Tech Brew Ride Home - (Portfolio Profile) Hypercubic.AI

Episode Date: November 15, 2025

A portfolio profile episode of Hypercubic.ai, a seed-stage company that wants to not only preserve knowledge in legacy code, but legacy knowledge in enterprises. Learn more about your ad choices. Visit megaphone.fm/adchoices

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ends June 30th, terms at AKA. m.m.S. college PC. Today, Hypercubic is allowing Fortune 500 to understand, preserve, and modernize their old legacy infrastructure. And so what I mean by legacy infrastructure is there's a lot of enterprises that still run cobal and mainframes. And this was technology that was built back in the 1960s that runs almost all of the big

industries today that you can think of, such as airplanes, airlines, banking, financial services, logistics, the government, like IRS, Social Security, and so on. And so there's a lot of legacy technology out there that's still running. And what we do is simply help these enterprises understand them and modernize that into a modern tech style. Welcome to another bonus episode of the Tech Brew Ride Home. I'm Brian McCullough, as always. This is a portfolio profile episode, which it's been a couple months since we've done one of these. But I'm super excited today to talk to the founders of Hypercubic, which you can find out more about at Hypercubic.

cubic.AI.AI and Ayush, please introduce yourselves. Hey guys, I'm Sai. I was previously a machine learning engineer at Apple for a couple of years, where I worked on the iPhone multi-touch. And before that, I did research in grad school and undergrad on LLMs way before they became mainstream. And today working on Hypercubic with Ayush on using AI for the mainframe ecosystem. Ayush. Hi, I'm Ayush. I'm a co-founder's CEO of HyperCubic. I was a software engineer at Apple AIML before this, and my research has been at grad school around robotics and I'll be AI and excited to be here today. So for whoever wants to take this, give me the one minute elevator pitch of what HyperCubic is doing, and then let's dive into what HyperCubic is doing.

Yeah, so today, HyperCubic is allowing Fortune 500 to understand, preserve, and modernize their old legacy infrastructure. And so what I mean by legacy infrastructure is there's a lot of enterprises that still run cobal and mainframes. And this was technology that was built back in the 1960s that runs almost all of the big industries today that you can think of, such as airplanes, airlines, banking, financial services, logistics, the government. and like IRS, Social Security, and so on. And so there's a lot of legacy technology out there that's still running. And what we do is simply help these enterprises understand them and modernize that into a modern tech stack. Do you have any stats? I mean, you're mentioning like, you know, major pillars of the economy and things like that.

But do you have any stats off the top of your head in terms of how pervasive these legacy systems are? Like, are we talking about 30, 50% of, like, the systems running out there? Like, how big is this problem that you're solving? We have a few stats. Some of them are, like, something like 70% of all Fortune 500 use mainframes for something. Another is, like, I think Kobol, Sai mentioned is like a programming language that many of these systems run on. There's something like between 200 to 800 billion lines of COBOL still running out there. So these are pretty big numbers.

People think like Cobol has died. This is like a dying market, but there's just so much effort out there. That's not true at all. Well. And I think the key stat, one more thing to mention is the average age of the developers working on these systems is about 55. And rapidly going into like 60s, 70s, there's plenty of 70-year-olds working on these systems today. They simply cannot retire. Well, I was going to, I'm glad you interrupted me because I was going to make a joke about dying.

You said dying. But the problem you're solving is that it is, it's not just legacy systems, it's, it's legacy knowledge. It's knowledge that can age out of an organization. And so the code is still running, but the ability to tweak the code, to improve the code, to fix the code, that sort of knowledge might be, and I'm not going to say dying, but walking out the door, getting a gold watch, that sort of thing. And so that is the problem that you're trying to solve.

Exactly. So it's the system understanding of these legacy infrastructure. Today, what's happening is a lot of people are focusing the competitors or the incumbents are focusing on the code aspect, line-by-line explainers, code explainers, and so on. But the other missing part of the puzzle is basically the institutional knowledge in the minds of these subject matter experts. like I mentioned, these 70-year-olds that have worked on these systems for about 30, 40 years of their careers. And when they leave the workforce, all of that simply vanishes or leaves with them. And so capturing all of that is the key mission that we're going after.

So, yeah, go ahead. Adding onto that, like, when you're talking about this knowledge, like, a lot of the common question we get is, like, isn't all of this knowledge, much of this knowledge already there in, like, large language. models or aren't they eventually going to, isn't it eventually going to be in these models, right? Right. And I think one key thing to understand here is, yes, like this system, like, operational or like, manuals are going to be there in the large language models, but like every company has its own culture of doing certain things.

And that is almost never going to be in the language models. And that is like one of the key things that we're focusing on right now. Right. The language models can be trained on COBOL. You can have it code in COBOL. you, but it is more of that institutional knowledge mixed with the legacy coding that you're trying to fix here. Correct, yes.

Can AI agents, can they fix this problem in the sense of what do I have to do if I'm, if I come to you and say, hey, we've got all these legacy systems, how can you help us? Like, what is the installation? What do I have to do to get up and running? Like, aside from showing you my code base, like, what do I have to do to get hypercupic up and running to help me? I'll start and then so I can continue. We have, like, this crawl, walk and run approach that I think we've talked about internally,

where crawl is like an easy way to get started with us, which is just, I think you mentioned or even before the call like hyperdocs and hyper twin. So step one is just the understanding part, right? We try to come in. We will understand your code. We'll sort of interview your experts using AI to get the knowledge in. And that's the crawl part. Like we're not like the AI agent's part come later.

Like there's more to our vision. Like eventually like I think we said initially we want to be able to move entire systems or to modern tech stacks. But I think going straight there is like it's been done many, many times and has failed. so we're not sort of going there directly. Right now, we are sort of doing two things, like I said, capturing knowledge from code and capturing knowledge from experts. And to go a little bit deeper into that,

just to elaborate on sort of what we're speaking about, when we say going deeper into the code, we have a documentation platform that ingests all of this legacy code. It's millions of lines of code, tens of thousands of files to spread across their mainframe systems. And we simply ingest all of that and converted into a very readable, highly structured documentation, that their engineers, business analysts, and leadership can simply go through to understand

these now black box systems. So that's one part of the puzzle. That's the hyperdocs, right? That's the ingesting, okay. And you're using like what, a hybrid of like deterministic and generative AI? Like how are you making sure things like accuracy and audibility? and trust are part of that ingestion process. One of the things that we're doing is like sort of having,

for many of the things that we're generating, we're sort of trying to tie them back to proofs. And by proofs, I mean when we're just ingesting code documentation, for every statement or every paragraph or every, like a diagram that we generate, right? We sort of link back to like, okay, these are the code, like these are the five. these are the code blocks where we're sort of extracting this business logic from and this is where it comes from.

So it's like there is an element of like human verification to it, but we make it very easy for that verification to happen. And we have all kinds of like editing tools and like versioning to make that like as verifiable as possible. And like you said in the beginning, there's like, yeah, we have like a bunch of generative models and like some determinists of stuff together sort of making it. more reliable than like a purely generative system would be. The second part being the hyper twin part. That's that's the, I think you said the knowledge layer that so like you're building a digital twin of the experts inside the organization. How does that work? How do you how do you like are you learning from like observing their work and stuff like that?

Yeah, so this was a correct. So this is a combination of a few different modalities of information that we collect. And the whole goal is to basically replicate their mental model of the systems, the processes, the expertise that they're working with. And so the way we collected without going too deep into the proprietary stuff is first is ingesting the existing documentation they have written. So this could be SharePoint, GitHub, Jira, anything that's been out there. The second one is an AI-driven interviewer. So the air-driven interviewer would ask very targeted questions to the subject matter expert. For example, there is an issue last month.

The interviewer would ask, hey, we've seen this issue with this specific LPR. How did you resolve or debug this specific error that we're seeing? Could you walk me through the problem-solving process? So that's one way. And the third modality is simply workflow capture. And that means recording the screen of some critical task or workflows they're working on, and then converting the information from that task into structure workflows. And that essentially means, hey, Richard, you know, the SME maybe, has gone through these different files.

He's accessed all of these different things. Here's the functionality he's implemented, and this is the outcome of that. And so we take all of that structure knowledge of how we went through that task. And by combining these three modalities of information, we can create almost a digital twin of the subject matter and the way they work, problem solve, and architect solutions. Is that, though, is that something that, again, when I'm getting set up with you, you do it for a period of time and then it's ready to go, or is it something that's always there and always learning so that, again,

maybe it's a two-week process or whatever to get set up or do the interviews and things like that, or is it something that you're monitoring so that when new problems are rise, you can still, it's like, hey, you'll get a ping. We need to talk to hypercubic again and give them more info. I think it's kind of both. So one, like the interview process itself, like, you would, like I was surprised to learn that many would want to, because the knowledge that's in these experts has been accumulated over like 30, 40 years, right? Like, it'll take them a year of just doing interviews to like just put everything in. So there is like an aspect of like we might not even need like like the current,

like the really current or present things are not even just getting what's in their head is just like a year long or even longer process. Right. So that's one part of it. But to the other part, which is like the there's the workflow capture that site talked about. And that is much more like proactive or like even reactive in the sense. Like if things happen or there's like a new thing, the expert can just do the thing and hyper, like hyperquin can watch. you do the thing and that just goes into it automatically and the next time it happens there's like it's captured and it's like it's continuously learning yeah and ingesting yeah yeah so the twin is

almost self-evolving uh in the sense that as new information is being fed in continues to get updated over time so for enterprises the value proposition is obviously um you know preserving knowledge but also So is there like risk reduction and like cutting efficiency in terms of production ready, like producing things? Like is there efficiency here beyond even just the preserving the knowledge? So the key value proposition is it's preserving the knowledge, but then what is the outcome of preserving the knowledge, which is really just risk, of these legacy systems. Because if you think about it, when a large bank like JPMC or Bank of America, when they're running these systems with millions of customers and tens of billions of dollars of transactions,

they cannot afford to lose or any downtime on these systems. And so it's really about mitigating any and as much risk as they can that could potentially occur on these systems, whether if it's subject matter experts retiring, some critical issues within these systems, trying to mitigate all of those issues is sort of the business outcome that enterprises are getting out of the tools we're building. Just to add on to that, even like the initial conversations that we've been having for like pilots and all, they've all come out of like, okay, here's a risk, here is like potential people that we might lose. How do we mitigate it? So like it's always like all the conversations we've had is always in the background, larger background of like mitigating risk.

That is like the main thing that they're doing with our tools. And is there a layer of proactivity in the sense that hypercubic, once it's up and running, once it's learned and ingested, that it can proactively solve problems that might arise? Ready to soundtrack your summer? With Red Bull Summer All Day Play, you choose a playlist that fits your summer vibe the best. Are you a festival fanatic, a deep end DJ, a road dog, or a trail mixer? Just add a song to your chosen playlist and put your summer on track. Red Bull Summer All Day Play. Red Bull gives you wings. Visit redbull.com slash bright summer ahead to learn more.

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so you mean, like, if that is, like, Like, if there's an outage, can I turn to hypercubic? And maybe you're not there yet. And again, I don't want to go into product as it's developing. But like, can it say, okay, this has happened, this outage has happened. I think this is why. And this is the solution that I can make for you. So I think it's more like they're still going at its currency.

Like there's still going to be a human kind of involves like the person who's going to go into resolve this incident. Instead of them just having to go in without any context, they would sort of go in and just like our interface is like very minimal. If we have like a demo, we'll show you. All you do is like hit a shortcut and just quickly ask a question. It's able to look at your screen, look at stuff on the screen and just like give you like some idea of what's happening very, very quickly. It's still it's still guidance. It's not agent in the sense that it's going to fix it for you. But that could be down the road.

Which is like fixing, modernizing, actually building stuff like because we were. want to be able to get into enterprises with like an initial like that's like a advantage right like this is how we get in this is what we're going to do initially solve an actual problem that helps them out but we do want to have like do bigger things eventually so i think more of like actually we are detecting these systems that is something like or eventually what we want to do is be able to take an old system and like completely really are detected based on all the knowledge we've gathered so far and like how can we make it with as minimal risk as possible. Like that's the like longer term vision that we're thinking of.

Yeah, it's extremely early in the sense that I think I only spoke to you like what in April of this year or something. So like I yeah. Actually, let's go into that for a second. When I spoke to y'all, you still hadn't really, you know, really, launched yet. You were your former Apple folks. So can you tell me where this idea came from, like what the inspiration was and why you felt like you were the ones that could solve this problem? Yeah, absolutely. So as you know, I usually and I met at Apple, we were working at a hackathon, randomly sitting next to each other and then just happened to hit it off. And over time,

we started building a bunch of different projects together. And we had a similar vision in terms of the things we wanted to work on and so on. And one of the components of the vision was knowledge and knowledge retention was something very big that we focused early on. And so we realized, you know, there's a lot of institutional knowledge in these enterprises. Let me interrupt you real quick. Have you personally, either one of you encountered that where there's a gray hair, there's a veteran that has this, knowledge, you were working at Apple or wherever, and there's a problem that you couldn't solve and you're like, man, that person solved it for us because they know.

Like, have you ever had any, like, personal events like that that maybe inspired this? Yes. We've had, like, so many, even though, like, for our situation at Apple, even though it wasn't like a gray head veteran, like, we've come across that situation, like, oh, if that person X had left and if he had, like, his brain somehow. or like if he had his mind somehow. We'd or, because I'm saying he because I know exactly who I'm thinking about when I'm saying all of this. Like if that person had not left, we could have just solved this in minutes, but it took us days.

And like I think some version of the problem of like knowledge loss or tribal knowledge retention. It's more most critical in like the mainframe space, but like I think almost all companies have to deal with this. Well, I was going to even even, even, again, we're using gray hairs. I don't want to be a pejorative to anyone that's that's older than even me. I'm older than you all. But it's also like someone could get hired away or go off and do a startup somewhere or something. And so like that's the institutional knowledge that you can leave. So even even the larger thing is, is like if it's not about retaining talent,

talent can move on, but you don't lose the talent if you have like the legacy knowledge of that talent. All right. I interrupted. So you were doing a hackathon and you hit it off. I'm sorry. continue. Yes, no worries. I mean, are you sort of hinted, foreshouted some of this as well. We, you know, we realized institutional knowledge was a very big problem, especially at some of the workplaces without going too deep into what that is, was. And we've had a hypothesis that

maybe there's other large enterprises out there that are also facing the same issue. And so we just simply went out there, spoke to tens of, you know, tons of, you know, tons of, you know, people out there in different sectors such as banking, defense, food tail, government, and so on. And they realized this was a major problem across all the other industries. And throughout those conversations, we've discovered there is a specific space called the mainframe ecosystem and specifically cobal engineers who are facing this as sort of a hair-on-fire problem because of, you know, the average age is about 55 and 60. A lot of them are retiring. these are very critical systems that are close to the heart of the business.

And we realized we might have struck some gold here. And so we started pulling the thread on this. And over time, evolved into the company we have built so far six months later. And where is the company right now in terms of the product? We'll get into more if you're interested how to get in touch in the end. But like if I am an airline or a, an automotive company or a bank, somebody that's using these legacy systems. How ready is your product for me to use and in what way can I use it?

Currently, we're working with a few pilots right now, some of the largest enterprises today. And so we have fully functioning products of the two things that we've just talked about, the docs and the hyper twin. And so we are working on design partnerships, pilots with these folks. And so if someone is interested in coming along to test them, we would be happy to onboard them, use the products and see how we can work together on some of the challenges that they're facing in their orgs and institutions. And we're thinking we're thinking of things like financial services, aerospace, utilities, government, sectors like that, right? Correct. And one thing I was just going to add, because we're sort of like in earlier stages and like doing design partnerships, right?

So the one thing, the one kind of benefit that people who just reach out to us right now is like, we're able to sort of build the product towards their use case. Like it sort of develops with them and with their cases. So it becomes very specialized for like the kind of early design partners will have like a lot of say in our products. So that's like a both. It's a real. That's like. Let me ask you a personal.

question in the sense of you were both at Apple, that's where you met. Good job, great company. Personally, what is it like to, when you're at a big tech company, good salary, stock options and stuff, to say, you know what, we're going to go off and do this on our own? Is it something that you had considered before? What is the thing that, for both of you, that's, that's says, you know what, we really, we're going to do this. I think one of the things we had in mind was, without going too deep into specifics off the workplaces we've been at, one of the things we had in mind was, you know, we're super young, there's just a lot of opportunity right now.

And the most important was there's a special window of opportunity right now with AI and LLMs and sort of the applications that are possible, that were impossible, you know, one or two two years ago. And so it seemed like the perfect time right now to sort of take a bet on ourselves, take a leap of fate. And that's something that was a shared vision for both of us, if I used to have something very specific as well. Yeah. I think for more personal, if I want to say more personal, I think this holds true for both of us. Like from the perspective of personal constitution, I think both of it were just like, we would honestly go mad if you'd stayed back then or back in, it's not it's not a, it's not a, it's not a dug on Apple or

anything, it just talks about like how fiercely independent or fiercely we wanted to go and chase out something. So like it was going to happen. Like both of us were like even before we, long before we met, like there was like a main bucket list item. It's like you can't too strong to say a bucket list item. Like we want to start our own thing. That was always there even if the AI we have had never happened. So I mean, AI we have just like made it more urgent than ever, but I think we were going to do this no matter what. Well, and it right. Right. So you, you, you, you, you feel like this is our moment, let's meet the moment. But also, as mentioned previous, AI background sort of helps.

So, like, it's sort of like, let me use the analogy of, like, if, like, you're in a city and all of a sudden there's a music scene that hits, and you're like, well, look, it's all taking off. Let's start a band or something. Like, did you sort of, like, feel like your background sort of made you ready for when this moment hits. Yeah, I think absolutely because like Sari talked about, we met at the hackathon and we built like something related to tribal knowledge capture even back then. Like that was our like original idea and we're just like playing around with it and even the whole like even our research that

came before that. So like a lot of this stuff like it was really easy for us to pick up like as things were moving really quickly. I mean there's not like we know everything but we do. We do. I think we do have like many younger folk or like who just sort of doing research in AI or like built in AI before coming into the AI way. Like we had like an upper hand off. Oh, we already understand a lot of it. We just, we just keep being like all you need to is be at the forefront and keep being at the forefront and we've got this.

And the interesting thing I'll add is at least personally, a lot of the dots looking backward started connecting immediately. For instance, in grad school, supposed to, you know, specializing computer. vision. Luckily, that department wasn't that great in my college at Virginia Tech. And so I chose the second best option, which was sort of natural language processing. GPD two just came out. You know, there wasn't much of a buzz out there. But then I did my research, published my papers, and over time, you know, sort of this LLLNGF just started taking off. And so I was already at that forefront of this frontier that was just opening up. And now looking back, it's all just connecting

the dots, basically. As you get older, you sort of see the dots where you're like, well, that obviously was going to happen where at the time you didn't know what was going to happen, but you were in the right place and knew the right people and had done the right education. So you were mentioning earlier projects and things like that. You are going through Y Combinator right now. Demo days coming up at the time of this recording, November 10th. But I think you had applied to Y Combinator a few times before. Like, this isn't your first rodeo. What, tell, tell me the story of applying before and not getting in.

I think size applied a bunch, like size applied like seven times. And I think we've together, we applied like four times. So we've been rejected a bunch of times. And this, the last time we got accepted. But before that, we've applied with all kinds of ideas. Like, there was a time, like, I think it was last. last winter where we applied with like a dating app because we were just like throwing ideas on the board and it's like, I don't know. Si, if you want to add more color to that.

Yeah, I think, you know, I replied a bunch of times during grad school and undergrad almost every semester. I was looking for a way to simply leave college and just go out to San Francisco and build something. And eventually, you know, the seventh time was a lucky charm, basically. But, you know, in the last six months, and what Aeush and I did before we started this was basically, We started throwing different ideas along. We made a list of 100 different ideas that we can basically work on and started just checking each one off in terms of like, what is the feasibility of it?

Could we actually go into that space? And over time, narrowed down into this space. I think a couple other second contenders were sort of something in the health space, if I remember correctly, and so on. But eventually narrowed down to this mainframe space. Well, you know, a dating app or a dating whatever, like that's how YouTube got started. There's a lot of,

there's a lot of startups that started out as dating apps and then people pivoted to things. Yeah. Let me ask you this though, again, like connecting the dots later, like do you feel like, um, uh,

this was, this was the idea that probably, maybe you shouldn't have done it seven times. Like this was the one that was really the one that, uh, it could hit. Like, what is it like when you apply? And then you apply so many times you get turned down and then you get accepted.

Like, um, what is, how does the application process differ versus just throwing something in versus, okay, we want to talk to you more. We want to talk to you more. Okay. Uh, you're in. Whatever your thing. It could be anything. Canva helps you make that thing a thing.

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On Wired's uncanny Valley, we take that curiosity even further. Each week, journalists from Wired break down the biggest stories in tech while speaking directly with the people building, challenging, and reshaping the future. Is the AI boom sustainable? How do you protect your privacy in an age of constant surveillance? Uncanny Valley tackles the questions driving today's tech debates and lighting up your group chats. Listen to new episodes every Thursday, wherever you get your podcast. Brian, I think that's a great question because that was something very clear I noticed in the application where we got accepted compared to all of my previous applications where it was simply a matter of I was working on something else on the side. YC deadline is coming up.

Hey, let me go ahead and apply with an idea that I have. Whereas the application that we actually got in, we were very intentional in terms of making progress, regardless of YC worked out or didn't work out. We were super focused on this idea. We knew there was a big market and a big opportunity for us to go into this space and simply submitted an application. And then we simply forgot about it after. And then we started doing our customer discovery, building the product and just building the business. And so whatever YC says in terms of their advice on building a startup and PG's advice about, not caring too much about YC is actually the truth, which you should be focused on building

your business. And YC is just a byproduct. If you get in, you get in. If not, continue building your business. I take some more color to that, just to give an example of, like, every application that we submitted before, I remember, like, just doing the YC application was so hard. Like, every question was just like, we don't know how to answer this. Like, for every question, we have to spend hours and hours to come up with an answer.

And I think for a last application, everything was. just so clear, we just, I think, Sy just like during like one of, one free evening, he just filled it in the summer. It was just like a very off-hand thing because all the things were just so clear. So I think, uh, just adding on to that, like, if you just build

something with intention and not really in the space full time and just like focusing, the answers sort of just right themselves. That is, that is so amazing in terms of clarity of if you apply, to YC and this can apply to anything. Like you apply to college, you apply to, you know, try to get an agent for a book or something. If it's like, well, the application or what you're trying to do is, well,

this will only work if you accept me. Like, or even apply for a job. Like, it's almost like a hiring agent can understand that, that like, you know, I'm coming to you and I'm crossing my fingers versus what you're saying is on this eighth time or whatever of applying, you're like, well, we're going to do this anyway. We're applying this time because, you know, it would help to get into Y Combinator, but we're going to do this one way or another. And it's almost like that sort of intentionality comes through in an application. I don't know if you agree with that, but I think that's an interesting lesson. Absolutely. I think that's precisely it, which is, just simply not caring about the outcome and just focusing on the, you know, you want to do, you know, you want to build a business, basically.

Yeah, I'm going to make this happen one way or another. Exactly. When you do get in, and this is the last question about YC, but just give me a little bit of color on. What are the resources? What is the onboarding? What is the excitement and things that you didn't expect that Y Combinator does for a cohort company? One thing that was like really, really useful that we kind of, even though we kind of expected, we were still surprised by it. Because there's like a weird heard like when you get into YC, it's really intense. It's really intense. And every time I've asked people like, what do you mean by intense? And there, no one's really been able to describe like where that incitecy comes from. but it's sort of a combination of like your partner's helping you set like really ridiculously ambitious goals

plus you looking at your peers doing really well and like there's like the social pressure there's like a combination of things that's like makes you want to work really really hard and just push what you thought was possible so that was both surprising and not surprising that was one of the things for sure for me I think to add on to that, I think it was second week of YC, and we went into our group office hours, and sort of our partners made us commit to this absurdly high goal that we've never even thought about. And so we walked out thinking this was our vision, but it actually should be up here, essentially. And so I think YC raised up the bar in terms of, you know, sort of shooting for the start in terms of your vision. So that's been incredibly helpful. And then obviously all the partners are they've seen and worked.

with hundreds of startups over the years. And so the amount of information and insights that they have on company building, you know, how to build a product, talking to customers, how to scale. It's all incredible information that we get access to on a weekly basis. What do they do in terms of introducing you to investors?

I think you meet with investors before Demo Day a little bit, but also Demo Day is announcing yourself to investors. But like what are the introductions like in terms of do they do they sort of match make where it's like you would be right for this person I want to how do they do that? The heavy lifting is done by sort of the YC ecosystem and the YC brand. So what they've done is like made it a norm for make the investors sort of approach us in the sense. They kind of they go to like the YC startup directive page.

Look at all the startups and they kind of come in. So the majority of the work is just done by the brand and like the way the whole I think during the whole like three months They partners heavily recommended to not talk to investors just like sit down and just build stuff and Work and do all of that so Rather than it just being like a matchmaking or like an introduction It's just because it's just more Investors sort of just reach out to you because you're a part of IC and you're doing something cool Yeah, sorry.

No, I think the only thing I was going to add is it almost helps you as a founder because during the duration of the time, fundraising isn't even on your mind. And your only focus is to talk to customers, build your product. NYC takes care of the legwork of reaching out to investors, getting attention, booking meetings, and all of that. And so just the brand itself helps you with the entirety of the seed fundraising process. and it culminates with a demo day at the end of the batch where you pitched to some hundreds of the investors at their dog batch HGP. Well, let me ask you about that.

Final question about YC, are you feeling pressure? Are they giving you training on Demo Day? How are you thinking about Demo Day and do you feel prepared for it? Yeah, Demo Day is in about two weeks or so. We're super pumped up to go into the meetings that we have scheduled the next two weeks. Most of the hot companies tend to raise before Demode.

And by Deimore, they're sort of fully, the allocation is complete. But, you know, we've been getting a lot of investor endowed, a lot of interest, just because of the niche space that we're working on. So, yeah, just super excited about what's coming in the next few weeks. Okay, so let's wrap this up by saying, I'll put this out this weekend, I think. If you want to get involved with this, first of all, if you're a company that could use this,

how should I get in touch with you all? So you guys can go to hypercubic.a.i and simply book a demo, and we will reach out to you within 24 hours to schedule something on your calendar. And we'd love to show you the products that we have and how we can fit the challenges that you guys are working on in your old. What about if I want to get involved, are you hiring anything like that? Yeah, we're just getting started. So like we've talked about fundraising three, four weeks from now.

We probably have been done with our fundraising and we'd be really, really looking for amazing founding engineers. So if you're someone like someone who's like who wants to build a company eventually, but once you get really like, wants to compound themselves and like learn like crazy over the next year, two years, three years. We'd love to have you. Love to meet amazing people. And for that, I think, just reach out to me or Cy on LinkedIn or email at us at team at hypercubic.a. We don't have a formal careers page yet because we're quite early, but we are starting to look at hires. And that's like the biggest reason for us and most companies to fundraise. Quick edit here. There was one more thing that, uh, uh, Sai wanted to include that, uh, I forgot to

So, Cy, go ahead. If you have any leaders' connections to leaders in the mainframe ecosystem or large enterprises in the Fortune 500, we would love to speak to them if they're maintaining or have ownership over these mainframes or if they simply have connections to those that are working on these systems. We'd love to get in touch with those. So please feel free to reach out if you have any connections with that space. You don't even have to be those people. You can be the people that know the people. is the point. Yes, exactly. I would also say if you're a listener to this pod and you know me well enough, email me at Brian at ridehomefund.com and I will put you in touch with the guys. And an amazing opportunity. Like literally, if you think this is a good idea like I did eight months ago to invest in it,

you can get in on the absolute ground floor. Listen, again, I want to repeat hypercubic.a.i. Ayush, good luck on Demo Day, but thanks for coming on to tell us all about this. Thanks for having us. You can't reason with the sun. Trust us. We've tried. This summer, it's time to put that angry ball of fire on mute.

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