Interview with Aman Mahapatra

You're listening to One Sharp Sword cutting through to what Matters Most with outfluencer Dr. Wayne Purnell. Welcome to One Sharp Sword Cutting through to what matters Most. I'm your host, guide and coach on this journey. I'm Dr. P. Dr. Wayne Purnell. Today I've got a really special guest. I am joined by Aman Mahoptra, Chief strategy officer at Tribeca Softech where he sits at the intersection of strategy operations and go to market execution. Tribeca Softech focuses on strategic growth, bringing together relationship driven consulting with modern tech execution and including AI led acceleration with digital transformation. In this conversation, we'll talk to Iman about his journey into this operator investor seat and what he's learned about building momentum in complex environments and the patterns he sees in companies that scale. Well, we'll talk about the future of AI and we'll focus a little bit on what that means for financial institutions as well. Aman, welcome to One Sharp Sword.

Thank you so much. Pleasure to be here. I've been looking forward to this. It's nothing short of an amazing opportunity to speak speak with you today. And I'm looking forward to our conversation.

I'm looking forward to it as well. It's been great. I've gotten to know you over the past few months. We've even met in person and you're just, you're a joy. You've got so much to bring and I mean you are uniquely qualified in so many areas. So I'm just, I'm, I'm delighted to be able to talk to you. Let's, let's do two things. I want to talk about what it is you're doing now. I mean, Tribeca Softech basically started a year or so ago and with a beautiful idea and I've watched it morph and scale and wow, it's, it's like the fuse has only just been lit because it's going places. So I'm just curious at this point about how you got from where you were to becoming the chief strategy officer at Tribeca SoftTech. Like, talk a little bit about your journey. Let's, let's talk about what you're doing now. Let's talk about, let's wind the clock all the way back to when you were a kid and where you started. Okay, sure.

So yeah, right now I'm serving as chief strategy officer at Tropical Software, which is more or less a venture accelerator as I, as I tend to explain it. And we are helping high growth companies in financial services, technology, healthcare and different like across different industries, scale through AI, through automation, through modernization, through transformation. And we bring the best of the breed across like technology and the companies and help them like find success and find momentum. In industries like healthcare, finance, energy, we are operating almost in multiple verticals. But like finance has been the strongest sector where we have been operating. And it's a fun role. It's part digital transformation, part go to market, understanding where the gaps are, thinking about where we can position them. So every company has its own struggles and successes and pain points and the creativeness that it brings and we have to just unlock it across the stack of processes and governance and AI that we see and tend to deal with in a broader landscape.

That is a big bucket.

That is a big bucket. It's an ever changing bucket as well. Technology has been changing, compliance is on the rise, governance has become a primary guardrail across many industries. So things are changing. It's a very dynamic environment. We get to work with CEOs, CTOs, CIOs, C level executives across all the Fortune 50, 100 institutions. So we have like a two fold model where we have all these cutting edge companies, but we also have these people who are responsible to drive transformation across their institution, which is a big effort. It starts as maybe a boardroom thought of, hey, we want to implement AI or change the way we look at our legacy data and do something about it in the next fiscal quarter to achieve certain revenues and bring our market share up. But like from that thought to the whole journey of actually implementation of those whole thing, we are the trusted partner who help them deliver the impact by bringing in best in technology to help them go through the whole process. So that's.

In some ways you're like a broker, in some ways you're like a repairman with a bunch of tools that you come on site with. Right. When I say broker, it's sort of like, well, here's this institution that has these emerging issues. They've identified that they want to be ahead of the game with AI. They want to streamline their processes. Like in banking. I know that you do this with loan processing that like happens so much faster, cutting days or even weeks down to almost seconds. And that's based on the technology that's out there that you go look for. You. We're going to bring in this. Is that a good way of defining what you do?

Yeah, sometimes we have to understand before we execute on what the requirements are. Everyone wants to look at AI maturity or that's on the rise, but so is quantum. So is Cybersecurity. So is every other thing in any frontier they want to make progress in. More or less. There is this guideline of we want to do this, but there is siloed, fragmented ecosystem that have been running as a legacy model for these larger institutions forever. And the question then comes down to, okay, where do we start? What's our primary strategy to achieving the amount of transformation we want to deliver? Does it fundamentally come down to data? Is it people who needs upskilling? Is. Are these processes, this culture? So we take those strategic lens and we evaluate the company like, along with our C suite execs. We do this as a joint exercise, bring them up to speed, empower them so they can make better and strategic decisions on how they approach the complexity of the transformation they're set out to do.

I love that. Right. Because it is about people. Process, platform. Right. Yeah. You know, and I was just having a conversation recently with another C suite gentleman who. His. His focus was on strategy. And the, the truth is that culture creates the strategy, right? That, that it's like, well, we want to do this, but. But you need a group of people to be able to make it happen. And so you've, you're working with technology, but you're also, you're also empowering people in their own organizations as well as in yours to be able to utilize that technology. So this is. I, I think it's a, it's. It is a very strategic place you're in. Especially like you as chief Strategy officer, you. You have to do that, right? So that's great. I. I want to know about it. Well, you said that there are silos and fragmented ecosystems, and that just really piqued my interest because one of the things that I do with, when I consult is I go into organizations and I help build bridges between silos because everybody says, Everybody says, you know, that siloism is bad, we shouldn't have it. And yet pretty much every organization has silos in it. So how is it. Right. And when I say silos, it's like this group is operating in this way, and this group is operating in this way, and they might have resources that they could use or share, but no, they're doing their own thing heading forward. My. Yeah, the work that I do is to build those bridges, to build culture and that kind of thing. How are you seeing that play out for yourself when you work with organizations or when you, when you use tech, I mean, to, to bridge silos. How. How's that happening? Or is that happening?

I think it Came down to I guess like you captured it and I like, that's our blood and DNA, like people process and platform. So we have to look across like everything along with like the guardrails and the governing bodies and like what, what are the external factors? Like it's more like, I think more or less a consulting engagement in ways where we think, okay, what's the, what's the root cause of a situation? What's the internal drivers? What are the external drivers? Are, are we responding to competition in a way that like it's compromising some of the integrity of the data or the approach? Are we tracking some of the systems which needs more diligence before we enter the market? So it's a multi threaded, very complex problem, depending on which thread we pull across, what sector. But in a nutshell, I would say, I would say a legacy framework. If we talk about banks, let's just take that as an example. So bank is not have a system, they have a museum of systems.

Well said. Oh, that's well said, right? It's not a system, it's a. Yeah, yeah.

The way I define it is like you have loan origination, you have payments, you have fraud, credit cards, data warehouses, document systems, call centers, identity tools, and everything is stitched together over like years. And modernization isn't just like migrating infrastructure. We have to untangle these workflows and identify how do we sort of create an entirely net new distribution layer where things are synergistic. Data is talking across all these channels and silos are kind of broken down, which is a big thing to imagine given that we have been operating like this for better part of 25, 30 years.

There's great legacy with banks. Obviously with most organizations that have been around a long time, banks seem to be one of the ones you can go. Well, especially banks, institutional knowledge, institutional ways of doing things, which these days they no longer work. And that's the pressure I think a lot of businesses are feeling. I want to ask you very specifically, like where you, you know, given the AI is changing so fast, where do you see, what are your views on AI? Where do you see it going and maybe weave that into, especially for financial institutions. I think you hinted at it just now with like we have to untangle it all and create new solution where, like that's, that's awesome. That may be your answer, but I'm curious, like where do you see AI going? And then, you know, tilt that toward, especially in, in institutions that have such deep legacy that now needs to be disrupted.

Yeah. And I Would like turn the dial back to the changes or the origination of how things came to be and how things have been changing.

Tell us the story, Iman. Tell us the story. This is great.

So we saw the cloud revolution. Everyone was skeptical then. Everyone is on cloud now. The machine learning kind of training, these systems became a thing, I think with OpenAI.

Pause for a second. So when you start talking tech, I'm going to interrupt you just a little bit. Machine learning is a. Is a phrase that's out there, but I'm not sure that my whole audience knows what that is. Can you just like in a sentence or two, say, okay, machine learning is when. Right. And how does that differ from large language model?

Okay, so it's like basically what, what we do with machine learning is we are trying to figure out a way to train a system to. On a particular data so that it can automate or perform a specific function of a task. The very basic and simple example is like in 2015, 16, everyone was on the hype of like implementing machine learning and the way I learned it and everybody else did image recognition. So you feed them a data set of dogs and cats, and it's just a mixed bunch of animals. And then you ask, do you think this is a dog? And the machine kind of has been training on this data so it can identify and classify if this is a dog versus a cat, basic classification problems, A versus B, or basically mapping problems. So it's. It started solving some of the like recommendation problems, for example, Netflix or any of the video streaming platform where have you watched the. If you like this, do you want to watch this?

So that's a great example. So right. So for machine learning to recognize your preferences and go, okay, you like the whatever science fiction, fantasy, drama genre, you watch this and then it. With. You said it was about classification. Right. So based on classification, you might also like this. I love that. That's recommendation. Cool.

These were like older sessions. This was in like 2015, 2014. This has been going on for a while. Like your shopping carts were designed in that way. Like if you have purchased something, do you want to add like this to the basket? So all these questions were like sort

of AI and now we're inundated with that. That's like, that's just so standard. We're almost used to that kind of annoyance or convenience. Depends on how you shop. Yeah. All right, so that's machine learning. Then what came after that, I think and LLM is, you know, there's a

lot of things that happened like technologically, there are multiple steps. We went into like reinforcement learning, deep learning. Couple of different things happen. But the breakthrough happened in 2023 when OpenAI came out. That was big, like ChatGPT and everyone started looking into AI as a mainstream thing. It became a consumer tool for interaction, for research, for going to like. It became a replacement of Google in terms we started going there for answers. So that as a concept was built on large language models where you have insane amount of data. We just scaled the backend of the technology to have TVs or multiple terabytes of data as training data, where now your system can actually talk to you, you can have a conversation. The simplest use case became knowledge basis. Think of it as a documentation repository and you're fetching something out of a big pile of documents and so do I have this? And the system would say, hey, I found this. It was somewhere here based on what I know. So I think that was the onset of like big revolution. And then after that it has been changing every day. So you have to every day open up your research papers and things and stay up to date on, okay, what changed today? So from there I think it changed like three more times between 2023 and end of 2025 when we are talking today. So it's just like super insane how the evolution has been. The next phase was rag. It's called retrieval Augmented generation. It's rag. Rag, Retrieval Augmented generation. So you kind of feed in a prompt, let's say, I want this, I want to write an email based on the conversation that has been happening. So you feed the conversation, it takes the reference, and I take the reference as my guiding principle. I'm trying to break it down in simple.

I love this. Yeah, thank you.

So I'm as an AI, trying to take that sample you gave me as my guiding principle to look for what you need and trying to match it in a database and see that is this close enough to what is being asked and that data has been fetched and given back to you in a generated way. Like you fetch like 10 sentences, it adds 10 more and gives you a quick prompt on, is this the email? Does this look good? So that was the next wave.

Now that's not happening now because when I use chat, I know that it has picked up my style.

Yeah.

Because I've trained it like, I'm like, I would never say this or I would say it this way. And so I know that Chat GPT has picked up my particular style. And I know that it does that to Every single individual, so that it's biased toward that. Isn't that what's happening now where with chat? Or is that not yet kind of the rag

it has been happening? This was the norm, I think in 2024, where a bunch of companies came out in the venture space. Everyone was like, hey, build your model on top of us. We will provide the ecosystem in the guardrails and we'll help you create your custom, let's say agent or bot or something which will resonate your identity, be it artificially generated sales call assistant who would make calls on your behalf. So it'll speak the tone of your company, be it, let's say an answering machine. Right. Like if they want to stick to the policies, the guidelines and the AI would understand the boundaries where it's operating and will speak your company's language. So that became the next wave. This was in 2024 and since then we have seen one more wave. It's called agents. Agents are basically small fragments of autonomous pieces of AI who would execute a task for you. For example, right now I'm not sure if you have experimented with it, but you can practically go there and ask like, hey, I want to book two tickets to Miami and it'll do it. We are now there. So we're looking at a multi agent system. We have like multiple agents working on your behalf. So that's where the current state of technology is. And it's being. That's where I think what like our company and other companies, we are trying to push this as the next layer of like, how do we implement this to operationally, like reduce our burden across like multiple things. We can think of it as like multiple analysts trying to do small pieces of your work. Then you can have agents or supervisors who monitor those pieces of work. So you can create a whole fabric out of it.

Is. Is this why people are worried about their jobs?

A bit. But it wouldn't, it wouldn't really take away jobs. It's just going to reshuffle them or repurpose them. You're going to be the human.

I'm glad, I'm glad you said that. Right. That's, that's the point is that with any evolution of technology there's been this wave of I won't have a job if we have whatever. Right. Robots doing XYZ from. I mean they basically can do everything at this point. Folding clothes, frying food, packaging. Like it's already happening and jobs have shifted. Right. It's not that the jobs disappeared, it's that the Job, the nature of the job has shifted. So when we get somebody booking two tickets to Miami and it's not somebody, it's. It's technology. Awesome, right? That's awesome. So it, it opens the door for maybe somebody to coordinate that. Right. So I just, I wanted to point that out because it just seems like there's the, a wave of fear around any kind of change in the way we do things. And the truth is it's happening. So you could be afraid or not afraid. I prefer curiosity. Like, what does that mean? Right. So. Yeah, yeah. So the next phase for you and the next phase in, in banking and, you know, some of the other areas that you're specializing in, what does that look like?

I can give you a few examples. But like, just, just to complete the whole thought, like, oh, yeah, please. While we see endless opportunities and possibilities, it also opens up to a huge amount of cyber risk because now you're talking about like AI who's making purchases on your behalf. So it does have access to. It should have access to some of your finances. It should have access to some other things. So it's about access now. It's about permissioning now. It's about governance now. It's about like, observability now. So those aspects of the guardrails and mechanisms in any institution are on the rise. It's the front and center of focus, or it should be. As we talk about the implementation of AI in any sector, be it healthcare with patient data or looking at images across, let's say, organs or biopsies or any of those use cases, we can talk about hundreds of use cases where we can make a significant difference. But you have to think about the security, access, permissioning, and the guardrails of the operating system we're talking about here as well. That is the key message that we want all our audience or customers, clients to be behind. Like, we want them to champion governance as part of the whole playbook.

I love that. Champion. Champion governance. It's, you know, more rules. Who says that? But, you know, we. Right. When we're looking at what, what you're terming or has been termed permissioning, it's an interesting, it's an interesting verb that it, it isn't recognized as I write it down, it's like it's underlined in red. But it is a verb, right. We're using it these days. Permissioning. When we're looking at that, I, I think it makes so much sense that we're giving access to basically AI agents. The wrong people could get in there. So we do need guardrails. We do need, we do need security and governance around permissioning. I love that. All right, so what, what's next? Where to.

Oh yeah, I think I can answer that. The other question, like what, what's happening or in the space or what could be the implication of like the cool technologies we have been talking about? And I can probably give you an example. Like I think that'll be fun to see what's possible or what could be possible. It's, it's, I think to me it's a great example of financial, inclusive financial inclusion, trade access, just empowering people. Like what, what we think about like modernization and how we can enable like banks to do better, financial institutions to do better, healthcare ecosystems to do better. Well, what does it mean in the end for the likes of us, like the common people, the consumer on the ground, what does that look like or how it can transform their lives? And there is this one use case that I can maybe talk to which can just paint the picture on a high level that how significantly it can help with a multitude of things. And I think I've written a paper on this a few few years ago.

It's called I might have just written a paper on this. That's awesome.

Yeah, I named it Banking the Unbanked. And it was like a lot of people like we, we take, I guess like take it easy or take it like as a norm that we have access to the financial instruments that we have. But there are a lot of people who don't see the same things as we because they are in a situation or are underprivileged or like maybe they don't have access. So it's all about like democratizing access. Right. So the example I want to start with is I guess this one company called GCash. I think they're based off Philippines or they did something, I'm not sure if they're based out of Philippines, but they did something in Philippines. They use like non traditional data as a mechanism of like understanding credit worthiness.

So digital, what is non traditional data?

Digital behavior, transaction history, app usage. Then like payment behavior, wallet balances, cash flows, digital engagement patterns. Right. Like they kind of made a proprietary tool which analyzes these to kind of create a proprietary credit score. It's impressive to see how we can bring or go back to our roots and do a first principle thinking on what defines this credit score and how do you kind of factor new patterns into it. Just like figure out a way to deploy more capital to an access to people who never, never had any sort of like finance to them. So it ended up, and it could be, and the way they did it is in forms of like micro loans. They wanted to be an alternative to loan sharks. Right? Like, so the way it turned out that like 2 billion, they deployed 2 billion or enable 2 billion micro loans over a course of like a year or something, they reached 7 million people who never had a bank account. And they replaced like regulated, highly regulated credit and loan sharks with sort of like this alternate mechanism. And the credit penetration prior to this was less than 1% in the entire country, which then became multi million user engagement. So this is one of the many examples on how like, if we can just like tweak in a few things, you can just expand the scope of like a better consumer outcome. Right? Like for access, for inclusion.

Amazing. Okay, so, so when you say better consumer outcome, there are some people that would be like, no, they're going to have access to everything and they're going to find out this and that about me. When they say they, they mean, you know, AI agents are going to find out all this data. It could go the wrong direction for the consumer sometimes. Right, but it's also, it's also better in terms of analyzing risk for the financial institution. Is that right?

I mean I, I might have to disagree because there the, if done right, we have a mechanism of like risk aversion. That's why I say the governance and guardrails have to be baked into some of these solutions even before we think about the benefits of it. There are ways of encryption, there are ways of anonymizing or creating a digital identity for a user which replaces your driving license, passport, any of those things where we are scared about like, hey, this is gonna go out. But we can be a digital, we can have a digital twin of an identity that like, hey, you are XYZ 3, 4, 1, whatever, whatever, right? So it's encrypted.

It's not, it's not like your actual data's out there. There's an encrypted code that represents your data.

There are technologies like blockchain is a big one. The whole concept is called decentralized identity or did. That was also part of my paper. So you can have this as baked in. And this is like some of the cutting edge technologies as well. So we are looking at implementation in South Africa, the continent. They're doing great things in Tanzania. There are a couple of other countries that I'm forgetting. So they went there and these Guys didn't have a national ID system, right. So they created a decentralized digital identity for them which they could use to open like banks, go to, go to open bank accounts, have access to telephone accounts, Internet, any of those things. So feels very basic. But they were able to like come up, they were able to come up with a system which is like completely digital anonymous and it's like one central identity which can be used across hundreds of systems because it's all interconnected. So amazing stuff is happening.

It is amazing. It is amazing. Let me ask you this. We're gonna, we're gonna close in a minute or two, so let me, let me ask you this. Is there something that we hadn't touched on? I know we didn't get much into your own personal history, but I think what we covered today was pretty amazing. And, and was there something you were hoping we'd touch, get to. And part two. Wait, part two, because I want you to answer this together. If people want to reach you, to find out more about you, bounce ideas or questions off of you, or even find out about how to work with Tribeca, softech, how do they reach you? So what would you, what were you hoping we'd cover that we didn't touch on? And how do people find you?

I would say I think we kind of skipped on the personal journey, which I think is, could be a thing. We can always have a part two because there are lots of things and I want to disclose or maybe give a disclaimer or a teaser that we are spinning off another AI entity from our company, which will be live in mid Feb and March. So there's a lot that we are kind of doing ourselves as well to lead this next round of whatever is happening. So we can talk about that. It's been all hush hush for now. We want to keep it that way as we are figuring out how to keep our competitive advantage. But I would love to dive a bit deeper into why we wanted to go behind those use cases and solving those big problems, pivoting into those things. That's something we can briefly touch on. The personal journey or how things happened and the way, the best way to reach me would be on LinkedIn or if you can email me, that'll be perfect.

So, okay, so on LinkedIn you are LinkedIn.

Just my first and last name, Aman Mahapatra. So that'll be. Hopefully I'm easy to find. So yeah, open to connecting with like leaders, operators, anybody wants to build, solve, create operationalism, Doors are always open. We'll love to hear your story and

help you and, and the, your email. You want people to email you?

Yeah, like, I guess, like I just, I think on, on hindsight I shouldn't give it out here.

Well, maybe LinkedIn is the best way to reach you and then. Okay, good. Awesome. Do you want to just talk a little bit about your personal journey and then, and then we'll wrap up?

Yeah, I guess like the only thing I want to touch there is and in all humbleness is like, I never thought this, the path till here would, would happen the way it happened. It has anything but linear. It has been all over the place.

Most successful people's journey and you're not, you're not even close to done yet. But most successful people's journeys, it's not most every. It's never linear. Like, it's never like, oh, I'm starting here and I want to go there. There's side quests because you say yes to opportunity. Right? So yeah, you started just real quick, you started in what part of India?

I started that was born in, I think eastern part of India and traveled all across growing up because my dad was in the military, so we had to move every two years. So never could like associate myself with the home. But every, every place I've been to was my home. So I think just putting yourself out there and you have to, you have to like stop being the new kid every time you move. So like I think the communication and blending in and just like getting along with like different people, culture, backgrounds, ethnicities just became baked into the way I was growing up. And things were very dynamic as I was growing up. I think I changed like 13 schools in 12 years.

13 schools in 12 years. Wow.

Wow. It was, it was, it was challenging, but it was also fun. I got to see a lot of different places. And then I wanted to come to US for my undergrad, but took a detour when I started building my own company. Like 16 was always into business, tried a few like smaller gigs, sold that build that like kind of going in that direction. The final breakthrough happened when I kind of like built a system which captured an attention of our incubation company and they connected me to a consulting firm and they were like, okay, we would love to work with you. So I worked there for two years before deciding maybe I should go do an undergrad because eventually it's going to come down to some form of credentials as well. So did like triple degree in math, CS and engineering and from there spent some time in industry. It was Like M and a consulting venture, building companies. My last company that I was working on was in retail. It's a retail marketplace. I wanted to solve for short term accommodation needs. So it was Airbnb for subleasing for if you can simply put that. So we have like platforms to find houses if you want to stay less than a month which is like Airbnb and the likes hotels and you have Zillow, Redfear and all the other like plethora of companies. If you want to find like longer term lease, what about like 2, 3, 4, 5, 6 months in different cities for people who are traveling. There are students, there are commuters, workforce. So I wanted to solve for that ecosystem. Turned out it's a big problem. I bit more than I could chew. But we raised capital, we scaled it, it was going fine and then I kind of thought like there are better people who can, who are better equipped to scale this. So exited that and realized that I was always fond of like venture space and I'm still, I love venture capital because you get to work with somebody who's dreaming to disrupt the status quo. They are always looking to build something which has never been done before. It's an opportunity to see and be at the frontier of like every single thing. So I still love that. But I kind of realized down the line that building has been democratized with, with advanced AI we can, it's conversational now we can build things. You and I can build things. The differentiation in the market will come down to scaling who's going to be in front of the right set of consumers and people and how quickly. How do we find adoption and stickiness in record time. How do you beat your competition in that way? So Tribeca Software was that layer which enabled that and we are still enabling that for all different companies. And that's how I find my way in. Starting as a program manager, then chief of staff, managing bunch of different things for our entire team and then now stepping into chief strategy officer. That's my whole story in a nutshell.

That is a, that's a long story in a, in a very short time. Very good. Really appreciate it. This is, it's really good to talk to you and we'll, we'll do more. I'll, we'll follow up with you as you can release more information about the, the TE that you offered. You know what's coming out, what's the spin off. I really appreciate it. So I am tripping a little bit on your last name. I'll do it as best I can. I'm on Mahatra Mahapratra.

Yes. Okay.

Awesome. Very good. So that's easy for me to say. Aman Mahapatra, thank you for being here. It's been great. What a great conversation. I'm so glad we. We finally landed the time to be able to talk like this.

Yeah, likewise. I've. We have had so many conversations outside of this platform as well. Always amazes me your. Your understanding of the space depth and, and the insights that you bring out and especially the, the way you kind of like help ease the conversation take its natural course. So I guess like, all thanks to you. Like you, you're the master or the genius behind the. The things that, that have folded today for all the viewers.

I'm so glad. Thank you. Those were, that was, that was nice of you to say. Thank you and you know, yeah, appreciate it. I love how big you're smil is really great. Thank you. All righty. All right. Well, Aman Mahapata, I'm gonna get better at that. Aman Mahapatra. Mahapatra.

Mahapatra. Yes.

Thank you. Aman Mahapatra has been my guest today. This is one sharp sword cutting through to what matters most. What an amazing conversation we had. I'm Dr. P. Dr. Wayne Purnell, your host, guide and coach on this journey. And we will see you again here next time. Thanks for tuning in. Thanks for listening to one sharp sword cutting through to what matters Most with outflensor Dr. Wayne Purnell. For more information, please go to onesharpsword.com.