corrective work, stemming from those existing strategies. So strategy, reliability, efforts and roadmaps are really merging in a way that they'll be complementary. And not only to each other but to other applications within the product, like health. And I'm going to talk a little bit about that. So I'm going to transition a little bit into the next topic here, which is, around using a risk based approach for strategy development and optimization. And when we talk about asset strategies within the application, we're really talking about the maintenance activities that you're performing to mitigate specific risks and failure modes associated with those pieces of equipment, but also the monitoring activities. And we'll talk a little bit about how the strategy tool was inning is being integrated in a way to support both of those, throughout the lifecycle of the strategy itself.  
 
So one of the main offerings that we have, in addition to, of course, the tools like criticality analysis, RCM, FMEA methodologies, as well as our standard and flagship asset strategy management application, which is really the hub of the asset strategy tools. We've we've given customers the ability to have those tools to, to do the, development of strategies in the optimization and the implementation of those strategies. We also have seeded these applications with what we call accelerators. And we've talked about accelerators extensively because we know that it's a high value area for customers who want to go down this journey. And that's a journey of optimizing your existing strategies for a brownfield scenario. And that's all around you. Rotating mechanical equipment, electrical and safety equipment. But it's also around greenfield scenarios where you're starting from scratch to develop strategies for, new assets that are coming online. You may be using the tool to optimize your existing strategies through things like transitioning, away from time based or traditional time based maintenance and more condition based monitoring activities.  
 
You may be using the tools to, cost out by extending intervals on existing, maintenance plant, maintenance activities, etc., which have a downhill impact on your cost structure through both the, consumption of labor as and materials as we just talked about around spare parts. So the, the advantage of the accelerators is that you get a lot of this, content in terms of what the major failure modes and prescribe mitigating actions per asset type would be directly out of the box. Now, most customers have many pockets of excellence and subject matter expertise across their organization. The struggle is to get a standardized, centralized location to house all of that subject matter, expertise and experience in one place that can be leveraged in bulk across their enterprise to, again to help them standardize and ensure that what they're prescribing as a best practice, maintenance and monitoring standard across their organization is something that they can truly develop and enforce and, implement across the application.  
 
So we want to get away from the custom experience where you have to go out and and build this content by hand every time. So we want to get business results much quicker. Right? So if I, if I spend less time having to develop all the failure known failure modes for a piece of equipment and leveraging someone's expertise and time that they could be doing something else, we have that stuff out of the box. And so we're saving hundreds and thousands of potentially hours, depending on the size of, of your implementation to quickly get to getting the strategies in place, getting the strategy optimized and implemented so you can get that return on an improved strategy. And, we've seen, of course, tons of evidence where this is 100% true for our customers. It just takes longer to start anything from scratch, as we know. And so embedding this intelligence and subject matter expertise into the product in the form of these asset strategy templates, is, is a big, game changer. And step up for many of our customers who see starting from scratch is a real barrier to adoption of the product to be able to they know the value is there, but it's a matter of time and resources and subject matter expertise that customers have.  
 
So leveraging this embedded technology and, in the form of these templates is a really big improvement. The last thing I'll do to kind of bring this together in terms of accelerators, and we've already seen this alluded to both in Luke's presentation as well as, Martha's and Vipin’s and Janet's presentations are really bringing these elements together in a way that holistically delivers workflows across various elements of APM and the integration that we've done on the on the actual product. The work on the product continues in the form of these accelerators, because customers don't see and accelerate a strategy independent of, the health monitoring that they're going to do as part of that strategy. Right? It's all a continuous process, and it's all interlinked together. So we saw a really good example of the AHMAs, that have been developed by the accelerator team and implemented at various customers and, and those are all based on, again, subject matter expertise that's embedded in the product. And we we know that in the strategy application and the strategy templates, we define specific failure modes for different types of equipment. And I've just got a simple example of like a pump here. Right. So we've got specific failure modes.  
 
We know that we're suggesting and prescribing specific mitigating activities like maintenance periodic time based maintenance, but also monitoring through condition based, analytics or other, physical means of collecting data, censoring data, manually executing routes through a rounds application, etc. to pull data back in that feeds into these health indicators that tell us whether or not these assets are healthy and whether or not we're at risk for any specific failure modes that might be, emerging or looming ahead of us so we can visualize, see the, obviously the issues before we experience any failures. Now we're going to be providing a ton of tools to be able to help you if you do experience failures, move on.  
 
But this is an example of the integration of not only the product, but the content that plugs into that product. And for my last bit here, you know, I won't go into everything that's on the strategy roadmap, but you will see some common themes here around integration with not only applications within the, the strategy collection of solutions, but also with other, applications outside of strategy. The other thing I'll do to wrap this up is just mention again, spare parts. This is something that I'm actively engaged with customers on developing requirements and something we do want to start working on this year. So this is a topic that we've shared through all of our focus groups and continue to discuss with customers. But if you have feedback for me or any of the other product managers on any of the topics that you've heard, please reach out to me. Schedule some individual time and we'll be happy to listen to you and and understand where you're coming from and what what we might be doing from product manager perspective to help you get there. And with that, I'll be, turning it over to the next presentation. Thanks, Vipin. Thank you, thank you.  
 
Mark. Team, before I get into this, I just want to remind that the Q&A, section is, on. Please, please put your questions. Thank you. So in this section, I'll be covering how to improve your plant safety and integrity and also to enhance your mechanical integrity program. So when Luke presented, earlier, if you remember that slide, you know, there are a few critical components, which is performance, emissions, reliability, integrity and safety. So those are the primary pillars and the value drivers. And what I'm going to really focus on is the integrity and the safety component of that, image. And with that I would like to just focus on what do we currently have within this portfolio, because I know there are a lot of, new users who have just logged in. So for those who are not aware of the, mechanical integrity solution, this is one of our flagship product, been in the market for the last 20 years, and we have only matured and innovated over these, 20 years in this space. First of all, we do have a world leading risk based inspection capability, which gives you a of various of options, right? From carrying out a semi quantitative to a fully quantitative, as per AP 581, and also a flexible RBA option to model your own, risk methodologies. We do have, holistic inspection database management system to, manage your inspection plan and also tracking your inspection recommendations. Third, which we have recently innovated and invested in is our paperless inspection, which is our mobile capability, which we are calling Integrity Mobile. And this is to really improve the productivity of your inspectors as they go out on the field to capture your inspection results. Then we have a module for managing your corrosion analysis and carrying out your remnant and, remnant live calculations as per AP standards, which is thickness monitoring. The fifth element is, capability to help you, give you some situational awareness and contextual visualization.  
 
And as a part of that, we have launched something called 2D visualization or a schematic view. And also we have partnered with Visualize to provide you a There is also a component called compliance management. Because we cater to different geographies and there are different compliance standards across the globe. So in order to support that, we do have a module called Compliance Management, a last but not the least, which is Integrity operating window, which is a step towards a dynamic risk assessment can be enabled by, cloud components like data, time series ingestion, policy designer analytics, which help you establish these integrity operating window monitoring and also determine process excursions. And the best thing about all these modules is that they are not siloed. They all talk together, and they all work together to provide you a holistic mechanical integrity work process. With that, let's just focus on, what we have delivered. And these are the big tickets.  
 
We we have a lot other that we have worked on, but I really wanted to focus on in the last 12 months, our focus has been one is providing you a out of the box deferral management within inspection, modules. So now you can defer your inspection task or recommendation. We have been talking to various users who had configured it over a period of time. And now you have a baseline workflow to support your deferral program. It is also highly, configurable in the way that we can support customers in our cloud to define their own deferral, settings. Because we all know deferral is very specific to owner operators, requirements. So that has been one of the major focus areas in the last year.  
 
The second area of innovation, or where we wanted to really improve, is combining your inspection program and your corrosion monitoring program. So as I mentioned, thickness monitoring is all about managing your corrosion data. But one of the key components of that is optimizing your TMLs. And that was not possible just because, there was not a, a great integration between both the modules when it came to your planning of your thickness monitoring locations. So that was a big gap. And in order to bridge that gap, now we have integrated your inspection task with your corrosion monitoring program, which allows you to link your thickness monitoring to a, inspection task, and also to provide that your field inspectors to carry out your thickness monitoring. So that has been a core, component. And we will talk more on how we are continuing to innovate on that space to really bridge that gap between inspection and your UT programs.  
 
The third element is we talked about how we launched We started that in our thickness monitoring module. But now we have extended the use case to RBI. And the first place where we have integrated that is with corrosion loops. So with our latest version, now you can visualize a schematic view which you can see on the right side, a PFD drawing or a could, process flow, drawing. And you can see all the damage mechanism, highlighted on it. You can drag and drop your RBI components and you can see the most important critical information of that RBI component right on that image. So these were the three most important ones. And as I said, there are much more that we have done in the last year. And now let's have a look ahead, which is what we are currently working on, what we are actively working on in the next 8 to 12 months. As I mentioned, inspection and TM integration, a tight integration is a very a highly focus point for us. So we are working on phase two, which makes it further interesting. So now not only will allow you to link the TMLs using a data loader, we are really taking it to the UI. So from the product user interface we will be giving you a smart TML scoping view, where we will provide you out of the box criteria based on which you can easily identify the most critical TMLs that you should be scoping as a part of program. So this should really bridge the gap and enhance the program of TML optimization.  
 
We are really excited for this capability, and we look forward to working with all of you on this, on the segment too. The second one is all about our continuous work towards giving you a better user experience. So one component, which we are really, taking it up and we are going to hone in, is inspection management, the asset view. So we are going to show more data within that asset view so that you don't have to have multiple clicks to get the information. So we have an asset view for inspection very similar to how we did it for RBI and thickness monitoring. So you will have all the information for for that asset from an inspection perspective in that one view. Again, 2D visualization, it's you know, one thing about our quarterly updates is that we are able to deliver you much faster and quicker. So the first phase of 2D visualization and RB was corrosion loops. Now we are also enabling that for an asset view. So if you are not running a corrosion loop program which is fine, even if you are doing RBI at a component level, you will be able to define a schematic view and drag and drop your RBI components on that schematic view. Last you know, one of the differentiators as I mentioned is our RBI And one of the demands is always to keep up with the standards. So as you are aware, that API and will be slowly starting to embed the changes that has come.  
 
Within that are the two most critical ones which we want to deliver this year is, combining the thinning and lighting damage factor calculation and also the updates to your management system factors. Now, I would like to spend some time on vision, right, because that's the exciting part. So we talked about what we delivered. We talked about what we are working on currently, to support your program. But also important is to stay ahead of the curve and where we want to take this program moving forward. So one of the segments, which we are really honing on is linear asset management. So we we do support all the asset types like pressure vessels, piping, storage tanks, pressure relief devices. But one of the critical asset types that is critical for the upstream and the midstream, segment within the oil and gas is linear asset. And we are currently working with, oil and gas major in the Middle East to envision how it would be to support linear assets within APM. And we identified some of the core components of that would be the new data type, like ILI data, the video inspection which comes out of subsea inspection. So we are thinking how will be able to handle those inspection types. We are also thinking of, you know, how we can really emphasize on GIS visualization because we know how critical it is when it comes to a linear asset.  
 
And finally, anomaly management. So before, recommendation management, there is a key workflow for anomaly management based on your, corrosion analysis. So those are a few of the key components that we are looking into as we explore supporting linear asset management. The second one, as Luke mentioned, you know, as we get into our cloud journey, one of the strength that we get is the power of AI. And Mazen and Neha I would be talking more about it in the next slides, but you use cases that we think is really critical from a mechanical integrity. Talking to our customers is one having an assistant for text summarization or data summarization because we all know how. So how how much data is kind of spread across asset performance management. And we your planning the power of summarizing it during inspection planning is so critical. So that's one of the use cases that we are exploring it from our perspective. And the second is, which Neha would be covering in a slide is using computer vision to drive insight. It could be driving insight from your images, or it could be driving insights from your PDF data using optical character reading. And finally, integration with future ready inspection technologies. We know that it's, you know, we are going through, a change here.  
 
We are going from a visual inspection to mobile inspection, robotic inspection and drone inspection. And we, being on the forefront of the change, would like to understand how we can adapt to this change and how we can analyze this unstructured data that will be getting in the future. And finally, I do we do see that IOW program is going to change drastically with the power of time series data that we have on our cloud solution and also the ability to run analytics on this. So these are some of the core components that we would be focusing in next couple of years and will be working on it. So I would like to get more feedback on what you think about the vision. And if it's aligning with your journey to a lot, spend a lot of time on this. This is just, a roadmap slide. I typically cover this during our focus group. So I've covered most of it in the previous slide. But with that I will hand it over to, the next presenter. Neha, back to you. Thank you and good day, everyone.  
 
Thank you for joining us today. My name is Neha Joshi and I'm the product manager at GE Vernova currently leading innovative inspection and monitoring solution within our APM portfolio. So let's get started. So in the earlier sessions you have heard, the significant challenges that our customers face in the asset intensive industries that are truly worth solving and how our APM suite of products are helping them in various areas. So now let's focus on some of the process related challenges that our customers are facing. And in terms of like manual inspections or the sensor based inspections. So you all know that sense resource, sensor based, manual inspections are, very resource intensive. It demands significant allocation of, human resources. Inspectors need to be trained, scheduled, transported to the various sites to pick up the data. And it of this process is often time consuming. That involves deep, detailed and repetitive tasks that need to happen and the thoroughness and the accuracy of those tasks. In certain cases, Inspector required to go in the hazardous or the remote areas like it could be confined spaces, high elevation, climbing up the stairs. And they, they might get exposed to the toxic substances and in certain cases as well, that increases the risk of accidents, injuries, and that can lead to legal liabilities. Another area where, we saw the manual inspections are having challenges are because these manual inspections are typically scheduled on a monthly periodic basis, like, monthly or a weekly basis. Inspectors go and check the areas. Sometimes, on the extended period as well.  
 
So this delayed and reactive approach can result into, you know, unnoticed problems or, it, it and that leads to higher repair cost and increased downtime. On the other hand, the sensor based inspections, they are, difficult to identify the, identify the surface level issues, like, for example, corrosion or cracks. It really lacks that visual context and it also, intrusive process. That means sometimes you will have to bring the asset down to install those sensors. So to address these issues, we have developed a computer vision based inspection solution called Autonomous Inspection. It is our image analytics solution that runs on our essential cloud platform. So let me walk you through the four step process for the workflow. How it works. So step number one is collecting images on prem.  
 
Currently we support fixed cameras like RGB cameras or infrared cameras. We also support non-fixed cameras. Like you know, mobile devices that Vipin mentioned earlier. It could be a robot, a drone or satellite as well. So step number. So step number one is collecting images on prem. Step number two is transferring those images from on prem to our cloud solution. And this is done automatically to our cloud platform. Step number three is analyzing those images. So we have deep our deep learning models running in our AWS cloud. These these, you know, these particular, image and these, models run image analytics on top of the image data that is captured. So we have, we have currently released few algorithms like gauge reading, thermal profiling, corrosion detection, corrosion severity level defect detection, and MCC panel readings. So we have these models running right now. And step number four is basically running the image analytics on top of that and sending the results into our APM solution. So we send two types of outcomes. One is time series data in our APM. And the second one is APM alerts that, that is those are the threshold based errors. So what value our customers get with this is images. First of all number one is images from all the sources like think about like camera mobile, robot base, data. Everything is available in single repository. As a single source of truth in our APM.  
 
Those are available for your historical analysis and reporting within our APM. The second most important, value that our customers get is because the data readings, from, from from our image analytics is available in terms of, as time series assets and tags. So those can be accessed from broader set of our, other applications that are running in the cloud, like health, accelerators, health and reliability, workflow or integrity that Vipin mentioned. It is also well connected with our essential platform based applications like policy, advanced visualization, asset 360. So as a part of, this current release, the thermal profiling model is actually collecting the data, it analyzes, it will run the algorithm and it will find the temperature anomalies and send the alerts or the threshold values that you have set for gauge detection. It reads the gauge, it understands the needle position. And you can set the other, alerts based on the threshold levels. For corrosion detection, We have two models. First model will detect the change on top of the surface, and the second model will actually give you the severity level analysis. In in terms of, asset, tags. So again, just to, just to remind the product capabilities are like sending alerts. Everything is, part of our APM cloud solution. So same user login and password and you get access to this particular, application. So we have we are now expanding our capabilities for remote inspections.  
 
We recently successfully completed a POC, where we, used a robot, that followed a automated route, to capture the images of the various assets in the plant. You can think about the robot as your mobile sensor. So it sends images to our autonomous inspection app and, to run the image analytics on top of that, the results are available into, APM, APM, time series as well as APM allowed. So you can see that now the robot is, picking up the pictures in the plant and the results are available in APM times. It is. So we get that entire flow from, from the plant to APM and everything is run automatically. I'm not able to play the video at this time because of the time constraint. But feel free to reach out to me one on one and I can, give you more details about how the how the robot works. Robotic inspection works. So, the at a very high level, the, the flow would be like, you know, robot is going around picking up, RGB or thermal images at the plant. This particular robot that we are exploring is a multimodal. It can pick up data on one go with different type of data. It uploads that data into the, into the service called RaaS robot as a service.  
 
That is the application running in AWS. And we use our SDK API to access that image data, pull that image data, run our AI ML algorithms to analyze that data and generate the APM alerts and provide actionable insight in policy and time series. So this approach is especially valuable for, for hazardous environment or hard to reach environments, enabling safer inspections for, for for the manual inspection. We have customers looking at using the robot, and they want to run it in the night shift or over the weekend so that, you know, they get, 24 by seven coverage. They are also, the robot that we are exploring is it takes zone one compliant robot from ANYbotics company. So looking ahead, in, in the second half of the of the year, we will be exploring like, tighter integration with, with, with the, with this robot and with this robot is bringing a lot of opportunities as well. Like Martha mentioned in her, in her presentation, we will be starting a POC with our Rounds Pro product.  
 
It will further automate Rounds Pro integration with robot based inspection. This will enable us not only to analyze the sensor based data that is coming from the robot, but we will be able to also manage the robot missions remotely from our APM Rounds Pro application. So that's that's what we are. We will be working on in the second half with this robot. There are also some emerging use cases that are, appearing because this robot has multiple sensors like gas leak detection and ultrasound detection. So this will we will be able to identify potential hazards, happening in the field. We have recently published a white white paper to showcase the value of combining robotic inspection with, with our APM solution. And I would highly encourage you to read this, paper. You can download that paper using the QR code that is displayed. And we will be also giving, it in the In the resources tab here. So again, that brings into my, my session. Thank you for your attention. I look forward to discussing how these innovations can help to drive your operational excellence in your organization. Let me hand it over to Mazen to discuss more about our scalable platform and infrastructure and other gen AI initiatives. Thank you everyone. Thank you so much, Neha, and welcome everyone. Nice to have you all here today with us. So Mazen Younes here, I lead product management for our platform, inclusive of Essentials, our APM Foundation, Accelerators, Autonomous Inspection and our, I also own our AI strategy. So with that, what I wanted to cover today is really the, at the bottom green part, the bottom box there for essentials, an APM Foundation. Just so you have an idea of some of the recent capabilities that we have.  
 
And I'll also cover, some of the, AI roadmap that we have and then give you a preview of some of the work that, both Janet and Vipin touched upon. When it comes to AI within, SmartSignal and AI within, APM. Now with that. So, when we say, Essentials, this is our, SaaS based enterprise platform for data and event management. So really, it's, the nuts and bolts of, all the applications that we're, we're, we're providing today in the cloud as a SaaS offering. We're providing basically a data monitoring and event management platform, which is enabling everything from your data ingestion from OT and IT data sources, all the way to enabling you to do analysis, visualizing this data, taking action, and, making sure that you're monitoring and responding to any events as they occur. So with that, some of the recent capabilities, you know, this is this is just a snippet of some of the capabilities of a subset of the capabilities that we've provided for the last 12 months that I wanted to highlight.  
 
So first off, is really in the, connectivity space. So we've been continuing to ensuring that our, integration tools, are modernized, scalable, and provide the ability for us to, tap in to more data sources. So we've, we've actually done, a change from our prior tool to Boomi. So we're now running more of a lightweight, execution container runtime that is scalable. We've simplified a lot of the, integration points that we've had with the, IT and OT interfaces. You now have better workflow scheduling. We're now also providing a low-code, no code workflow, development for our customers and more importantly, providing a better experience for troubleshooting, integration points with, with the systems, whether it's on prem or in the cloud. We've also, modernized a lot of our data loaders, all data loaders that you have today, go through API ingestion. So that's been, a big ask from, from our customer base that we've, we've worked on and pushed out, as a, as a capability that's now available. Now shifting on, we, we also have invested a lot in the, analytics space, especially in the whole, MLOps, realm.  
 
So, we're providing the ability as well to bring your own analytic, basically you're able to orchestrate, configure, deploy, execute, and monitor that's analytic against the subset of data that, that we have in APM. So really this gives you the ability to leverage, your own built, built, built analytics and bring these over to our platform to run and execute and get real time, understanding and, real time monitoring and alerting against your, the failure modes that these analytics are running against. And lastly, we've also embedded a BI capability for advanced visualization within our tool to basically provide the rich experience for, reporting and, collaboration across, across our system. Now, as we, as we think through, the disruption and you know, some of the, more cutting edge technology that we're seeing, right, in the market. So when we look at, our AI, ML roadmap, you've heard a lot from Neha on, autonomous inspection, investing in computer vision. So on the left hand side, that's the second piece there within our essentials, platform.  
 
The first section, we're, we're, we're investing more in, AIML capabilities for false positive alerting, right. Making sure that, we scrap our knowledge base and ensure that we're providing, recommendation for our users to better dispose of these alerts. And we also provide a feedback loop, making sure that there's a human in the loop aspect there, to ensure that, we improve on these models. We're also, as Luke mentioned at the beginning, we're enhancing the bring your own analytic model and the whole MLOps experience to also enable our customers to, build and train their own models on our platform. And as Janet also mentioned, this is going to come with the capability of, using gen AI to generate a model or provide drag and drop capability for, data scientists and even the capability of writing your own Python code, doing, data wrangling. Right. Training, retraining these models and then be able to do inference on, within APM. So that's also capability that we're excited to, to move forward with, to publish. And I think that's going to play a big role.  
 
And also enhancing things like concepts like, assets intelligence, right, where now customers can bring in their own, analytics, their own expertise onto the platform and then tie that, these failure modes and back to the assets, back to a presentation of the, asset intelligence on the, on the APM side. So the middle box there, we've spoken about APM Smart Signal, right, with the prescriptive analytics, I’ll give you a, a sneak peek into some of the work that's been done there. Right after this. And we've also been doing some, work on the gen AI assistant for text summary of structured and unstructured data via chatbots that we're testing out with APM integrity. But we're really we're building the foundation there to use this chat bot across all of APM. And lastly on CERius and you'll hear more, shortly around, around the capabilities that we're building there in CERius a lot of work happening still to refine the models that we have for assets on scope one, two, and three. Advancements in terms of, recommendation engines for prescriptive emissions.  
 
That's also, work that, that's been ongoing on that front. Now, with that, just wanted to give you a brief sneak peek into, some of the, APM Smart signal prescriptive recommendation, work that we've been doing. Really, this is, the ability for, plant manager. Reliability engineer. Right. Whether it's data scientists or even maintenance, maintenance engineers, once an alert is triggered and it's positioned as a, as a true alert within the cases module that we have, you can now, autofill the recommendation piece with the recommendation section with specific, actions that, an SME can, can follow to address the specific issue. And really, we're taking that a step further as well to, embed within, an agentic workflow as well of automating the whole case creation, keeping human in the loop aspect as well for feedback. So making sure that the person clicks. Yes. Agreed. So that the, AI agent can continue to the next step, and looking at other ideas, whole thing over to, creation of strategies. Right. And, and taking it a step further towards, towards these, these, these capabilities that can, better thread the experience across our APM, portfolio. And then the next slide that I have here is across the gen AI assistant that we've built. Sorry, this is giving you the ability to query, query our databases query, data sheets that may have been applied to the structured and unstructured data that, that we're talking about.  
 
And really getting information around the historical of your equipment. Right. The historical of inspections, whether the inspection options that you're covering for, your assets are, you know, are covering every, every step that you need to think about, so as we start with this specific journey, we can, you know, we've been we've been, developing, best practices when it comes to gen AI. And we've been taking our time as well, just to make sure that we test out, you know, things like the model, context, protocol, RAG making sure that performance makes sense, the cost profile makes sense as, as we introduce these technologies and more importantly, that the output from these technologies is truly reliable. So really exciting times. And I think we're looking forward to more partnership and feedback from our customer base as it comes to this area. And with that, I will pass it on to Laura so she can cover the exciting, aspect of CERius. Laura, over to you. Thank you so much, Mazen And, thank you everyone for joining. I am going to take us home with CERius, so I am a senior product manager for our our new emissions monitoring and emissions management software, CERius.  
 
And I'm going to walk you through how you can use the same building blocks you already have for APM, and then scale those to ensure you're monitoring and managing your emissions with CERius. So as you can see here and what Mazen and really walked through is, the general foundation and core components are shared between CERius and APM. So you can very much use CERius on its own. It's a standalone product, but the key here is will be able to leverage data between the two systems to drive emissions management in CERius and operational efficiency with an emissions lens in APM. And this can really allow you to balance the operational performance and emissions reduction. So this is a kind of a next click down. This is highlighting kind of from the previous side. So on the left is what we're talking about when we we're going to leverage APM connectivity with that historian to get started in this ecosystem. So on the left is where we will pull data from. So historian will be that scope one OT data, scope two with utility connections and billing information, and then scope three, through an ERP platform. Once in CERius, kind of in this light, light green box, you get those AI based analytics. And then we move that cleaned data into a carbon ledger and then use that data as needed. So this could be for audit information.  
 
This could be configured reporting which we'll go into for different scenario planning. And then you'll see some additional capabilities at the bottom that we can look into with this data. So this could unlock the ability to change dispatch or operations depending on data. It can help decide on future projects or other carbon accounting or carbon credit pathways and carbon intensity. So this is just going to kind of walk you through the value that CERius can bring to our customers. So if you look, we have two, two parts of the tool that can either work kind of separately or together. One is the emissions monitoring and reporting piece. So on this side CERius can automate, verify and correct scope one, two and three emissions data across various sources. This can be a real relief for people in charge of manually collecting and importing this data, as well as the risks that can be associated with not reporting accurate data to those regulatory bodies. Once the data is in CERius, the next step is then to track your near real time emissions.  
 
This will help your organization understand their current emissions footprint, as well as how they're tracking towards their target, many of which have been publicly communicated. You can then use this data to complete regulatory reports, such as annual Greenhouse Gas Reporting Program or the EU Emissions Trading System, and then moving into the emissions management side, you're really able to forecast and strategize future sustainability investments using predictive analytics. So thinking about questions, you know, how am I going to reach net zero or how are we going to reduce our carbon footprint. Do you need to upgrade your equipment. You need to make operational changes, perhaps model different renewable energy options. You can also then develop and manage your overall emissions reduction plan. So setting those targets and then tracking those programs to get closer to net zero. This is going to jump into those modules of our platform that can unlock the value we just discussed. So the emissions monitoring and reporting side of CERius is really comprised of these three top modules collection monitor and reporting.  
 
So again collection is going to be your centralized emissions accounting ledger. Pulling in emissions data such as carbon, carbon intensity sulfur oxides and nitrogen oxides. So those greenhouse gases and also air pollutants. In the monitor module, you can visualize key metrics at an asset plant or fleet level. And then you can compare performance and measure the decarbonization impact. So this is really nice because it brings value to to the plant level itself, where you're looking at the assets and getting a holistic view of emissions and operational data. Of course, on the APM side and then at the enterprise level where you can compare, plant level information or site level information and then reporting, we use all of the data that's been collected to prepare for and submit regulatory reports. This can be a really heavy lift, resource intensive manual. And what we've built in to CERius is predefined report templates as well as customizable report templates. So these may be on a quarterly basis. Or an annual basis. And then you'll also have access to all of your report history. Moving into the management side of things that are strategy and planning modules. So now you have all of your data in one place, and now you can move forward and set that decarbonization strategy. So this module will allow you and your teams to do scenario modeling.  
 
What if we invested in carbon capture or what if we built a solar farm things to kind of offset our emissions. And then you can compare those different scenarios, integrated resource planning and then measurement of performance timeline and the return on your investment. So your finance team is very likely going to want to see is this going to give us a good return on investment if we, move forward with this project? And then on the planning side, you now have your strategy with your targets, all of your data. And now you need to map a path to hit those targets. So the planning module will allow you to define your decarbonization projects or emissions reduction projects. Track the milestones to get there, and then visualize the reduction and identify any underperforming projects to see if you kind of need to pivot your strategy. I'm going to finish up with a quick case study. So we work with, one of our customers, Xcel Energy. And they had a kind of a number of challenges, including pressure from leadership, the market and investors to achieve net zero emissions. There was a lot of manual data collection. I think thousands and thousands of rows in, spreadsheets. Their environmental health and safety team was involved in audits, which was very time intensive, and there was increased regulatory requirements to disclose which which many of you may be facing as well. The goals of their CERius pilot program are to automate data collection, standardize processes, and eliminate spreadsheets. So we want to simplify reporting and preserve internal knowledge concerning data verification and reporting.  
 
The results of the pilot have been moving to a centralized emission software, providing transparency from the plants to executive management. Integration with APM and Historian connectivity. This helped eliminate a third party software. Automated collection of that scope one, two and three data, and then support for finance initiatives and gaining of executive oversight to track and meet those emissions targets using intuitive dashboards and visualization. We also work with them on a lot of our kind of beta testing. And as we go through that, those are features that of course, everyone will benefit from.  
 
So thank you all so much for listening. I really hope to hear from some of you soon and would love to talk more about CERius. I will hand it back to Christy. Yes, thank you Laura, and thank you all, for the speakers for your sessions. I appreciate all of you guys attending the the webinar. Reminder We do have information on the resource center. Fill out that contact us form, and we'll get you to the right person. To get answers and also extend the conversation. Again, thank you very much. And you guys have a great rest of your day.