November 12, 2025 • 13 mins
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.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Inside the Win, We'llbreakdown 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 Telaris. And with me today is Mike
Kowalski. And we're going to discuss an
Inside the Win. So, Mike, go ahead and introduce
(00:22):
yourself. Yeah.
Thanks a lot, Chad. And my name is Mike Kowalski.
I'm a solution architect for Telaris, 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 covertoday.
(00:42):
Terrific. That sounds great.
This is something obviously thatwe've worked on here with a
Telaris partner and their end customer.
So if you can just kind of take us through it from beginning to
end since you were a key part ofthis, that would be great.
Yeah, absolutely. So the, the beautiful, sexy
thing, if I could say that aboutAI is that it's the buzzword.
It's going around the industry. Everybody wants to have some
(01:04):
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 andcannot do if the need ever
(01:25):
arises. That was very similar to this
particular client. They are in the healthcare
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
(01:46):
consider is #1 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
(02:07):
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 of other routes.
One is heavily modifying an existing AI platform that could
support the use case or build itthemselves.
And building it themselves is kind of like a house, right?
(02:29):
You're either going to move intoa house that's pre built and
ready and the rooms are where they are and you're, you're
going to lease it, you won't ownit.
Or you can say, Hey, I've got the time, the and, and the
resources. I, I want to build this from
scratch. So we started going down that
route. At the time, we had two
different AI suppliers in the running.
(02:50):
One had each to offer because ofour value.
There's no right or wrong answerhere.
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 thisparticular opportunity is just
now getting kicked off. Phase one of any AI or ML
(03:10):
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 specificopportunity did this fall under?
(03:31):
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
(03:53):
human intelligence would be considered a IML 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.
(04:14):
And that would be the ML On the AI side, you have sentiment, you
have different interactions withhumans and, and, and things like
that. So with this, I think you could
really look at that as a machinelearning and AI at the same
time, because they're going to have a front end and they're
going to have a back end. And the two of them will work
very well together. Going back to Phase 1 of this
(04:38):
approach, when you have an AI orML opportunity, you have to get
the data right. So they had just a diverse set
of analytics, reporting mechanisms, ERPCRMS, everything
was just kind of discombobulated.
So the first step in this whole approach is getting the data in
(05:01):
a unified location so that they can just securely access it,
keep it managed to 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 healthcare space.
They have HIPAA compliance, theyhave clinical documentation,
standards, licensing and credentials need to be properly
(05:24):
stored and available for previewor 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
(05:45):
would be a 7 or excuse me, it's a seven week development, it's a
one week architecture and it's aone 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.
(06:06):
Then you get the frame built, that's phase two.
And then the fun thing is you'vegot a house, you get to decorate
it and that's that's phase three.
And then phase four, as you livein 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 going to go back to it.
You're going to say, look, we want, we thought we knew what we
wanted, but we want to take out a bathroom.
We want to add in a powder room.We want to make these happy to
(06:28):
glad changes that since they ownit, 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
(06:51):
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.
And I think that it really kind of goes a long way because when
(07:13):
you're building a home, you really are.
When you're building an application for 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 hitthose objectives.
And with all of the complexitieswith their data and everything
that they're trying to accomplish with this being phase
(07:33):
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 analyzing that they have and all of these reports that
they have. They want to manage those
workflows because they want to scale up past multiple thousands
(07:55):
of providers and they just can'tpossibly do that today.
So let's talk about the the plusside.
The business, the client is veryhappy with the commercials of
this project. It's being broken up into four
different phases. As I mentioned, the first phase
is over $100,000. It's professional service,
(08:16):
considered a professional service, which means they're
going to come in, they're going to build it, they're going to
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, youalready have the framers coming
in. So it's a nice little overlap.
So it'll be 50% down for the forpayment one.
Once it's completed in nine weeks, they'll get 50% of the
(08:39):
other payment and then they moveon to the next phase where each
one of these phases is approximately 6 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,
(09:00):
you're going to get that monthlyrecurring revenue of what does
it cost to run this home, but what are the utility costs of
running this home? And that is right now in the,
it's this early phases of 35 to $5500 per month.
It's going to be based on how many users adopted, how many
they need to support. And so that will be the ongoing.
(09:20):
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 lotof money.
Azure is helping offset some of those costs by using their their
programs. In this particular client's
case, they're getting $30,000 off from Azure to run this in
(09:41):
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 30,000 that Azure pays is
doesn't come off the top. It is included in, as far as I
(10:03):
know, with this particular project as part of a
commissionable opportunity. And I think that's a key thing
to note, Mike, is that MicrosoftAzure, 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
(10:26):
types of things are really help being offset by some of these
hyper scalers that are willing to step in and say, hey, if you
put it on my platform, I'm goingto cover in this case 30% of the
cost. In some cases it's 50%, in other
cases it's 10%. But they're willing to cover
some of the cost involved so that they can gain that long
(10:47):
term business, which equates to that monthly recurring number
that our partners want to get aswell.
Yeah. And not to mention if they want
to have a retainer for the supplier to be on call or
constantly managing and tweakingthis for them so they can
operate on their business, theirhealthcare providers, they are
not data scientists, they are not AI engineers, right?
(11:08):
They they're not infrastructure experts.
They know about healthcare. 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.
(11:28):
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 going to 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
(11:48):
the road they want to call it like how does that deployment
going? I've something that's very
similar. I'd like to get started on that.
But you know, we're talking about AIMLS here, AI and machine
learning. It all is going to start at the
data level. We got to 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
(12:11):
address those customers data points.
So if you want to 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 ofthose technology bingo words, I
think it would go very far as far as getting that client to
(12:35):
trust you and know that you are the resident expert.
Yeah, for sure. And again, the Telaris
engineering team is here to helpas 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 2 to this so we
can find out a few months down the road how this is going and
what this project looks like andmaybe follow it all the way to
(12:57):
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 theoverview 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 healthcare.I think that'd make a really
(13:19):
good follow up. Great.
Thanks again. Look forward to talking to you
soon about this. All right.
Thanks, John.
Welcome to Inside the Win, We'llbreakdown 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 Telaris. And with me today is Mike
Kowalski. And we're going to discuss an
Inside the Win. So, Mike, go ahead and introduce
(00:22):
yourself. Yeah.
Thanks a lot, Chad. And my name is Mike Kowalski.
I'm a solution architect for Telaris, 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 covertoday.
(00:42):
Terrific. That sounds great.
This is something obviously thatwe've worked on here with a
Telaris partner and their end customer.
So if you can just kind of take us through it from beginning to
end since you were a key part ofthis, that would be great.
Yeah, absolutely. So the, the beautiful, sexy
thing, if I could say that aboutAI is that it's the buzzword.
It's going around the industry. Everybody wants to have some
(01:04):
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 andcannot do if the need ever
(01:25):
arises. That was very similar to this
particular client. They are in the healthcare
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
(01:46):
consider is #1 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
(02:07):
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 of other routes.
One is heavily modifying an existing AI platform that could
support the use case or build itthemselves.
And building it themselves is kind of like a house, right?
(02:29):
You're either going to move intoa house that's pre built and
ready and the rooms are where they are and you're, you're
going to lease it, you won't ownit.
Or you can say, Hey, I've got the time, the and, and the
resources. I, I want to build this from
scratch. So we started going down that
route. At the time, we had two
different AI suppliers in the running.
(02:50):
One had each to offer because ofour value.
There's no right or wrong answerhere.
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 thisparticular opportunity is just
now getting kicked off. Phase one of any AI or ML
(03:10):
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 specificopportunity did this fall under?
(03:31):
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
(03:53):
human intelligence would be considered a IML 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.
(04:14):
And that would be the ML On the AI side, you have sentiment, you
have different interactions withhumans and, and, and things like
that. So with this, I think you could
really look at that as a machinelearning and AI at the same
time, because they're going to have a front end and they're
going to have a back end. And the two of them will work
very well together. Going back to Phase 1 of this
(04:38):
approach, when you have an AI orML opportunity, you have to get
the data right. So they had just a diverse set
of analytics, reporting mechanisms, ERPCRMS, everything
was just kind of discombobulated.
So the first step in this whole approach is getting the data in
(05:01):
a unified location so that they can just securely access it,
keep it managed to 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 healthcare space.
They have HIPAA compliance, theyhave clinical documentation,
standards, licensing and credentials need to be properly
(05:24):
stored and available for previewor 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
(05:45):
would be a 7 or excuse me, it's a seven week development, it's a
one week architecture and it's aone 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.
(06:06):
Then you get the frame built, that's phase two.
And then the fun thing is you'vegot a house, you get to decorate
it and that's that's phase three.
And then phase four, as you livein 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 going to go back to it.
You're going to say, look, we want, we thought we knew what we
wanted, but we want to take out a bathroom.
We want to add in a powder room.We want to make these happy to
(06:28):
glad changes that since they ownit, 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
(06:51):
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.
And I think that it really kind of goes a long way because when
(07:13):
you're building a home, you really are.
When you're building an application for 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 hitthose objectives.
And with all of the complexitieswith their data and everything
that they're trying to accomplish with this being phase
(07:33):
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 analyzing that they have and all of these reports that
they have. They want to manage those
workflows because they want to scale up past multiple thousands
(07:55):
of providers and they just can'tpossibly do that today.
So let's talk about the the plusside.
The business, the client is veryhappy with the commercials of
this project. It's being broken up into four
different phases. As I mentioned, the first phase
is over $100,000. It's professional service,
(08:16):
considered a professional service, which means they're
going to come in, they're going to build it, they're going to
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, youalready have the framers coming
in. So it's a nice little overlap.
So it'll be 50% down for the forpayment one.
Once it's completed in nine weeks, they'll get 50% of the
(08:39):
other payment and then they moveon to the next phase where each
one of these phases is approximately 6 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,
(09:00):
you're going to get that monthlyrecurring revenue of what does
it cost to run this home, but what are the utility costs of
running this home? And that is right now in the,
it's this early phases of 35 to $5500 per month.
It's going to be based on how many users adopted, how many
they need to support. And so that will be the ongoing.
(09:20):
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 lotof money.
Azure is helping offset some of those costs by using their their
programs. In this particular client's
case, they're getting $30,000 off from Azure to run this in
(09:41):
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 30,000 that Azure pays is
doesn't come off the top. It is included in, as far as I
(10:03):
know, with this particular project as part of a
commissionable opportunity. And I think that's a key thing
to note, Mike, is that MicrosoftAzure, 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
(10:26):
types of things are really help being offset by some of these
hyper scalers that are willing to step in and say, hey, if you
put it on my platform, I'm goingto cover in this case 30% of the
cost. In some cases it's 50%, in other
cases it's 10%. But they're willing to cover
some of the cost involved so that they can gain that long
(10:47):
term business, which equates to that monthly recurring number
that our partners want to get aswell.
Yeah. And not to mention if they want
to have a retainer for the supplier to be on call or
constantly managing and tweakingthis for them so they can
operate on their business, theirhealthcare providers, they are
not data scientists, they are not AI engineers, right?
(11:08):
They they're not infrastructure experts.
They know about healthcare. 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.
(11:28):
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 going to 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
(11:48):
the road they want to call it like how does that deployment
going? I've something that's very
similar. I'd like to get started on that.
But you know, we're talking about AIMLS here, AI and machine
learning. It all is going to start at the
data level. We got to 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
(12:11):
address those customers data points.
So if you want to 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 ofthose technology bingo words, I
think it would go very far as far as getting that client to
(12:35):
trust you and know that you are the resident expert.
Yeah, for sure. And again, the Telaris
engineering team is here to helpas 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 2 to this so we
can find out a few months down the road how this is going and
what this project looks like andmaybe follow it all the way to
(12:57):
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 theoverview 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 healthcare.I think that'd make a really
(13:19):
good follow up. Great.
Thanks again. Look forward to talking to you
soon about this. All right.
Thanks, John.
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