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Rahul Bajaj

Senior Vice President

Persistent Systems

Ranju Das

CEO

Swan AI Studios

Swan AI and Persistent Systems team up for enhanced patient care and operational efficiency

In the ever-evolving landscape of healthcare, a major change is taking place — one fueled by the fusion of cutting-edge technology and artificial intelligence.Persistent Systems Ltd. and Swan AI Studios are looking to harness the power of AI and machine learning to reshape healthcare, improve patient engagement and streamline administrative processes.“We want to make sure that we are improving patient engagement, customer consumer engagement — it’s a common complaint across the board,” said Ranju Das (pictured, right), founder and chief executive officer of Swan AI

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Rahul Bajaj | Persistent Systems & Ranju Das | Swan AI Studios

Rahul Bajaj

Senior Vice President Persistent Systems

Ranju Das

CEO Swan AI Studios

>>Welcome back everyone to this Cube conversation, which is part of the Persistent PSI program.

>> I'm your host, Rebecca Knight. We've got two great guests for this segment. I'd like to welcome Rahul Baja. She is the Senior Vice President at Microsoft Sales at Persistent Systems, and Ron Judas, he's the CEO and founder at SWAN AI Studios. Thank you both so, so much for coming on the show.

>>Thank you.

>>Glad to be here.

>>I wanna start by having you familiarize our viewers a little bit about your,

>> your organizations and your roles at the organization. Ron, I wanna start with you. Tell us, tell our viewers a little bit about Swan ai.

>>Yeah. Swan AI Studio is a recently launched healthcare company that's focused on inventing

>> platforms and building companies in this unique intersection of healthcare technology and machine learning. The advent of machine learning is, is disrupting every industry. It's very common knowledge at this point. There's a lot of in impact of AI ML into healthcare already, and we are trying to harness that fo focus towards administrative efficiency for payers and providers for higher engagement with patient and for a better outcome for help for all population in general.

>>Okay. Raul, tell us a little bit about Persistent and your role there.

>>Absolutely. So persistent is almost 33 years old, and we are leaders

>> in, earlier in software engineering, and now we call it digital engineering enterprise monitoring, you know, a fastest monetization, I meant a fastest growing company in this space, really focused on the US market. So almost 1.1 billion in revenue with a market cap of around $5.3 billion with the 23, 20 3000 employees operating from around 21 countries. Yeah, so our mantra has always been to, you know, stay close to the customer, understand their needs, stay ahead of the technology so that, you know, we can beat our competition engagement model has been, you know, with our size, you know, stay nimble again, customer initiatives, you know, and, and SWAN AI is, is one of the story. So, yeah. And about my role, you know, I'm Raul Bajaj and a senior vice president, Microsoft Sales. Been with Persistent for almost 18 months now. Came through an acquisition company that I co-founded along with my partner, and we got acquired last year, again, have, you know, have created GTM strategies set up offshore delivery centers for Microsoft, both for application as well as, as well as cloud, cloud factory models. So I'm based out of Princeton, New Jersey.

>>Got it. Now, both of you're relatively new-ish to your, to your organizations,

>> of course, your, your veterans in, in the, in the industry, but, but both new to the organization, your partnership is really cool and, and Swan ai, there's so many, it's, it's such an exciting ambition. There's so many exciting use cases for, for AI in healthcare. Tell us a little bit about the partnership ran you and the challenges you faced and why Persistent was going, why, why it appealed to you as a good fit.

>>Persistent has been a great almost personal partner to me for many years now.

>> During my time in my prior roles, be it in Amazon Beach in UnitedHealth, every time I needed external vendor to come in and solve problems that are, you know, time sensitive, needed high quality skillset in very, you know, short burst, persistent was always there. Through that journey, I've had created a, a deep relationship and trusted relationship with Rahul, who I've found to be very customer obsessed, which is what I always look for in any vendor. So as I started this journey with Swan AI Studio, and you could imagine in the early days in this nascent form, when we are looking at multiple ideas, many partners asking us to solve multiple problems, speed to market is important, right? And within that, the quality is a important part of earning that credibility and, and trust with the market. And it was an easy decision for me to look into a, a, a vendor, a partner that I've worked with many, many years, and particularly my relationship with Rahul in that journey to, to collaborate with them in trying to solve some meaningfully impactful problems, if you may, that has that time to market high quality need and, and a sensitivity from a customer, if you may, a vendor, if you may, of that balance of, of quality and, and, and speed.

>>So, Rahul, I wanna hear about it from the persistent perspective. I mean, we are hearing so much about this,

>> this trusting relationship that you both have and clearly you have collaborated on, on various projects and partnerships in the past. What was it when you were hearing about the issues that Swan AI was dealing with, how did you ensure that you were the right partner and and which approaches did you use?

>>I mean, so it is, it is always critical. I mean, first is to understand the requirements,

>> you know, from a customer perspective. Now, important in this case was time to market. I mean, we had very little time for this product to be launched and, you know, and to be market ready. So, you know, we knew that solution would be very highly intuitive. We have to make it scalable. So we leveraged the experience of our gen AI team, our healthcare team. So at Persistent, you know, we've been, been incubating GE Gen AI for a while now, ensuring, you know, we have the right pool of resources for engagement like this. And we started this journey around, in early 2022, launched more than 50 solutions, which are very focused in gen ai. And yeah, I mean, so we, we knew that we were ready for this. And of course, as I said, you know, HLS is, is, is very close to us. I mean, we had persistent 20% of our revenue is HLS focused, which is healthcare focused. So yeah, I think, you know, the approach came out naturally. I mean, we were, we knew that we are ready for this. Yeah.

>>Randi, walk us through this engagement. Talk a little bit about what you were, the, the kinds of solutions

>> that you were coming up with with persistent.

>>Yeah, so there, there are almost two classes of problems we are going after, right? One,

>> we are trying to build companies, as I started saying, right? And that starts with us identifying a, a meaningfully large customer problem. Then we internally, and that's an internal process of the studio, we explore these ideas to some depth to understand is there a true solution that has enough motivated buyers is as a commercial aspect to this product, right? And healthcare is, as everybody knows, healthcare is a extremely complex space. So we are being very thoughtful, upfront and in the journey. We wanna build some pilots and prototypes that we could take to customers and show them to deal with the ambiguity that is inherent in this space. And I think that's where the persistent team comes very handy, right? They, we can almost parachute this special ops group of people that comes in, works with us very closely, hands on glove with us, and, and build up the solutions that we can then take to some of our key strategic partners and customers to get the validation before we decide to move into forming a new NewCo or a startup.

>>On the other hand, as we are in this journey, some of it is because of our track record of the

>> partners in so on studio, there's a lot of partners and, and vendors that are customers are saying, Hey, we have this very burning problem, can you solve it for us? And in those cases, especially when it relates to technology and machine learning, it's about framing that problem again, right? How much time does a customer have to get it solved? And then once we have identified kind of the architecture viewing, then we can immediately bring in a partner like Persistent who can then come in, of course, provide their own, own direction and angles in that and help us delivering those solution in a timely manner, right? With the quality that we have bar. And so far it's been very successful. We have had a very successful customer delivery in a very, very aggressive timeframe. And we are very close to launching our first startup in December and persistent resources and have a lot to do with getting us there.

>>Can you talk a little bit about the specific problems that you're trying to solve in terms of,

>>Yeah, so yeah, absolutely. Sorry, I've

>>Done my homework and it's really cool stuff and I wanna make sure the viewers hear that. Yeah,

>>No, no, no. I think it, it, you know, I think on, on the companies that we are trying to form,

>> they're in the triangulation of interaction between a provider, which in the healthcare world, think of your doctors, your nurses, your hospitals patient who is us in some cases or our, our, our loved ones or our family or friends or whatnot. And then there's this pair, right? Which could be either insurance companies or your employer. So we are, we are trying to look at meaningful problems in that space, which would allow us to apply machine learning to, for instance, an, an example could be how can we connect a doctor and a hospital system to patients more easily without needing more time for this from providers who are already burnt out, who are already over leveraged, right? While creating a high engagement for patient. And it seems like an intractable problem, right?

>>The way to connect them is take more time from the doctors or more doctors. So we are trying to see can we bring technology and

>> machine learning to solve that problem, right? So that's one plus a problem. Other cluster problem would be all around, there's a lot of administrative in inefficiencies, right? From your claims to you, to your payments, to how you schedule. Can we help with machine learning and technology in there? So there's a second class of pros problems. We're looking there on the solution side that a very interesting problem we had to solve was, you know, a customer of us wanted us to do a prototype around can we detect safety of their end customers, patients in their bed while they're in the hospitals, right? And that was a very interesting problem. 'cause you could imagine a real world hospital room with different lighting, different occlusion I coverage of, you know, curtains and people walking around. And within that, being able to detect with high accuracy, a human being that's supposed to be in the bed, whether in the bed or they've fallen off the bed, was a really hard problem. And we took it on, we applied some cutting edge comfort ion technology to that. We brought some edge computing to that problem and we were able to solve it with some very, very high efficiency, with very high accuracy.

>>Wow, that's really incredible. And you can just really see how beneficial this would be to patients

>> and, and healthcare organizations and of course the loved ones who are worried about how about their, their loved ones safety. Rahul, talk us a little bit, talk to us a little bit about how you approach building these solutions. I love what Ran was saying about the sort of the, the parachuters coming in and, and applying this ml. How, how do you collaborate particularly since you two do go way back, how do you come up with finding the right core technologies and making sure you're defining the problem in the right way?

>>I mean, it's so important is to understand, and again, as, as I said, like,

>> you know, almost 20% of our revenue comes from healthcare. We work with three out of the top five healthcare payers in, in us, five out of the 10 top healthcare providers. So we, we have immense experience as a, you know, as a service provider. Now when, when this problem or when Raju came or SWAN came up with this requirement, I mean, yeah, absolutely, it was, it was a complex. I mean, but you know, all kudos to our CTO office. I mean, you know, they had worked on similar solutions, similar product engineering capabilities, which, you know, which were improving, you know, improving consumer experience. And so we had, you know, we had the pieces, some of the pieces ready. I mean, we worked in a, in a patient very recently. We worked on patient care next, which is, you know, end-to-end patient journey application. So, so little little bit pieces from there. And of course all our requirements from swan, I think, you know, together with our CTO office, which had worked on gen AI capabilities, I think it was, you know, it was a easy way out. So yeah, overall, I think this was a collaborative effort between persistent team and SWAN team to come up with a solution.

>>Ranj, what were the key performance indicators that you were tracking? How are you measuring success here?

>>Yeah, it's, you know, I think for me, this is a, this makes it life easy for me,

>> right? Be the, be it my software development or machine learning resources inside, or whether I'm outsourcing, be it from a partner or a vendor or a third party. I think it starts with a quality of resource and it starts with an objective goal that we believe can be assigned to a resource with a very specific timeline that's realistic, right? Once we have those, and when the resource agrees upon that, at that point, it's about meeting these goals. And that gives us a very clear understanding of quality of resource. That along with, in the software world, it's easier to measure, you know, code quality or, or quality of any deliverables, be it algorithm or tuning or thereof. The second aspect was important to break down this otherwise complex project into milestones.

>>'cause we knew the customer requirement. Well, we did the work of breaking down the milestone

>> and then holding all delivery, both internal and external teams to that milestone timeline, right? Any milestone timeline was would've added risk. And so we were effectively managing that delivery timeline. And then finally is really the, the, the, the, in a, in a product case, what you would call a product market fit. In this case, it was the solution fit to what the requirements were, right? So with every sprint, every two weeks, we were looking at what we are building and is it really a step towards where we're going or are we diverging off, right? So kind of the product effectiveness was a third piece that we were always measuring in near real time. And we had to make adjustments in the journey, right? Both in our approach, in some of the resources, in some of our, you know, the one, one challenge that we saw as we started building that part of our success is customer got excited and they asked more.

>>They're like, Hey, hang on. And we, we said this, but can you do this and that?

>> 'cause this is looking so good, right? And you know, one of the thing that, you know, this is probably a bit of Amazon in me, customer obsession trumps everything else, right? So we said, all right, we'll try. And it was amazing purpose and him to say, okay, let's collaborate, let's see through how we can, you know, deal with this changing requirements even more important became even more important to understand those three, right? What is the quality of our resources? How effective are with our timeline? And how effective is the solution we are building towards what the customer has asked of us?

>>Well, Rahul, this is, that's a really interesting point RTE just brought up about this customer obsession

>> and, and customers who have these voracious needs and say, this is great. We need, we need more. Can you do more? Can you do it again? How do you make sure that you are being responsive and proactive about delivering on what your customers are asking for without frankly burning out your DevOps team? Because they're also working hard and trying to also be customer obsessed. But, but their, but their jobs are hard and the technology is relatively, relatively new.

>>Yeah. Yeah. I mean, again, I I think as Randu mentioned a little bit that,

>> you know, two week sprints, that's, those are good checkpoints to have, you know, a global delivery. I mean, I mentioned earlier that, you know, operating from 21 different countries have a global delivering methodology. So you have teams in different geographies. Absolutely. I mean, there's always, there's always changes when we are talking about customer requirement changes immediately, you know, we turn round. But yeah, I mean, we are blessed with a good team, solid foundation. I mean, product engineering has been, if you go back history, I mean almost 30 years, that's been our forte. I mean, persistent is recognized as a product engineering company. So yeah, I mean, we are used to such, such changes in the requirements and, and this was no different. I mean, yeah, as mentioned earlier, that timeline was a little aggressive. You know, we made sure that, you know, in today's market where, you know, capability, I mean you have to find the right capability, but to get it done right on time is, is always key. And, and you know, it's challenging sometimes. So yeah, I mean those were the few things that we were, we were kind of always focused on that we have the right team available at the right time. So yeah. Yeah, overall, I mean that's, that was the approach.

>>Ramji. What is next for SWAN ai? What does your growth look like for, for next year and the years to

>> come?

>>E exciting time for us, very early starting days. We are exploring

>> multiple ideas at this point, right? And the simple foundational approach has been, we'll start with exploring ideas. Once we feel an idea has some legs, some merit, we'll spend some time in framing that idea into a product, right? Which I'm calling an incubation. Once we have incubated an idea with some validation of potentially some customers feasibility of the engineering and, and science or clinical aspect of it, then we'll choose to decide whether we wanna build a prototype out of it, which I'm calling a protocol. Once we have built a protocol, at this point, we want to get customer validation, we want to get user experience validation, and then we get to graduate into what I'm calling a, forming a startup or a NewCo. And at that point, it's racist to a scale company, right?

>>So we understand that journey. Everybody understands the journey of how take a startup to a scale. What

>> we want to do is pin a lot more energy in validating the foundational aspects of a good business upfront. So by the time we form a new core, by the time we form a startup, we have higher conviction of success there. And that big, that means some ideas would get killed upfront being the incubation of the protocol phase, but it just guarantees a better environment for the team that's working. 'cause you know, having been in startups, you don't wanna be in a startup 5, 7, 9 years and then you have lost team as well as for investors and customers, right? It's, it's a, it, I think it's a win-win situation. So we are looking to focus on, as I said, even before the triangle of payer patient and provider problems around helping with operating income or operating expense reduction.

>>'cause I think commercially successful businesses have to deal with that as a direct corollary

>> to that. We wanna make sure that we are improving patient engagement, customer consumer engagement. It's a common complaint across the board. I haven't met a single person in my journey in my lifetime, and especially in my last four years with in the leadership role of UAG, any customer who's saying they're ecstatic about their healthcare experience, right? So we wanna make that better. And, and by the way, it's not a lack of trying from any organization, it is a complex space, right? And yet there is an effort needs to be made to improve on that. So keeping in mind how to improve patient engagement, keeping in mind how to reduce the healthcare inequity that exists, the disparity of access that exists, that's what we are excited about. And then we are bringing in this experienced operator.

>>We are bringing in specialists of healthcare, machine learning technology all

>> together to ensure that we have a robust go-to market and, and scalable products in the market. We are very close to announcing our, our first startup we are looking at in the next few weeks to, to be able to announce to the market our first startup. We actually, I know the process is working, we actually went through a rigorous journey of incubation for one idea, and we said it wasn't ready yet. And we are exploring three more ideas and, and we are, you know, scaling the team. We are raising more fund and we are in continuous conversation with strategic partners, you know, like-minded health systems and health plans to see where are the gnarly problems that we can help with.

>>Yeah. How about you? How about persistent? We're we have very few weeks left of 2023.

>> Whatcha looking at for next year and beyond?

>>I mean, again, the focus is, is Gen ai, I mean, we are preparing our teams for that.

>> Absolutely. I mean, now more than ever, I mean, if you, you know, again, I'm from Microsoft side, so you know, you go to a Microsoft, even technology companies that they're asking for how ready you are, industry aligned. So yeah, I mean, gen AI absolutely is from the technology perspective, but more industry ready, you know, ensuring both, I mean, BFSI, again is, is almost 40% of our revenue and healthcare is 20%. So yeah, we'll be ensuring that, you know, we have technologies who are ready, but they are industry aligned. So that's, that's pretty much it. I mean that's, those would be our key focuses.

>>Well, Rahul and Randu, thank you both so much for coming on the, this has been a really fascinating conversation.

>>Thank you.

>>Thank so,

>>I'm Rebecca Knight tuned for more of the in enterprise.