Listen to the MB&A and Plauti podcast to hear from industry experts as they discuss real-world use cases. Discover how MB&A seamlessly integrates Plauti’s AI automation capabilities within Salesforce to modernize field inspection services for agencies. Allocate and improve data consolidation to enhance productivity and ensure security compliance.
AI Enhanced Inspections & Work Order Automation
Anthony Jimenez
Welcome back to care cast the podcast from Carahsoft trusted government IT solutions provider subscribe to get the latest technology updates in the public sector. I'm Anthony Jimenez, your host from the Carahsoft production team. On behalf of MBNA and cloudy we would like to welcome you to today's podcast focused around using AI to enhance work order automation, Joshua Millsaps, CEO of Millsaps, Ballenger and Associates and Frank Costa Solutions account executive of cloudy, we'll discuss the advantages of using AI to enhance both citizen and employee experience. Right.
Lucia
Good afternoon, everyone. My name is Lucia. And on behalf of carahsoft technology, I'd like to welcome you to our webinar, revolutionising government operations, AI enhanced inspections and work order automation. With that said, I'd like to turn it over to our first MBNA presenter of the day, Josh Millsaps.
Joshua Millsaps
Alright, thanks so much for that. This is going to be really exciting. You know, I think as you run through this, what you're going to begin to understand is if you have the right data infrastructure, there's really an opportunity with AI to improve data confidence and work order, velocity, velocity, and that's an example. Right? So you know, where we see this landing is about 80%, faster work order handling, and about to X number of work orders managed. And so just as we go through this, I want you to be thinking how, you know, how can I improve my data confidence, and work order velocity. So just take a step back from that. And, you know, I mentioned at the beginning there about having the right data infrastructure. And that's something that MBNA that we're really focused on, I talk an awful lot about collecting, managing and understanding. And that seems kind of abstract when we start thinking about work orders and facilities management and things that are concrete like that. But so much of what we do in facilitating that, and especially in the process of facilitating the leveraging of AI to automate some of that is based on this thin layer of information that we need to collect, in order to make a difference in the business, right? Once we get that information in, we're able to leverage it, we're able to manage it through the business process. So doing a good job up front of collecting the right information, helps us do a better job of managing it through the process. And of course, as we go through that process, it needs to be a focus on understanding how that information is flowing through the system. And I think, you know, as we start to talk about how AI can help accelerate, in this case, work orders, you start to really understand why it's important to have the right information available. And with that, I'm gonna hand it off to Frank, please.
Frank Costa
And thanks, everyone for joining. So quick bit about Plotly. Before we get into it, right? Proudly, we focus on bringing a true customer 360 in Salesforce by automating the processes of consolidating all the data that comes from all channels and teams. Under one master record. The solution is built to handle your different enterprise edge cases, so So you have the flexibility to fully automate the data management. And this allows organizations to operate with speed and efficiency. And it also makes your actions that you're using state more effective since you have all the correct and up to date data available at their fingertips. Now, right now over the past few years, cloudy has also become a key piece in another major digital transformation taking place. And that's the AI revolution. A companies are discovering that many AI initiatives require clean and consolidate data. And as you can imagine, the unification of data under a master record that's continuously enriched with up to date information gives your organization reliable data that you can feed your AI or train it on and newly launched is a capability to hyper automate case and request management by integrating GPT into the workflows, we will cover this more in depth to show you the impact it has in today's session. But yeah, before we get into that, I want to get into few a few of these terms that you know we will be using or that you might have seen and of course this will be shared at the end so you will have this as a reference. But but for anyone that's new to this subject, you know, when we talk about AI AI is not anything new. It's essentially the simulation of human intelligent processes by machines. Right? It can be classified into two different categories that narrow AI and general AI and and narrow AI is something that has as been around for some time now and which is pretty much autumn needing more specific tasks. What's truly exciting now is the combination of this machine learning. That's the ability of machines to learn from data without explicitly programming it. The natural language processing the ability of machines to read, understand and derive meaning from human language, which extends to understanding a request without code. And generative AI, which is a subset that focuses on creating content from a range of text images, to music and code. And without this, we can remove the need for human oversight or intervention on broader scopes. And it can significantly increase the productivity of the general public, since you can do much more now without coding or being specialised in a solution. Now, I want to ask the audience this question before we kick it off. What could help your organization better adopt AI and low code, no code solutions? Is it understanding compliance and security, changing management support? Knowing where to start, or better data visibility? Okay, well, this is quite interesting. So there's a little bit to touch on everything and thing that Josh, Josh will be taking on kind of covering from Crump from a high level.
Joshua Millsaps
Yeah, so, you know, I said at the very beginning that, you know, having good information was the foundation for this, this automation that we're that we're talking about here. And so, I want to talk about a little bit about how our two companies came together to help facilitate this in order to be able to facilitate work order, qualification, and routing. And really enable what was a group that was being overrun by requests, right. So if you look at the business processes that our customer was experiencing, you know, they're they have a need to go out and perform lots of inspections, they're out, performing work orders, they've got to do all, you know, repairs, maintenance, all of these things, at the very beginning of all of those is sort of a qualification process, right? If it if an issue comes in, is it a duplicate of something else? Is it a real problem? What is the problem look like? And, you know, our interaction with cloudy actually started around deduplication, trying to get better data in and then of course, it's grown into what we'll discuss as we move forward around being able to to use AI. But when you look at these core services, and what exam provides, it's about providing really solid data around these, you know, what we think of as kind of traditional facilities management, management facilities, maintenance services, right? So, you know, for a lot of people on this call, they've got a portfolio of facilities that they have to manage, how do you group those things? How do you? Or how are you able to segment them out so that you can route things appropriately? How do you schedule and manage field inspections? And of course, there's always somebody that's sitting at the top that wants to understand how you report against those things? How do I manage my assets? And then of course, a big part of repair and maintenance is work orders, how do I get people out in the field to go do those things. And just through the implementation of, you know, leveraging the exam platform, and Salesforce, we've been able to see a significant reduction in that time. And that's because there's a standardized list, there's a there's a digital mechanism for facilitating those kinds of transactions. And so the instead of being emails on people's desks, instead of being phone calls, that stuff all flows through a digital platform, and it moves from one step automatically to the next person's desk to the next person's desk, and then onto their mobile device out in the field, so that they can take action on it. And I think that's, that's it's just, it can be game changing for folks that are involved in the space and are used to managing things on the phone, and through email. So let's, let's dig in a little bit more into the work orders because I think that's this is really the piece. You know, that's it's kind of the focus of, of how we're leveraging AI in this in this case. And so, one of the things that exam, the exam platform allows you to do is gather better information in the sense that we walk you through what we termed decision trees, right? If somebody has a hole in the wall, how big is the hole is you're exposed to wiring? Is there moisture present? Those kinds of questions step you through a process says so that we can provide the right data to a person who is then can dispatch the right type of resources to go qualify that. So when they get out on the scene on their mobile device, they're able to provide feedback on on the issue that they have there, we're able to track where it is in the process, right? So that we can give feedback out to stakeholders and say, Hey, we've dispatched somebody out, they've taken a look at it, this is the status of it. And then, of course, we're able to report on a macro basis, you know, we talked about that portfolio of facilities that you may have, under your purview. How do you report out against those and say, Hey, this is how many I have, how many work orders that have opened, here's how many are in progress. This is how many we've closed this month, you can look at it on the basis of, you know, maybe it's building by building or property by property. But understanding how work is flowing through your system is a really critical component to continuous improvement. And then of course, distribution. How do I get in this is this is kind of a key part that I know Frank's going to touch on later on. But how do I qualify it, and then distribute it out, get it to into the hands of the person that can solve the problem more quickly? Right. So there's, those are two key pieces of this is qualifying the issue and getting it in the right hands? Over to Frank, tell us how it's gonna Yes, yeah. So
Frank Costa
I'll take you through a bit of a demo, right. And the thing is, a lot of the a lot of how it works is under the hood. So so I'll walk you through kind of the front facing experience of what this looks like. Now, qualifying and categorizing cases and requests is necessary for several reasons. Like Josh mentioned, it could be for reporting, or it could be for forecasting, resource management, or any other operational need, right. And now typically, what happens when an organization receives a request like this is that it lands in someone's inbox or in some queue, until, until it sits there until someone gets to it that day, or it could be that week, right? So so whenever someone gets to it, if they're well versed in your classifications, they can quickly act and complete the task. If they're not, or they're new to the team, they will have to refer to a resource or take up an experienced persons time to complete that task, which can further delay it getting done or getting done correctly, we can now have GPT take care of a lot of that mundane work, so so that you have the much needed data to run the operation efficiently. And your team has more time for strategic tasks, right. So so as a request like that consent, right, say that there's a leakage in in one of your service location that needs immediate attention. As such a request come in, you can trigger GPT to scan this request, and categorize or qualified to your operations classifications. And to do this, you can create a prompt to give GPT context for the output. And in your prompt, you can give a context to define each classification to help help GPT make this decision. And of course, once a prompt like this is set up, it's it's there to process every case until you decide to make an adjustment to it. And and what happens there. GPT will look at the look at the description, or the the copy. It'll it'll refer to the context, it's good, you've given it and they'll put out this output, thereby, you know, qualifying it or classifying it. Now, you can also do this with images. So here you can actually add an extra layer extra layer of verification by by having GPT check the image to see if it matches the description. If it doesn't, you can have GPT populate a false or incorrect value that can trigger another workflow to maybe reach out or send a message to ask for the correct image. And now assuming here that the image is correct, GBT will qualify it. And if needed, you can also ask GBT to transcribe this image the way you want. And you know, this information can go into the NBN s exam platform. So you don't have to populate this information and that can be handed back off to the business processes for execution. So now the same with routing, right? As an organization, especially if you're if you're in the government, you you certainly receive a lot of requests and things like that. Right and maybe cases on it. data bases. So how do you ensure that these cases and requests go to the right person? So if you if you look at the same request as before, how does that request, get to the right, right department or person, now someone will have to take a look at it, and complete that as a task. Now, you can have GBT scan each of these cases and label it with the correct either department, team or individual by giving it the context of what each handles and it can, it can even be Team aboc internally, but in the prompt, you can give GBD the context of what each team handles and it'll use that to route or label this request. And, and all of this can be done right on interface right on Salesforce. So an admin or power user can easily manage this one setup. And you also have the granularity here to assign permissions. So the end result here is an automatic routing of the of the case or request that comes in and this is significantly improved average response time, by reducing as much as 80% of processing time from from what we've seen in some cases. So this has been super effective. And it's actually quite straightforward to implement.
Joshua Millsaps
All right. Thank you, Frank. Yeah, I think that last, that last part is really important, right? It's, the savings is because you're not sitting on desks, right, you're not having to wait for somebody to triage their inbox, and then get it to the next desk, you're able to take some of those steps automatically. And distribution is exactly the same thing, right. So again, if you're leveraging this, you know, in this case, check GPT to help you validate and categorize and then drop it into the right queue for the right team to do the work. Well, you know, now in the traditional environment, by the time it gets there, now, you're, again, you're waiting for somebody to send an email to somebody to get it out, in working. And what we've done here is we're able to allow exams, that base distribution, to be able to take into account the context that work order. So if something is ready for assignment, or in a lifecycle stage, that means that it's ready to go out to somebody in the field exam is able to look at, you know, at a for example, a record and understand, hey, this record is ready for assignment, it can understand who the who the available team member is. And it can automatically send that email, or push that assignment out to that person such that they're able to take it on, so that they can get out and start doing the work. And we talked about what we're really trying to do at every step in this process is take the things that are really just kind of administrative process ds, and automate them to the degree that we can. And so with distribution, we're able to get that out in the field faster. And from there, we're able to easily start tracking it. So you know, is is the person's now received the work order for them to go out and execute against, have they arrived at the place? Have they checked in? And, you know, we know that they've made it to that building to begin performing that repair? Are they working on the progress on it now? Have they completed it? All of those things we can facilitate through distribution? I think one of the other key parts in this is the ability to get sign off, right? So if we say if the person who's out there that makes the fix says it's completed, is it really done, it's what's nice is being able to automatically when that repairs completed or that work orders completed, also been able to trigger feedback, right? So being able to send something out to that person and say, Hey, was this work completed to your satisfaction? In the case of exam, if you wanted to ask a few more questions, you know, once the person friendly you, you can make it a real feedback. But at the end of the day, one of the key things you want to know is, you know, does the toilet work now? Is the light on Is there still a hole in the wall and you're able to get that that feedback and close that out definitively in a way that's highly managed? Right, you're hearing automate that process. So I think, you know, what we're trying to do here is show that by collecting the right information up front, running it through automation and leveraging artificial intelligence, we're able to take a lot of the stops that are really just administrative stops out of the process so that you can really focus on the part that's important, which is doing the work getting it done, and check in with your stakeholders to make sure it got done. One other route When you take the lens up a little bit in the look at how we're able to accomplish these things, and Frankel talks about cloudy, but I'm gonna talk about exam, one of the things that I think really should hit home is ease of use the ability to be what we call a low code, no code. So all of the things that you saw today from the API prompts to the configuration of those distributions, you're not writing code, a fear of sophisticated business users can come in, and they're able to make those adjustments on their own. In the case of the exam platform, we have what's called the exam command console, you can come in the templates that I talked about, for in taking work orders, you can build those out, it's all point and click, customer feedback, it's all point and click, you don't have to write any code to do it. The consequence of that is speed the launch and speed to evolve, right, because you don't need to have a programmer in the loop, you're able to get there faster, you're able to if you need to add a question to something or you need to build a few more form elements on your form, you're able to get there with points and clicks instead of code. Because of that, it's more sustainable, you're able to sustain it with your own business resources. In the case of exam, we make it really easy to take those things and put it on your mobile device. Most of our customers, they're not tied to a desk all day long. They're out in the field, they've got their mobile device sets where they work. And we try really hard to make sure that you can do that work on your mobile device, and offline. And then of course, exam planning, we're available on the Salesforce platform makes it very secure, we hit on a lot of the compliance touch points that you're going to need to hit on, it's really easy to integrate with as well, open API's are a big part of what we do. And of course, the last thing that I'll touch on is just this idea of actionable reporting. It's great to get the work done. It's also nice to be able to prove that you got the work done, and to be able to take credit for it. And one of the things that having good data. And being natively available in Salesforce is all of that stuff is reportable. And you're able to easily provide that information to management, and brag on the improvements that you've made through the course of implementing this type of solution. All right, so with all that said, just a little bit about us, we've been around for about 10 years, we service a large, a lot of large customers. Public sector is a big part of that. And we've been supporting, you know, inspections, audits, compliance, facilities management, asset management use cases for a really long time in organizations that prize security, scalability, and flexibility. So with that, I'll go ahead and kick it across to Frank.
Frank Costa
Yeah, thanks. And, you know, like I said, again, I think these systems are out there, too, of course, collect the right data on time, so you can take actions on it, right? That's, that's, that's the reasons we use most of these systems. And the DAP product is a is a one to many solution. And this product can standardize segment and enrich data to improve your business processes. And it's a product that also helps you manage Salesforce data at scale by automating actions like Mass Update, mass, delete, mass assign, or mass manipulate the data, right? With this product, you can ensure that the information in your Salesforce stays complete and current, making it making it a reliable source of truth for your operation. So internally, we actually call it the Swiss Army knife, this this component, and it's right there available on the Salesforce interface, making it very easy to use and set up. And cloudy as a company. We've existed in the ecosystem since 2012. Right? That's a very long time. And and we are industry agnostic, you know, working across many sectors and industries. So if you would like to learn more about how we can help your organization, especially around data management, Salesforce data management and standardization and enrichment, please visit our website. You know, it's it's cloudy.com, you can make a request there, or you can jump on the chat box. That's right there for your questions. But I'm curious to hear from the audience again here, right, based on what we have covered today. How many here attending can benefit from automating your cases or requests for classification or routing, right and for that, well, how many cases are all errors. Does your organization process a week?
Lucia
Yeah, and with this question, we're seeing a pretty even split from 35%, saying one to 25 24%, saying 50 to 135, saying 1000 Plus? Yeah,
Frank Costa
so of course, you know, if already from 50 on, you know, it's a question of bandwidth. Does your, does your team have the bandwidth to handle that? Or would you rather have them maybe investing that time to do something else? And definitely, if you're above 1000, it's, it's something that should be considered to automate? Because certainly that can that can bring some significant improvements to, to your operation on on many levels. And with that, yeah. We're both curious to hear what questions you have.
Lucia
All right, we're gonna go now move into the Q&A portion of our webinar. If you have any questions, please put them into the Q&A pod below. And we'll do our best to answer them live. We'll give everyone a minute or two to enter your questions now. Okay, so a question that I'm seeing here is, what are some of the biggest technology challenges you've seen in the work management and work management that your solution solves for state governments?
Joshua Millsaps
I think I can speak a little bit to this, Frank, feel free to jump in. You know, I think there really is, The Big Leap here is from these email chains that go from desk to desk, right? And you get a little bit of a ping pong ball. And every time you go, you know, every time that ball goes from one side of the of the court to the other, there's some sort of delay. And what we're able to do here through the combination of having good data at the beginning, because it was ingested through the exam solution, and then being able to use cloudy to do things like deduplicate and then to leverage Chechi to Jeep chat GBT to be able to do some qualification of that. What we do is we cut out a lot of those intermediary steps, you know, and I think that that is a huge challenge. When you look at, you know, one of the things that, you know, City Council's and oversight committees look at all the time as well, how long did it take you to get to the fix? It was reported on this day, why did it take you 22 days to get it get it completed. And one of the one of the places where a lot of time is lost is just going from desk to desk, if you can't start the work until seven or nine days later, because it had to it had to bounce around through a bunch of emails, you're going to be really challenged to to, to compress the timeframes that you're delivering it to. Frank, any anything to add to that.
Frank Costa
Did not Josh, I think I think I think you had on point. Right? That's, that's this. There's nothing much more I can add to that part. But I do see another question here from from Eric. Does the tech connection to GPT keep your data local? Or is it a part of GPT is learning that? And also a question if it's only tied to Salesforce only? Yes. Starting off with the second part, yes, it is only tied to Salesforce. We are native to Salesforce, or the other components are native to Salesforce. And with the business API, enterprise API of GBT, they do not use that, well, they have stated that they do not use that for for learning. Right? to to to train their models. So and what you will only be sharing with GBT is just whatever value you send over in a prompt. So for example, if you are just sending kind of like the, the body copy of, of an email, or some some description, that's all it'll get, without any associations, and it'll then just look at the kind of the, the values that you ask it to pick out of, and then make its decision. So not You're not sharing anything. Next to that.
Joshua Millsaps
You know, just to build on that. And if you're, if you're using a structured tool to collect that information, for example, digital form, like you could build an exam, you really have tight control over that information in terms of being able to limit any type of oversharing that would occur. So there's there's a lot of options here to comply with local state policy around what type of information you're able to use.
Frank Costa
Thank you, Eric.
Lucia
All right, I see one more question here. How do you see AI changing work management and the short term specifically as it pertains to generative AI?
Joshua Millsaps
Right, you can go, I've got my own thoughts, it's up to you.
Frank Costa
So how do I see see changing work order management? So? Well, one thing is, of course, as we saw, it's going to remove a lot of these kind of like tasks based work. And that's, that's actually a big part of kind of processing a work order these days, right? You first have to take a look at it. Where did this go? What kind of how do we classify it and kind of update our records, so we can, so we can track this. So we can forecast on this and use it for other business processes. So all that needs to be done, somebody has to do it. What I see this will already the direction it's going in is removing the need for somebody to do all that work, thereby freeing up that person's time to do other strategic work, but also giving more immediate responses or putting it into the business process immediately. So one by significantly, of course, cutting the average response time, which we've seen, it could be up to 80%. In some of the cases that we've worked on, and also saving up the team's time and giving you accurate data to work on. Right, I think it will also improve the the end service of it because well, I know like when I have to do some kind of task based work. If I'm being honest, when you're busy, you're just trying to do the bare minimum and just trying to quickly filter out a couple of things in and we'll pass that. So this will also allow you to collect more data, because now you don't have to rely on someone to insert and fill everything out or classify this or qualify this the right way. You can have have something like GPT make sure that you get all that data. And what do you think, Josh?
Joshua Millsaps
Yeah, I think, Frank, I think you're spot on. I mean, it's the I think the one of the really big things that you're going to see, is this movement more towards more differentiated work. I think that's the that is that's one of the one of the things that is really going to increase. The value that, you know, that end users get out of this is that instead of spending time checking boxes, they're able to focus on solving that problem, right, you're going to need to have a human in the loop. And I think, you know, one of the things that we should all recognize on this is that, you know, because of how new this is, there's going to be a lot of effort put in to, you know, how do we secure it, make it safe policy, things like that. And so obviously, there's going to be a learning curve, to what we're able to do early on. But I think as you look out over the longer arc of time, it's really about, you know, especially if you're in facilities, management, things like that, the more that we can have skilled craftspeople, people that are skilled niche trades, focused on the part that adds value fixing the thing, as opposed to filling out a report about fixing the thing, I think the better off that we're going to be and we're going to be able to deliver that value to them faster. And, you know, if you're providing citizen centric services, you're going to be able to deliver to those citizens faster. People are going to spend less time shuffling emails and more time, really taking a hard look at what the problem is and being able to focus on solving that big picture problem. I think that's that's what this is kind of interlocking, unlocking, which I think what we show today, is that how a very narrowly focused effort can lead to two fairly large results. Over time, you're going to see more, you know, intelligence, summarization, there's going to be a lot of other things we can do. I think the you know, the idea here was to show how tightly focused effort could lead to big rewards. Everything else out there question.
Lucia
We have one more question here. And actually, we have two more. Is this system easy to set up? How long does it take to get the system up and running?
Joshua Millsaps
So well, from from our, from the person who said it easy to set up? I think, you know, that's obviously a loaded question, right? What the solution that we're talking about today, I think was fairly straightforward to set up You know, there was there was a fair amount of work that went into the early part of it. Right? It depends with any kind of system implementation. It's about, you know, How broad is what you're trying to accomplish. In the case of this particular solution, there was already a system in place to collect issues in the field, there was, there were there was already a set of well defined work orders. And so the work that our team did to, for this automation, and I think Frank can talk to a lot of that, you know, especially around chat, GPT components of it, a lot of this happened in a fairly compressed time timeframe, it was set up by having good data at the beginning, right, the, you know, that that infrastructure that we put into place when we pulled when we took this organization from spreadsheets and emails, and put them into Salesforce and exam, and then started using cloudy to do data deduplication. And then we came in, and we focused on this AI piece. Well, by the time we get to the AI piece, Frank, can you tell me I mean, that was that was not that wasn't a, you know, six months plus or years of effort, that was a really tight turn that you that you guys LED?
Frank Costa
Yeah, certainly no. So you know, we did some testing for about about a month, I would say just, of course, that's also depending on everyone's availability, right. So stress testing that searching that out. But, you know, that was because we were able to roll it out with that much speed, because they already had defined and, you know, define how certain what certain categories or how they should be directed, they already had a clear way of collecting that data and it accumulating on the back end, for us to work with? So I think, yeah, I believe it was, it was your, your team, Josh, that had that all set up, they're making it much easier for us to come in at that point. And and automate the rest of it from the back end. But I think you definitely have to have a clearer idea of how you want to collect the data and how you want to classify and categorize this data. And once you have the structure set up, the implementation of the of the GPT part can can go much, much quicker there.
Joshua Millsaps
Yeah, I was just gonna say one other thing there that, you know, and I know, I know, we're kind of stepping around the question, but it's, it really does depend on the complexity of the organization, how many things you're trying to get done. But I think one of the things that's really helpful, at least in this case, and with the focus being on you know, a lot of these all of this is writing on top of Salesforce is this low code, no code piece to it, right. So we didn't have to come in and build something that was custom from scratch, you know, exam is, you know, low code, no code components you come in, it's a lot of pointing click, a lot of the effort is in working with business teams and facilities teams to be able to understand what their process is, right. And so the better define those processes are, the more rapidly we can deliver solutions. So I know, that's a little bit of stepping around the question, you know, is it six weeks? Is it 12 weeks? Is it a year? But you know, obviously, it depends on the organizational complexity, organizational readiness, and familiarity with the kinds of solutions that we're delivering.
Lucia
All right, thank you. And so, we have one more question, which is, have you set this up for workforce management, specifically, the Navy?
Joshua Millsaps
No, this is the this is not an AP example. Has, it wasn't set up for the Navy, we would love to, you know, if there's interest there, love to talk further about it. And we know that, you know, in many large organizations, right, there's there's a massive opportunity for improvement. But no, this was this is not a navy centric example,
Lucia
already. And I think that is all the questions we have for today. I do, I will pass it back to you, Josh, and Frank, for any closing remarks you guys have today.
Joshua Millsaps
Right, I want to go ahead and, and ya know, first
Frank Costa
of all, you know, Thanks for Thanks for being here and giving us the opportunity to give me the opportunity to talk more about this case, it was very exciting for us, because we definitely saw kind of that promise of this AI really being delivered in a more concrete way more than just you know, you going in to like TPT and asking some questions. Right, that's, that's, that's what I've at least seen for a while. And, you know, this, this allowed us to kind of stretch that out and really test the limits of it. I think it's, it's, it was definitely some definitely a really cool experience for us. And if you are, if you're looking to learn more, like I mentioned earlier, please feel free to reach out to us, Josh is here, here as well. You know, we work in collaboration with that team. So reaching out to any of us, you know, we are happy to help.
Joshua Millsaps
Yeah, I'll echo that. I mean, I think, you know, this is I've spent a lot of time over the, over the past few years talking about how important can data can be in a facilities context. And I felt like this was really validating in terms of being able to just show that the stage was set for, you know, just incredible acceleration of the business process. So it was really an exciting challenge, to work with the plaidy team, and, you know, excited to talk more about it and to learn about the challenges that you guys out in the audience may have, and see if there's a space for us to help bring some of this to the, to the challenges that you face. So yeah, please reach out to Frank reach out to myself, you know, huge thanks to care software, facilitating this. And, you know, being a great provider and connector, for all of us out in the IT space, trying to solve customer challenges.
Lucia
Yeah, so thank you all for attending. Our contact information is displayed on the screen. So if you have any questions at all about anything today, please do not hesitate to reach out to us. I'd like to thank everybody for joining us and we hope that this information was helpful to you and your organization. Thanks for
Anthony Jimenez
listening, and thank you to our guests, Joshua and Frank. Don't forget to like, comment, and subscribe to care cast and be sure to listen to our other discussions. If you'd like more information on how MBNA can assist your organization, please visit www.carahsoft.com or email us at Salesforce isv@carahsoft.com. Thanks again for listening and have a great day.