[0:00] What is his startup? What is pricing strategy?Hello, the guest today is Akhil Yengar. He is the class of 2026 but is on the leave of absence from Cornell to work on his startup full time. Hi Akhil, what does your startup do? Hi, Tony. Yeah, thanks for having me. Yeah. I know at Operand, what we're doing is we're building these AI systems that set strategy for consumer brands and retailers. So think of what a big brand or a big retailer would typically hire a team of management consultants to come in and do in terms of helping them set the revenue growth or PNL strategy. Our system basically sits on top of their data and is capable of doing a lot of that complex analysis for them instead. Gotcha. Well, what kind of analysis are these? Yeah. One of our initial focus areas has been pricing strategy, and it's not something that I was super familiar with while I was studying at Cornell, but it's something that spent a lot of time learning more about over the last few months. A key element of setting pricing strategy is being able to do something called elasticity analysis. So figuring out how price sensitive customers are to a price change. So if a retailer, say Walmart, changes the price of 1000 of their products, they want to understand or try to predict how a customer would respond to that price change and make sure that they're only changing the price for the right products by the right amount so that they're making as as much money as possible. And that involves a lot of data science work to go product by product and figure out what is the relationship between price and a price change and how will it affect change a change in customers demand. Is the goal to price them at a point where they make a bigger profit or is the goal to to sell more of the product? Like what are the typical thought process behind? What's a good price to set? Yeah. It's very company specific and different companies try to use pricing for different strategic objectives. So we've worked with some very large retailers that wanted to navigate all of these recent tariffs very intelligently and ensure that they're responding to all of these changes in their cost structures by protecting as much margin as possible. And so as a result, we'd come in, do that price elasticity analysis and then give them recommendations for optimal prices to set for their products to protect as much profit. There's some other clients that we've worked with that are quickly growing consumer brands and their goal is to continue gaining as much market share as possible. And so a lot of the analysis and the recommendations we give to them are oriented around helping them out compete competitors in strategic pockets of inventory so that they're gaining as getting more customers and and driving more top line. So that that makes sense. So, so So what kind of things are the inputs into your startup and what are the outputs of your startup? Yeah. You see, you can think of our company as a replacement to like a substitute to management consulting. So there's no one defined input, no one defined output. We have built the system in such a way that in a similar way that when a client asks a team of McKinsey consultants to come in, the team of consultants figures out what types of data they're working with, what the clients goals are, and what the different stakeholders that the client would want to see at the end of an engagement. Our system is capable of flexibly handling all of that. So I'll give you some examples. When we work with much more mature larger businesses, they have enormous data rooms and our system is capable of working with those enormous data rooms as inputs, doing a lot of the data cleansing, enrichment and future engineering work that goes into a lot of the data science behind a recommendation. And then when we deliver recommendations to the client, some members of the client organization would want SKU level pricing recommendations in Excel files. Some of them would want very intuitive calculators like demand forecast kind of calculators and simulators in an Excel file. Some of them on their data science team would want to see the underlying Jupiter notebooks and code and models produced by our system to come up with those recommendations. And we're oftentimes also working with like the CEO or CFO of these companies and they want to see a very business friendly business impact oriented report or deck that our system is also capable of producing. So these are all different types of artifacts or deliverables that is capable of generating at the end of an engagement. And it's meant to appeal to all of the different members of, you know, a client organization that we're working with. So it's very consulting like a much easy kind of style. There are these like how long is a typical engagement then is it like well defined problem in and out or is it more like you get in create all the pipelines and you just recurring revenue afterwards? Yeah. I mean the goal of our company is to productize management consulting. What you would typically get out of a management consulting engagement that's predefined static, it's like a 2 month long engagement. You get a deck at the end and everyone parts ways you now have access to a system that's capable of reproducing, using that analysis or completing those kinds of projects anytime you want over the course of over the course of your access to our systems. The way that our engagements work is there is an initial kind of pilot phase. And this really acts like a traditional consulting engagement where a member of our team, often times an X management consultant uses our system, uses our technology to solve a defined business problem for the client. And they run an AB test. And by the end of the AB test and by the end of this consulting engagement, we would have already generated value for them. At that point, we transition to a deployment where we basically deploy our system like a Palantir would coming in, configuring all of the data, configuring all of the business context. And then we hand off to them at the actual system in a platform that they pay for access to, to continue setting off as many of these strategy projects as they want. Gotcha, So what are examples of like the input data that you would connect like a JIRA or Confluence? Like what are the inputs into your system? Yeah. I mean, oftentimes it's very, very large data warehouses. So like a retailer will have a point of sale system, they will have some sort of ERP, maybe they have a lot of data just sitting in enormous Excel files. Like a part of that deployment process is we go on site, we work very closely with members of their FPNA team, members of their data and IT team to figure out where all of the major sources of data. And then we just build the connectors to them so that the system is ready for them to use. Gotcha. OK. Did you also put your engine into? Is that part of the thing that you install as well? That is exactly what we install. It's that system. What goes into deploying it and making that system usable for a business is making sure that it's hooked up to all of their data and that it understands their business. That's what we call business context. So what we will do as a part of the deployment process is make sure that the system has access to all of the businesses data and also understands how the business operates. So we will talk to a lot of members of a client's team, for example, let's say they're head of pricing, figure out what are all of like the what is all of the knowledge or context that sits in their head when they're making pricing decisions. For example, a lot of retailers will have implicit rules like we will never increase our price for XY and Z product by more than $5 in September because we know that customers hate it. And I know this because I've done this work for the last five or ten years. So these are these are like idiosync pieces of information that are very, very company specific that to set up a system like this that's going out and now setting strategy for the business it needs to be aware of and we will provide that the system all of that context. So it's capable of setting that kind of strategy for them. Is it mostly like quantitative strategy? Like what prices to set where? Does it also have qualitative strategies as well as like how to manage a category? It's both. It's both. For example, we were working with the client and our system autonomously identified that there were structural issues with their loyalty program. There were specific tiers of their loyalty program that were much higher churn than they should have been and it identified that problem and gave them recommendations for how they should restructure the loyalty program to address that issue. Huh, interesting. So so so after the deployment is done, does the client like the pricing manager does? Does she interact directly with a human or does she interact directly with the software?

[8:28] How clients use their AI softwareYeah. So she, she on a day-to-day basis, she's interacting with the software as a, as a part of how our like just the core thesis of our company is that to be successful in the application layer of AI, there still needs to be like a heavy, heavy services component. And so each client will continue to have access to that like XMBB consultant or operator who helps deploy the system for them. So if they ever need help with navigating the platform or making sure that it's connected to the latest piece of latest source of data or the latest piece of business context. And also if they just need help using the system to complete some sort of like complex strategy project, they can have that member of our team who's been designated to them help them on on their behalf. So it's, it's, it's a mix of both, I'll say like most of their usage will come from the software, but for kind of ad hoc requests and ad hoc help and to act as a strategic sounding board. They will also have this really really smart ex consultant working alongside them. Gotcha, that makes sense. So it makes the AI system much more usable as a. Result exactly like we want to build a company that doesn't just build an AI product for the sake of building an AI product, but is building an AI system that can really drive make money for companies. And to do that, you can't just hand off a piece of software. You need to hand off a piece of software that along with a really, really smart person that can help people use that piece of software and make the most out of it on a day-to-day basis. Makes sense. So after you do the deployment, everything's hooked up. The pricing manager is happily using it. How do you show to the company that you did create value that value creation was there? Yeah, that's a good question. So initially during that pilot engagement, which operates as a consulting engagement, we make sure that no consulting engagement ends without an AB test or some sort of test that we can run to demonstrate Lyft that our technologies strategy generated for the company. After we hand off the system to them, we have kind of inbuilt into it that'll run like continuous post mortem analysis on recommendations or strategies that were ended up implemented by by the client. So that we're consistently auditing our systems recommendations and holding ourselves accountable so that for these pricing recommendations that our system generated and that you use system to generate in March that you ended up implementing. Here was the lift generated by that strategy, here was the forecasted lift. If there is a difference there, we'll come up with potential explanations for why and give them those recommendations for the next time that they use our system to set off a pricing strategy. Gotcha. Well what is like the state-of-the-art before your system comes in? Was it that they were using Excel and they were trying to figure out the prices and that the lift comes from using more AI?

[11:17] How the AI creates valueBut yeah. That's a good question. For most of the businesses that we work with right now, the lift comes from just being able to apply much more rigorous data science and analysis to come up with these strategies. In an ideal world, these businesses would, these very, very large companies even would have access to a team of very smart, very capable data scientists and consultants at McKenzie, Bain and BCG day in, day out set their pricing strategy for them. But they don't have access to it because a team of BCG consultants can cost a company multiple millions of dollars for a two or three-week long engagement. And we have with our system encoded that top tier talent and top tier ability into software, which means these companies now can set their strategy much more intelligently anytime they want, right. And that naturally generates a lot more lift for them than what they'd previously noticed. Does running these like once a month fresh every month? Does that add that much lift? Where is it more of like once every couple of years? You need the BCG consultant to comment update your prices. I think it's it's very business dependent, Tony. So some retailers just inherently changed their prices much more frequently than others depending on, you know the category that they operate in as well as their overall like operational constraints. For example, if you're a retailer with a lot of brick and mortar locations, there's a cost that is associated with those like retail taking your products, your skews when you have to change prices. So it is very, very business dependent. One other thing that I'll add here though is what we have built is not just something that captures the ability of a top tier data like a top tier strategy team, but it also scales it right. There's only so much actual analysis that even 5 very, very capable consultants can can do over the course of four weeks that if you have a system like this that's just constantly iterating and honestly doesn't rest like it will work while you are asleep, it'll work while you are enjoying time with your family over the weekend. It just inherently means that it's capable of doing a lot more analysis in a much more rigorous way and it's capable of identifying more opportunities than people can, right. So that's the the other kind of delta that that we're able to to generate. Like that loyalty program example you gave earlier that AI could catch it earlier so then you can fix it earlier. Yeah, exactly. And these kinds of opportunities can, I mean it, it often times adds up to, to multiple points in, in bottom line, which when you're working for with businesses at scale is, is multiple millions of dollars in, in growth that our system is generating. Gotcha. And how do you make sure that you're the one that's the most up to date on data science and the best performing one?

[14:03] How to become the best in pricing strategyYeah, well, I think part of it is we're always just talking to and learning from the people who are operating at the cutting edge of data science. I was just selling months ago at Cornell to try to finish my undergrad. My 2 Co founders were doing the same at Dartmouth. None of us were expert data scientists by any regards. I think the first step was just understanding and acknowledging that, you know, we make it an effort to just reach out to constantly talk to and learn from people who have been doing data science for a very, very long time. And I think the other element to this is it's very easy to set up ways to evaluate the way that these large language models perform. So we're constantly evaluating the data science work that our system is is producing, comparing it to what actual like expert data sign to how actual expert data scientists would approach similar projects or similar works and benchmarking it so that that enables us to keep track and and monitor how it's performing over time does. This does the pricing theory go very deep, like PhD level deepness. Like it's very, very deep, at least like the way that we approach it. We use use like certain machine learning techniques like double machine learning. These are like the cutting edge ML approaches to doing pricing. We, we learned about it and, and developed it into our system by talking to pH DS in economics and statistics who've, who've been doing this pricing work for a very long time. I'll say the reason for that, Tony, is because to like when you're doing pricing elasticity analysis, you're trying to understand, you're trying to determine the causal relationship, right, between a change in price and a change in demand. And the issue with doing that in the real world is there's a whole host of other factors that could, that could influence that relationship. And so a lot like that's where you would use a lot of these very, very powerful machine learning models is you're trying to determine what is that causal relationship. And if you're able to do that very, very accurately and give pricing recommendations that are derived from that, then you make businesses a lot of. Money. That makes sense and and then you're only using the clients data. You're not bringing in outside data of your own that you could use. So I mean mostly we will use clients data, but we have also built in features into this technology that it is really good at going out and also enriching it with with as much data as it deems relevant externally. So we'll use like different API providers and the scrapers to do that data enrichment work as well. OK, gotcha. And then that data you can use between different clients as well. Yeah, yeah. And that also does remind because we've, I mean we've built a lot into this system. We have built also just probably the best in class product at scraping competitor prices. So it's capable of going out on retailer and consumer brands websites and scraping the current as well as historical prices, which previously was not not possible. And that's that obviously goes into the pricing analysis that we do as well. Gotcha. This started very very cutting edge in data science. Is it easy to convince those stores to buy into it? Like are they also excited, as excited as you about it? Yeah. I mean, I think it just comes down to being able to explain things in the terms that matter most to the different people that you're talking to. That's actually one of our team's strengths. So, so me and one of my Co founders, we were debate champions in in high school before we, we went about trying to to start companies. We hire people who are very articulate, as well as people who have done a lot of client facing work in the past. Like I said, many of key members of our team worked in management consulting And you know, as a part of the skill set that you developed there, you just, you really understand, look, when you're working with five or ten different people in, in like a, in a gross in a grocery chain, each of those people care about different things and want a different thing out of the system that we're building for them. And if you understand that, then like that's honestly a superpower. And we make it a priority to always try to understand that when we start working with or start or start talking to anyone. As you've been going on this journey, how do you figure out like what the ideal customer profile would be like? Yeah.

[18:10] Private equity firms are the ideal customersAt this point, so we work with a lot of very steady state PE backed retailers and and consumer companies. And so these are companies that have like an inherent maturity to their their business, their business model, they're operating at scale. They have a lot of underlying data to work with and they're always thinking about driving bottom line. And one of the the most reliable ways to do that is with very smart pricing strategy. So that's that's kind of the customer profile that that we've landed on. Gotcha. Interesting. How did you land on this customer profile? Did you try a lot of different ones and you have found that these were the most tech savvy ones? Yeah. I mean, I honestly don't even think it's it's a question of tech savviness. I think it's just we talked to a lot of people, we started working with a lot of people and we just converged on a type of business that would generate the most value out of what we have built. And this was the type of business and that's that's where we've been focused on for the last few months. And then over time like to productize this so so like what would like the shape of this product look like? Yeah. So we honestly don't, we don't have like a specific category focus. So we'll, we work with like we don't just work with, with, with grocery chains, we work with like retailers that sell across the board, consumer brands that sell across the board. We're working with like some, some restaurant chains as well. Just there are a lot of businesses out there that are, that are very large that are sitting on enormous amounts of information and would like to have access to management consulting level and business strategy and data science talent on hand, every single data set strategy for their company. And we have figured out a way to bring that ability and into software using these these latest AI models. And so that's, it's, that's a very, very large market. And and that's, that's kind of the the goal here. Yeah. The reason why we like also really working with people in private equity is that we're all, we're similarly like very, very outcome oriented. So we make it an intentional goal of ours to prove value before we we sell. So you want a larger contract or try to scale across the portfolio. So we'll always start these engagements with like a four. Or eight week long consulting ask engagement where there's always lift generated that they can see on a piece of paper that our system provided for them. And once they see that, then it becomes a lot easier to not only sell into that individual port go for kind of like a a longer term engagement, but also to your point expand across their portfolio. Gotcha. That makes sense. So that pilot's really important. Yeah, yeah, yeah. And then like, did you figure out the right way to to price your own, your own startup like?

[20:57] Pricing for their own productYeah, that that's a running joke that we have internally as well because the pricing in in today's world, especially like with companies building an AI seems to be becoming very, very complex. Like you have some companies playing around with a usage based model. Our our approach is very, very similar. It's it's, it's sorry, very simple. There is a fixed fee for that initial consulting engagement. And at the end of the consulting engagement, if you want to work with us long term, you pay an annual fee to retain access to that underlying platform or system that we deploy for the company. And that's priced based on how much value we think the system can and should generate for you once deployed for you. That pilot is really helpful then because it sets the the bar for like how much value? Yeah. Is there any like the like, because it's like at the very retail end at the pricing part, are you able to, are you able to get like a percentage of whatever value creation as well? Where did you, why did you, how did you decide to set on a fixed price rather than like a percentage? Yeah, that's something that we have considered and you know, we're talking to some some people about implementing that. The only issue with that typically is that it can be kind of ambiguous and just confuse things initially. If there is some uncertainty about how much potential, like the reason why for example, a lot of like CFOs don't really appreciate that is like there's inherent unpredictability about the, the impact that this investment would actually have on their PNL. At the end of the year, maybe we generate $10 million for the company, maybe we generate $2,000,000. So for a lot of them they, you know, they prefer predictability and this kind of annual license fee provides that. And for others and because we're so outcome oriented, they they're willing to entertain that. And so that's something that we have, we have thought about as well. That makes sense to the overall picture now. Like what was your process from being a student at For now to today? Like what was the journey like? Yeah, I mean, it was, it was, it

[22:49] Journey from being a Cornell Junior to being a startup founderwas a very interesting one. A lot has changed very, very quickly. So I was in class of 26, so I was going to, to graduate next year. When I came to Cornell, I didn't really know what I wanted to study. Like I said in high school, I, I did a lot of debate and kind of, you know, my freshman year just took classes across the board. The, I mean, the end of my, my first semester, my, my first semester as a freshman at Cornell was when ChatGPT came out. And I remember this was like right before my first final season and I was using it to prepare for every test. And I just had a realization that, I mean, this piece of technology was going to change a lot. And I think since then, most of my time at Cornell was not really spent at at classes. It was spent just working on different startup ideas working like trying to learn as much about this technology as I could by by building things with it with different friends at Cornell. And that naturally just kind of led me to where I am right now. Last summer, just reconnected with two of my closest friends, one of whom I, who I went to high school with. We started working on startup ideas together. I was working on the underlying idea behind operand while while I was a student at Cornell in the fall. Just a few months ago, we applied to Y combinators winter batch, got accepted and then took the leap of faith to see this idea through. And that's, that's been where, that's where I've been over the last few months. So it was a sophomore year winter that you got in. A junior year winter. Junior winter. Gotcha. We had like two 2 1/2 years of courses in CS and econ at that point. Yeah, yeah, yeah. Interesting. Coming into Cornell, were you already pretty entrepreneurial? Yeah, no, honestly not not entrepreneurial at all. I grew up in the Bay Area, so both of my parents were in tech and I was surrounded by a lot of people who were very interested in in entrepreneurship and and building building startups. But me and and one of my Co founders, we were debate partners in high school and so we really enjoyed public speaking and the competitive element of that. But I think for both of us, it what would really spark our interest in startup building was, yeah, just large language models becoming increasingly potent and what we realized like just an unbelievable opportunity to create a lot of value and change a lot of a lot of how the world works by by using this technology. Did you worry that if you wait until after May of 2026 that the opportunities would been gotten by then? Yeah. I mean, I think we this, this window won't, won't be open for, for a very long time. There's maybe like, I don't know how long the window, like how, how large the window is. But there's, there is a window right now to, to create very, very large companies because everyone is figuring out how to use this technology and, and generate value using, using it. So that was that was a big reason why we, we decided to take this leap is, is because we felt like that there's no better time to, to really try to, to execute here. Is the timing risk, is it that McKenzie would develop their own and take over, whereas the time risk that another startup might come and take over? Yeah, another startup. We're not. We're not nearly as as afraid of of McKenzie. And then how did you decide to apply to Y Combinator then? Yeah, I mean Y like Y

[26:02] Applying to Y CombinatorCombinator. Me and my 2 Co founders obviously gone to a lot of Y Combinator related events while we were at school. And you know, all three of us, we, we honestly decided to apply just because we thought why not? We didn't really, we didn't really apply with the thought that we were actually going to, to get accepted. And, and what the, you know, the ramifications of that would be. We, we applied because we'd heard of Y Combinator, obviously such, such a reputed accelerator. And we thought, yeah, might as well give it a shot. And then we got an interview after the interview. This was right before I, I, I left for, for Thanksgiving break this past fall while I was at Cornell. We didn't think we, we would get in. And then we got in. And then I mean the rest is history. You say you went to a couple Y Commodore events before, Like what kind of events were they? Yeah, I think YC has has done a lot of work recently trying to appeal to a college students because they also realized that especially with with AI, they're no one really has an inherent advantage over someone else. Like everyone is more or less working on a level playing field and trying to wrap their heads around how to use this technology to build products and companies. They've had like some startup school events and and different events catered to to college students. And I attended one with a few Cornell friends, like spring my sophomore year is, is an example. Yeah, it's, it's events like those and, and also just like coming across content that they put out online on YouTube and, and on Twitter and all of them. Did you already know other people who went through Y Combinator before work? Yeah, yeah, definitely. New people who'd applied and done it before. And some Cornell alumni were very, very helpful here. So, you know, after we got an interview, I called some Cornell alumni who'd been through the program and asked them for for help, and they helped prepare me, which was incredibly helpful. But yeah, no, definitely we knew. We knew founders who had gone through the program. That was the big reason why, you know, we'd only heard good things from them. That was a big reason why we we decided to reply Super. So this is the winter batch then like, well, what's the well, like what kind of things the YC? Help with yeah, so it's basically a 2 month programs. It's very, very, it's accelerated. But for us, most of the, the value that came out of YC was the mentorship. So when you go through the program, you work primarily with one partner at YC who is also the one who interviewed, who basically came across your application during the application process, handpicked your application, interviewed you and then decided to accept you into the program. So they're very, very hands on with kind of like providing mentorship and advice while you're going through the program and even afterwards. Our mentor was, is, is, is a partner named Tom Blomfield, who had previously founded 2 Unicorns. So he had just tremendous amounts of knowledge and, and experience actually doing this work. And so, you know, during Weiss, he helped us out a lot with just thinking about a product, thinking about building a company, thinking about marketing, all of these different things without without which we know we definitely would not be in the position that we are we are in today. So how did the company change during the course of the two months? No, so actually you know the initial idea that we were working on was like a an AI data analyst for e-commerce stores. So I think the big thing that YC did for us was push us in the direction of thinking much bigger. And the the vision that we have for the company now as well as what we have built and what we sell to to companies is something that's just a lot more, a lot more powerful and just operates on an entirely different scale. That's I think that's another element of going through IC that was very, very healthless. It encourages you to kind of push your boundaries of thought and just become even more ambitious about the type of company that you're trying to build. They make you feel more ambitious, like was it surrounding you with other people who are ambitious? I think that's part of it for other founders, but for us it was honestly just working very closely with Tom, our mentor and partner. He had a much more ambitious vision for the company than we did. When we when we got into YC, he had this vision, I'm guessing when he was reviewing our application, interviewing us, etc. And so when we went through the program and were working very closely with him, he helped bring that that that vision to life in our heads as as well. And he encouraged us to kind of think about things a lot more, a lot, lot more ambitiously as well. Interesting. That's because he's been thrilled to start up, so he thinks big already. Exactly, exactly. And I mean every, every partner in YC, they, they've worked with hundreds if not thousands of, of startups, right. While working at YC, they're mentoring so many of these companies. They've seen a lot of these companies, companies apply and go through the process of, of growing. So they've seen it all right. And so they're probably the best people in the world for getting this kind of like trustworthy, very reliable advice that it can be very helpful for, for building a company. Yeah. What motivates the people? Like is it like an intrinsic motivation to build something bigger or like. Yeah. I mean, I think it's very person dependent. I know for, for YC at least, I think most of the motivation comes out of just, at least at the partner level, it just comes out of really wanting the companies that they're working with to succeed, really wanting the founders to, to succeed and knowing that you have someone like that in your corner is, is, is such a huge value. Add How do they demonstrate that they want you to succeed? I mean, I, I just, I think it just comes out of the mentorship that they provide. They're willing to just like the relationship that we have with, with Tom at this point is anytime we have a question or anytime we're worried about anything, we'll have like we have like a messages group chat with him. We'll just text him and he will hop on the phone with us even if he's traveling. So that that that level of of support is is huge. So, so it's been like a couple months since YC ended. What has your experience been since that? Yeah, things have only gotten busier and also a lot more exciting. So we have we've, we've hired, we've made a few hires. The team has has grown. Working with, with new people on the team has been such a such a fun and exciting experience and our company has grown as a result. I mean, building a company is very difficult. It's it's a grind 24/7 every single day and that's more or less what it's been like since since YC ended. But what's the typical day like like? Yeah, honestly, there is no typical day like that's and that's the one of the most fun things about doing this work. You start every day with, hey, like each of us goes around and tries to figure out what should our priorities for the day be to move the company in the right direction and you spend the day executing on those on those priorities. But other things will always come up. You know, a client will reach out saying we need XY and Z or a few people that that you reached out to to for potential for potential sales or warm introductions will reach out back to you and say, like, look, XY and Z opportunity has arise. You want to hop on the phone. Something with the within the product breaks for a customer. You have to spend time putting out that fire. It's, it's a lot of just initial prioritization, but also just get, get some, get things, get the things that need to get done every single day and do that consistently. And that's just how a company progresses and moves forward. One common thing I hear about,

[33:20] Startup fundraising processlike about the fundraising, how difficult is to fundraise like what was your experience with the fundraising aspect? Yeah. I mean, honestly, for us, Tony was pretty easy. So we wrapped it up within a day. You know, we had a number in mind that we wanted for for that seed fundraise and we got an offer for it and then we just went back to building the company. So, so at that point, the company was already pretty solid with revenue and everything, yeah. Yeah, Yeah, we made made for some very, very good traction. So the way that the YC fundraising process works is you tee up all of these meetings with investors at the end of your YC batch. And so all of your meetings are then. So you have the the entirety of like the actual 2 month long program to make as much progress as you can. And you know, if you make a lot of progress very, very, very quickly, it helps with with with raising the money that you need at the at the end of YC. Gotcha. So, so it sounds pretty straightforward so that we can focus on your business. Yeah, exactly. So this is like such a different life than your classmates who are still like going to prelims and then taking classes like what? Like what were some big surprises on the transition from class thinking to startup thinking? Yeah, I mean, I think the all of the transitions just kind of occurred naturally. And I think my my biggest take away from it is that it hasn't registered registered for me yet. Just how much has changed in, in my day-to-day life and, and also my goal since like what, December of last year, just a lot has changed for me very, very quickly. So that honestly hasn't even fully registered. That was, that's been my biggest realization about it is that, you know, if you, if you really take a leap like this and, and you just start doing something that you genuinely care about and you work very, very hard on it every single day, honestly, it's, it's a lot easier to just flip the script and start something new like this. So for the closing question, I

[35:17] Closing questionalways ask the guest, well, what was the kindest thing anyone's ever done for you? It's hard to name one, but everything keeps coming back to just like sacrifices my my parents made, both financially and is also as also as well as like the time that they have invested in, in helping me become the person that that I've become throughout my childhood. Yeah, it's it's hard to name a specific one, but you know, it's like that's what parents do like every single day. They're taking action, actions of kindness, to support you in in, in, in helping you become the person that you're supposed to become. And those things add up over time. What was the conversation like when you got into why comment either that Thanksgiving break? Yeah. I mean, I had not told them that I applied until the night actually before my interview. And the night before my interview, I was actually very stressed because you know, once when you apply, when you apply to Y Combinator, you don't really realize what the effect of actually getting in would be Like, at least for us, we just apply it on a whim. Just why not? And once you get an interview that, that, that becomes a lot, that feels a lot more like a potential reality. And so the night before my interview, I was just very scared. Like I, I couldn't like wrap my head around the possibility of, of getting in and, and potentially having to leave school. And that's what I call them because I was just like slightly anxious and they, they told me they had the exact opposite effect, the opposite reaction to it than I did. Like they were not stressed, scared, anxious whatsoever. They're just genuinely very, very excited. And that they've had that level of excitement. I mean, throughout this process, after I got into, I see everything that's happened over the last few months, decisions that I'm making now about, you know, doing this full time, these are all, these are all things that they are extremely excited about. And that's made my job and my life a lot easier. Thanks, Tony.