Hello everyone. Thank you for attending today's webinar, Monetize Fiber Smarter, Quote Faster and Build Where It Pays presented by IQGeo, Amdocs and the Fierce Network. I'm Rebecca, I'll be your moderator for today. Before we begin, I just have a few housekeeping notes for you. To learn more about our speakers, you can visit the speaker bios window on your screen and you can find additional resources from the presentation in the handouts window on your screen. If you accidentally close the window, you can reopen it by clicking on its screen. name in the top navigation bar. It should pop right back up. This webinar is being recorded. It will be available within 24 hours. And we will finally, we will close with a Q&A session at the end of the webinar. You can submit your questions during the presentation at any time using the submit questions window. All right, I'd like to go ahead and introduce our speakers now. We're going to be joined by Jay Cadman, Senior Vice President at IQGeo and Rob LeMend, Senior Vice President at Amdocs. All right, go ahead, gentlemen. We are ready to start. So we'll take you through that process today. The innovations that we think are coming to the market and are already in the market and what the combination of IQGeo as a software provider and Amdocs as a systems integrator and data provider can provide the marketplace. So first of all, just to set the scene, we want to cover in terms of what we see is the fiber fiber lifecycle here. And this can obviously apply to cable and HFC markets as well. So the objective we have is to provide a complete end to end solution from design side with the automation of planning optimization of HLDs, LLDs, the build size. How do I optimize the construction of the I optimize the construction of my network, optimize the relationships with my contractors, make sure that things are built on time and on schedule? And then how do I monetize? And then how do I operate those networks? And what we see is a great deal of commonality between how those systems work and how they interplay. So whilst each one is run, obviously by different departments in most of the organizations that we work with, each one is interdependence. And we see a great deal of optimization that can happen from say the build stage to the monetized stage to the operate stage. And from that process onwards, it's a continuous process of optimization and planning and building out your fiber network as we see the competition range. So those are the areas that we'll cover today. But the focus will absolutely be on the monetized aspect and how new technologies can help you grow. And then an expanding presence in Asia pack. We work with over 500 telecom and utility operators. So anyone has a physical network complex infrastructure that's critical for consumers and businesses. About 150,000 people use our solution every single day. And we're very proud of being able to provide, say, critical infrastructure solutions to our customers. About 350 people now. And about 29 of the top 50 North American operators use our solutions in some shape or form. So either throughout the build process, the planning side, the operations side or an increasingly on the monetized side of things. And we are a software provider. And that's our critical thing is to provide software solutions to our customers. And as such, we work with a number of customers who complete our solutions to provide other areas. Very happy to be joined by Rob today from one of our key partners, Amdox. And I'll let Rob describe what Amdox does and how it fits into the scenarios that we're talking today. Yeah, thanks, Jay. And really appreciate the opportunity to partner with you guys on this conversation and the work that we do together across the fiber ecosystem and industry. For those of you who don't know Amdox, Amdox has been around for a long time. We actually birthed out of AT&T as a telephone book distributor quite a long time ago. And since they have taken a software centric path that leads us to a global customer footprint today. We are headquartered out of Tel Aviv, Israel. And currently we have around 28,000 employees. And because of that global presence, we cover about 90 different countries with over 400 different customers. About $5 billion in annual revenue. So we're a fairly large company in the software space and definitely a globally leading discussion around service integration, billing, OSS, our consumer digital experience, all award-winning products. But about five years ago, we established a very small fiber engineering services business, which has really exploded in the last few years. The reason why we're having this conversation is we've become victims of our own success in a way with a lot of growth in the last three of those five years. We serve all the major tier one and tier two telcos across North America when it comes to cable or fiber. And we're growing into the tier three and municipality areas with a holistic offering we're bringing to market called Fiber One, which is essentially all of our fiber related products loaded into one package that a brand new ISP can establish and stand up end to end through one engagement with Amdocs. It's a pretty exciting area for us. So thank you again for letting us be part of this. Happy to have the conversation. Thanks, Rob. So what is the problem we're going to be discussing today? I know that I would say the vast majority of the people listening to this webinar understand this and live this. But we wanted to illustrate a couple of things that we think are differentiators that people should be considering. And so whilst at the moment we see a race to the bottom for connectivity in terms of pricing, pricing consumers, businesses, considering it a dumb pipe. And I know people are layering additional services on top of that security IP services, etc. On top of that to try and differentiate. What we're really trying to do is build a machine to help enable you to go to market in the most effective way possible, given your fiber footprint and given where you're expanding. So we're seeing the cost of customer acquisition increasing as competition raises. We have in one area in North America, we have five of our customers all building in a certain area. So somebody has their business case wrong. That's up to them to determine. But we're obviously seeing an increasing amount of overbuilding and competition as markets get saturated. And with that, we're starting to see increasing customer churn, especially in the business side of things, where we're seeing 25, 30, 35 percent customer churn on renewal of one, two or three year contracts. And at that stage, it's very, very hard to manage your revenue. And obviously, cost of customer acquisition goes up as you increasingly lose your market space. So what we are seeing is most places we go and have discussions with have some sort of homegrown system that they've done or a combination of a homegrown system and a manual system in terms of generating quotes, market prospecting, understanding their near net, understanding the revenue opportunity in that. And we think that the reason for that is there hasn't really been a software solution that you could derive and implement that is effective and actually drives customer change. So people build their own. That's that's that's that's they fill the gap, obviously. We really feel that the time now with AI and integration and modern markets, it's a time to provide a software solution that you can configure to your solutions. The other thing we see is people being reliant on what we classify as generic marketplaces. Now, generic marketplaces is a fantastic way to expand your footprint and and make sure that you're penetrating markets in an easy way. But we do not see this as a competitive advantage. We see that as table stakes. So what we're trying to show here is how do you build a system and a machine that enables you to go to market that allows you to differentiate either by speed or by pricing analysis or by competitive analysis as quickly as possible. And we'll take you through that today. Before we do that, what we did want to do was we wanted to run a quick poll just to get a sense of the market space and who we have working with us today on this webinar. So really, you can classify Neonet in many different ways, but let's just classify it in the generic way at the moment. Everything within, say, 2500 feet, 5000 feet of your network footprint. And how are you generating Quests today? Is it a manual process either in-house or we see a lot of our larger providers outsource that to third party providers? Is it in-house tools that you've built? CAD tools, things on top of Google, top of Google Earth. We see a lot of that. Third party tools that you've built, that you've purchased and configured to your usage or more than likely a kind of client. A kind of combination of all of the above. So we're going to run this poll quickly and then we'll just get a sense of that and that will help us target our conversations going forward. Okay, I'm going to give it a few more seconds and then we'll see the results. Okay, so just a live interpretation of the results as we expected. About 40% obviously are using a combination of the above. As we see in the marketplace, a small number of third party tools being used because there really just aren't that many third party tools that we see that are that useful. Obviously, in-house tools we see a lot of. And there's still a lot of manual processes out there because if you can't build the tools and you don't have a solution that are effective, then what do you do? You do it manually and you get the quotes out as best you can. So thank you for that. And we'll use this as a kind of underpinning for our conversations as we go forward. So I just want to give a quick piece of background on the kind of journey that we've seen that these technologies go through. So when we started about 10 years ago, our objective was to take mobility and drive that into the physical network. Basically provide anyone in the field with a mobile solution that could allow them to do any particular workflow process from, you know, Polemate ready to construction management to field techs doing break fix and out in the field. And we feel we've succeeded with this and providing solutions to this. But what this didn't succeed in is providing a way to actually capture and bring back in quality data for your digital twin of your physical network. We got so far with it, but we still don't have something, a solution which provided you a physical twin, which you could then digitize key processes with. As much as we try, it's just, you know, the human element in this and the pressure of doing your work day to day just means you don't gather enough information. So the transition we've seen, and this has happened over the last six months, has been the integration of AI into this data gathering process and a gentrification of the, an automation of the things that you do in the field. And what this has allowed us to do is to now finally create something which captures information in the field without the human having to be engaged with it, bring that information back and automatically update your digital physical digital twin. So finally, your GIS or your physical network inventory or whatever you call it within your organization is up to a standard to where you can actually base automation on. Now, once you have that automation in place and you have, you actually know where your network is, you know what it's connected to, you know the capacity of it. You can then drive what we classify as a revenue twin. And essentially, if you take an expansion from where my physical network is, I can now model my revenue around my, around my near net. What we tend to see is there's an obsession with build and we get it. You absolutely have to build. There's a, there's a fiber rush to go out and get to as many consumers as possible. We see a huge gap in the marketplace about an obsession with gathering as much revenue as possible from every foot of fiber that you construct and build out into the marketplace. And that's what a revenue twin is, is there to enable you to capture and work with. So in essence, what we're doing is we're taking a network twin that you, that you now can build and is now accurate. And now we're deriving this into what you see on the right hand side, which is what we classify as a revenue twin. And what we do is we model every single revenue opportunity, either revenue that you've already captured, revenue that you know your competitors have captured, or revenue that you see as coming in the future with new builds and expansions and edge outs of your network. We model that. And then from that, you can proactively target and you can use other AI agents, obviously, to go out and target those even without human intervention. But you can focus your sales teams proactively to go and address the market space that you already have fiber or HFC or network capacity available. And we see this as a tremendous opportunity to make it even more effective rather than a lot of customers that we see are having to result in spray and pry. So here's your phone book, here's your white pages, here's your Dun & Bradstreet, start calling, this is your area, how am I going to make my numbers from this? So let's give the sales teams a bit more of a constructive effort as to how they target and make their jobs a bit easier in terms of making the numbers. So what are the steps of a revenue twin? So essentially, we need a good quality physical network twin in order to do this. Otherwise, you're basing your information on incorrect network capacity. You need accurate business and environmental data in order to build this model. You must have correct costing information, environmental information in order to derive the basic costing which allows you to get the data. And you must have a continuously updating model. So this is, we don't live in a static world anymore. I can't do this once a quarter, once a year. Your model must be continuously updating based on all the different data sources that you have. And the technology is now in place to do this. And then once you have this, you must integrate this into the rest of your solutions. You cannot have a walled garden, which is this fantastic revenue twin, but it doesn't integrate into your CRM, your sign off processes, your capacity information, your future build, and your build planning. So there's obviously a huge element of integration from that. And that's why it's not just us on this webinar. Obviously, systems integrators like Amdocs provide a huge amount of capabilities in taking a piece of software and then integrating it into your business processes and working with the change management side of this. So just quickly, in terms of the network digital twin. So first off, you need a powerful network model. So what you'll start to see now is a lot of network models out there in the physical side of things will not have the capacity to work with the increased amount of information we can now gather with AI in the field. So you're going to start to see limitations. So what you need to ask now is, where is my network model going to be in three years time when I can basically capture a great deal more information and store that in my digital twin? So your model needs to not only meet your requirements now, it also needs to meet your requirements in the future. And then you have to remember that your model is constantly changing. You get new vendors, you get new standards, you get new pricing and capacity information. All of this needs to be taken into account in your network model as it grows and develops. We've seen several large vendors start to blend different types of network models from distributed tap to flex snap to centralized and distributed split, all in a common network model based on the best economics that they can have. So your network model must support that in order to allow your revenue model to respond effectively. You must have connectivity. Obviously, that goes without saying. And then on top of connectivity, you must have capacity. Don't think we're in the situation anymore where I can just sell it and figure it out. I think we were there five years ago, three years ago. Some people might still be there, but we have enough information and enough technology to actually be selling existing capacity and understanding the capacity that we have in order to sell those services. It's no more sell it and we'll build it. That doesn't work when you're trying to meet SLAs and connect businesses as quickly as possible. You must have planned work. So again, there's the capability to understand where your network builds are, where they are effectively coming online. 90 days to go live, 60 days to go live, 30 days to go live. I need to understand that and proactively get that to my sales team so they're not surprised, so they can go seed the market. And this isn't just your greenfield builds and your new builds. This is every network extension needs to have a capability and availability for your sales team to go and leverage. And then these solutions need to live in parallel to your network inventory. Obviously, we provide a network inventory solution and this plugs in, but many of you will have network inventory solutions that work adequately. So these types of solutions need to be able to sync data with your network inventory and all the other data side of things to build a network digital twin that's used across the organization. So as you can tell us, we're pretty passionate about making this a usable system for across the enterprise. And these are the things that we see, again, as table stakes. This is not something that you can have and run your business anymore. You must have an adequate and efficient network digital twin in order to compete in the marketplace. So I'm going to hand it over to Rob and let Rob cover why you need accurate business data and environmental data to feed into this model. So as everyone knows, garbage in becomes garbage out pretty quickly. And so one of the most important aspects of any good fiber design or network digital twin is really the accuracy of the information being provided to it. Everything from an owner type and a basic address when we get design areas handed to us, making sure that we're capturing all the necessary addresses within the design area and functionally using all the publicly available information to us to make sure that we are operating within a correct set of data to provide the best possible output. We want to make sure that when we're addressing all the potential opportunities in that space, we're being thorough and accurate. We have a proprietary set of data that we use to do that within our fiber design space. We bring in as much public data as possible, including municipality data, right away information, as many above ground and below ground utility sets of data that we can get to provide the best set of data that will allow you to do the best design for your near net expansions. One of the things that AMDOC has become very strong with is our permitting sections, probably one of our most awarded essentially from our customers point of view interpretations of our skill set. We take a lot of pride in how we expect permits to flow through the system and be giving green lights for moving forward in construction. But the information we do associate in is obviously making sure we understand all the different business and consumer types, helping to determine all the inputs so that at the end of the day you do get a robust and accurate build cost for these kind of quotes that the IQ Geo tool can present to you. Thank you, Rob. And there's a reason that we're in software and not in data because the permitting, the data and all sorts of things is incredibly challenging. And I think software is actually easy in comparison to that. So we do see more and more people outsourcing that type of information, not relying on internal teams, because it really is that an added benefit to sourcing that if you can get somebody else who's an expert in that. And has scale in terms of that. Once you have that information, then you must build a complete costing model. And we see there's three areas of costing that you need to take into account. There's area costings. So within area costings, you can support things like I know in this particular area construction costs are high because we have we have rock. And I know in this particular area, it's it's difficult to get permitting. So I'm going to increase the cost because it's it's it's a challenge to work with this local authority. Or I have a utility that's really, really challenging to work with in terms of getting pole access. So I'm basically just going to put an adder on here in order to get the most effective cost possible. So you can do that from an area perspective. Then you add in a linear model. So from a linear model, you can take in different different street designations, obviously from from a costing perspective. But you can also take into account different types of existing infrastructure. So if I have pole lines that I know available conduit that I know I can use easement information, as Rob mentioned that I have, I can put costing information on those linear assets as well to add into the layers of costing that I can do. And then obviously point costing as well. We have things like railroad crossings, river crossings. These can be linear or point information. And then on top of that, which type of infrastructure am I able to put in this particular area? Is it an overground? Is it an underground build? Is it a combination? Do I have different sets of costings for different sets of equipment based on where I'm building across the country? And so whilst this sounds like a big task, it's actually relatively simple to put this in because you're building up a layered approach to costing, not trying to imagine what costing is and just put one variable on top of it. So by having this layered incremental approach, you can build a very sophisticated costing model that is incredibly accurate and is easy to update over time as you change vendors and understand your marketplaces in more depth. So I'm just going to cover a quick video now in terms of just to show you quickly how the costing model works. So in this particular instance, we've got a series of a spreadsheet of different addresses that we've thrown into the system. We can also add in incrementally any individual building that you have just by adding in. This particular one is an MDU. There's lots of different variables you can set in terms of daisy chain, point to point, different types of costing models. And then you basically run this solution. In this instance, this is an on demand running rather than running this across the whole of your near net. And you can see each one of those particular areas is particular points is connected with the lease cost. And then we also look at not only what the lease cost is, but what's the most revenue potential that you can generate from that. For each one of these, you get an incredibly detailed bill of materials in terms of what the costing is that you can then put into a design scenario. All that then you can go and run by your engineering team. So we're not saying this is developing a perfect model that your engineering team will sign off on. But we are saying that basically this gets you 80, 90% of the way there in order for your design and engineering teams to then sign off and move forward. So it's a good green light, red light. And it's also a good way to move an actual route into your design process, which absolutely speeds up the end result. So now we're going to jump into another quick poll so you don't just listen to me and Rob talking at you. So this one really is what we find is when you're analyzing your near net is how frequently do you proactively analyze and visualize your near net revenue opportunities? So we do see a lot of people do this. Lots of times we see it on a quarterly basis. Sometimes we see it on an annual basis. We don't tend to see it done more proactively than that, but I'm sure there are people out there that run it on a monthly and weekly basis. And so really interested to know essentially how often is that analysis run if you're running this analysis at all. Obviously, if it's a manual process, that might be a bit more challenging. So just run this poll for another 20, 30 seconds and we'll get the results and then we'll discuss them. Sorry, I have to have a sip of Diet Coke. It's my guilty pleasure. Okay. I'm going to move on and show the results. So yeah, as we expected, that's a pretty good number of people that are doing monthly. Obviously, the nether. That basically means probably you're restricted by a manual process and that's just almost impossible for you to run in the near net. Quarterly is what we tend to see the most of out there and annually as well. It's part of your planning process. And really, you know, we're sympathetic to the sales teams out there. What we're trying to do is provide tools for your sales teams. So whenever your sales team are complaining and I am in a sales team, so I understand we complain a lot. But really, it's because we want the best information possible in order to sell the network. So those are the tools that we're trying to generate here. So one of the things we see is we believe you should strive for a model that changes every 24 hours. And whilst a lot of people say that's overkill, if you have the processes set up, there is no reason why a model shouldn't update within that timeframe. And actually, if you do that, because there is a smaller number of incremental changes that happen within a 24 hour period from a systems perspective and a performance perspective, it's actually easier to do once you get the systems in place. So having the cost modeling, what we see is you run it. Essentially, you run it on your near net all the time. So anytime anything changes, any data changes, street moratoriums, MDOPS gets you more information about the environmental cost changes, some competitive information comes in about a particular area. This automatically churns into what we call the revenue twin. And then that automatically updates within a 24 hour period. And what you do need to be careful of is having the ground change underneath the feet of your sales team. So you do need to be able to manage quotes and not have those automatically change. I'm working with a prospect. I'm working with them for a month. I don't suddenly want my costings to change, things like that. I want to be able to go through and have consistency. But for forward looking things, you absolutely need to be able to have this model that changes. And just imagine the competitive advantage you will have over the other people in your area if you manage to put this model in place. So complete capture of all the costing, including on net connection costs. So this is not only off net one net. This is once I'm on net, what are the splicing costs that I need and connection costs that I need in order to derive a correct connection? And that's point to point connection as well, obviously back to your CEOs as well. Revenue capture analysis. So ARPU, et cetera. How do I understand where my most profitable business is? Not just any business that I can win. Where my revenue potential is. Where should I be focusing my sales team's efforts? Competitive analysis. Is somebody coming into my area? I'm starting to win business that I need to respond to. Lost customer tracking. So very easy to put in place triggers, which on one year, two year, three year anniversary of losing a customer, automatically solicit that customer in order to go out and try and generate that 30% churn that your customers, that your competitors are facing. And then an even easier assessment we see that isn't always done is the lease assessment. So every time, you know, you're 90 days from a lease being renewed, an assessment of should I be building to that particular building property? Can I capture enough revenue to justify it? Can I target my sales team on there to generate two or three sales that basically justify me not renewing that release? Sorry, that lease and making sure that I build that property to sustain and improve my profitability over time. So that's something that we see is really a continuously updating model allows you to implement these types of automations. I'm going to hand it over to Rob to talk about how do you take this fantastic revenue machine that you've built and incorporated into the rest of your organization. Well, I think to your point, Jay, that the industry is so dynamic right now and there's so much change happening consistently. There's never been more energy blowing into this industry as there is today. The idea of consistent updates makes a lot more sense. But in order to do that, you really do have to make sure that all your systems are properly integrated. This is where Amdoc's bread and butter really comes into play. We are known as a systems integrator. And our partnership value really comes from being able to take very disparate systems and integrate them seamlessly into the IQ Geo framework. All your critical systems with really a focus on ease of transition to make sure that your data stays relevant, that your systems stay as much uptime as possible. But dealing with your network inventories, your CRM tools, your inventory from a logical standpoint, all critical to make sure that in this dynamic environment, these integrations happen as seamlessly as possible. We do have global experience doing this for international tier one and two carriers. As I mentioned, we span the globe in 90 different countries. We have been doing this as well as in the domestic terrain for quite a long time. We also have consulting practices that bring in and help with business process optimizations, as well as establishing a consistent feedback loop for tracking those wins and losses and making sure that your actuals versus quoted costs are relevant. We try to make sure that an IQ Geo system that is allowing you to do these near net quotes in basically real time is associating all the right data, all the right systems, integrating seamlessly within that overall ecosystem. Yeah, and one of the things that often gets forgotten from our perspective is the close loop validation. And that's something that's very important in terms of improving the effectiveness of your quotes, making sure that your casting is correct, taking the actual build cast and feeding that back in. And that's an easy thing to say, but it's really hard to do. And that's where it really takes some third party experts who built networks all over the place to be able to come in and help you build a process to take the actuals and feed them back into your machine so you can improve your customer time. So what's the business outcomes? So we've been talking about how to set up essentially this machine, the data inputs that come in, the costing that you need to put in place, the automations that you need to run on a 24 hour basis. But essentially, and these are conservative numbers. So we understand there's a pragmatism in terms of implementing these types of systems. But consistently, we've seen a 5% or greater increase in win rate in terms of established markets. So this isn't really a greenfield market. This is in established markets where you're competing for business. We've seen 10 plus percentage accuracy improvements in terms of the quotations that you're putting out. Obviously, accuracy can hit you in multiple ways. So it's easy to produce a quote that is conservative, that has a high cost. Doesn't mean you're going to win the business. And that's not helping your sales team. That's not helping your business. So getting as accurate a possible quotation is obviously a bit of an art at the moment. And so that's where that closed loop comes back in. And how do we develop this and get it as accurate as possible from that? So I'm winning the right business. I'm winning a profitable business. And then some of the simpler things we see is the time to quote. How do you put tools into the hands of your sales team, of your engineering team, of your sales engineering team, however you are structured in order to generate quotes quicker? And this is probably the most conservative number on there. So we see 55% as actually the baseline. And we see 65%, 75%, 80% faster times to quotes. And then optimizing that sign off process because I can get a great quote. But if I can't get it through my internal processes and get that signed off and get that out the door, that kind of negates the advantages that you're putting in. So if you fix one thing, it can break other areas. So there is a whole flow, business flow that you need to take account of when you're doing these things. So we hope this gave you a flavor of what we call a revenue twin. There's obviously a massive amount of details underneath this, which cover the technology aspects of putting this in place, the systems process, the systems integration, the data that needs to be fed in in order to make this machine work. So what we want to do now is just give you a brief view on what we see as next. So obviously this is something that's going on right now. So just in terms of just in terms of what IQGEO sees is next is it's really this combination of the self updating physical twin. And it's really AI driven, which leads to the revenue twin and then leads to what we see as basically the proactive management of your physical network. Now, I know there's lots of work happening on the logical side and the BSS OSS side of things. Our bread and butter of what we work with is the physical network. So that machine you've got outside of your office, the ISP, the OSP combination out there, which is really challenging to manage because it's over tens and hundreds of thousands of miles. And we feel AI is going to have a fundamental difference to how you do this and allow you to do all kinds of exciting automations, which is kind of what we're looking forward to. So I'm going to hand it over to Rob and Rob's going to describe next generation thoughts and views from an AMTOPS perspective. Thanks, Jay. So we're really excited to be part of this conversation and certainly from a partnership with IQGEO and a great product. We see this as being more of a holistic solution from all the way inside of our AMDOC studios, which really runs the gambit from finance and banking all the way through to networks and operators. We bring a lot of strategic M&A conversation to this. We've made three major acquisitions in the last several years that bring more skills to this, including Amiggy and Procom, which are consulting businesses. We see this as a bigger ecosystem. And I use the conversation all the time or the catch phrase, you know, one plus one equals three. In this case, our partnerships with you at IQGEO and NVIDIA allow us to go out and bring this to larger companies from a field standpoint, from a brown field standpoint. We can switch out tech stack when needed or we can drop this in holistically. But it's all really powered by the way AMDOC sees AI through our AMAZE platform. We are very much on the leading edge of agentifying, I suppose that's the right word, for all of our interactions. And AI becomes the backbone for all of our data iterations as we look through which systems are need to be integrated, what data is considered good data or bad data. We do validation through AI. We move our processes through AI. We try to take a lot of the guesswork out of it and make the system do the majority of the work for us so we can be faster to market. We can be scalable more easily and we can bring a cost control to most operators that they haven't experienced before. So I think the software that IQGEO brings and what this provides alongside an AI driven backbone company like AMDOC with a deep expertise in systems integration is a wonderful solution for any operator to really bring home. Thank you, Rob. And we appreciate everyone's time today. We're going to jump and answer some questions now, I believe. Yes, hi. Thank you all. And we will go ahead and go to our Q&A now. We'll get to as many questions as we have time for. And you can still submit your questions using the submit questions panel on your screen. To get us started, we are in the process of acquiring another network operator. How can we incorporate their network data into our revenue twin, especially if their systems or data models are different than ours? Yeah, I'll jump into that initially. It's well, firstly, it's very common right now. Obviously, it's a lot of large acquisitions and small acquisitions happening. And one of the biggest reasons for the acquisitions is to be able to quote effectively and over a larger network in terms of on net quotes. And so one of the advantages we see is by having your revenue twin as almost a separate thing that operates separately from your physical network. And so the integration and sync between those can happen between, you know, we've seen solutions where even within our largest customers, they may have four or five different physical network inventories that we have to integrate to. So this sits separately, syncs to that and is really a solution for your sales team. And you don't actually want your sales team to be bogged down by all the nitty gritty of what's happening in your design systems, arguments about how your data model is. The sales teams want to be able to have a prioritized list of information, be able to get quotes out quickly, and they don't want to be bogged down by all the arguments between IT and other areas that have to put these systems in place. Rob, I don't know if you have any additional comments on that one. Rob, I do actually. So I think the base of the question is around data translation. So there's two aspects that we think about at Amdocs, and that is moving data from one another and making sure that those systems are integrated and the data is consistent. We do that already with multiple tier ones and tier twos across the globe, essentially. And the second part is moving between legacy and frame and going to cloud, whether it's on on-prem existing systems going to cloud or vice versa. Yeah, we definitely have experience of that. We understand the necessity of doing something like that and making sure that data stays as clean as possible. Going back to the slide, garbage in becomes garbage out very quickly. And so we take data translation very seriously in this. I understand it's just a part of the ecosystem. And as you mentioned, with the heavy M&A lift right now, I think it's going to be a massive part of the way we have to deal with this going forward in any systems of record. Okay, moving on. How long does it typically take to implement the revenue twin model? You can actually do it very quickly. So what we would recommend is an incremental approach and not trying to kind of boil the ocean. So we have a set of customers that start and basically all they start with is basically the street information, customer information or prospect information, and then a point of where they could be served from. And that's it. And you can get something like that up and running in four to six weeks. And so then from that process onwards, obviously, to build the kind of nirvana of a fully, you know, sinking or singing or dancing revenue twin is an ongoing process. But something like that can add value very, very quickly for your sales team, as long as you have the right data there. And that's where I think, you know, the Amdocs discussion and do you have the right data services in order to do even the simplest thing is really critical. All right. All right. Next question. We're still working to improve the accuracy of our network digital twin. How important is that foundation before we can actively implement a revenue twin? I'm going to talk out of both sides of my mouth now. So I just said you can do it really quickly with just some point data about where you can service and what the capacity is at that service point. I think previously there really hadn't been solutions that would allow you to, if you had any network of any size, update the information. But I think I would encourage everyone to go out into the marketplace and look at the visual AI solutions out there that are being developed right now. I mean, obviously, we have one, but there are other ones out there. And really start to incorporate that into your construction, your build, your inspections, and all of those types of processes to capture the information out there. And I think, you know, within 12 months, if you have not implemented one of those solutions, you will be behind. As I see, you know, I would say 50% of our customers are looking at those types of solutions right now and are actively pursuing proof of concepts in order to roll that out into their solutions. And that basically drives the quality of your digital twin, whereas before it was a real challenge. It was basically people in the loop and that was ineffective. Let's put it that way. Moving along, what kind of supporting data can I include in the revenue twin analysis, for example, DNB info? And what is typically included? I'll defer that to you, Rob. You're the data guy. Yeah, well, obviously, your existing systems will define a lot of that. The supporting data that we really need in place is obviously going back to a strong network twin. And it's almost impossible at this point to expect that we would see a revenue twin opportunity without that. A lot of the data we pull in regarding your right of way in easements, municipality space, as I mentioned, the underground above ground utilities all become very relevant to us. A lot of the public data that we have inside of our process that we analyze and bring together from an AI-driven least cost routing standpoint is already available out there. But it comes back to what your network inventory looks like and the accuracy of that. So a strong network digital twin, a strong network inventory place is probably your most basic requirements. You mentioned revenue twin as a part of M&A activity. How is that defined within that context? How do I answer that? I mean, I think it's a great way to, well, I think, first of all, as part of M&A, you need to understand with the acquiring asset if they have a good quality enough information in order to support this. So I think that should be a consideration now in any M&A activity. And I know the sales aspect is obviously a critical part of the M&A. I do think it's a quick win. I think it's something where you can start to stitch together very quickly from the point of acquisition closing capability, which will allow you to integrate your sales teams and allow to quote opportunities across your your whole service territory or newly acquired service territory, which should open up opportunities for the larger kind of corporate and commercial sales. I'm not sure I quite answered that very succinctly, but that's what came to my mind. Rob, I don't know if you have anything on top of that. I think the M&A ecosystem right now is so complicated. And you have to think about from almost a scale and size standpoint. There are some companies that will emerge and go through an M&A at a scale in which network, you know, revenue twinning is probably not the most important thing for them. But smaller companies are looking to take advantage of where they're currently located in the network, where they're located by geography, could certainly use a revenue twin model to help assess whether or not their new spaces they're going into, present them with the most profitable opportunities to capture new clients and customers. So I don't think it's necessarily a requirement for every M&A, but there are some M&As that would definitely benefit from it more than others. All right. Well, thank you both. It looks like we are at the end of our time here for the day. We did get a lot of great questions. If we didn't respond to yours on the webinar, we will try and get back to you personally afterwards. And just as a reminder, this webinar has been recorded. It will be available for you to watch on demand within 24 hours. Thank you all for joining us today. We look forward to seeing you next time. Thank you.