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Built for Good is for the community builders driving missions that matter. Host Kian Alavi shares real stories, hard-won lessons, and data that challenges assumptions, helping you move with more clarity and less guesswork. Each episode delivers actionable insights to accelerate decisions and make meaningful work less of a solo struggle.
Now… let’s build for good.
Built for Good is brought to you by Mazlo. Mazlo simplifies accounting, banking, compliance, and fundraising—so you can focus on your mission, not your back office.
Sunita Mohanty [00:00:00]:
It's this very enticing value prop of like, I can do 10 things now, twice as you know, I can do 10 times as many things as I could before. If I can get AI to write this for me and do that for me and write, you know. But I think it's very easy to forget the importance of thoughtfulness and quality and like, precision that can be traded off.
Kian Alavi [00:00:20]:
I'm Kian Alavi and this is built for good. This show is for community builders, the ones leading missions that matter. We're here to help you move with more clarity and less guesswork. Real stories, hard won lessons, data that challenges, assumptions and insights that accelerate decision making. One of my favorite sayings is if you want to go fast, go alone. If you want to go far, go together. Meaningful work does not have to mean going alone. Now let's build some good together.
Kian Alavi [00:00:55]:
Welcome to the show, everybody. Super excited to have Sunita Mohanty with us. I know Sunita. We are members at South Park Commons together and I have also served on a board with Sunita and I've always admired the way that she thinks and approaches her work. She's now building Vibrant Health and normally I would have nonprofit builders on this show. And the reason why I brought Sunita on is because she is the CEO and co founder of Vibrant. And Vibrant is working to improve healthcare and she's gonna share more about that. And they're also out on the forefront with AI.
Kian Alavi [00:01:30]:
Now, my thesis is whatever happens in healthcare around AI will also trickle over or transfer over to the nonprofit and social impact space specifically. There's a lot of ethical issues to work out and PIIA or personal information to work out here. So I thought this could be a great conversation to have, see how they're approaching their work and potentially learn from it. So, Sunita, welcome.
Sunita Mohanty [00:01:55]:
Thanks for having me on. Kian, it's excellent to be here in conversation with you. I really appreciate the opportunity.
Kian Alavi [00:02:01]:
Thank you. And Sunita, you're going to be speaking a little bit later today at UC Berkeley.
Sunita Mohanty [00:02:06]:
Yeah. About healthcare and AI. So it's very much a hot topic and lots to talk about. Yeah, that's great.
Kian Alavi [00:02:13]:
So tell us a little bit about what you're building at Vibrant.
Sunita Mohanty [00:02:17]:
So what we're building at Vibrant is an operating system for modern medical practices. We actually, we're focused on supporting clinicians and independent practices specifically who are doing functional, integrative or longevity medicine. Right now. We have built the full suite of tooling, the vertical stack of what they need to run successful practices. So it's a mix of more so the basics that you get in EHRs, which is, you know, collecting your patient's information, being able to intake, schedule, all those kinds of things. But now it's not just about episodic care, which is how most EHRs are built for. It's also not just about insurance based care because a lot of these practices are doing cash pay models and you need to think more about how you're engaging patients on a longer term, retentive basis. So we also have a patient app that's really about helping share data with your patients and share things like protocols and keeping, you know, checking in and being able to engage patients more.
Sunita Mohanty [00:03:20]:
All of this is really time intensive and onerous. And so the clinicians who are running these practices today, like, God bless them, I see them and what they're doing and they're oftentimes they're like bleeding hearts who are just like, well, this is the best way to actually, you know, get these people healthy to care for patients. But it is just bending over backwards to doing to do a lot of, you know, pulling data from all these different sources and researching and creating lots of very detailed, robust protocols and plans. And it's all manual. And that's what is so difficult. And so a lot of these practitioners are struggling because of the lack of good interconnected technology to make this kind of mode of care and model of care work. And so yeah, we just, you know, my own journey as a patient, actually when I was product leader at meta and in 2022, right about, right before we met actually, I had an autoimmune disease diagnosis and was able to find my way back to health with the help of a functional medicine provider. And it just really opened my eyes to the potential of more data driven personalized medicine because that was what allowed me to get back to remission rather than, you know, other routes of allopathic care were suggesting I needed to have surgery, just kind of slotting me into the same pattern that you do for most people.
Sunita Mohanty [00:04:41]:
And anyways, I went through this whole health journey was thinking, well, if there's this happened to me, there must be so many people like me who could benefit from more personalized root cause care. But the ability to do this for the doctors that I worked with and that I was seeing was just really difficult. And they're working with all these challenging antiquated systems and as more and more powerful AI tools are coming up, there's just more that can be done and should be more kind of integrated and, and so that's where this concept for vibrant practice came from. And we are, you know, we think of ourselves as a vertical AI SaaS company, which I think we'll see more and more. And we see, you know, so many of these kinds of companies in all these different verticals of. I was just talking to another builder, entrepreneur, friend in real estate who's building a similar kind of suite, you know, for real estate agents. And. And I think now, you know, and I'm really excited for know the nonprofit space will also be able to benefit from in terms of every single job and role and category of.
Sunita Mohanty [00:05:44]:
Of work as. As we know, it is shifting right now with the use of AI, and it is making all of these workflows a lot easier and tighter. And so there's just really interesting opportunities for builders like us to build these vertical AI companies.
Kian Alavi [00:05:58]:
Yeah. Wow. And there's so much there. So for us at Maslow, we're building a vertical company.
Sunita Mohanty [00:06:04]:
Yeah.
Kian Alavi [00:06:04]:
And we don't normally say this to folks that we work with, but the verticalization is taking all these disconnected workflows.
Sunita Mohanty [00:06:10]:
Yeah.
Kian Alavi [00:06:11]:
And bringing them together. And by bringing them together, you can rethink them. Not just integrate them, but rethink them and really lift often undifferentiated work. Like, everybody's got to do the intake or everybody's got to do this work over in this space and lift that. Lift that burden so that you can get to the real work.
Sunita Mohanty [00:06:28]:
Yes.
Kian Alavi [00:06:29]:
Or the work that differentiates you anyway.
Sunita Mohanty [00:06:32]:
Yeah, yeah, yeah. 100%. And, you know, we talk about this a lot in the field of health care and AI, and, you know, there's hot conversations out there about, like, AI is going to replace doctors and all of that. And, you know, our big philosophy is, is to not think about AI as this replacement tool, because I think we're far aways from that. And I don't even. I mean, I think in a lot of different categories, health being one of the biggest ones. So much of the delivery of care is about this element of humanity and connecting and understanding and. And that aspect of, you know, all good doctors know that that's so much of what their role is.
Sunita Mohanty [00:07:08]:
It's not just kind of going through the process of containing all this knowledge and giving it back out and spitting it out to patients. There's so much more there. But I think with AI and with the benefits of technology, what we're seeing is doctors can hold eye contact with patients again because they don't need to be staring at their computer typing a bunch of notes and Having worn out keyboards, they can, instead of spending five minutes after each session, which then compounds to hours, week, in following up on a lab order or following up on all these things, they can, you know, some of that exactly should be lifted. That workflow should be lifted so that it can free up time for doctors to focus on the act of really understanding their patients, delivering care. And. And so that's what I think is really exciting. Yeah, yeah.
Kian Alavi [00:07:57]:
Well, and you touched on it a little bit. And I think this is kind of really flying around all different sectors, which is, AI will replace me and all of these things. But historically, when you look back on technology, it usually creates more opportunity and more work. I do believe there will be jobs and job functions and things that will be disrupted, but I also think there'll be far more jobs created. Generally speaking, there's going to be more to do. And obviously we're growing as a populace and a humanity. So when you think about AI in the workflows, this verticalization, same thing over in nonprofits. It's more of like a back office problem.
Sunita Mohanty [00:08:32]:
Right.
Kian Alavi [00:08:32]:
It's like all these disconnected tools. How do you decide what AI does, what AI doesn't do, or what AI should be good at or shouldn't be good at? And then when you start to get into patient information, what is like the decision tree there around what AI can touch, how does it touch, et cetera?
Sunita Mohanty [00:08:49]:
Yeah, really interesting question. So the first premise we've used is to build our system with the concept of a human in the loop. And so anything that is going out to the patient right now, we actually don't have patient facing AI. It is AI that the doctor or the clinician interacts with and has to review kind of a first draft or, you know, suggestions or approve the actions, and then that information gets sent on to a patient. And so it really is providing this, like, thought partner and support to the clinician. I do think we will add a patient facing AI as we build trust in our system and we build trust in how people. Yeah. And just understanding of how people are using AI.
Sunita Mohanty [00:09:36]:
So I think that will continue to evolve. Things are moving so fast. So within that, you know, there's still so much like, where do you apply AI in this workflow? And we first just did a bit of a time study of looking at, like, where can we save time and where are there some obvious things where it just feels like there's too much time spent. Perfect, you know, task versus now, what you could, you could use AI to do. So documentation is a really big one. I think that's like something that nobody likes. And you know, with all of the scribe tools we already are seeing a lot of where that documentation AI is helping in. AI would say some of the fastest growing companies right now have been these AI scribes for health care because it's saving a lot of time.
Sunita Mohanty [00:10:20]:
And so we are, we have our own native one kind of built into the app so that you're not porting context back and forth. And so we've picked out a few different spots and now we see, you know, the, the real vision of what we're building is to have a team of agents that are working. You know, everything is becoming agentic. As you think of. Why do you build a vertical stack? It's because you can start to do more with those automations. And what does automations mean? It's me mainly means, you know, more and more agentic workflows where AI can do more than just like, you know, one step, it can do multiple steps and you can tell it like to take a certain role and it can go complete that set of tasks more autonomously, which is super valuable. And so that's the direction that we're building towards. But for a few reasons, we didn't start there.
Sunita Mohanty [00:11:11]:
One is like the technology is not, you know, it's still emerging. There's still so much everyone this year is calling 2025 the year of AI agents. And there's so much as builders that we can leverage from the tools that are coming out as they come out. And so it's partially like we don't need to be building the infrastructure for AI agents, we need to be leveraging the infrastructure that comes out and supports what we do. But then also I think the other piece to it has been we just built the company, we just built our platform starting at the end of last year. And so the first part of building the vertical stack is just getting every kind of connecting point of the workflow in first and then you can do agentic work on top of that. So we need to have the ability to send prescriptions, we need to have the ability to send lab orders to, to have each of those different parts of the workflow kind of addressed through some integration. Like we're not building all of this ourselves, so we have like a foundation we're building and then as soon as that's done, we will be able to start building more and more agentic stuff on top of that.
Sunita Mohanty [00:12:12]:
I think your second question was around data security and PH in health. It's Phi or, you know, part of what we've done is decide to be the system of record where we store all of that securely because that gives us the trust. And then, and we actually, we work with a tool called medplumb, which is an open source EHR that allows us to build a secure layer. It's like a structured layer of data that is secure that we build on top of. So it's both allowed us to move quickly, to have a high degree of trust because we're not rebuilding a bunch of the systems like first party. And you know, coming with all the challenges around that, you know, at the end of the day, like the principle of security around individual data is so critical and you know, that's first and foremost like what our product has to do. It has to make sure that, you know, individual patient data is secure in and of itself. Individual practice data is secure.
Sunita Mohanty [00:13:14]:
And so I think the ways we've approached that are to, you know, weave that in as a core principle from the very start of what we're building.
Kian Alavi [00:13:23]:
Data security as a core principle. There's so much here, I mean, I almost want to take this in a vertical SaaS because, you know, those who are building vertical SaaS study vertical SaaS and try to understand what this means. Vertical SaaS software as a service for those who don't know what SaaS means. But getting the workflows right means you're generating the data, means you need to store the data right and in a secure way, which is what you're working on. How do you choose what data is stored and what data isn't stored? Is there design thinking around that? Is there security thinking around like we shouldn't be taking this data or we're taking this data and this is extremely sensitive data. How are you approaching that?
Sunita Mohanty [00:14:03]:
It's an interesting question. I mean, I think the way that we are able to build is, and another principle is like we need to collect a lot of different data points around very personal and specific things that are important signal in our case. And so all the data really does matter. And in this case, given the health context, it's like you need, you know, as much data as you can because if you don't have certain important data points, you might not draw the right conclusions. So the premise is less, you know, not collecting certain data, but rather what do you do with that and how do you make sure it's secure and who acts so access layer controls, both within even a clinic, within our teams being very clear and deliberate about those data retention policies. With, with how we leverage, you know, data retention of the AI models we use currently. You know, that's, that's our approach.
Kian Alavi [00:15:00]:
It makes sense that when you're talking about somebody's health needs, that you need to collect as much signal as possible.
Sunita Mohanty [00:15:06]:
Yeah.
Kian Alavi [00:15:07]:
Over in the social impact space, there's generally, there's certain things that you don't need to collect, so you won't collect. And it's design, you know, that makes sense. There's more of a design, building trust in through the design. But, but let's talk about data flowing over to a large language model.
Sunita Mohanty [00:15:23]:
Yeah.
Kian Alavi [00:15:24]:
So you're talking about these business agreements and whatnot. And when we unpack that, that basically means that, hey, large language model, whether it be anthropic or OpenAI, we have an engagement going on. We're passing you information. I want to talk about what you're like, not what you're passing specifically, but like, like at what level of personal information is getting passed and then what is the agreement around that data that's being used, looked at, is it being stored, et cetera?
Sunita Mohanty [00:15:54]:
Yeah. So the level of personal data is, you know, accessing a patient's record in the context of our system. So it should not be used elsewhere. It should not be also used. Like the information within one patient's record will not connect to another patient's record. Like, that's also kind of an important thing for data leakage and understanding and also like how the AI understands across all of these different data stores that we've created. So you can think of them as individual silos that the AI kind of accesses, when we call it to, and then comes back out. And so we.
Sunita Mohanty [00:16:34]:
Yeah, you know, it does. We have access to all of the available data there, but the retention policies and the usage of the data very clear, both in like, the terms of our product and the terms that we have with any vendors. So I think this is where it's so important to understand the terms and the agreements, you know, on both sides of user data of any vendors that you're working with to make sure that you have the right policies that make sense for your sector and your segment. Because I can imagine in nonprofits, yeah, there's certain things that, like protected information that should not be aware, made aware to anybody. And so ways to silo that data and make sure that, you know, the AI will never understand or pull in that data will be very important.
Kian Alavi [00:17:22]:
Yeah, yeah, Interesting. And tell me about what you think around. There's a lot of concern, like models could be biased. They're trained on, you know, the data doesn't have enough cultural context, et cetera, which I believe translates into the healthcare.
Sunita Mohanty [00:17:36]:
Space 100% cultural context. Yeah.
Kian Alavi [00:17:38]:
So what do you think? Where does your mind go around that.
Sunita Mohanty [00:17:41]:
The bias is true? And I think there are other cases within larger hospital settings and more diverse patient populations where those issues do persist. I mean, even if you look at a use case around surfacing insights about someone's lab results and just looking at. I mean, the problem is the models are trained on the data that we have, and the data that we have is biased. Right. So the data that we have doesn't necessarily represent differences across ethnic groups. You know, for example, oftentimes I'll, you know, as a South Asian, I have high levels of, or I'm starting to, as I'm over 40 now, like have high levels of cholesterol pop up, which, you know, seem concerning to some people. But then other people, other doctors I work with will say, well, you know, I'm not so concerned because I do see that with South Asians. But like in AI is not actually aware of that context, obviously aware of like the difference in cultural norms there.
Sunita Mohanty [00:18:41]:
So I think that's where we need better data sets. And it really is a problem of what data sets are available. But also, like, you know, ways to mitigate this would be when you are leveraging these large language models, like inform it of. These are ways that we want to correct for the bias or this is the type of population we might be living. So you can actually prompt and try to understand, like where its lines are and then. And test it too, to see, like, what it's coming out with. Like, how often will it give you insights that reflect a bias or don't reflect a bias. But I mean, the thing is, it's just perpetuating like, any biases that we have as humans in society.
Sunita Mohanty [00:19:24]:
Right. And so the only way we will know is if we're both conscious of those biases and then can ask and test and kind of measure around those biases. So it's very hard to do that sometimes.
Kian Alavi [00:19:35]:
Beautiful explanation, though, about actually testing for the edges of the model where its biases live, and making sure that you're very intentional about how that gets put into practice. Yeah, let's talk about the human in the loop part. I think a lot of folks feel like AI is going to replace everything. It's just going to be this super intelligence. But I heard this one metaphor, which I thought was pretty good, was like, you're going to get the plane off the ground and set where it's going. And then most of that trick is going to be done by AI and then you're going to land the plane. Plane and make sure that it went where it's supposed to go and things of that. So there will always be this need when it comes to work with humans, that humans are in the loop.
Kian Alavi [00:20:15]:
So talk about how you're experiencing bringing a product into a space, the humans in that space, and what it means to explain the human in the loop idea and how AI can augment and support versus take away.
Sunita Mohanty [00:20:30]:
Yeah, I love that analogy. Specifically, I was talking to somebody else yesterday, one of the clinicians that, that I'm talking to and her son is a pilot. And so she was sharing the same analogy, but her addition to it was, you know, it's crazy. Cause he can sit there and he can fly. You know, he goes up in the air and then most of the time he can like read a book and he can be doing all these other things and doing crossword puzzles and he's like, you know, but for the moments where WI fi goes out or something happens, you know, or they're like, then he is on point and he is there and yes, landing the plane, those kinds of things. Like you have a human who knows where the nuances are of these challenging situations. And then the other interesting thing she was saying was younger pilots these days kind of don't know how to hit some of those challenging situations and are not. They don't know what to do in these circumstances when they don't have AI to support them.
Sunita Mohanty [00:21:27]:
And I think that that's a really interesting challenge right now, which is like, we're finding this also with our dev teams. I don't know if you see this, but our best kind of developers are the ones who are a bit more senior and excellent, but also know how to guide and like find the quality issues with AI. And then the ones who are maybe more junior and don't know where to spot those things, like their taste isn't quite like honed in on is problematic because then it just perpetuates the problems of AI having kind of garbage in and spitting out garbage. And so I think in terms of, of what, you know, I see happening right now is there is the leveling up that will happen kind of across the board of being able to do basic tasks. But I do think we see experts being able to leverage AI to become even more like efficient and at being an expert, especially when they Train and hone and there's sort of this process of like grooming your assistant or something. Like you're, you're really working with the AI so that it understands you and you find its edges. It finds your edges, all of that. But I don't think it necessarily is you going on autopilot.
Sunita Mohanty [00:22:40]:
Right. Like, it's, you having to be involved with understanding, you know, what's working, what's not. And so what we're seeing with our clinicians is it really is like a, like a first draft thought partner in some ways. And, and it even surfaces things that are, you know, it helps extend their brain in a, in a way that is. Is so powerful where they might not have known it. There's actually lots of studies now on the combination of like, like AI only, AI with doctors and then doctors only and how AI is outperforming doctors. And AI is even like the AI doctor combo. Sometimes doctors are overriding what AI is suggesting and they're performing worse then because they're being dubious of trusting AI.
Sunita Mohanty [00:23:23]:
And. But when you have like, doctors who are trusting of AI, there is a higher bump in what, you know, what output is happening. And so, yeah, there's just a really interesting moment right now to find the edges of what capabilities of AI are and how to keep training. How to keep training it to work with you and to work with the expertise you need. Yeah. So I think that's going to be an interesting continued path that we see.
Kian Alavi [00:23:50]:
Yeah, there's so much here. And this is a conversation about AI, clearly. So a friend of mine works at a large law firm downtown in San Francisco, and they have just. He's on the approval committee for AI and they just released Harvey, like, for use, like official use across the law firm. And so I was asking him, like, what does this mean? And he says, you know, it's amazing. Yeah, it does. Like, these are experts, and now they have incredible junior attorneys, like powerful AI at their sides. And he said, but what it also means is that we're a little concerned that junior attorneys aren't gonna get experience.
Sunita Mohanty [00:24:27]:
Yeah, exactly.
Kian Alavi [00:24:27]:
So they won't get that taste that you're talking about.
Sunita Mohanty [00:24:29]:
Yeah, exactly.
Kian Alavi [00:24:30]:
And so how do we work with that process?
Sunita Mohanty [00:24:33]:
Yeah, I think that's a huge question that I keep coming up against and people keep coming up against is like, if we, you know, if developers don't have junior developer jobs anymore, how do you build expertise? If, yeah, lawyers don't have junior lawyer jobs, how do you build expertise? I don't know the answer to that question yet. I think we'll really have to understand that as a society. How does this, you know, process of learning continue to happen? I think about that a lot. We both have young kids, you know, what is that going to mean for our kids and our children of what kinds things of, of skills they need to have? How can they learn skills? How can they keep accelerating? But I think that's a really big question that's top of mind for me right now, definitely.
Kian Alavi [00:25:13]:
I mean, again, I know the folks that'll be listening are mostly tuning in from the social impact space, but it's the same problem there. Right. So as you continue to adopt it into your workflows and it picks up things, for example, one of our clients, they were able to reduce their back office by three FTEs or full time employees. Now they're not doing that. They're taking that capacity and doing more work. But like there's this real fear. I guess it's just where are people going to get time on and learn and build that expertise? It's an interesting problem. We're not going to solve it here, but it is a societal problem.
Kian Alavi [00:25:52]:
I mean, this technology is clearly here and it's like any major inflection point. It's, people see its value and are trying to work with it, to adopt it and to increase outcomes.
Sunita Mohanty [00:26:03]:
That's right. That's right. It's, it's. I mean, especially in sectors like healthcare and nonprofits where there's just this very difficult cost equation that people are running into. Right. Where it is becoming harder and harder to do your job. It's, you know, become more and more expensive. The sort of weight of that ROI is constantly something that, you know, you're struggling with.
Sunita Mohanty [00:26:23]:
AI is really powerful in providing, you know, the, the possibility of. Right. Sizing that, that equation. But the human cost is also, you know, questionable.
Kian Alavi [00:26:34]:
There's a pretty popular study that's flying around where they studied. I guess it was writing in college. You may have picked up on this, but you know, GPT first, writing versus you did the writing first and then you use GPT a little bit later to like kind of tune your ideas in your writing. And what they did was they studied synapses in the brain, right?
Sunita Mohanty [00:26:55]:
Yeah.
Kian Alavi [00:26:55]:
They found that like the synapses were dropping drastically. If you were just relying on GPT. Yeah, I think that just kind of dovetails into this overall conversation which is like humans are still important and the human experience is ever evolving and you need to be there and experiencing it and you're just not going to be able to set life on autopilot with.
Sunita Mohanty [00:27:18]:
That's right. It just is. I mean, yeah, both for the purpose of, like, it's probably not going to be as effective and also you're just going to start to atrophy around how you understand things. Even internally in our company, I feel like one of the tensions we have is like, what can we do faster, better, easier with AI? How can we use processes of AI within just what we do every day? And then also, how does that not make us lazy? How does that not compromise quality of written communication? Especially how does that not just compromise on those important pieces? And I think it's a true challenge. It's this very enticing value prop of I can do 10 things now twice as you know, I can do 10 times as many things as I could before if I can get AI to write this for me and do that for me and write, you know. But I think it's very easy to forget, you know, the, the importance of like, thoughtfulness and quality and like, like precision.
Kian Alavi [00:28:20]:
Right.
Sunita Mohanty [00:28:20]:
That can be traded off.
Kian Alavi [00:28:22]:
Yeah, definitely. Kind of bring us back over to vibrant and, and the space and you know, the thing about folks who build nonprofits, they are entrepreneurs as well, so. So I think your builder story could be helpful here in the nonprofit space. There's 2 million ish nonprofits and there's always new ones every year because there's new problems to solve and problems that pop up. As a builder and a builder that I respect, how do you think about in general, how do you decide which problems to solve? How do you go about solving those problems and learn about your pathways forward to solving problems.
Sunita Mohanty [00:28:59]:
Yeah. Interesting. Thanks for asking the question. I think I was really excited about wanting to start a company broadly in the space of health and AI after my own personal experience. And I was a AI product leader at Meta and so bringing these two worlds together, but I actually didn't know what to do and where to start. And I was sort of like, okay, what problem is calling me? You know, and so went on this like, search, which is why somewhere like.
Kian Alavi [00:29:25]:
When I met you at South Park. Exactly.
Sunita Mohanty [00:29:26]:
South Park Commons is amazing. Exactly. It's amazing for that. Such great, you know, it's so great to have community to talk through that especially and to reflect, I think on not only like, you know, it's great to kind of have a hypothesis. And I went through a few different cycles with maybe different co founders exploring different ideas. And I think the thing that started to become most Clear was like where I had the most personal authenticity and energy. I didn't even really know at first, but started to find that by this process of exploration of. Because I think often too, if you are intellectual, you can try to kind of talk yourself into, sorry, anything being a great problem to solve and like something being, you know, worthy and how you can retrofit, like this is the thing.
Sunita Mohanty [00:30:14]:
And I was finding myself really intellectualizing the process rather than personalizing it. And I remember like one advisor I was working with, she's like, you're kind of thinking about the world this way, but I really think like your idea is this way, but you're the way. What you get excited about and where your passion is and what you're talking about is around this other thing. And I feel like you should lean into that. And that was actually a really eye opening moment for me when I realized not only there's something there that I'm very excited about, but part of me was like, oh, you know, there are all these reasons why it might be a bad idea and all these reasons why I could talk myself out of it. But then as I realized like the energy I had around that problem and in talking to the customers that I run into in that space, I think that was the other piece is I just got so energized working with these clients, with these doctors who I respect a ton. I think they're so interested. I just love building for them.
Sunita Mohanty [00:31:08]:
And then it started to. There's just like a virtuous cycle. Like the pace of like building momentum just started to go faster because I think I brought this very authentic energy into why I'm excited to solve this problem and what unique insights come from that. And getting out and talking about it just became like much more easy of a flow.
Kian Alavi [00:31:32]:
Yeah, yeah. Passion, authenticity, like really extra caring. I mean, there's this founder fit idea.
Sunita Mohanty [00:31:39]:
Yeah, yeah, yeah, that's right. But I do, I mean it does also come back to like, you need some intellectual aspect of like, is this a good market? Is the timing right? Like a bunch of these things, you kind of have to understand and triangulate between those things. But I think the way to do that is to get out and talk to the people you're building for, to not just be in your head, but also so the process of validation, of taking really early, really rough ideas, getting feedback on it. One awesome thing that you can do with AI now is even prototype things and show people examples of what you might be talking about without having to build a product. Fully. And you get so much great insight from that. And I think that was a really big part of our journey in landing because we were iterating for a good year, honestly. We started the company in 2024 and we pivoted to this vertical AI SA at the end of the year.
Sunita Mohanty [00:32:36]:
And we were able to do that for a few reasons. One, we like, spent, you know, the first part of the year building practices from the ground up and really understanding the problem alongside the doctors we were working with and understanding the workflows as a non clinician, like, I needed to build that. That firsthand knowledge so that I knew the pain. And then I think we could bring our unique insights as technologists into where can we add value, what can we do? And I wouldn't have just been able to like walk in and kind of build this product without knowing that that amazing.
Kian Alavi [00:33:06]:
Yeah, there's a lot that I could translate over to the nonprofit space, but, you know, in a general sense, like having the balance between passion and. In intellectual work around understanding and sizing things and sorting things. I think philanthropy, and by philanthropy, I mean the funding side of the market often will over intellectualize the work and they'll. And, and then you'll get into this kind of weird philanthro speak kind of, kind of gated language space where it's like they're trying to get too intellectual around the work and really it's just about go do the work or go give them funding or go see what's going on and to test and to iterate. We're going to close here. Give me a lesson. It doesn't have to be like you decide what it is. It could be a big loss or a not loss.
Kian Alavi [00:33:53]:
And how that changed you and changed your approach to life and work.
Sunita Mohanty [00:33:57]:
Oh, gosh.
Kian Alavi [00:33:58]:
And I did not prime you before, so great.
Sunita Mohanty [00:34:00]:
Yeah. Okay, let me go through my catalog of so many lessons.
Kian Alavi [00:34:04]:
If there's one that like, stands out, that helps as a builder.
Sunita Mohanty [00:34:07]:
Yeah, yeah, yeah, for sure. I think the biggest lesson right now that I'm kind of constantly reminded of, especially in early stage teams, is this balance of optimism and realism. And, you know, you have to believe in a future that doesn't exist and you have to like, be relentless about that belief, but you also have to be so honest with what's not working and what, you know, is like, and. And being like, almost, you know, obsessive about being on top of it, being, you know, working around the clock, like making things, you know, come to life, but being realistic also about, like, what capacity you have to do that and not do that. And I think that's where, you know, know, teams can get caught, maybe. And if you don't have the right balance of optimism and realism and even across co founders. I think I read something recently that was actually really helpful about co founders falling into, like, roles of being the optimist or the, like, pessimist and having to, like, hold all of that. And I have recently found myself, like, in my co founder, who I love.
Sunita Mohanty [00:35:19]:
He's amazing. Like, we definitely can fall into those patterns and then become, like, versions of ourselves that we don't, like because, like, I'm, you know, too far in one direction or the other. So I'd say the lesson out of that for me that I'm continuing, like, I think I've, I've, you know, kind of hit this in different points with early stage teams before, but it's really on how to constantly strive for, like, a balance of motivating a team, you know, being optimistic to your, you know, your. Your customers, your clients and everything, but also, like, creating a sense of trust and reality and, and just, like, relentless focus on the details that you're not getting right and, and don't let the optimism blind you. I think is like, a really big, important lesson.
Kian Alavi [00:36:09]:
Yeah. I love it.
Sunita Mohanty [00:36:10]:
Yeah.
Kian Alavi [00:36:11]:
Thank you.
Sunita Mohanty [00:36:11]:
Thanks so much. Yeah, thanks for having me. This is awesome. Awesome. So, so much fun to be a part of this conversation.
Kian Alavi [00:36:17]:
Appreciate you.
Sunita Mohanty [00:36:18]:
Thanks.