 Welcome back everyone to this cube conversation, which is part of the persistent PSI program. I'm your host Rebekah Knight We've got two great guests for this segment. I'd like to welcome Rahul Bajaj She is the senior vice president at Microsoft Sales and Persistent Systems and Ranju Das He is the CEO and founder at Swan AI Studios. Thank you both so so much for coming on the show Thank you, Rebecca. Glad to be here. I want to start by having you familiarize our viewers a little bit about your organizations and your roles at the organization Ranju, I want to start with you. Tell us tell our viewers a little bit about Swan AI Swan AI Studio has recently launched a health care company that's focused on inventing platforms and building companies in this unique intersection of health care technology and machine learning The advent of machine learning is is disruptive in every industry. It's very common knowledge at this point. There's a lot of impact of AI ML into health care already. And we are trying to harness that focus towards administrative efficiency for payers and providers for higher engagement with patients and for a better outcome for health for all population in general Okay, Rahul, 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 modernization 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 23,000 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 Swan AI is one of the story Yeah, and about my role, you know, Rahul the judge and a senior vice president Microsoft sales being 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 your relatively new ish to your to your organizations of course your your veterans in the industry, but both new to the organization, your partnership is really cool and and Swan AI there's so many it's such an exciting ambition. There's so many exciting use cases for for AI and healthcare. Tell us a little bit about the partnership around you and the challenges you face and why Persistent was going, why why appeal 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 beached in United Health. Every time I needed external vendor to come in and solve problems that are, you know, time sensitive needed high quality skill set in very, you know, short burst persistent was always there. Through that journey I've had created a deep relationship and trusted relationship with Rahul who I've found out 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're looking at multiple ideas, many partners asking us to solve multiple problems. Speed to market is important right and within that the quality is an important part of earning that credibility and trust with the market. And it was an easy decision for me to look into 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 sense of sensitivity from a customer if you may or vendor if you may of that balance of quality and speed. So Rahul I want to 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, and 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 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 incubating Gen AI for a while now, ensuring you know we have the right pool of resources for engagement like this. 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 knew that we were ready for this and of course as I said you know HLS 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. Ronju walk us through this engagement. Talk a little bit about what you were, the kinds of solutions that you were coming up with with persistent. Yeah so there are almost two classes of problems we're going after right. One we are trying to build companies as I started saying right. And that starts with us identifying a meaningfully large customer problem. Then we internally and that's an internal process of the studio we explored these ideas to some depth to understand is there a true solution that has enough motivated buyers 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 want to build some pilots and prototype that we could take to customers and show them to deal with the ambiguity that is inherent in the space. And I think that's where the persistent team comes very handy right there we can almost parachute this special ops group of people that comes in works with us very closely hands and 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 new core 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 son studio. There's a lot of partners and vendors that our 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 if you may, 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 the 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. Yeah, yeah. Yeah, absolutely. Sorry. I've done my homework and it's really cool stuff and I want to make sure the viewers. No, no, no, I think it, you know, I think 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 loved ones or our family or friends or whatnot. And then there's this pair right which could be their 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, and an example could be how can we connect a doctor on 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 population 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 class of problem other class of problem would be all around. There's a lot of administrative 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 problems we are 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 hospital. Right. And that was a very interesting problem because you could imagine a real world hospital room with different lighting different occlusion, I coverage of, you know, curtains and people around and within that being able to detect with high accuracy a human being that's supposed to be in the bed 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 computer vision 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. So that's really incredible and you can just really see how beneficial this would be to patients and healthcare organizations and of course the loved ones who are worried about how about their loved ones safety. We'll talk to us a little bit talk to us a little bit about how you approach building these solutions. I love what Ranjee was saying about the sort of 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 so important is to understand and again, as I said, like, you know, almost 20% of our revenue comes from healthcare. We work with three out of the top five healthcare pairs in in the US five out of the 10 top healthcare provider. We have immense experience as a, you know, as a service provider. Now, when, when this problem, or when Ranjee 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 or very recently we worked on patient care next which is, you know, end to end patient journey application. So, so little bit pieces from there and of course a lot of requirements from spawn. I think, you know, together with our CTO office which had worked on gen AI capabilities. I think it was, you know, it was an 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. Ranjee, 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 beat my software development or machine learning resources inside or whether I'm sourcing beat from a partner or vendor or a third party. I think it starts with the 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 quality of any deliverables beat algorithm or tuning or thereof. The second aspect was important to break down this otherwise complex project into milestones because 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 have added risk and so we were effectively managing the delivery timeline. And then finally is really the, the, the, the, in a, in a product is what you would call a product market fit in this case with the solution fit to what the requirements were right. So with every sprint every two weeks we were looking at what we're building. And is it really a step towards where we're going are we diverging off right so kind of that product effectiveness was a third piece that we were always measuring in near real time and we had to make adjustments in the journey. 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 a success is customer got excited and they asked more. They're like, Hey, hang on. We said this but can you do this and that because it's 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 for persistent team 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 that what is a quality of our resources, how effective are with our timeline and how effective is the solution we're building towards what the customer is asked of us. Well, Rahul this is that's a really interesting point run to just brought up about this customer obsession and customers who have these voracious needs and say this is great we need that 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. I mean, again, I think as run to mention a little bit that you know two weeks Prince 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 and different geographies. Absolutely I mean there's always, there's always changes when we're talking about customer requirement changes immediately you know we've done 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 assistant is recognized as a product engineering company. So yeah I mean we're used to such such changes in the requirements. And, and this was no different. I mean yeah as mentioned earlier the timeline was 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 on right on time is always key and and you know it's challenging sometimes. So yeah I mean those are 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 was approach around you what is next for swan AI what is your growth look like for for next year and the years to come. It's an exciting time for us very early starting days. We are exploring multiple ideas at this point right and the simple foundational approach has been will start with exploring ideas. Once we feel an idea has some legs of merit will spend some time in framing that idea into a product right which I'm calling an incubation. We have incubated an idea with some validation of potentially some customers, feasibility of the engineering and science or clinical aspect of it. Then we'll choose to decide whether we want to build a prototype out of it which I'm calling a protocol. Once we build 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 new code. And at that point it's racist to scale company right. So we understand that journey everybody understands the journey of how to make a startup to 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 code 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 beating the incubation of the protocol phase. But it just guarantees a better environment for the team that's working because you know, having been in startups you don't want to be in a startup five seven nine years, and then you have lost team, as well as for investors and customers right it's it's a it's I think it's a win win situation. So we are looking to focus on as I said, even before that triangle of their patient and provider problems around helping with operating income or operating expense reduction, because I think commercially successful businesses have to deal with that as a direct corollary to that we want to make sure we are improving patient engagement, customer consumer engagement, it's a common complaint across a 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 G any customer who's saying they're ecstatic about their health care experience right. So we want to make that better and in 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 in equity 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 altogether to ensure that we have a robust go to market and scalable products in the market. We are very close to announcing our first startup. We are looking at in the next few weeks 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 the nolly problems that we can help it. Well, how about you how about persistent where we have very few weeks left of 2023. What are you looking at for next year and beyond. I mean, again, the focus is is Jenny 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 to align. So yeah I mean Jenny I absolutely is from the technology perspective but more industry read ready. You know, ensuring both. I mean bfs I again is is almost 40% of our revenue and healthcare is 20%. So yeah we will 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 Ranju. Thank you both so much for coming on the Cube. This has been a really fascinating conversation. Thank you. Thank you so much. I'm Rebecca Knight stay tuned for more of the Cube. You're watching the Cube your leader in enterprise technology coverage.