Home Care U - The Careswitch Story—And How We're Building the Future of Home Care (Ilya Vakhutinsky)

Episode Date: July 8, 2024

Home Care U is brought to you by Careswitch. Co-Founder and CEO of Careswitch, Ilya Vakhutinsky is here to share his founder story, why we went all in on AI, and his vision for the future of home care.Connect with Ilya on LinkedIn: https://www.linkedin.com/in/ilyvak/Enjoying the show? Send me a text and let me know!Learn more about Careswitch at: careswitch.comConnect with the host on LinkedIn: Miriam Allred This episode was produced by parkerkane.co

Welcome to Home Care U, a podcast brought to you by CareSwitch. I'm Miriam Allred, your host. Today we've got a treat for you. Many of you avid Home Care U listeners don't know a whole lot about CareSwitch, the company bringing you this show. So we're going to flip the script a little bit today. At CareSwitch, we believe in free education that creates value and helps businesses, home care owners and operators succeed. But at its core, CareSwitch is a software company that provides an operating system to home care businesses. And today we want to tell you our story.

So I brought our co-founder and CEO, Ilya Vahutinsky, onto the show today. This is a first for Ilya and I to do a session together, but it's a story and a journey that's really important to both of us. So we are excited to share. Ilya, thanks for being here. Hey Miriam, glad to be here. I know we're both a little nervous. This is getting us out of our comfort zone a little bit, but like I said, this is a story that is really near and dear to you. And we've talked about this for a while and now we're excited to kind of, the deeper story and why behind CareSwitch. So who better than yourself to tell the story?

So I want to start as far back as kind of the beginning. I want to start by talking about your founding story, really starting as far back as you can remember to your first encounter with home care. So let's start there. Why home care? When home care? How did it all start for you? I'm going to do my best not to break the fourth wall here, as they say. So take this very seriously. Yeah, my first encounter with home care really goes back as far as I can remember. I essentially grew up in this industry. My family immigrated to the U.S. from

Ukraine. Like most immigrants, the jobs available are typically, you know, the hard, difficult, you know, grueling, dirty jobs. And I think it's safe to say that many would agree that, you know, being a caregiver ultimately is one of those jobs. And so, you know, like I said, like, like many immigrants, my mom was able to get a job as a home health aide. And so, you know, growing up, I remember her taking me around to some of the facilities she ended up doing cases. And I remember, ultimately, a lot of the sights and smells of those places and, you know, and realize that, and I don't know if I realized it then, but, you know, in the future, there's gotta be something better than, than, than that. Eventually she became an RN, this was around middle school.

You know, I watched her progress in her career in this industry. She did, she did pediatric home health for some time and even, you know, brought me around to some of the families that she helped and took care of. I remember one girl in particular who she cared for for quite a while, a pediatric case. And this girl had a feeding tube at one point she had an umph uh i struggle to even say this word but umphalocel which is basically like uh when the internal organs are kind of outside of the the walls of of the abdomen which is kind of a birth defect that um you know can happen and there's kind of a silo that they create around it to to sort of eventually push the organs back in. Anyways, I share that story to point out that at the time, I was very,

you know, sort of shocked and kind of at this for a thing what I was seeing and just kind of the way that my mom sort of, you know, took care of her helped her grow up. Ultimately, you know, that defect was was erected. And, you know, that girl went on to live a, you know, a happy, filling life. And my mom is still very close to her in that family. And so that was a really impactful, you know, part of my growing up. Eventually, my mom then became the on a nursing director for an agency. And this was high school. And then I actually ended up going to work for that agency as a summer job. And, you know, nothing too particularly important, you know, answering the phones, you know, cleaning up the closet, getting papers, you know, things like that.

But it was my first exposure on the business side of home care, which really ultimately taught me a lot. Yeah. Let's lean in there a little bit. What were some of your first impressions? I know you were in high school. I know this was a while back, but what were some of your first impressions? I love you saying too, like pushing paper, like you were like, you know, doing some of the paper management.

So what were your first observations? Yeah, I mean, I think two of the biggest things that I personally took away from that experience, what I call kind of the war room of coordinators that they had basically of the war room of coordinators that they had basically was the biggest room. It was filled with the most people. And there was kind of six or seven people in there speaking kind of multiple languages, papers strewn all over, calendars everywhere, you know, banging the phones constantly, phones ringing. And it was, you know,

both remarkable to see that, you know, what they were doing, but also, you know, somewhat nerve wracking to see that, you know, they took all of this to kind of solve that puzzle of coordination and making the right matches. And it was impressive to see that, you know, a lot of them had all of this insight about both the clients and the caregivers just in their heads. And they were sort of utilizing each other and what was in their heads to kind of solve this puzzle. You know, ultimately, over the period of the last kind of decade, I've come to believe that there's got to be a better way to help them sort of scale this process, which is obviously some of the things that we're tackling. But that was one of the biggest things that I took away. The second probably was around the sort of HR and hiring process. What I was sort of shocked by was, one, how lengthy that typically was this was a an agency that was both medicare and medicaid so they

did home health and they did you know home care and you know oftentimes there's there's extra hoops to jump through in the hr process at the time it was still very much a paper process so there was pages and pages for caregivers to go through oftentimes the caregivers would show up to the office to to do this stuff and you know I would even overhear often they'd say like, oh, well, man, I just did this thing like last week or just the other day because, you know, the reality was they were actually applying for a bunch of different agencies. They may have already been working with some in the area, you know, and also kind of coming in doing this and realizing like, oh, man, I'm missing this or missing that. And, you know, this kind of very problematic process

where like, you're just kind of repeating the same things over and over again. And so similarly, I thought, you know, there's got to be a better way. And ultimately, kind of what would transpire after they do this kind of two week long HR process. By the time that war room of coordinators called in saying like, hey, we've got a great match for you. Here's the shift. We're ready to go. Are you available? And then they'd be like, oh, no, sorry. I'm, you know, I'm working a shift or these hours somewhere else. And so I'm not. And that was just a constant revolving loop that I was seeing. And frankly, I don't think that that has actually changed very much since then.

And potentially it's only gotten worse. Yeah. So you saw a lot of these inherent challenges firsthand when you were relatively young, when and where was the idea for CareSwitch born? You know, you saw all of these challenges, you know, at some point, like an idea started formulating, was it as far back as then? Or when did like an idea start to take shape in your mind? And then how did you start to unpack that and start to kind of formulate a company around the idea? Yeah, I think it really has taken the better part of a decade now to make kind of the ideas of what we're doing more and more salient. But I would say definitely those moments kind of working at the agency were the sort of pivotal seeds that kind of grew in my mind over time to develop kind of the plan that we're on. Basically, you know, it started then. I eventually

won something called the Thiel Fellowship that was started by Peter Thiel around when I was 20 years old. He was the founder of PayPal, was the first investor in Facebook, eventually the founder of Palantir, a lot of other, you know, really impressive and notable things. He created this program basically with the belief that, you know, the and notable things um he created this program basically with the belief that um you know the current system of higher education was sort of overinflated overvalued um you know saddling uh young people with debt um ultimately kind of preventing them from attempting to and trying to tackle big challenges, you know, because they have to ultimately pay it off. And so it was a $100,000 grant to kind of pursue entrepreneurship. And ultimately,

that was kind of like my first foray into trying to start a business. And it was around, you know, home care at the time. Basically, you know, it was really kind of version one of a lot of the things we're building today. But frankly, you know, I was inexperienced, young, and frankly, at the time, didn't have the tools to make it happen. But it was a tremendous learning experience. I ultimately went off to join another venture backed startup in the mental health space, where the focus was a concept called collaborative care. Basically, the premise is that if you treat a patient both from their physical health and their mental health, ultimately, you get better outcomes. And the business case for it is that you theoretically can save, you know, insurers a lot of money, because ultimately,

you know, if you're not treating somebody's mental health issues, that means they can end up hospitalized, they're not following through on their treatment plans, and all sorts of other, you know, negative externalities. So I was an early employee there, part of the early founding team, was there for a few years and kind of learned a ton. I helped recruit my now co-founder, Mark, who's our CTO to CoreTet Health, that was the name of the company, where we ended up working together, you know, on the product. And so, you know, taking what was good there and kind of replicating that with, with care switch, you know, working together, but basically around 2015 and really 2016 after the FLSA law had passed, that was what really kind of got the gears turning again and really wanting to come try to tackle

solving some of these challenges in home care again. That law for those, well, I guess everyone listening probably is very familiar with that, but I'll repeat it anyway. But the point was, you know, there's this move to W-2 employment away from contracting. And so, you know, a lot of the challenges that I spoke to earlier around caregivers kind of going through this process and having to apply to work and having multiple kind of employers likely, it was a lot easier probably before when they can just be contractors. A lot of those kind of challenges, both on the caregiver and agency side, were sort of exacerbated by that. You know, ultimately, when you make someone a true employee, that comes with all sorts of additional costs, workers' comp, insurance, benefits,

unemployment insurance, and the list kind of goes on and on. That's really kind of what, like I said, got the gears turning and wanting to kind of get back into this. So Mark and I, quick quartet, ended up doing the very cliche thing, mom's garage. Well, in this case, mom's basement. We drove around, talked to agencies and caregivers for quite a while, just literally like banging on doors, trying to get a better sense of what I'd already come to understand some of the challenges, but just get it more and more in depth. From there, we ended up kind of landing on this idea that, you know, is something we've sort of pivoted away to from, but really still ties back to kind of the future

we'd like to build. And it was into this, in and around this process of that two-week HR process, we realized like, you know, there's this shortage of caregivers, but ultimately it's the caregivers that have to go around and apply to all these businesses and are doing these repetitive steps. And so our vision was like, well, could we get this kind of standard application, the standard HR profile that caregivers could create and ultimately share with different employers? And actually, we kind of bootstrapped that and it did pretty well. We grew to a dozen or so agencies had like 2000 caregivers or so kind of using it. But ultimately, we sort of realized while working with those agencies that there was just this critical mass of challenges kind of around the administrative side of the business. These technology platforms that they were all using were sort of outdated and ultimately didn't help solve a lot of those complex challenges. And so we pivoted. We decided to sort of scrap that for now. We started from the ground up. We got into an accelerator in New York,

which was kind of our first money in. We ended up making a small acquisition around a company called Rappora. Dan, who is our head of design, who leads a lot of our product effort, came by way of that, as well as an engineer. And then from there, you know, it was kind of off to the races. We started to build our first product. We tested, you know, a lot of different things. We even, you know, went as far as to implement kind of payroll ourselves to try to understand how all that fits into the puzzle, which then led to kind

of this pivot last year to AI, which we'll talk about more, I'm sure. And then, you know, that brought us to today. Yeah, that was awesome. Thanks for sharing all that. I don't think a lot of people know, like kind of the phase by phase beginning of CareSwitch. So thanks for detailing all that out. I think people will really enjoy hearing your journey to where we are today. Before we talk about where we are today, AI, what we're doing, what we're building, let's talk about the name CareSwitch. It's a little bit unique. So I want to give you a couple minutes to talk about, you know, why CareSwitch and what, what it is behind the name. Yeah, sure. So like at the sort of most basic level,

it stands for CareSwitchboard. You know, and ultimately, again, starting with the ideas from back when I was working for the agency to this sort of marketplace we tried to build, it's that premise of being the central point around coordinating this care, whether it's for the actual agencies and the coordinator and staffers in the office to ultimately and hopefully becoming kind of that center hub in this kind of hub and spoke model where we can kind of map caregivers to employers through a central platform. You know, that's kind of the more practical reason for the name and what we're hoping to accomplish. The idea that caregivers can kind of more easily do what they're doing today, which is switch

between these multiple employers that they're working for kind of in one centralized system and ultimately give, you know, agencies the ability to tap this broader network of potential labor. And then obviously all through kind of a layer of AI optimization throughout all that kind of going a layer deeper, something that I think is sort of really powerful and kind of keeps me going in all this is sort of this concept of the letter C. And we use it pretty heavily in our brand. It's for those who've seen kind of our jerseys, we should probably talk about that a little bit too and why we do that. But, you know, the C is a very prominent kind of thing. And there's really two parts to that.

The first, this sort of C in the shape, right, is kind of this, you know, broken loop, right? It kind of can represent just the sort of the life itself, right? This kind of journey from start to end. The way I like to think about it is what we're doing all is really playing in that gap. You know, it's typically, right, people who are getting home care typically are sort of towards the end of life. They're in the sunset of their life. You know, we're there along with, you know, obviously our customers to kind of support that journey.

And ultimately, what I'd like to see is that we play a pivotal role in sort of keeping that sort of fire, that light alive with clients to ultimately, you know, keep them around longer and sort of keep that gap, you know, a gap, right? So I think that that's one really cool part of that and then the second is just more kind of scientific right the letter c denotes the speed of light and this kind of getting a little nerdy here but basically you know in the concept of special relativity um when a human is moving near the speed of light, the thought is that time slows down. And so, you know, what I like to think about is, you know, when a person is moving at the speed of light, they will therefore kind of age more slowly. And again, so going back to kind of

the point earlier, that is ultimately what we hope to accomplish and play a role and actually allow people to age more slowly. And that's the mission we're on. So this totally wasn't intentional, but on the topic of light, before we start talking about CareSwitch AI and where we're at today, I want to give you one more question, which is like, what lights you up about home care? And I just want to start by saying I've been on the team for two years from day one, you know, back to when I was interviewing and talking to you early days, like I could feel your passion and your sense of passion for this industry. But I look at both of us, we're both relatively young, you know, and who are we to be trying to solve some really,

you know, inherent challenges with home care and with caregivers in this country. But what is it that you alluded to it a little bit, but what lights you up about home care? Like what gets you up every day to, you know, pound the pavement and continue to chip away at these challenges? Yeah, well, actually, before I jump into that, I think I want to tackle part of what you just said there, kind of being relatively young and sort of who are we? I often think, and at least this has been proven kind of time and time again at least in the broader kind of technology industry that like it often takes the sort of naive to come in to think about things a little bit differently to ultimately have the naivete to like attempt and try to do it to change

things and so i think ultimately in a lot of ways that that is to our advantage. But yeah, what lights me up in something, I think in a lot of ways I was made to tackle this. I told you the story about my mom. The other half of me, my dad has been a software engineer my entire life, primarily in the healthcare industry. He's worked for some of the largest insurers, some startups in between, and, you know, spend an entire career building complex systems in this space. So, you know, in a lot of ways, I'm sort of taking parts of them and channeling it into what we're building. You know, I think the other thing here, I always sort of admired, you know, the idea of Robin hood and how he sort of played this role of kind of

helping the the disenfranchised or the overlooked and i think in in what we do there's sort of two parts to that there's obviously the clients who just by the nature of needing care that there's sort of this element right where they need the help of others. They need the agencies, they need the caregivers, you know, less so us, but, you know, the systems that kind of manage and are responsible for that. But the part I think that sticks out to me is more on the caregiver side. I think there's more and more talk about the caregivers and sort of being caregiver first, but I think there's more that has to be done there. And again, you know, seeing my mom and go through this journey, and that's always been the thing that kind of lights my fire to sort of pursue this. I think we're not doing enough yet

for caregivers as an industry. And I'm glad to see that there's sort of a change in the tone of how we're talking about this. But I think there's a lot more to be done. And I really hope that we can play a role in that. I think everything we do fundamentally from the products we build comes from a lens of the caregiver and sort of, you know, they're at the center rather than kind of on the outskirts. So I think, yeah, that's probably fundamentally what it is for me. Thanks for sharing that. I mean, that resonates with me personally as well. And I think you really like emulate these concepts, you know, the things that light you up. I think you emulate them, you know, in your own life and what you're doing. Let's talk about where we are today, a pivot. You referenced it kind of in the story. About 12 months ago, we made another pretty large pivot as a company and went all in on AI. So that was intentional. You know, let's talk about why we did that and, you know, kind of just the thinking behind AI coming on the scene and making this change and, you know, really embedding it in our software? For sure. I think I'll start by just first reiterating that, like, our mission and the bigger picture has not changed at all. You know, the things I touched upon earlier are still the

driving force behind what we're doing. But, you know, it would be a huge miss for us and really anybody else, you know, in business and in technology and, you know, in home care agencies to overlook the impact that this new wave of technology can have. You know, historically, when there have been big kind of disruptive shifts, whether it was, you know, mobile devices or, you know, the internet or even things, you know, much older than that, it was those who sort of said, oh, that's not for me or, you know, the internet, or even things, you know, much older than that, it was the those who sort of said, Oh, that's not for me, or, you know, we don't need this in our industry, were the ones who were, you know, out competed and ultimately, you know, lost legacies over the

long run. So, you know, it was, it was sort of, it behooves us to do this, you know, even though it's a challenge to ultimately say, okay, we're starting over. You know, we're thinking about this all from this new lens that kind of got dropped in our laps. But there's really no other way to ultimately stand a chance at being successful and to make an impact in the ways we want to make. But, you know, sort of more importantly than that is the fact that, you know, we realize how powerful an impact these tools and technologies can have in actually solving some of the critical, you know, underlying on the ground challenges that we've heard time and time again from, you know, the industry, from agencies, from caregivers, from just kind of being able to recommend caregivers and find the right match between clients and carriers to just the sheer volume of documentation that has to happen, whether it's in that initial kind of sales and

intake process through, you know, the HR process, through the clinical process, there's just so much that needs to be documented and ultimately so much that needs to be documented and ultimately so much that needs to be reviewed, right? When you think about how many shifts are going on at any given day, and then you scale that across months and months, it's just an overwhelming amount of data that, you know, typically, historically, someone at the office has to consistently be looking at. And so if you just fundamentally look at some of those challenges and you realize like, well, these latest advances in AI

can ultimately help alleviate a lot of that burden and do a lot of the analysis, can do a lot of finding the needle in the haystack type of scenarios, that realization ultimately leads only to one destination, which is like we have to embrace it. And we have to make this the sort of essence and core part of our product. Yeah. And I want to clarify, when we talk about AI, and we say, you know, we pivoted,

you know, 12, 18 months ago, AI hasn't been just hasn't just been around, you know, for 12 to 18 months, it's been around for a lot longer than that. And can you just speak to briefly, you know, the AI that we're talking about, like the generative AI, L to briefly, you know, the AI that we're talking about, like the generative AI, LLMs, like what AI we're talking about? Because sometimes people misconstrue like, oh, AI has been around for a long time and other people have, you know, tinkered with and built with it. But like, what is it exactly that we're dealing with and talking about? Yeah, for sure. So, you know, ultimately we're now talking about LLMs, so large language models. You know, these are companies like OpenAI, Anthropic, many others.

You know, there's some open source models like Lama that, you know, is sort of started by Facebook. And this generation of AI technology kind of stands out dramatically from kind of, you know, prior generations. You know, in the past, we had things like machine learning and other tools. But, you know, what's different about kind of the tools now fundamentally is that they're all text oriented. What they're extremely good at is sort of replicating kind of our understanding of language. And so when you have, you know, the vast amount of data out there, you know, through the internet, you can ultimately, you know, make really, really good sort of assumptions around kind of, hey, what's that next word that comes next to sort of,

you know, replicate human kind of cognition. When you think about the relationship to sort of a lot of what we're doing, well, you know, there's, like I said, there's so much text, there's so much data being generated. These tools can ultimately help to process that. And what's really cool about it is like, you don't need to be a programmer, you don't need to be highly technical to ever kind of make use of or implement some of those tools that I mentioned earlier. You know, it's very highly technical, you need to really know how to train these models and use data. But now, as long as you have a kind of a great grasp of the English language, and many would argue that, you know, today, the sort of the person in humanities

and the English major might actually be better off than somebody who is highly technical and, you know, had a computer science background in leveraging the powers and capabilities of these AI technologies. I've never actually thought about it from that angle, which is actually kind of amazing. Before we talk about some breakthroughs that we've seen with our users, let's start a little bit broader than that and talk about just some of the practical impact that AI can have on this industry. When we made this pivot, again, it was very intentional. We thought through, you know, what does this technology do best? What are some of the weaknesses or the weaker points in home care? And how do we kind of marry the two? So what are some of those more practical

solutions that we do think AI can bring to home care? Sure. Yeah, I think it boils down to three main principles. The first and most important is margin expansion. Fundamentally, all of the challenges that arise in our industry stem from how, you know, sort of margin slim this industry is, generally speaking, right? Your primary cost is the labor itself. There's constantly, you know, an increased pressure in rising wages and other costs associated to it. Home care is not necessarily the most affordable thing for necessarily everybody, right? So the prices continue to kind of rise. It's difficult to sort of maintain profitability between those two things. And what I believe is one of the critical elements of how AI plays a role here is that it makes the people in the office kind of almost infinitely more scalable. Well, we can talk about some of the specifics of that, but I think it ultimately saves agencies money

by not necessarily needing to hire as many back office staff to maintain a much larger and larger kind of field staff operation. So obviously, that goes directly to the bottom line. On top of that, I think there's tremendous optimizations to be had around the actual kind of staffing and matching and understanding kind of what's the best path to profitability. You know, I have these resources on one hand and this kind of set of clients and schedules and needs on the other. And how do you kind of optimally position both of those things to maximize profitability? The second big element to this is, I think,

just the kind of competitive edge and the speed of delivery. So again, when you look historically, you think about whether it's like the paper process or even some of these kind of legacy technology tools, somebody goes into a new client's home, they, you know, do an assessment, they, you know, typically will get back, they'll have to do data entry, key it in, maybe they'll do it live, but you don't sort of rarely. Well, when you when you look at an example like that, you know, imagine a world where the care manager or nurse who's in there doing assessment is literally, you know, recording an audio of the conversation he or she's having with the client. Naturally, that data comes in as processed and automatically creates the assessment in the exact format that has been defined by the business. From there, it's a one-click button to generate

the care plan, taking the sort of first draft from the assessment into the care plan, again, with the same requirements and templating and formatting that the business requires. All these processes often take many hours to do. And sort of what we realize is we can cut them not only in half, but oftentimes, you know, in fourths and in eighths, you know, we've heard a story of one customer who said that whole process would often take kind of four hours. And that's four hours before everybody else can start to do their job around like figuring out the right matching caregiver, the schedule. Well, you know, with our process, we cut that down to an eighth of the time, you know, potentially 30 minutes. While that's happening in real time, other parts of the

business can actually start to take action around that data and start to build the schedules, can use AI to start to build the schedules, can use AI to start to build the schedules and some of those other things. So the speed by which we can get from first contact to staffed is just getting faster and faster and faster. The reality is the agency that's going to win is the one that when they receive that first call can get a response and a caregiver back sooner, right? Over the ones that are going to take hours and hours and days and days and weeks to make it happen. So that's kind of the second. And the third is really about just quality. Like I mentioned

around just the sheer volume of data, the amount of documentation, things get missed, especially, you know, you're trying to scale this business. Like I said, there's kind of this office staff where you ideally, you know, aren't trying to necessarily expand beyond a certain point for profitability reasons. Well, that burden falls on all of those people. And it's a difficult, difficult job. the sheer volume of stuff to look through, like care notes that come in on every shift, every single day. With AI and things that are live in our product today, your business can create rules around what exactly it is that the humans look for and alert you to those things, so that the vast majority of shifts that are okay can move along through the process into billing, and people only need to pay attention to the most critical things. Similarly, when you think about kind of reassessments and those kind of clinical processes, you know, there's a lot of kind of human cognition that happens

on those decision points. And what AI can be really, really powerful in doing is helping that clinician actually gather insights and pull information to kind of do that reassessment. For example, you know, it's time for a three or six month reassessment, the AI is pulling in the prior assessments, the care plan, and all the care notes for the last, you know, six months that the caregivers were providing and can point to the sort of nuance of like, well, you know, it looks like there's been this kind of change in condition and that there's, you know, there's all these notes about the caregiver mentioning that the railing and the, you know, the front steps is shaky and falling apart. You know, that might be a silly example,

but you can kind of gleam like where this goes and providing the team internally to be able to do just a much, much better job clinically, as well as in the matching process of like finding the right caregivers for the right clients. You know, again, another really, really powerful tool live in our products today is the kind of recommendation engine around caregivers. Not only is it taking into account the all the data about the client, like I mentioned, the profile, the assessments, the care plans, but it's also taking into account the full profile of the caregivers. So we have this very, very complex kind of matching criteria profile that the offices can kind of fully customize and annotate. And it goes well beyond what you would

expect from kind of a legacy system, right? Where, yeah, sure. Maybe there's five, six, seven, let's say 10 check boxes that you can kind of sort and search a list with. Well, you know, in our system, you can actually go beyond and say things about availability that go beyond just like four or five rigid blocks on a, you know, on a calendar. You can say things like, well, this caregiver has said blocks on a, you know, on a calendar. You can say things like, well, this caregiver has said that they are, you know, prefer mornings, they're available most mornings and late evenings. But if it's a early morning, they're going to need more than one hour notice to actually be staffed on a last minute case because they have a child and childcare is a concern. So, you know, all of

that nuance that goes into what historically these coordinators kind of did and built up in their minds, the AI can actually process all of that and provide those insights and recommendations at the point of the decision around a coordinator saying, okay, well, I've got this case, whether it's kind of long-term planning or short-term planning, who are the best caregivers that fit this based on everything we've got in our system, the shifts that's been worked, the schedule, the profiles, and ultimately can improve quality throughout that whole process. So let me do a quick little recap here of, like we talked about, the practical implications of AI, the three that you highlighted, just to recap, margin expansion, competitive edge and speed of delivery

and improved quality. And you were already kind of getting into these early breakthroughs that we're seeing. I want to click in to each of those on a little bit more depth. You did a really good job of kind of overviewing them, but I want to, yeah, click into each of them. You talked first about care planning, then scheduling, shift review, getting all the shifts onto invoices. And then you were ending there with availability and matching and what that looks like. I want to go a little bit deeper on all of them so people can really paint this picture in their mind of how this actually works. So let's start with the one that I maybe think is the most exciting or has had the most time and cost savings for our

customers is that AI powered shift review. So let's talk a little bit more about that. Just the manual time it takes to review every single shift. I think everyone listening to this, that resonates with them. There are somebody's eyeballs looking at every single shift, every single line item, making sure everything adds up before those get onto the invoices. So you mentioned this concept of writing rules. Let's talk a little bit more about what that actually looks like and what that means and how AI is powering that. Yeah, for sure. This is probably one of the biggest concepts that I think will be expanding and unfolding within our platform that will save kind of untold amount of hours and dollars

across many parts of the operation. It's this concept of, you know, English written rules, right? Historically, if a competitor is building a workflow into their platform with, you know, a lot of business logic that comes from, you know, insights from the field, from customers. There's a lot of code that has to be written to kind of build those. We're building something kind of far more abstract, powered by these AI tools, where we give the customer the ability to actually write out their own, you know, rules in English. And they can be as quantitative or qualitative as they want.

For instance, when you look at, you know, you mentioned the kind of shift review, there's obviously all of this nuance about how certain clients or how the business thinks about, you know, the clock in and out and sort of, are we, what are we billing for? How many of these minutes are getting billed? How many are not? What's for payroll? And there's all these kind of like consistent edits that need to be made. And, you know, the example of like, well, we know Bob is a stickler and it's sort of no minute. You know, I'm not paying for any minute more than, you know, the reality is maybe the caregiver was there for the extra 10 minutes. And and, you know, you could actually write a rule that says if any of the notes provided by the caregiver show any sign of violence or harassment, alert this to staff. This is really, really powerful analysis that can be done on the text. You can do things like say, well, if a temperature reading was past a certain point, we want to be alert to

that. The business has tremendous amount of flexibility to actually create and define how this workflow and how their operation functions. And that ultimately saves tons and tons and hours of time throughout this process, which these people can be ultimately doing something more creative to the business. So that's one thing. I think expanding out further where we want to take this and what we realize that there's different stages in this process where these kind of AI rules engines make a lot of sense. Another big one that we're focused around is kind of the authorization, kind of EVV and billing process. You know, so many agencies are losing, you know, hundreds of thousands, if not more, because the kind of auth process and

tracking of all that is ineffective or, you know, revenue gets trapped in kind of the cycle for 45 plus days and kind of or more because it gets rejected and comes back and so forth. I think there's a tremendous amount that you can sort of define using these AI rules around that process that catches all this stuff, both kind of before a shift and after. So for instance, you might have one of your payers requiring that a caregiver's social security number, you know, be part of their, their, the record, perhaps, you know, for some reason that the social is missing, you can actually build a rule that

that's kind of catching all that, you know, and there's sort of, as I'm sure many listeners know, there's kind of a laundry list of requirements for each payer, for each state, where we're headed and what we've kind of proof of concept and in really exciting ways is like you actually send over your authorization. We use vision to analyze it and we build out not only the authorization and kind of configuration in our system, but a whole set of rules based around that to alert you to anything that might be kind of falling outside of that process. This is a tad conceptual. I think people are starting to probably like get a grasp on what this means. I'll put in my first plug here. If anyone wants to see this, like we're more than

happy to show you what this actually looks like. It's live and care. So it's today. We're not like blowing smoke here. It's like we actually have users writing these rules, shaving time off of their, their shift review to get things on invoices and get things sent out. So first plug, just to let people know that this is real, this is live. You know, we're talking about something that exists today and we're happy to show you one thing that I want to know beyond the time and the cost saving that you've mentioned, you cited the example of like change in temperature, you know, alert the office if the temperature is up over maybe a hundred degrees. I think one of the more beneficial

aspects here is actually the client, you know, the care, the quality of care, which is, you know, you may have a client on service for 12 months, 18 months, multiple years. It's difficult for the office to have a really good understanding of all of the historical care that's taken place. But now you can be alerted in real time of changes in condition. I think that's one of the things that we're trying to improve upon in home care is monitoring these changing conditions, reducing hospital readmissions. but it's really difficult to do that. But now, you think of the caregiver in the home every single day, taking notes, leaving notes,

someone in the office has to look over those, make note of them, take action on them. Now it's whenever something is reported, a change of condition is reported, the AI flags that, notifies the office. Now it's all real time and proactive rather than reactive. Okay, let's look back at the last 90 days. What happened? What went wrong? It's always looking back. Now it's real time and looking forward. So I just want to call out that, of course,

the time and cost savings in the office are instrumental and are incredible when it comes to margins. But I think even more important, the quality of care, tracking outcomes, reducing readmissions, being proactive in the level of care is probably more beneficial. So just want to call that out again. I know this is a little bit conceptual, but I hope people are understanding just the concept of writing these rules. Yeah, for sure. And I think just to give that a broader context, I think the industry has been talking for quite some time about this kind of feeling of being sort of the ignored younger payments shape up and regulations and where we're pushed in different directions. There's obviously a big desire and need to prove what we all already know, which is that not only is home care a cost-effective option, it's effective in terms of reducing costs and hospitalizations and all sorts of other

things, but also just extending life and quality of life for clients. I think one of the most critical things there is your platform, your technology, being able to sort of play a role in assisting you and proving out what we already know. Yeah, absolutely. Let's talk a little bit more about the care planning piece. You cited this a little bit. One of my personal ahas, I think, has been the impact of AI for nurses. Any nurse or anyone with a clinical background will probably relate to this. You went into nursing to avoid typing, to avoid documentation, but that's a massive part of a nurse's role of a clinician's role is to document all of the care that takes place and to do reassessments and supervisory visits. It's there's so much documentation. I think one of the things that I love most is seeing these nurses use that

microphone, dump in all their notes, take a picture of their notes. You know, it generates an assessment. There's all of these, these unlocks for nurses with AI that I think has just been, you know, aha for me that I wouldn't have expected. You mentioned the example of, you know, one of the customers that we're working with, their nurse, you know, is taking four hours to do the intake, get the assessment back to the office, get the documentation in. So then they could build the schedules. Talk a little bit more about, you know, like the AI magic there of like snowballing information. You know, you gather all of this information on the front end and the intake,

and there's usually a lot of like dual entry step-by-step. How is AI creating a lot of efficiency when it comes to that like duplication of information through the care planning process? Sure. I think you sort of hit the nail on the head there with that sort of snowballing concept. When we get a new referral, right, and there's kind of those initial touch points with a client that often might be over the phone, might be a form, that might happen with the sales team, right? And it's sort of a completely different set of roles. You know, typically that information is not really moving well through the pipeline, right? It's like, you know, it might be caught in some form somewhere else in some CRM, or even if it's an internal CRM in the system, it's some other place that the salesperson entering that information,

and it's not necessarily playing any role in the platform utilizing that information to then help the next person in the pipeline to actually do their job. And so that plays a very big role in what we're doing. having this kind of initial qualification conversation, there's this data being gathered, whether again, like you said, on the through a microphone through X, just being kind of brain dump of information into a text box, to things written down in the picture to PDF, all sorts of formats that we can use to actually like digest and format that information. But what's critical is that it actually moves along in the process that then the kind of next person can actually utilize that in the analysis of what they're doing, right? So if it's the salesperson with the intake

to then the assessment that's more clinical, well, hell, we can get you a first draft quickly based on a lot of what was already done. And we can actually then guide the nurse on, hey, here are the things that we don't quite know yet that you should really pay attention to. These are the questions you need to ask. Oh, and by the way, there's a dialogue that you can kind of have here. These systems are, you know, approaching, you know, we're not there yet, but we're getting closer and closer to sort of, you know, human levels of cognition, you know,

obviously not there yet, but the point being that they're already really, really smart at kind of helping you look around your blind spots, right? You, it can analyze what you've written and basically say things like, well, hey, you've noted this person has diabetes and, you know, this nutrition section is feeling a little light, I mean, you should probably ask about if this person has any dietary concerns related to that condition. Oh, and by the way, you want to ask about what might be some recommendations for somebody who meets these specific conditions. We can actually spit out a dietary plan and that can actually be injected right into the care plan, you know, in the system.

And it's not all these rigid forms and checkboxes and dropdowns. This is kind of the sort of natural process by which most people kind of do their work today, talking with, you know, each other. So it's incredibly powerful throughout all that. I hate to cut us loose here, but I want to, you know, kind of put a plug in here of we're scratching the surface, you know, care planning, huge unlock, AI powered shift review, huge unlock. This is just to kind of illustrate, you know, the possibilities here, get people's minds turning. And again, we're scratching the surface. There's so much more we could run, you know, a couple more hours on just like use cases and ways that we see our users using AI.

But I think the underlying message here is there's a lot of potential and power with AI. There's so many manual tasks that are happening in the office and in the care delivery that can be automated and assisted by AI. I don't want people also to get the assumption that, you know, we're not to the point where we're replacing people, you know, making decisions without the human in the loop. Everything that we build is AI assisted, you know, in our software. You can do all the manual checkboxes, you know, more efficient, smarter, making better decisions, focusing on higher yield tasks, you know, where the AI can supplement, you know, some of the

lower yield tasks. So I want to shift gears a little bit and talk about the future, not the long-term future to start here, but just kind of the next maybe six to 12 months. We have seen massive changes, updates, improvements the last 12 months. You know, this technology is moving extremely quickly. What are just a few things that you are excited about or maybe hopeful to see in the next maybe six to 12 months regarding AI? Yeah, for sure. I think just to touch upon your last kind of comment there, there's a reason our tagline is care is human and for everything else there's care switch i think that that care is human element i think what gives me

comfort with all of the you know shifts in technology and if you're you're sort of in tune with what's happening there's a lot of fear even from kind of the most technical people out there around uh this concept of agi and artificial general intelligence and what does that mean for the rest of us and how we get by and this kind of fear. I think what gives me a lot of comfort in what we're doing and what we're helping customers do is that care element. At the end of the day, sure, maybe there's some AI that you might use for a client or senior to sort of get some social comfort and having a conversation, which I think is going to be really good today. And frankly, there's probably some use case there. But the reality is, at the end of the day, nobody wants a robot coming in and potentially giving them peric care, right? Like the sort of care that we expect, we want that skin to skin,

like we want that human contact that we're wired to be that way. And ultimately, that gives me a lot of, you know, sort of confidence in what we're all doing in this industry. And then sort of the fears, you know, at least aren't so impactful. So that's kind of the first thing I want to know, as far as like how I see that future. So it's not that, right? It's not, you know, robots coming and sort of doing that, the skin to skin contact, that human element is just something that, you know, we're wired for and can't be replaced. But what we can do is automate a ton more stuff around the documentation process, the clinical process, the back office and administrative process to actually just get to more of the care.

Well, that's kind of like the bigger picture thing. You know, on the shorter end, I think, like I said, this kind of rules machine is sort of what we're really excited about. I think it can save so many parts of this process, the HR, billing, operations, time, and then sort of catch certain things. And I think that's going to be a big time and money saver as we expand that. Beyond that, I think there's a whole move to actually not just alerting the person and the user to these things, but sort of more agentic kind of AI systems. And what I mean by that is where the AI can actually sort of take certain

actions for you based on a certain criteria, right, where you're instructing it to do more on your behalf. For instance, one kind of version of this that we've proof of concept earlier on, it wasn't quite fully baked and wasn't ready, but we're getting closer and closer to this sort of thing. But, you know, the example being like a no-show where people are kind of obviously always paying attention to that. It's one of the most critical things that can happen, the fire drill of no sign of the caregiver, right? There's no clock in. It's 10 minutes after the client's calling in. And it's like, you know, an entire emergency can ruin, you know, businesses and, you know, can ruin your reputation in these sort of situations. And, you know, the thing we sort of proof of concept was this ability for the AI to actually automatically reach out to the caregivers via the app and actually have a dialogue.

I mean, hey, what's going on? Where are you? Why aren't you, you know, what's going on? Where are you? Why aren't you, you know, what's the situation? Gather that information, bring that back to the coordinators and ultimately have them drive, you know, a decision from there. Well, it's not much of a stretch to get to a point where you're sort of preemptively being able to do that. Hey, if a caregiver is, you know, it's their first shift, they haven't worked for us before, it's 15 minutes before. We should reach out, get a response. Are they traveling? We should know that. And if we don't get that information to sort of then kick off an entire AI-driven workflow around, well, okay, well, I actually know who the three best caregiver options are who match all of the same criteria who are fully vetted have their profile this person actually has worked you know 10 shifts with us in the past is pretty

reliable let me actually reach out with a last minute you know message to this caregiver and see hey you're 10 minutes away can you get there asap they say yes and we make that schedule change get them there and can even ultimately alert the client that like, hey, actually, this new person is going to be there in 10 minutes. I think that's where we're headed. Is that six, 12 months? I don't know, but it's not that far away. And that's kind of the feature that we're pushing for. I think another big kind of piece of this relates to kind of data and these insights, the sort of world of kind of reporting and how you understand data, I think is changing. We already have versions of this in our platform, but it's only going to expand more.

I think the age of like the KPI dashboard is sort of close to over. While we have one, we're building one, we're going to have a basic version. The reality is you can actually leverage the AI to give you the insights you're looking for and slice and dice data in ways that potentially your platform hasn't thought about yet, as long as that information is there. So the world of having to go to support, be like, oh, I need a chart for this. Can you guys add an export for that? You can literally talk to Looper, the assistant and say, show me, you know, all of the shifts that we've potentially offered or were declined. You know, an example, I give you like an unemployment report. We never conceptually, you know, had built one, but, you know, we had a user come by and sort of tell us like, hey, actually, I was able to have Looper build me

a report because I got an unemployment insurance claim and I needed to be able to show them a spreadsheet of all of the shifts that were offered to this person and why they declined it. And they were literally able to just ask that question and Looper actually spit out a report and made it into a CSV that they could ship out. I think that is the future of reporting. And that's even closer than some of the agentic stuff I mentioned earlier. Like we're there today. There's more to do on that, but it can literally build graphs. It can build charts. It's doing that now. So I think that's another big piece of this. Yeah. I'll lean into the hot take here on reporting. The way that I think about it and the way that we're thinking about it is reporting is historical. It's looking back. It's looking at

things that have happened in the past. AI, we're looking for insights. We're looking at the past. We're looking at the present. We're looking at the future. AI can extract the insights. You know, when you look at a report, you know, the data is telling you something, but what is it telling you? You know, I think AI does a much better job of giving you the insight that you're looking for when you're looking at a chart or at a graph. And the beauty is you ask it, you know, it can generate the graph, the report, the chart, the spreadsheet, the PDF, like it can generate all of that. But the more impactful power that it has is ask it a question, and it'll give you an insight based off of that report. And so I think, you know, maybe a little controversy here, you saying, you know, we might erase the KPI dashboard,

I think that may, you know, strike a chord with people, but it's, you know, AI can do so much more than the way that we've thought about reporting. There's insights to be gauged from this information. Instead of erasing the KPI dashboard, instead of telling us the KPI dashboard you want, you're able to actually design the KPI dashboard you want using AI and having it, you know, spit back all of those data points. Yeah. So for the most part, I think home care, the industry owners, operators are receptive, excited, you know, forward thinking. I think that's, you know, the majority of what we see. We have met with people that are a little hesitant or resistant or, you know, anxious about AI in the future. So I just want to take a minute and talk

about, you know, what you or we would say to those people that are hesitant, or we see a lot of people, not a lot of people, some people kind of waiting in the wings, like, you know, I want to see it improve or progress or this or that. What would you say to some of the people that are a little more hesitant or concerned about AI and its impact in home care? Yeah, I think there are three points there. I think the first, I want to reiterate again, to not take advantage of this is shooting yourself in the foot. The companies that are taking advantages are out-competing in their geographies. They will win, they will scale quicker, they will be more price competitive for all of these reasons. And you will inevitably lose if you don't take the next step with the wave of technology that is taking hold. Like I said, this has happened many and many cycles before.

And if you're not doing it, it's a recipe for disaster. The second point is, ultimately, it's actually a lot easier than a lot of those other things before. We're giving you the tools right there, uh, you know, in front of you. And like, if you can write a paper, if you can send a chat, if you know how to text message with your friends, you're, you already got the skills to, uh, to leverage this and make it happen. Consider this basically the, uh, the assistant in, in, in the, the real life assistant that, that you wish you've always had that, uh, never goes to sleep, you know, never complains, doesn't need overtime, is never asking for a raise and who ultimately like it has the competence level well beyond the years of any, you know, intern. So that's the way to think about it. And so, you know, it's actually really, really easy to use. Um, and, and frankly, there's a lot of tools out there that, that you can kind of wet your beak on, uh, I think our team goes, it is light years

beyond what's available and what's out there. And we think about and take seriously the fact that, you know, things need to be easy to use. They have to be understandable. They should be beautiful. I think that point alone is something that the rest of our industry, for whatever reason, entirely avoids, or, you know, maybe it's, you know, I don't know why, but it's a weakness that I see. And we take that very seriously. I think the products you use for work should be as, you know, beautiful and impactful as maybe some of the things you use in your spare time as a consumer. And so kind of to summarize that last point is like, we take usability incredibly

seriously. If you take a look at our products, the design is some of the easiest stuff you'll see out there. And we've heard time and time again, that that training is really sort of borderline unnecessary because of how easy and straightforward everything is to use. And what's really cool is that you can actually ask Looper like, hey, what can you do? What are some of the things that I can leverage AI to accomplish within the system? I can actually answer those questions for you. And obviously, it's expanding every day. This is incredible. I want to close out by zooming out. I know we've kind of like lasered in here, zoomed on, you know, use cases, breakthroughs, et cetera. I want to zoom out as we close out this conversation and have you speak to the future, the future as you

see it, the future that you hope for, you know, what, what are a couple of the key problems that you personally, you as the founder of CareSwitch want to solve in home care, you know, that go bigger than AI, beyond AI, what are? What are one or two of the inherent challenges that you personally want to see solved in home care in the coming years? For sure. I think at the most highest level, it's this kind of expansion of home care to its rightful place, you know, in my opinion, on the throne of what it is and what it can be for seniors and any other, you know, kinds of clients that have home care. Because ultimately, as we all know, it's miles ahead of, you know, being in the facility and some of these other sort of settings.

Obviously, there's a place for those things as well. But for a lot of people, there's a preference and desire to, you desire to age at home. And there's lots of studies that point to the fact that the quality of life and years are extended through that process. And so that is the thing that we hold in the highest regard. Obviously, to make any of that happen, we need the supply of caregivers. We are already in a massive deficit. So the most kind of critical thing that I think about and that we think about at CareSwitch is how do we make an impact to that very specific thing? And, you know, obviously a lot of this boils down to making this a career path that draws in more supply of labor right into the space, you know, obviously that that's ultimately

a financial question. It's like, how do you help reduce costs? How do you help expand margin? How do you make this, you know, how do you make it more affordable to increase wages and all these sorts of other things to create a better environment for the economic viability of caregivers and this as a career path. I think, you know, sort of sub point to that is basically like elevating not only home care, as I mentioned, but that role of the caregiver and kind of the eyes and ears that they are and the power that they have and, you know, holding them to the same kind of high esteem that we have for other roles in the healthcare ecosystem. And I think, you know,

continue to prove to everyone, both quantitatively and qualitatively that, you know, home care is critical, cost effective, life extending. You know, another example of sort of how we think about the role that we could play in something like this is something I think a lot about is like benefits cliff. You know, fundamentally in this sort of near term, like our vision for our platform is to erase the need for caregivers to necessarily have kind of two different systems that they have to pay attention to. The scheduling system, you know, and the HR payroll system. When you think about the average caregiver and how they're working for likely many employers, you know, that's two systems multiplied by maybe three, maybe four agencies, right? Who knows?

And that's six, eight systems that they're kind of paying attention to potentially like even having trouble using the same email address to log into these things. You know, our vision is like that should be one central point. And not only does that kind of help solve and alleviate some of the pain points for the agencies, but when you think about something like the benefits cliff for caregivers, something that I always think about is that there's a very likely chance that because of the nature of how theseuctions and the way that they do their tax setup is not necessarily very optimal when you think about it in kind of in total across their multiple employers. And on top of that, you know, when you think about that average caregiver, there's a good chance that they're on SNAP or Section 8 or some other, you know, sort of benefit.

And there's obviously a very, very real fear of losing those things, right? You never want to be in a position where doing more work actually puts you in a worse financial position than you were before. And unfortunately, for I think much of this industry and the labor that we have, that's actually where we are, that there's a point in which doing more work and caring for more people actually hurts the caregivers in the sort of short to medium term. And we call this the benefits cliff. And I think one sort of idea around this is like, well, if this caregiver was in this central place where we had all their three employers and we understood both their HR payroll situation and their

scheduling situation, we can actually give them the insight that, well, look, those extra hours that you have available here that's being offered to you is not going to come at a cost to you, right? You're not going to lose this X or Y benefit. And we can actually show that to them. I believe that there is a huge untapped kind of margin of capacity that we have in our already existing labor force that is not being met because of this fear of sort of hitting and crossing that boundary. I think if we can give caregivers the insights and data around that, we can uncover more and more of those hours that

obviously benefits everybody. That's just one taste of sort of many things that we're kind of thinking about behind the scenes on how we actually make this broader, bigger impact. If these last points that Ilya is hitting on, you know, get any of you, your wheels turning or, you know, are resonating with you or points you want to discuss or debate, this is really Ilya's bread and butter here. And I love that we're ending here because I think people have really gotten a taste of your journey and the journey of CareSwitch, but even more important, your passion for this industry. And so I usually say this at the end of any session,

but connect with Ilya on LinkedIn, shoot him an email. We didn't even mention at the top, he's in the New Jersey market. So if you're in that area, connect with him, reach out to him. Like you've seen and heard today, he is passionate about home care and about caregivers and about the future of this industry. And so he always warmly welcomes the opportunity to discuss or debate these types of topics. And so I kind of tee you up for that, Ilya, because I think you have a lot to say and a lot to share, maybe a tad like controversial, but I think, like you said, you know, we are this younger, maybe a slightly naive generation, but we want to see home care improved and brought to the throne across the continuum. And it's, it's up to people like us to make that a reality. So this has been an incredible

session. I think you've delivered even more and better than what I was hoping or expecting. So I just want to thank you. And, you know, even on a public stage, I feel like I don't probably thank you enough or express enough gratitude, appreciation, you know, just how proud I am of you and of what we're accomplishing. And so I want everyone to know that we're on this mission together and I'm excited and believe in the future of what we're building. I appreciate all that. And, you know, I think the same back to you. I think what you're doing here with our pod here is incredible. I know there's a whole session we can have about our vision for just that piece alone and kind of the impact we want that to have separate and outside of any, you know, impact we have with our software.

So, you know, similarly grateful for you and being a part of what we're doing. Can I close with one last thing? We didn't talk about the hockey jersey. And I feel like we have to jump in. Jump in. Let's tease this because I was actually going to say, Illya and I both will be speaking at the HCAOA conference in Seattle. You'll probably see us in

our jerseys. We'll both have some stage time. So yeah, tee up what people could expect if they see us. All right. So the hockey jersey, the Karasuch hockey jerseys, four part of our lore here. We'll give you the first hint, Happy Gilmore. The premise of the movie and why it's our favorite movie, the entire premise is that Happy gilmore is trying to win this golf tournament you know he's a he's a former hockey player but he's trying to win this golf tournament to actually save grandma's house and so that she can stay in her home where kind of in the midst of all this she's sent to the the big bad uh facility where she's kind of you know spending her days and he wins this tournament to get the

house back, to get her back home. So at the very highest level, that's fundamentally everything we're doing here, but there's sort of a deeper layer here, which ultimately is that kind of, you know, that naivete that we talked about is sort of the desire, the confidence to just kind of think about things a little bit differently, like come to the golf tournament with the hockey stick, be willing to sort of maybe look odd for some time before everyone catches on. And that's what it's all about. If people are still listening, this is the PS. This is like the bonus teaser here at the end, which I love. Ilya, great session. Thank you so much again for everything that you're doing. I am, you know, beyond excited for the next six months, the next 12 months and beyond. So much potential and so much passion in home care. So thanks for everything

that you're doing. And to be continued, like Ilya said, we've got a lot more to share, to talk about. So to be continued in a future episode. So thanks everyone for listening. Hope you're having a great week and we'll see you again next week. That's a wrap. This podcast was made by the team at CareSwitch, the first AI powered management software for home care agencies. If you want to automate away the menial of your day-to-day with AI

so that you and your team can focus on giving great care, check us out at careswitch.com.