The Product Manager

How to Use an Operations Mindset to Scale Product Delivery

• Hannah Clark - The Product Manager

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Finding product–market fit isn’t the finish line—it’s base camp. In this episode, Hannah Clark sits down with Amit Shah, COO of Virta Health, to unpack what really happens after you’ve “made it” and the mountain suddenly gets steeper. Virta is scaling a fully virtual metabolic health clinic in a fast-moving healthcare landscape, and Amit brings a rare perspective shaped by nearly a decade inside the company—and a career that started in operations, not product.

Amit shares how Virta navigated rapid growth while building a scalable product organization, why simplicity becomes harder (not easier) as you scale, and how they make deliberate choices about where humans add the most value versus where AI should step in. If you’re wrestling with org design, prioritization, tech debt, or the ethics of AI in real-world products, this conversation offers a grounded look at what it takes to scale without losing your north star.

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Hannah Clark:

Finding product market fit can be like climbing the tallest mountain you've ever seen, only to reach the summit and find a taller mountain behind it. The challenge of entering that rapid growth stage and scaling product delivery is multifaceted; it tests your organization's processes and infrastructure, forces you to be ruthless with how you prioritize and deploy your resources, and demands enormous bravery to make bets that are never without risk. But despite all those dependencies, there are many paths to scale a mountain. So today we'll be taking a look at just one. My guest today is Amit Shah, Chief Operating Officer at Virta Health, and what makes his perspective especially valuable is that he didn't come up through traditional product management. He started in operations, leading care delivery teams before transitioning into product five years ago. That background gives him a unique lens on systems thinking and cross-functional collaboration, which has been critical as Virta has scaled rapidly while tackling one of the trickiest challenges in healthcare tech—figuring out when to use human clinicians and when to let AI tools do the work. You're about to hear how his team navigated the journey to product market fit, what decisions didn't scale well and how they're thinking about AI, tech debt in the next phase of growth. Let's jump in. Amit, thank you so much for making some time in your busy calendar in January for us.

Amit Shah:

Thanks for having me. I'm so excited to be here.

Hannah Clark:

Well, can you tell us a little bit about your background and how you transitioned from an operations role into product leadership at Virta Health?

Amit Shah:

Sure. I'll start way back. I'll start somewhat chronologically. I studied industrial engineering and so that leads pretty directly to operations. I spent some time as a consultant. I started my own company that made solar products in rural India. And then I got into very interested in healthcare primarily because I loved working on things that improved the quality of life for people. Then I worked in value-based healthcare, starting with direct primary care, and then came to Virta about nine and a half years ago. So I've been at Virta for about nine and a half years, and over those years, again, I started, as you rightly said, in operations. I was initially leading care delivery teams. As we have grown at Virta, our core delivery model is delivering through our products, delivering via technology. And so initially I was a partner to our product development and delivery teams, and over the years, I kinda got more and more involved there. And then about five years ago now. I was asked to lead the product development team because a lot of what we were doing was stitching together our human clinician care delivery with how our product delivered. And so yeah, that's how I kind of got into product. And just a quick moment on Virta so people know Virta is on a mission to reverse metabolic disease in a billion people, which includes diabetes, obesity, pre-diabetes, and we deliver all of our care completely, virtually. We're a full metabolic disease clinic. What we do is incredibly unique because we're personalizing biology and nutrition in order to heal people's metabolism.

Hannah Clark:

Well, that's very fantastic and always a very noble mission. I know that's something that affects a lot of families and their lives. So, very excited to have you on to talk a little bit more about the nuts and bolts behind the mission, how that's kind of going for you now. But today we're gonna be focusing specifically on the transition phase from a rapid growth to scaled product delivery, which is kind of right where you're finding yourself now at Virta. So what, in your opinion, Amit, does that shift actually look like on the ground? And what, in your experience, what makes this moment particularly challenging?

Amit Shah:

Yes. And just to give people some context over that shift, we were both rapid growth and scaled product over the last couple years. Last year, for example our growth rate clocked in at over 70% year over year and we're hundreds of millions of dollars in revenue. And so, and again, won't share specifics there for a variety of reasons, but you can imagine that like, that is growing really fast and a, a pretty skilled product. And so that shift has been, there's so much happening as part of that shift on the ground, it could feel super intense. I've talked to a lot of teammates and first of all, just as you scale that often necessitates just more people, you know, more squads, more product development squads, working on things, different people coming in to do things, and so there is a little bit of figuring out. What the pieces of ownership are for different people, creating the right systems and processes in order for you to be able to scale what worked at a, at, you know, at a smaller scale. So, yeah, I mean, I guess the main thing to share is that it could feel chaotic on the ground, and I think as a leader you have to spend a fair amount of time with what is a North star, what are we trying to achieve? And then what are the systems, processes, and organizational structures we are creating in order to achieve that.

Hannah Clark:

I wanted to get a little bit to what you mentioned before about your operations background, which I think is kind of an ace in the hole when it comes to transitioning to product leadership. So how has that kind of operations focused or systems thinking mindset shaped the way that you approach product development and especially during the scaling phase?

Amit Shah:

It's a good question, and I'm actually grateful that you think it's a positive, right? I think oftentimes when you look at product leaders. There are people that have grown up in product and I think there's a lot of value to that and there's a lot of knowledge that you get there. I was, as you said, lucky enough to grow up in operations and I think there's certain things around operations that are really helpful. I think execution excellence is something that as an industrial engineering student, I even studied in school. And so, thinking a lot about execution excellence, it breaks down into being a systems thinker. You know, thinking about systems being an analytical decision maker. Prioritizing effectively at simplifying and then being a good people leader. I mean, those are the core things that I think great operators do, and it turns out that great product leaders do many of those same core things. Now, there are other skills, the approach to doing those things could be a little bit different. Those are the core skills that I think have been really able to translate between operations and product development. And so when I was making the transition and I spent the time. When I first was asked to lead product development reading books, I read probably. 15 books. I listened to many podcasts, including this one, and then spoke to other product leaders, and I repeatedly was finding that, hey, if you could think from a system standpoint, help the team make analytical decisions, help the team prioritize based on those analytical systems and the systems you've created, simplify to what is most important, and then just lead people as you usually would. That's kind of what translates best.

Hannah Clark:

When you list those five kind of core skills, to me, all of them seem like very specifically and equally important in order to scale properly and are all things I think all organizations really struggle with and depending on the department. So I think it's a no brainer. It's a great set to have.

Amit Shah:

Just to go back to your last question on when you go from rapid growth to scaled product is, I think simplicity gets very hard too. So this is the fourth one that I mentioned, but just to say it out loud, very early in a business product market fit. It's very simple. You have to find product market, you have to know, and you know, as you're scaling really rapidly, it's actually kind of simple. You gotta keep the train on the track. When you're kind of making that transition to scaled product complexity creeps in a very interesting way. So I'll just add that as something that today, you know, as I look back, I'm like, yeah, that, that resonates.

Hannah Clark:

Okay, well you're not gonna get away without so easy. We're gonna have to dive a little bit deeper into that. You've peaked my curiosity, so tell me a little bit about what that means to you. I mean, I can kind of surmise, but I'm sure that the way that I would think about simplicity with my frame of reference would be very different from yours as like, you know, an operations person stepping into this scenario of having to, you know, what is simplicity in your role?

Amit Shah:

Simplicity is being able to boil down all the different opportunities to priority set. That the team can actually work on and work on clearly and work on without getting distracted. And so for us, we're in a space metabolic health that is moving incredibly rapidly with the admin of GLP ones and all. We're very distinctive in what we do. We have a nutritional lifestyle intervention that is scaled. Not many other companies have that. Scaled again, completely digitally. And so again, when you're at scaled product, there's the work to just keep the product delivering at the level and with the excellence that we have delivered to date, you know, all the way back to our clinical trial in 2015, like we delivered, really world changing excellent results for our patients and our goal is to do that every single day, even though we're. Literally hundreds of times larger than we were there. And so there's a lot of work in that, right? Like that doesn't just happen. In addition though, the market's changing rapidly. There's the advent of a new drug class. There are other companies and you know we are a B two B2C business 'cause that's how health tech often works. And so, we have different needs from different clients. So figuring out how to then prioritize those and then simplify those down to, Hey, what are the things that feed into that same core? And what are the things that we might wanna carve out and invest in differentially, invest in separately or not do? And so that's the type of simplification that I think we're, we deal with today.

Hannah Clark:

Okay. Okay. I wanted to clarify 'cause simplicity, I think you can look at it from the user perspective or internal operations or just to kind of set the frame, what's the scope of what we're talking about? Okay, so I wanna move on a little bit 'cause I'm curious about the really interesting aspect of Virta, which is how do you use clinicians for care delivery versus when to go fully digital? And you kind of got this marriage of, it seems like both of these things happening at once with this, your product. So can you walk us through how do you design that balance and what makes it really challenging to get that right?

Amit Shah:

Yes. So much of the magic of Virta and our product and our delivery. Is figuring out when to use technology. And I use technology in its broadest sense, AI, automation, et cetera, ML models that drive that automation, et cetera. And when to use clinicians and people, you know, just to state that some of our results are, you know, we have an average of 10% weight loss at one year. We significantly reduce the risk of heart attack and death. And the reason I share those out results is because these are real in the flesh results. It's not like, oh, like, you know, people are clicking more on their phone. No. This is, people are making lifestyle change that is improving their overall metabolic health. That's improving their life and their lifespan. And so that magic that figuring out is incredibly important to us. One of the systems that I rely on is that North Star. We are here to deliver that outcome. That outcome is very clear to us. It's the metabolic health improvement. And by the way, you can measure that. You can measure that we are asking our patients to provide us biomarkers on a daily basis. Weight, you know, ketones, glucose, blood pressure, et cetera. And so we can see that improvement regularly. And because we have such a clear North star, which is, Hey, we need to see these numbers improving, it makes it a little bit easier to figure out how to best improve them. There's certain things that even in today's age of AI, which by the way, we use machine learning and AI models extensively at Virta, but there's certain things that we still believe clinicians and humans do best, and those things are empathy, creativity, accountability, and complex clinical decision making. Those are the four things that we say, Hey, we are gonna prioritize humans doing those things. Most other things. Technology and AI will do better. And so our frame is generally if a clinician is doing something and it doesn't fall into any of those four buckets, it's not complex clinical decision making IE like prescribing a deprescribing medications and managing comorbidities and all. Or if it's not emotional accountability, which is like, Hey, this is something that really is helping motivate a patient because it's driven by connection, then we should try to have technology to it. By the way, those same things that clinicians are good at are also things that drive fulfillment for clinicians. So it turns out you have very fulfilled clinicians when they could spend their day being empathetic and connecting with other humans and helping 'em solve a complex clinical problem. And so anyway, that's how we think about it. That's the frame that we take where. If it can be done by technology, both in terms of driving a better outcome, and also there's some legal guidelines around that, if it can be done by technology, we should start there. But there's certain things that we think humans do better and it allows us to tee up our clinicians to be as effective as possible and delivering that outcome.

Hannah Clark:

Yeah. Okay. Yeah. Talk about fulfilling. I'm struck by how rewarding it must be to really measure the success of your platform based on the real results of the people using it, as well as the satisfaction of the clinicians who are also interacting with their patients through it, but also the tremendous amount of responsibility everyone must feel, making sure that we're delivering on that promise, that North Star day after day.

Amit Shah:

Yeah. I mean, again I, one of the, I've been here for nine and a half years. We are absolutely mission-driven and that is the thing that gets me up every morning is doing better at work literally means saving more lives and improving more lives for us. And that's true for every single person that works at Virta. And it is literally the fuel that has driven the both rapid growth and scaled product phases. You know, that's been the fuel, so.

Hannah Clark:

Yeah, absolutely. Wow. Let's talk about like a, just such a privilege and such a responsibility. That's amazing. Let's get a little bit back, more into the nuts and bolts here. As Virta has scaled, so when we think about some of the earlier decision making, either around our architecture or processes or team structure, what's worked well initially, but you've kind of had to lead by the wayside as things have picked up and you had to scale.

Amit Shah:

You know, there are two things that come to mind on this. One thing that has come to mind has been, I'll use one org structure example. And so one organizational structure that worked in the early days was to have. Product engineering, analytics, design, they were kind of their individual expertise areas, and that's how often things are. You know, there's a eng leader, there's a product leader, there's an analytics leader, there's a design leader. And so our original org structure was to have those and to have each of them have overlapping, but their own set of priorities. That actually worked when we were, when it was a smaller product development team, you know, it was quite effective. There was engineering excellence that needed to be created. There's product excellence that needed to be created. As we scaled and as we, as the teams grew, that's something we had to lead by the wayside. We eventually, we organized into having, now we still have those guilds. You know, we still have an engineering guild and there's engineering excellence and all, but most of our engineers are deployed to what we call a squad. And the squad is a cross-functional group of people that are attacking a business problem. And so that ended up being a lot more effective as we were going through rapid growth and even in this scaled product phase because that enables teams to be have high context on the problem. To have that North Star as to what they're solving for, they're solving for kind of the same thing or the same set of things. To problem solve more deeply in the more siloed approach, you know, then you'd swap out engineers that were working on something that would create its own set of issues, et cetera, et cetera. So I think that change in org structure from a little bit of a, when you're smaller and before you're rapidly growing, you can have a. More. It's everybody can kind of swarm on the same thing to when you are rapidly scaling and getting to a scaled product, you do have to carve out the team and make sure that each individual squad has the right cross-functional capabilities in order to execute on what they need. So, so that's one.

Hannah Clark:

I just wanted to jump in briefly. I hope I don't derail you, is that you touched on something that's, I've been thinking about a lot in terms of scaling as well, which is just the, I think it's underestimated the weight and I hesitate to say issues, but knowledge transfer. It can be such a huge bottleneck, not just between teams, between members within a team, but as organizations scale and you acquire new people, you lose older people. There's this often a dysfunctional engine for how you do really optimize the transfer of knowledge as an organization gets larger. So that's just something that's been on my mind and it's interesting to kind of hear you comment on that and like kind of focus on the squad approach as being sort of what you found to be the most efficient way to kind of manage some of that. I'll let you continue, but maybe we can come back to this 'cause this is an interesting kind of thread.

Amit Shah:

Yeah, I'll comment on that and say it is definitely better in the squad approach, but it is not a solved problem and it is still something that we have found and again, i'll even say this, and it's both a positive and it's a positive in terms of humans and a concern in terms of organizations, which is certain individuals are incredible, which means they hold a tremendous amount of context and execute incredibly well. And and if you end up moving them or they end up leaving or whatever those things happen, then you do lose some of that context. And like, I don't think there's a. We certainly haven't found the documentation solution to that today, but the S squad model does make it easier because again, there's more consistency in what is being worked on and what that outcome needs to be, et cetera, et cetera, so.

Hannah Clark:

Yeah, absolutely. I feel like we could do a whole episode on just that, but carry on.

Amit Shah:

The question that you had asked was around things that kind of didn't scale, or early decisions that didn't scale. I think a second one, and I've yet to meet a leader at a company, at a tech company that has picked it right from the start, though I'm sure they exist, I'm just saying I haven't met them yet, which is how we structured our data and eventual analytics. Very early on we kind of wanted to be democratized data. We put everything in tables and gave a lot of people access to those tables, and that actually worked great early on. Again, anybody can pull data and it, as we grew, the tables got messy. People were pulling different things. They were like. We would joke, there were two answers to the same question, depending on who pulled it and how they pulled it, you know? And so we've spent a lot of time and continue to spend a lot of time cleaning that up, making sure that, you know, we revisiting how data gets into those tables, making sure that it's kind of clean from the source. That's been an investment. Especially, I would say that's more in this when we've gone from into the skilled product phase, in rapid growth phase, that's still happening.'cause a lot of new stuff's coming in and by the way I want to be careful. I don't want to, I don't want to, I hope we're not outta the rapid growth phase, but we are both a skilled product. As I said earlier, we're both a skilled product that is rapidly growing and as we are a more skilled product, we've tried to be intentional about putting those controls in early on. What gets into table data tables and how it gets there and whatnot.

Hannah Clark:

Right. Well, now that we're into the data segment of this episode, let's talk a little bit about generative AI. As you mentioned before, there are a few areas in which the knowledge and the participation of clinicians really is the best approach, but then there are some things that if they don't fit in those four categories, it really is best to kind of outsource it to AI that can just do it more efficiently and not, you know, use that people's precious time on those tasks. But you know, I'm sure that there's more decision making that goes into how you integrate generative AI into the product. So how are you thinking about integrating AI and the different kinds of AI into the product in order to kind of compliment that human skill set?

Amit Shah:

As I said earlier, a lot of the magic is how we do that. And so I'll just say we're constantly iterating on the how here, but a frame that we have used is, first of all, we've been a data and analytically rigorous company from day one, and so before we even talk about generative AI, we have had highly predictive and effective machine learning models for seven or eight years at this point. And so one key thing that we often think about is proactive intervention. We use machine learning models trained on our proprietary data set of over 200,000 patients over 10 years. So like literally hundreds of millions of data points. And the types of data points that I'm talking about are, as I said earlier, are like blood glucose levels and ketone levels and weight. So like objective data points to predict what the next best intervention for a patient might be. That's when we predict what the next best intervention is. That's often where we're doing some experimentation on should this intervention be delivered by human? Should this intervention be delivered automatically? Should it be delivered by a generative AI tool that we've developed? So I'll come back to generative AI in a second. But the first thing I'll say is that we, this proactive intervention system and the set of it's on one, it's the set of machine learning models that we have built around that is a key for us to even know when to intervene. The second thing I'll say is, so that's proactive. Then there's reactive. There's in the moment help where a patient could be coming to us for something. And again, as good as your models are, you're not gonna always gonna be able to predict. And that is, I think for a lot of the, in the moment help, that's where we found pure generative AI to be very helpful. Again, trained on our systems, trained on our data, but we have deployed generative AI for that in the moment. Help for that reactive help. Where the patient can get an answer in that moment and you know, we can then flag a clinician for follow up if we deem that's necessary. So that's kind of how we've thought a little bit about, about AI. Now let me talk a little bit more about generative eye. There's also, we also have found that. We wanna build great trust with our patients. And so we are very clear when something is AI driven versus when something is clinician driven. I mean, the, one of the deepest things that we have with our patients is that we are, we're working with their health. You want them to really trust what, what's coming through. And so we try to make that very clear. We don't just try to, we do make that very clear and we give them the opportunity to escalate to a clinician or to work with a clinician whenever possible. And then the last thing I'll say on this topic is the flip, which is. There are certain things I'll go back to like when we know we need to intervene, what's gonna make the most sense? There are certain things that just with all the testing, we know it works better when a human intervenes, and so we again prioritize that for our clinicians. They want to help other people be healthier and so what can give them that opportunity? That's the best for them, that's the best for the patient.

Hannah Clark:

As you're telling me how you guys are thinking about this, something that really strikes me. I've been doing this show now for a number of years. When we started doing the product manager podcast, it was kind of right at the cusp, like right before AI really exploded to what it is today. And I'm noticing when I talk to you, who's somebody who's been working in a capacity where AI, you know, Virta was kind of using AI, like I'm using air quotes before, it was cool. And there's a bit of a difference in terms of the thought process and. Approach even to what AI looks like or the effective or ethical use of AI within a product. For, I think that subset of folks who have been more, who've been using it for a long time and not just because that's the thing to do now and everybody's kind of in this rush to figure out how can we kind of, you know, retrofit it into our product or build a new product that's kind of based on these new technological capabilities. I wanna prod a little bit on that because I'm curious if you've ever thought about how does your prior experience using AI in some capacity or machine learning, do you think it differentiates you in terms of how you think about the technology? And your approach as you kind of move forward and like continue to keep it sustainable?

Amit Shah:

That's a great question and I, and it is interesting for you to say that I love the thinking that we're OG users of of this technology. And so I'm gonna use that. I'm gonna use that moving forward. Yeah. I think it has been a frame for us. I think we know more than believe. We know that one of the most proprietary things we have is our dataset. Again, hundreds of thousands of patients over the course of a decade. Both objective, subjective and sentiment data that we have. And so unleashing machine learning models and training generative AI on our data is a superpower that nobody else has. To your point, you could come out tomorrow with a gen AI version of what we do, but because our North Star, to go back to it, is outcomes is metabolic disease reversal. We know that pathway for an n equals one individual based on all the data points that we have. I view gen AI machine learning models. I view all of those as tools, deploying clinicians. Those are all tools in the toolkit to drive to the North Star outcome. And the more technological tools we get, the more we're gonna use, you know, what's next after Gen AI? I actually don't know, but I believe that we will be able to use it and we will leverage it for that same outcome. The thing that again, will be proprietary is our approach and our data set that delivers that approach.

Hannah Clark:

Yeah, absolutely. I just had to harp on it a little bit because I think it's really nice to hear, like I'm really hearing in your voice like. There's a significant amount of respect for the data that you have. What kind of responsibility that is entailed with handling that correctly and really focusing on, you know, does this drive the outcome? Yeah, I think that it's a complaint throughout the industry right now, that there's a lot of the use of AI for AI's sake, and sometimes the deployment of that can feel a little bit ham fisted or, and sometimes irresponsible. So it's very heartening to kind of hear like a very human, grounded. Way of thinking about AI and how to use it in products?

Amit Shah:

Yeah. I would say our patients come to us to improve their health and our B2B clients hire us to improve the health of their population. And so our North stars improve health and we will use all the tools at our disposal to do that. And generative AI, don't get me wrong, is an incredible tool that's growing at a breakneck pace, and I do think it helps us do that. One of my favorite features in our app is something where you could take a picture of. A plate and it'll break down the macronutrients for you. And again, this is to go back on all the data that we've had for that individual. It'll also say kind of orange, yellow, green as to whether it fits into their lifestyle nutritional plan. We couldn't do that whole flow without generative AI. It's incredibly powerful. That last piece is what makes it, it drives to the outcome we're looking for, which is let's help this person make a decision that's best for them in the moment.

Hannah Clark:

You know, honestly, this approach is a little bit reminiscent to me. We had a conversation with the user research lead at Photo Room a number of months ago. She had a similar kind of observation in which like user researchers, I think have been kind of reluctant to adopt AI. And we're starting to kind of see a little bit more of adoption and trust in the technology now that there's a little bit more of a clear. Pathway on how to use those responsibly. And it seems to me the similarities I'm seeing is that Virta Health and with some of these other organizations, it seems to be a focus on keeping the AI focused on the kinds of like large scale data pattern recognition and like those kinds of things that wouldn't be humanly possible for someone like a clinician to digest and process and output. Those kinds of things perfectly make sense that you would output them to generate AI and then the aspect of judgment. You know, I feel like at this point we should all know that chat GBT doesn't have the best judgment. Yeah. I think it's nice to kind of hear and reiterate and always be kind of thinking about how to transpose very clear cut examples of what is AI really good at? What should we really lean into that technology for and what is it maybe not best for?

Amit Shah:

I was joking with my wife the other day actually. He was way more brilliant than I am for many reasons. But we were talking a little bit about generative AI, and I was like, yeah, at the end of the day, it's a, it's, you should think about it as like generative AI is basically trained on everything that's public, on the internet. It's like the best knowledge base, you know, out there. And there's a lot on the internet, so like, it's a lot. But to your point, like, I wouldn't say that everything on the internet has the best judgment. And so, you know, discerning, critical thinking, discerning how to leverage and where to leverage and how to present and all. That's all I think where a lot of the magic is.

Hannah Clark:

Yeah, well absolutely. And like I think that part of judgment is the empathy aspect. Empathy is maybe not something that we can measure with a metric, but it is something that we can really see an outcome from when it's kind of weighed into the picture. I wanna talk a little bit about some of the current challenges, especially moving into 2026. You know, we have a new year now, like you said, Virta is scaling and it's growing rapidly. What's the next big challenge that you're facing and how are you approaching the evolution coming in 2026?

Amit Shah:

Yeah, it's certainly very germane to the podcast we're having, again, we're a rapidly growing scaled product, and so what I am focused on, and maybe the reason I earlier brought up the simplicity point and pulled that thread was because it is an area I'm focused is just ensuring we have systems and processes to create that alignment all the way from our initial conversation with a customer on how we can help their population down through what an individual patient experiences. They sign up with Virta and we're doing that in the face of, again, a rapidly shifting market. You know, one of the things that we do for our clients is, well, I'll just, one of the things that we have that's special is we deliver the same weight loss as GLP one drugs. So ozempic week a v Zep, bound manjaro, but without the drug. So we are both a alternative to the drug, which by the way, about 70% of Americans who wanna lose weight wanna do so without taking a drug. So it's like a very popular thing. We have a lot of patients that come to us because they heard about us and they're like, yeah, I don't wanna get on drug. But we're also an offramp. Part of the FDA approval for the GLP one drug class is to pair it with lifestyle change. And we are a lifestyle change that work. We have a peer reviewed published paper that shows that we have maintained people. At about 11% weight loss through another year after we've deprescribed their GLP ones. Anyway. I say that to say GLP ones are a very hot topic right now. Orals are coming out, you know, there are all these different things that are happening. So we are agile in terms of how we're approaching the market because there are a lot of changes, but our core value proposition is the same, and so we are. I'm really focused on making sure we're agile, making sure that we can respond to the needs in the market, making sure we're meeting our, both our patients and our clients where they are, and also aligning us so that we can continue to deliver the incredible and distinctive outcomes that we've delivered to date that really help improve people's lives at a lower cost.

Hannah Clark:

To hear that you have similar to equivalent results to the drugs on the market is absolutely mind blowing. I mean, an enormous hats off to the entire team of Virta. That's such an important mission. It's amazing to hear you guys are delivering on it with so much success. Since you mentioned meeting people where they are, where can people find you after this show?

Amit Shah:

Yeah, I'm most active actually on LinkedIn and so please look me up on LinkedIn. And yeah, that's the best, that's honestly the best place to find me. And then follow Virta Health on Instagram 'cause well that's on my account. I often am involved there.

Hannah Clark:

Well thank you for popping up in here. We appreciate your time, Amit. It's been great.

Amit Shah:

Yeah, this has been wonderful. Thank you so much.

Hannah Clark:

Next on the Product Manager podcast. This one is for the enterprise leaders juggling multi-product portfolios. If you've ever watched the moms at Costco fighting their way through a crowd while shepherding four kids in drastically different stages of development and thought, Hey, that's what my job feels like. This episode with Rubrik’s Anneka Gupta is about to be your survival kit. Subscribe now so you don't miss it.