In the video, Chad Muckenfuss and Mike Kowalski discuss a recent success in AI within the healthcare sector, emphasizing the need for businesses to strategize their AI approaches. They highlight the importance of building a solid data foundation, particularly in a field with strict compliance requirements. The project, which spans nine weeks and involves multiple phases, focuses on predictive analysis and data management, with significant financial backing from Azure. As the project begins, the team plans to track its progress and engage in follow-up discussions to ensure success.
Transcript is auto-generated.
Welcome to Inside the Win. We’ll break down real world wins, showing you exactly how strategic partnership with our experts empowers you to tackle your most ambitious opportunities with confidence. Let’s jump in.
Hi, everyone. I’m Chad Muckenfuss, VP of Cloud for Telarus. And with me today is Mike Kowalski, and we’re going to discuss an inside the win. So, Mike, go ahead and introduce yourself.
Yeah. Thanks a lot, Chad. My name is Mike Kowalski. I’m a solution architect for Telarus, obviously.
And, yeah, we’ve been invited to to cover one of our recent wins, and I think this one is pretty exciting because we have a lot of chatter going around in the marketplace today about AI. But I really haven’t seen anybody talking about a recent win, so that’s what I’d like to cover today.
Terrific. That sounds great. This is something obviously that we’ve worked on here with a Telarus partner and their end customer. So if you can just kinda take us through it from beginning to end since you were a key part of this, that would be great.
Yeah. Absolutely. So the the beautiful, sexy thing, if I could say that, about AI is that it’s the buzzword. It’s going around the industry.
Everybody wants to have some form of strategy about what is their AI going to look like in six months, in a year, in five years. And if you’re not having that conversation as a business, then please get engaged with us. We can go through some of these conversations, and then you can go back and at least have some form of idea of what you can and cannot do if the need ever arises. That was very similar to this particular client.
They are in the health care space. They’ve got a ton of regulations, and they have all of this patient data, and they need to do some form of predictive analysis on future state of these patients. So they came to us thinking we need an AI solution. The first thing that we like to consider is, number one, what’s the business outcome?
And the business outcome for them is just utilizing all of this data in a helpful and useful way. Now that can mean a lot of different things to a lot of different companies, so I won’t cover that here. But once we are able to analyze that and we look through the marketplace and our suppliers, we found there’s nothing really commercially available. So I think the easiest path is what’s commercially available that we can slip in as a service and they’re off to the races.
Not available, so we look at a couple other routes. One is heavily modifying an existing AI platform that could support the use case or build it themselves. And building it themselves is kind of like a house. Right?
You’re either going to move into a house that’s prebuilt and ready and the rooms are where they are, and you’re you’re gonna lease it. You won’t own it. Or you can say, hey. I’ve got the time and and the resources.
I I wanna build this from scratch. So we started going down that route.
At the time, we had two different AI suppliers in the running. One had each to offer. Because of our value, there’s no right or wrong answer here. So we gave them a nice option.
Once they went down the path, they found they really need to build this themselves from the ground up.
And that’s where this particular opportunity is just now getting kicked off.
Phase one of any AI or ML approach is building that foundation, and the foundation here is data.
I think that the, you know, there’s a key differentiator there. Everything gets put under the label of AI, and sometimes machine learning or ML is is a key conversation starter too. Like, which part of this specific opportunity did this fall under? Was it more of the machine learning or was it a true AI project?
It might be a little bit of both. So AI is the broader concept of machines performing tasks that mimic humans. And I think the most commercially available aspect of that is the bots. Right?
You call in and you get a switchboard and you can have a conversation with the AI and get some information. So anything that would mimic human human intelligence would be considered AI. ML is a subset of that where the it learns from the data to improve the performance without being explicitly programmed. So it’s one of these things where we’re going to give it both of them a a framework and a foundation, but one of them can operate just solely on the data, and that would be the ML.
On the AI side, you have sentiment. You have different interactions with humans and and and things like that. So with this, I think you could really look at that as a machine learning and AI at the same time because they’re gonna have a front end and they’re gonna have a back end, and the two of them will work very well together.
Going back to phase one of this approach, when you have an AI or ML opportunity, you have to get the data right. So they had a a just a diverse set of analytics, reporting mechanisms, ERP, CRMs.
Everything was just kind of discombobulated. So the first step in this whole approach is getting the data in a unified location so that they can just securely access it, keep it managed, keep it clean, garbage in, garbage out, as you know, and then just really be able to hold it up against all the policies. Being with the health care space, they have HIPAA compliance, they have clinical documentation standards, Licensing and credentials need to be properly stored and available for preview or review by anybody that asks, billing and insurance compliance, and the list goes on and on. So there are a lot of things that we needed to take into account for this particular project so that it would be in compliance every step of the way.
So this first piece of this would be a seven or excuse me. It’s a seven week development. It’s a one week architecture, and it’s a one week validation. So we’re talking about a nine week project here.
The good thing is is this is phase one of four.
So with any good home being built, right, you start with that foundation. That’s phase one. Then you get the frame built. That’s phase two.
And then the fun thing is you’ve got a house. You get to decorate it, and that’s that’s phase three. And then phase four is you live in it. You live in it, and you operate out of it.
And then at some point, you may not like how big the bathroom is. So you’re gonna go back to it and you’re gonna say, look. We want we thought we knew what we wanted, but we wanna take out a bathroom. We wanna add in a powder room.
We wanna make these happy to glad changes that since they own it, they really have all of this intimate control over the ultimate outcome for this.
And I think that’s a key factor that you just said is if you’re building, this from the ground up, you have the ability to make adjustments. You’re not just working within the confines of a prefabricated. And I love the home example that you’re using because everybody understands that. A prefab house or, the sample on in the development is already there. You have to work within those confines. But if you’re building it from the ground up, it’s really yours and tailored to your specific needs and wants. And I think that’s a really, really good example you’re using.
Great. They I and I think that it it really kinda goes a long way because when you’re building a home, you really are when you’re building an application for a business, you’re building a a vehicle. We could also use a race car. Right?
You’re building a vehicle that is going to take you from point a to point b. But what does the business outcome look like? We have to make sure that we hit those objectives. And with all of the complexities with their data and everything that they’re trying to accomplish with this being phase one of four, the business outcome is is pretty simple.
For them, they wanted to increase operational efficiencies by reducing the time that they spend on aggregating all these data sources together.
And all this anal analyzing that they have and all of these reports that they have, they wanna manage those workflows because they wanna scale up past multiple thousands of providers, and they just can’t possibly do that today.
So let’s talk about the the plus side. The business, the client is very happy with the commercials of this project. It’s being broken up into four different phases as I mentioned. The first phase is over a hundred thousand dollars.
It’s professional service, considered a professional service, which means they’re gonna come in, they’re gonna build it, they’re gonna pay for it, and then they they move on to the next one. But as you know, with building a home, when the when the foundation is almost poured, you already have the framers coming in. So it’s a nice little overlap. So it’ll be fifty percent down for the, for payment one.
Once it’s completed in nine weeks, they’ll get fifty percent of the other payment. And then they move on to the next phase where each one of these phases is approximately six figures without any modifications going over timelines and things like that. So it’s a very nice sized project. The great thing also is as they build these this data lake within Azure using Azure tools and using Azure infrastructure, you’re going to get that monthly recurring revenue of what does it cost to run this home.
Like, what are the utility costs of running this home? And that is right now in the it’s this early phases of thirty five to fifty five hundred dollars per month. It’s gonna be based on how many users adopt it, how many they need to support.
And so that will be the ongoing. So you get the front end payment, you get the ongoing payment. And the beautiful thing also, six figures to get phase one started may sound like a lot of money. Azure is helping offset some of those costs by using their their programs.
In this particular client’s case, they’re getting thirty thousand dollars off from Azure to run this in their infrastructure. They get that every step of the way. So it may be different amounts, but as it grows, then they start getting more and more and more, paid for. Again, very nice incentive to help offset those costs.
To the partner, we would pay on all of that. So the thirty thousand that Azure pays is doesn’t come off the top. It is included in, as far as I know, with this particular project as part of a commissionable opportunity.
And I think that’s a key thing to note, Mike, is that Microsoft Azure, AWS, and even Google Cloud are offering funds to help develop if it’s going to be developed on their platform. So these big numbers that we hear for the pro services, the the data cleansing, all of those types of things are really helping offset by some of these hyperscalers that are willing to step in and say, hey. If you put it on my platform, I’m going to cover, in this case, thirty percent of the cost. In some cases, it’s fifty percent. In other cases, it’s ten percent. But they’re willing to cover some of the cost involved so that they can gain that long term business, which equates to that monthly recurring number that our partners wanna get as well.
Yeah. And not to mention, if they wanna have a retainer for the supplier to be on call or constantly managing and tweaking this for them so they can operate on their business, they’re health care providers. They are not data scientists. They are not AI engineers.
Right? They they’re not infrastructure experts. They know about health care. And so keeping them on, this would just be an additional bonus for for the partner that is able to, put a project like this together.
That’s awesome. It sounds great. So so where does the project stand as of now?
This is great timing for this. We just signed contracts yesterday, so we’re really at the very, very front end of this project. We get to attend a lot of meetings. We’re all gonna learn a lot about this process for this particular, supplier during this time.
Might have more to to update those on that they may be seeing this right now, and it may be a month down the road. They wanna call them, like, how is that deployment going? I’ve got something that’s very similar. I’d like to get started on that.
But, you know, we’re talking about AI, MLs here, AI and machine learning. It all is gonna start at the data level. We gotta get that foundation set. So approaching it from a point of view, whether it’s commercially available, build it, rent it, lease it, modify it, We’re going to have to address those customers’ data points.
So if you wanna get a little bit of an edge as far as having those conversations, brush up. What what what does it look like to implement a data lake? What different things are required so that you can have one be successful and compliant? And if you learn a little bit of those, technology bingo words, I think it would go very far as far as getting that, client to trust you and know that you are the resident expert.
Yeah. For sure. And again, the Telarus engineering team is here to help as well with some of those more difficult conversations, and, you know, we love the opportunities that are coming in. I think what we need to do is a follow-up part two to this so we can find out a few months down the road how this is going and what this project looks like, and maybe follow it all the way to the end, doing a few different parts of this. So I think that might be fun to to do. So, Mike, I really appreciate your time today. I appreciate the details and the overview of of this very recent win, and, look forward to doing a follow-up and seeing where this goes in the very near future.
Absolutely. We’ll we’ll cover all the capabilities that this a is going to provide for health care. I think that’d make a really good follow-up.
Great. Thanks again. Look forward to talking to you soon about this.
Alright. Thanks, John.