Deep Learning with PolyAI
PolyAI's CEO/co-founder Nikola Mrkšić and team invite guests to candidly discuss trends and tech in AI, voice throughout the enterprise, and nailing the customer experience.
Deep Learning with PolyAI
Why should CX leaders care about MCP?
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
When PolyAI built its Builder MCP, one client helped shape it: Vixxo Facility Solutions, one of the largest facilities providers in the US. In this episode, guest host Damian Sasso talks with Vixxo's CTO Derek Neighbors and VP of IT Bobby Hunnicut about getting AI agents into the hands of people who've never written code, from accounting to technicians in the field. They cover why an MCP or CLI has become their litmus test for adopting any technology, how they build and ship their own MCPs as they go, why REST APIs are still the foundation, and how to stop governance debates from swallowing the whole conversation.
See how PolyAI's Builder MCP works at https://poly.ai?utm_source=youtube&utm_medium=podcast&utm_campaign=podcast&utm_content=podcast
Yeah, I mean the ability to have an MCP or like a CLI for a technology has now become like a litmus test for us, I think. Like if it could be the greatest solution on the planet, but if it doesn't have an MCP or a CLI, then it's gonna become a lot harder for us to embrace it and kind of roll it out across the company. Cause now you've been you've created a walled garden where I have to now leave our agent harnesses to go into your tool to do actions that I want. And that's just going to be a new friction point. When I want to try and instill in our engineers and our people here at VIXO, is like try and do your entire job without having to leave the harness. If you have a solution that does not work with that, then that's probably an inflection point or something for us to kind of discuss.
SPEAKER_02Hi everyone. Welcome to Deep Learning with PolyAI, our continued podcast series where we introduce new technology, speak to business leaders, and encounter all the exciting developments and challenges in the world of AI. I'm Damien Sasso, group product manager at PolyAI, focused on our developer platform. And we've recently had a lot of exciting releases and technological developments that we've done in our agent studio developer platform this year, with everything from a REST API stack, where we've allowed customers to develop against our Poly AI agent studio functionality using APIs, an agent development kit for more development through command line interface tools. And recently we've launched what we're calling our builder MCP. And so on that note, I brought some of our contacts and leaders, one of our key clients, to talk a little bit about how they inspired us to build out our builder MCP and how they're encountering all the challenges that come with AI and new technologies like MCP. So just a quick introduction. Joining me is Derek Neighbors, CTO and Bobby Honeycutt VP of Information Technology at VIXO Facility Solutions, one of the largest facility solutions providers in the United States and a Poly AI client. Derek, Bobby, welcome to the show. Hello, thanks for having us. Derek, why don't you start just quickly by telling us a little bit about yourself, a little bit about VIXO, and then we'll start getting into some of the you know cool technological developments that you guys are facing and that we're working on at Poly AI?
SPEAKER_00Sure. I've been uh in the software industry for 25 plus years, largely helping private equity or VC companies either get ready to IPO or basically turn turn their business. In the last two and a half years have really been focused on AI and AI transformation, originally mostly in the engineering space, but fanning out into the organizational space. I think one of the things that I've seen in the last two years, year and a half in particular, is AI is starting to impact a lot more than just engineering workflows, engineering processes really, you know, basically being pervasive with inside the organization. And so there needs to be kind of a technical bent or engineering bent to help the business transform into that. Uh, but it certainly isn't just about engineering and code. It's about a lot more than that. And so from Vixo's perspective, we've been around for 20 plus years. We're one of the top leaders. If you if you think of a retailer, a convenience store, a QSR, like a think of like a Starbucks, think of a uh Circle K, uh Quick Trip, you name it. We we're probably in doing their facilities or part of their facilities work, or we have in the past. Any kind of retailer, you think of a Macy's, you think of, you know, anything you see in a mall, big box store, grocery store. We're probably in there servicing the air conditioner, the refrigerator, their coffee machine. You name it. A pretty kind of blue-collar turn-to-wrench type of business. You wouldn't think super high-tech. But the reality is it's a lot of moving parts around when something breaks, what information is around that asset, how to find a person a service provider that's in proximity to be able to fix that, fix it efficiently, effectively, to be able to get the parts needed for it and to coordinate all of that work. There's a ton that goes on. And that's, you know, we've had technology that has helped with that some in the past, but there really hasn't been a forward-thinking look about how to leverage technology to really go to the next step. And, you know, in the last year and a half, I think it's become very apparent that assets, machines, facilities are built for agentic processes, meaning self-referential learning loops within the facilities management process really can make a fundamental difference. The thing that's difficult is because we're kind of in a blue-collar world, none of the systems surrounding us are very agentic by nature. Meaning if we're in the SaaS world, it's super easy to go find everybody, their brother says, go try out my new MCP, go grab my new REST API, go, you know, get my CLI. You can really have that conversation. It's really hard to have that conversation with Fortune 50s. It's really hard to have that conversation with a facilities management group that, you know, is is dealing with, you know, some hardware that's 30 years old, right? And so, you know, I was kind of brought in to say, how do we transform VIXO into a modern facilities organization and produce facilities OS, which is the agentic form of managing facilities assets and everything related to outcomes for a facility?
SPEAKER_02No, that's great. And I think I think you hit it hit the nail on the head in how critical your business is to the operations of businesses that you know are important to the customers of all different types of industries and consumers across this country. Bobby, like just to throw it to you, like when you look at how critical like an operations business like Vixo is, what considerations did you have to go through when you start even getting to the point of rolling out like an AI solution, something like Poly AI or, you know, any urgentic processes across your firm? Like what considerations and planning had to go into that?
SPEAKER_01Yeah. So I I think a lot of the industry has been having a really hard time trying to figure out where the ROI with these AI tools is at. ROI seems very straightforward in the engineering space. You know, you're dealing with code, you're building apps, but a lot of the industry was trying to roll this out to non-technical users and we're kind of having some struggles there. And that's what we're trying to solve for. How do we put agents in the hands of people that are not in engineering, that are not technical focused at all? You've got these incredible models with these incredible brains, but they have no hands, they have no tools. And that's where MCPs and these other skills kind of come into effect, where they allow the end users to work with the data and the systems that they want to be in on a daily basis, driving these agentic use cases. So that's kind of what I'm helping Derek out with right now is how do we roll out agents to every single individual in the company? How do we build agent skill sets and teach them how to use these harnesses to do the type of work that they're already doing, but at a much, much faster and more proficient level using AI agentic tools?
SPEAKER_02That's great. Yeah. And I mean, it's such a critical consideration with the people that are involved in using any agent tools. So yeah, I mean, coming into this year, you know, we've seen so much technological development in just how AI tools get embraced. We spent a considerable time at PolyAI looking in how we can enable developers through things like agent development kits, which I touched on as we kicked off. But MCP has really been, you know, it initially launched as part of uh a protocol a few years ago. And it's it's this cool little like, you know, tech thing that that like, oh, is it going to really be embraced? Is it really, but it's really started to catch on. What does it mean to your organization in the practical sense? Because it's like, you know, something that we can always think of, and you touched on a little bit as being a technological protocol. But what does it mean to the people that work at VIXO? What does MCP like you know look like in the day-to-day? Mary, Derek, maybe you want to touch on that or or or Bobby, but I'll throw it to you to start.
SPEAKER_00Yeah. I mean, I think at the end of the day, agents are fairly useless if they don't have headless access to everything that a human would have access to, right? And so the the bets that we made is that giving AI and agents to engineers is fantastic, but it only scales so fast and so far. And so the reality is we really needed to say, how do we give agents to the edge? How do we give it to the end user, down to the technician out in the field, everything in between? And what we really landed on is that there's some fantastic harnesses out there already. You know, you could think of codex, you could think of cursor, you you know, you think of Claude, all of those are harnesses in their own right. And we and we said, look, we don't really have the resources to go build our own harness like a block does, but we could certainly take one of these existing harnesses, we could do a lightweight company type of brain that sits inside of that, load a number of skill sets, uh, skills that can load into that and load all of the MCPs that give everybody access to all of the data that they normally have access to via UI, only with their personal agent. And then we can teach them how to write their own skills and how to build their own processes and workflows with the skills as part of that to customize their agent and that harness to themselves. And immediately the first thing that comes comes in is somebody has a workflow. They have to be able to talk to the systems that they're dealing with every day, right? So for a poly AI is great examples. Hey, I've got a service request coming in here, I've got this happening, I've got a service provider, I need to reach out and call that service provider, I need to get some information, right? You know, currently that's you know, hey, I have or you know, before that was I have to go input something or go into a UI or I have to ask engineering to go create something that I can interface with then calls the API, right? And so now I'm I'm waiting. If I if I want new functionalities for my process or my workflow, I'm waiting on engineering, I'm waiting on their backlog, I'm waiting on product, right? And when we gave people the skills, they're able to now get in and do all of these things. They said, well, why can't I just have my agent initiate a call on my behalf, the poly? Why do I have to to tell you how to do it? And you've got to go do a bunch of stuff and write about, you know, stuff I don't understand and it takes six months. How come I can't just play with that? That's, I think, where we really approached and said, look, we really need to look at what does it look like to have MCPs? And I think you guys have been absolutely fantastic. We said, hey, we can take the APIs and roll our own MCP fairly quickly to get people access. I think you guys said, hey, we're already on the stack and we were able to do something very quickly. But that's really the game changer, right? Is it it really gives universal access to the users, to the data in the systems that they already have access to, only now you're giving it to their agent as well, right? You're you're making that data a first class citizen to the agent. When I say agent, I'm gonna say harness, I'm gonna say personalized agent. I don't mean like generic agent. I mean, you know, literally a personalized agent that is doing work on their behalf now has all of the same access that they have access to, right? And I think that's one of the things is as ugly as MCPs can be and as you know inefficient as they can be, the reality is they're very simple to connect and to get out to the edges. And the end user doesn't have to understand a lot. If I give somebody, hey, here's the SDK, here's the the API, go write some code to do this, like it's just not happening. If I say, hey, go create a skill or just talk to your agent and tell you you want to do things and you already have the MCP hooked up, stuff just happens and it just starts to work and the agent can figure a lot of the things out itself. I think that's one of the things we're really seeing with MCPs that is fantastic, is they do a ton of self-discovery, right? They'll try to do something, they say, I can't really do this. They look for a different thing. Hey, I've got a different way to do it. Maybe if I do it in a different order, I can kind of circumvent and do what you want to do, right? I mean, I I think they they kind of expose things in real time that a developer using an API would have to do a whole lot of work work through to do and have to understand a lot. And the end user is never gonna do that, right? And so it kind of abstracts that and lets the the actual AI or the agent decode a lot of what's in there to get the data that's expected.
SPEAKER_02Yeah, that may makes a lot of sense. You you touched on something that was really interesting, which is the act of having like MD files and skill files on behalf of certain personas, certain, you know, people working on certain accounts. You know, Bobby, is that something that you've seen kind of this transition in the world to making onboarding new new people easier, making you know, tailoring, I think, technology to your entire workforce more streamlined. Is that is that something that this is solving, or is it is there other even other elements to it that that's making you know, sort of the MCPs enable enable your workforce?
SPEAKER_01Yeah, and it I mean, the ability to have an MCP or like a CLI for a technology has now become like a litmus test for us, I think. Like if it could be the greatest solution on the planet, but if it doesn't have an MCP or a CLI, then it's gonna become a lot harder for us to embrace it and kind of roll it out across the company. Because now you've been you've created a walled garden where I have to now leave our agent harnesses to go into your tool to do actions that I want. And that's just going to be a new friction point. When I want to try and instill in our engineers and our people here at VIXO, is like try and do your entire job without having to leave the harness. If you have a solution that does not work with that, then that's probably an inflection point or something for us to kind of discuss. And to your other question regarding how do these markdown files or skills kind of propagate through the business and how do they help us bring onboard people? Absolutely. So at VIXO, we kind of have a skills hub that we've kind of built where everyone that has these harnesses are able to publish and build skills and then publish those to the skills hub. And then anyone at VIXO can go and pull down those skills, you know, fork them, riff on them, or just kind of use them as they're stated on the tin. And that's how I think you get these this kind of like groundswell activity of like, oh, I didn't know so-and-so in accounting had a skill. I kind of need, you know, 40, 50% of that because I'm doing something else. But over I'm over here in, you know, service part of management. How can they take that skill that has those built-in connectivities and tailor it to their own experiences? That's what we're trying to do. We're trying to help them figure out what is possible and have them share broadly so everyone can kind of be made aware of what's going on and what other people are doing with these same tools.
SPEAKER_02Yeah, it's really, that's really interesting. I think one thing that I was really curious to get your take on, because we've started to think about this a little bit from the poly AI side of things, is is you know, you can build MCPs for a number of things. Like we have data analysis, we have customers that use our platform to analyze their call data and analyze their business trends. And then we have, you know, different roles at at companies like yourself that are actually configuring agents, constantly updating agents, want to be that human in the loop. And we've looked at MCP servers as a way to kind of almost ring fence those audiences. Like maybe we have a builder MCP for for certain roles that just want to build and tailor agents. And then we'd have an MCP server for people that want to analyze their call data, analyze their business trends. Do you kind of see that as kind of a an indirect use case for an MCP where you can almost lock down certain access by only giving access to certain MCP servers that can reach certain capability? Or is that kind of too restrictive in a way for MCPs? What's your thoughts on that?
SPEAKER_00For me, I don't want to say it's too restrictive. I think what we've kind of defaulted to is really kind of two modes, and our preference is a second. So the first mode is I'm gonna say, like kind of wide open, like, hey, you've got some kind of a key or a token or or similar, and it's giving unfettered access to that resource, right? There's a lot of danger in that. There's not a whole lot of governance, right? But it's the simplest, fastest way to get access to something, right? And then the second way is to basically defer all activity or access to the MCP to that of the user credentials of the person that created the token or access or similar, right? So most of those are happening through some form of OAuth cash credentials. So, you know, hey, I'm gonna hit, I'm gonna hit something like poly. I hit poly, it asks for my login, I log in, I authenticate, it saves that token. And now my agent that I created and bound up to that NCP has all of the same access I have. So if I have access to build, it's gonna give me, you know, all of the tools to build. If I've only got access to to read data or to get that, I'm only gonna get access to that. And so for us, like we have a ton of financial data, invoice data somewhere, and it's all scoped basically on the user. So if a user can do it, they can do it with NCP. If the user can't do it, they can't do it with NCP. If there's something scoped where they can only see certain sets of data, that's gonna be scoped to to how they their agent. So basically they're inheriting this, that their agent is inheriting the same security rules, policies, governance that is bestowed upon that actual user.
SPEAKER_02Yeah, no, that that makes a lot of sense. Bobby, any anything else you want to you want to add there? Like AI governance in the world of MCP is certainly like a such a such a a thing for every organization to think about, you know, any other concerns that you know you could advise, you know, our listeners on, you know, that you flipped into.
SPEAKER_01Yeah, I think we are being fairly progressive at VIXO in terms of like just nothing built beats speed at the moment. Let's try and build and just kind of push things and push people to try and use these tools. And especially in terms of how we're trying to build our own internal MCPs that connect to our homegrown systems. We're essentially building as we go. We kind of push an MCP out there. The users try and hammer it with what they're trying to do, saying, hey, I can't do XYZ action. Great. Let's talk about that. Is that a quick add to our MCP to kind of continue the development and kind of get out of your way to allow you to continue to build and learn? Um, so we are definitely very much on the let's just kind of see what happens and build fast. But I don't know if that's a good fit for everyone, but it's what we're uh adopting right now.
SPEAKER_02That I mean, that's music aure. I mean, that's the same way we're thinking about things at times of poly AI because the ability to use this technology to enable organizations is so is so exciting that that kind of makes sense from an approach. You touched a little bit on some of the things you're building internally. Where do you see the role of like REST APIs going in the world of more and more agent use of MCPs? I mean, we talked a little bit about humans using their agents, but I mean, the beautiful thing about an MCP server is agents can use it that you've deployed like autonomous agents as well as humans. So where do you see sort of the role of REST APIs now in an organization from a development perspective with things like MCPs and agent development kits playing a role in continued updates and changes?
SPEAKER_00I think for me, REST APIs are still the foundation, right? Like at the at the end of the day, the MCP still has to talk to something to interface. If you're going to do CLI, it has to talk to something to interface. I think REST has proven to be a pretty stable paraby for interacting with objects, right? So I think kind of like the the this is a noun, and you can create, read, update, or delete that particular noun. And the way that it's nice and clean, like that. I think when people have a nice clean REST API to extend from, you can tell that they have a nice clean MCP. They have a nice clean CLI because things are very segmented and very easy to deal with, opposed to more of a graph or a SOAP type of mentality that's highly customized. That's a lot more difficult to deal with, right? And it it really comes down to discoverability, right? If you have good REST nomenclature, you can get away, even with an agent, to just point to it and it will just try things because it knows the structure of a good REST URL and API, and it can do a lot without even knowing that there's documentation under the hood. So when you expose kind of that same kind of principles and thinking and you kind of think of things as primitives, and you start to then expose your primitives as resources and you start to to to do that and and cascade out. I think a good, a good bare bones REST API, this well thought out, makes it super fast for engineers to build MCPs and build CLIs to then take advantage of that, right? So I mean, I I think it's just, you know, I don't think they necessarily go away. I I think they still stay as a building block. I just think you're not going to see as many people consume them directly.
SPEAKER_02It's a good point. I I think the auditability of it, what we've leaned into, at least as we've developed out an MCP server that we were, you know, working and and and partnering with you guys on, was the thought of auditability around APIs and what APIs the MCP server was using, at least as a starting point. Is that something that's really been important, kind of Bobby, as you guys have thought internally about internal tools? Because it's kind of a new technology and REST is not a new technology. So you kind of get to bridge that gap of like still maintaining sort of the API auditability behind an MCP. Is that something to think about?
SPEAKER_01Yeah, I think what's what Derek kind of shared earlier, like MCPs maybe aren't aren't the most efficient here? They're having to load a bunch of context in, they're doing a lot of discoverability, what's there, but they allow the end users to kind of do things on their own. They don't need to physically know everything that's in that API or what it can do. They can use natural language to explore and try and build what they want. Don't think REST APIs kind of go away, that they're still a great foundational block. If you're trying to do anything at scale or anything that's really reproducible, REST APIs are definitely something that play a part absolutely, especially you want to start refining these processes and bringing token costs and all that kind of stuff out of it. It's just another way of honing a process. Once you kind of have the business, say, hey, this is what I want, this is what good looks like. Great. How do we take what good looks like and make it really, really proficient and efficient to run and cheap?
SPEAKER_02So as you've kind of like really detailed some of the digital transformation that you've gone through at VISO, enabling your workforce via their agents, you know, giving access control to agents that human have humans had, building things internally. What's some stories that you have on some of the efficiency gains you've seen or some of the some anecdotes around it, or just data that you've been tracking to show how much more efficient or much more in touch your workforce is, you know, as you've enabled them via agents connected to MCP servers?
SPEAKER_00Yeah, I think I think just a lot of anecdotes of kind of what's possible, right? So I mean, I think one of the things that people get hung up on probably wrongfully so, is looking at AI simply as efficiency, right? And and what I mean by that is, you know, we we saw this in spades that the we we used to even tease around, you know, AI means I forget we automate the infrastructure, right? Because, you know, when you look at when you look at so many of the processes that are out there, people's first inclination is I do A, I do B, I do C, I do D, and I run them in order, and I'm having to do a manual touch point in each one of these. And now I have AI and I'm gonna automate that, right? And let's use AI automate. And we look at it and go, that's like the worst use case of AI ever because it's super expensive to pull in context, like you're not doing anything special with it. You're you're literally paying a really expensive tool to pick stuff up from A, move it to B, pick it up from B, move it to C.
SPEAKER_02You're almost just hitting an AI usage leaderboard at that point and not actually solving like the problem.
SPEAKER_00And what it's moving is the same stuff. It's like, okay, the subject line always goes from here to here. It's like there's no there's no logic, there's no nuance. It literally is like, I want to map this data, right? And so we started to say, like, whoa, whoa, timeout. Like we need to start to separate what's automation versus what's AI, right? And and so what we've started to kind of say is AI is really fantastic to do automation that you don't have to wait for engineering on to prove a point, right? So if you want to go A, B, C, D, E and automate that and not have to go get an engineering's backlog or have you know big product to get you to the top of the list or whatever, you can go do that, right? And you can start to show, like, hey, look, I saved a bunch of time by automating. So we'll give you a great example from us, right? Is you know, invoices, right? So invoices come into an email box, somebody from accounting was kind of picking those up, looking at those in a folder, dragging them from one folder to another, opening them up individually, copy and pasting them into an Excel spreadsheet, then loading that spreadsheet into another tool that then put them into invoice processing and then verifying that they processed out into the accounting system, right? So, like five touch points, a lot of like dragging, but no intelligence involved in literally mindless moving stuff from A to B. Right. Hey, great. Use AI to automate that. Now you're out of that business. You're you're not having to do that anymore. You got efficiency, right? What you used to have 300 invoices a day in the backlog or 3,000 invoices a day in the backlog, and you struggled to keep your head above water between four people. Now that's fully automated, cradle to grave. Nobody's spending any time on that other than the final reconciliation piece. That's fantastic, right? But it's not really the fantastic use of AI. And so we've started to say is do use AI to do those type automations. Let us know is where it's working going good. And what we can do as engineering now come back and say, okay, great. What does it look like us to take the token spend out of that, right? And start to then make it efficient, right? So so we gave you some productivity gains, but we didn't really get a whole lot of efficiency because the reality is your time and the time you were spending that might be pretty close to what the token spend is, right? So at the end of the day, it's like we just we we gave you more capacity, but we exchange that for a token spin that then may have been similar to the cost of you involved. But what it does is it allows us now as engineering to come out and say, like, hey, could we just do this all through AP back to REST APIs? Can we just do this all via REST APIs and some lightweight glue and basically pull that out of the system and get the token spin back? Absolutely we can, right? And so where we're starting to see that the real like power anecdotes of things are the things that really didn't scale well as humans, right? There's some nuance involved in what's happening to where a typical automation couldn't occur. I'll give you an example is you know, we we basically didn't get a service request in. That's that's a work order, whatever you want to call it. And my ice machine is dead, right? Ice not dispensing. I'm circle K. I have uh the drink cooler, it's you know, 108 outside, and nobody can get ice out of the ice machine and my water fountain's down, I need help. Okay, and we're gonna dispatch somebody. So we've got some automations already built in to go determine who the best person dispatches for you know, the best provider to dispatch for that, everything about that. But the reality is there's so much more to that. There's the facility that you're in, right? So I'm in the circle K, I'm at this particular location, and I've got all sorts of data about that. Every service call that I've ever done in that circle K, I have information about that. Every asset, so the air conditioner, the coffee maker, the ice machine, the beverage machine, the walk-in freezer, the POS system, all of those are assets that I have a bunch of information on from either a telemetry perspective or from a service perspective of what's happened in the past about those machines. Right now, an SR comes in, I'm just servicing that SR. I'm looking at that asset, that work request in that moment in time, and everything else is blind to me. Right. So we're able to turn on that the service request comes in, and I'm able to look at all of the history, let's say in the last 90 days around that facility, around all the assets in that facility, and around all of the service providers that have touched something in that facility. And I'm able to create an intelligence packet about how that should impact dispatch. Give you a great example with an app that ice maker. It's broken, it's not a beverage machine. We auto route and it says route out somebody to fix the beverage machine. Okay. That's what happens by default. That's what our system does by default. This agent picks it up and says, whoa, whoa, time out. You've had two toilets clogged in this facility in the last 60 days. This is the second time you've gone out with a beverage to a beverage machine. I don't think that this is actually a beverage machine problem. We think you've got a clogged somewhere in plumbing down the line. You need to make sure you don't send out a beverage person. Instead, dispatch out a plumber. Make sure that that plumber has a camera on their truck. They've got a water jet on their truck, and there's two people on the truck. So if they have to water jet it, they have two people to do the water jet and they don't have to come back for a second call. Turns out they show up. Sure enough, 20 feet down the line. There's a problem, they have to water jet it. In the past, that would have been we sent out a beverage guy, he clears it, says it's all good. Five days later, we get another call, but it's stuck again. We then send out a plumber. Plumber's gonna camera it. They say, Yeah, there's something pointing down there, but I don't have a water jet on me, or I have a water jet on me, but I don't have another guy. We're gonna have to come back in two days, right? The the meantime, the actual moneymaker for for this Circle K is a beverage machine that's no longer dispensing ice, right? So then that's kind of a real world, like nuanced way that we can look at that. And underneath the hood, there's all sorts of MCPs involved within that, looking at all of our systems, looking at our invoice system, looking at our asset system, looking at our you know, prior work order, looking at our facility, looking at the customer, looking at the service provider, and kind of gluing that all together. That whole system was not written by an engineer, that was written by an operations person with skills.
SPEAKER_02That story is an amazing story in a sense that like that use case is amazing because it really touches on, I mean, the value of like a solution like a pol, like what we offer with Polyai, where you're using an agent, a voice agent, chat agent, like a customer service focused agent that you can now connect to a number of systems to think through intelligently in a way that just a human being answering a phone would not necessarily be able to connect all these dots as quickly as agents can as to what's truly the issue at a client's site, not, you know, just a beverage machine, but you have a plumbing issue that's actually going to cause three additional service calls as opposed to being hit, you know, in a single, in a single service call. Something that I wanted to ask both of you as we kind of put a bow on this conversation was, you know, you're you're in an industry that to the average person might be perceived as not like, you know, as a blue-collar service industry, but you're thinking about things in such a technically advanced way. We're very excited that you're a client of ours and excited to continue to work with you on things like what we've done with our polyi MCP server. What should CX leaders elsewhere in other industries be thinking about over the next 12 to 18 months as they think about either, you know, their industry, if they're in a service industry in another, because in my eyes, you guys are at the forefront of tech of thinking about the right way to implement AI technology. You know, Bobby first and then yeah, Derek, just would love to hear your thoughts on that.
SPEAKER_01Yeah, for kind of calls back to what I stated earlier, like MCP and CLI availability is now the litmus test. If there are key platforms that we have at this business that don't have those connectivities, that is probably the step one of me either looking to solution that, trying to build our own MCP, or two, how do I pivot to either a custom solution or a new solution that has those capabilities? I cannot condone or tolerate having those types of really key systems that don't have connectivity with our agents because it's just going to put blockers or friction points for trying to roll this out in the way that we want to. So look at your systems, look if they have MCP access. If you don't, how do you engage with your vendors to see if that it's on their roadmap the same way that we engage with Poly AI? Said, hey, do you guys have an MCP? At the time you did not. We offered to kind of write our own, but we were wanted to use a first class one from you guys, and you guys built one. Like that's a great partnership to have. So I would, if you don't currently have MCP access for those key systems, reach out to that vendor to see if if that's on the roadmap. And if not, maybe adjust your thinking from there.
SPEAKER_00Derek, yeah. I think for me, you know, speak direct to the the CX leaders out there, right? And it's like no matter how fast you think you're going, you're not going fast enough. I can't express this enough to my peers. This technology is moving faster than anything we've seen ever by a factor of 10 or 15. And you can kind of look at it in one of two ways. One way to look at it is it's going too fast. I'm too afraid of it. I'm just gonna wait and until it shakes out and and there's a right answer, and I'll adopt that right answer. And and a lot of times in the past, that's been a pre-reasonable way to look at new emerging technology. Unfortunately, in this wave, I think those that are not riding with the wave aren't gonna have a place anymore. Right. I mean, that this is the the the kind of the separation that it's putting between companies is so gap-oriented. I don't know if that can be closed, right? And so I think the the biggest thing that happens in organizations that I see is it becomes a governance hell, right? Everybody is afraid around like, well, well, who do we give access to what? And how do we do this? And how do we control? How do we, and I'm just gonna say if that's dominating the conversations in the room you're in, you're not gonna make it. Um that's not to say governance is important. That's not to say that that they shouldn't be having those conversations. It's if it's dominating the conversation. Like if that is if that is the thing that is where the focus is right now instead of on learning, it's gonna be really rough. And part of that is I think for the first time, there there are executives has to understand how things work at a deep level. There is no more manager or leader, and somebody's an individual contributor. We're seeing CEOs, CTOs of the top companies in the world right now be hands-on, being individual contributors to how these things work. And I'm gonna say, as a CX leader, if you're not in getting your hands dirty with this technology, you're not gonna understand where the opportunities are for your organization and for your industry and your market. And the window to take advantage of those is not gonna be really, really strong without getting lost or getting behind.
SPEAKER_02That's that's great, that's great insight. And I mean, like from our perspective, you know, we've really enjoyed the partnership with you and your team on building out, you know, building out the MCP for our agent studio platform. We don't see it as just a release or a tool release, but it's kind of a shift in the way that our customers develop, you know, both you you guys integrating and and working with the polyai agent studio platform. But even since we started building out our initial MCP, we brought more functionality, rethought a lot of things that we know need to be included there, auditability that needs to be there. And we've been engaged, you know, we've seen increased interest as well since we put out documentation and and and started to talk more and more about it. And it's just things are moving so quickly in in all industries that as you touched on, Derek, it's important for even executives to be kind of in the weeds understanding how technology is is transforming their workforce. So, you know, it's been an absolute pleasure to have both of you on the show today. You know, Derek, Bobby from Vixo Facility Solutions, Derek CTO, Bobby, VP of Information Technology. For everyone that's listening, I encourage you to like, subscribe, you know, stay connected. And this is, you know, another edition of Deep Learning with Poly AI. Thanks again.
