Inside Hanover Park: Financial Infra for $100 Trillion in Assets | Chris Hladczuk

Chris, welcome to The Peel.

We're back.

The, the second ever second-time guest. First one was Dan at ChainGuard, which they went 0 to 40 in revenue in 2 years. It's— those are big shoes to fill.

I know we went 0 to $15 billion in under 2 years, 20 months to be exact. So we're going.

$15 billion in revenue?

No, no, that would be a lot. That would be a lot of revenue. I'm not Cursor. Assets on platform.

Assets on platform. Okay. So for someone who isn't familiar with Hanover Park, they didn't listen to the first one, which we'll link in the show notes for people if they want to listen to the year-old conversation. What is Hanover Park for people who don't know?

You can think about us as financial infrastructure for the investment firm. And so today there's like all these insane armies of human duct tape, we call it, that sticks together all the back and middle office to do all the financial reporting for the limited partners at these firms, for the CFOs and all that kind of stuff. And what What we've built is like unified software plus AI plus services to do all the financial reporting, which would be called fund administration. Um, and then we also have a bunch of portfolio management and monitoring tools and kind of like our thesis is that, you know, B2B SaaS is dead, which I said literally on this podcast a year and a half ago. That's come true with Claude. These like thin layers on top of data that you don't own are things that are being disrupted. And so our goal is like build the system of record for the fund. We did that, and then owning and controlling that data can deliver better product for the CFO.

So I know you just announced some news within the past day or 2, 3, whenever people are listening to this. What's the thing you just announced?

Breaking news heard here on The Peel. Uh, we, we raised a $27 million Series A led by Emergence Capital. Lux Sousa participated as well. to build the financial infrastructure for the investment firm.

And how did this kind of come about? What was sort of the setup sort of leading into it and the process of raising a Series A? Because it can be hard sometimes.

We were growing really fast. We went from $1 to $7-ish billion of assets on platform late last year. People were super excited and we were kind of like, what I wanted to validate is the size of the opportunity, right? It's like you have $100 trillion of global assets, Largely these investment firms are paying millions and millions of dollars a year to these, you know, outsourced providers. And we said, like, can I provably take this from a human-heavy services business and something that's scalable? And so that's what we validated. And then we went to the market. It was a very quick process. It only took kind of a few weeks or so. And there was this crazy moment where Jake Saper, who joined our board from Emergence, flew in from San Francisco to New York City on Friday night. He's like, I'm going to take you out to dinner. It was a 3-hour-long dinner. And I was like, when are we actually going to talk about the terms here? Are we just going to keep looking at each other eating omakase back and forth? And at the very end, he's like, all right, so let's talk about the terms. And so we end up negotiating these terms and literally nothing— it's Friday night at 9:00 PM. I go back to my apartment and at midnight I get a phone call. I'm like, why am I still up right now? And he's like, term sheet's in your inbox right now. What are you doing? You have 48 hours to make a decision. I was like, oh, that was fun. So I was like, this is great. Now we'd done a bunch of backchanneling with Jake and he had been super close to the team at Sousa as well. And he has kind of been the leader in what I call like AI-enabled services, which we can talk about. And so pretty excited to team up with him and the Emergence team.

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Yeah, it's very, It's like army of humans with like a set of disconnected tools that people are buying. And so if you— they might buy, you know, you have a bunch of people in Kentucky, they are fund accountants, they are CPAs, they buy QuickBooks, they buy Excel, they buy Bill.com. They don't build any of their own tech because they call engineering IT. They don't have engineers on their— at their companies. And so a lot of it's just disconnected SaaS tools with human duct tape on top. And so kind of the opportunity set is like, we're like, okay, if we build this ERP for the fund, right? Which is like this, like everyone told us from day zero, this is crazy. Don't do this. Don't build this insanely hard thing to build. You're building financial infrastructure for the most complex firms in the world. We did it. And it was like, if you build this, that gives us the opportunity to have AI agents action on top of that data. And so instead of having people clicking buttons, you can have agents actioning. And then kind of the paradigm that we've had is AI's preparing things and then Humans are reviewing things. And so we have well-trained fund accountants on our team that are reviewing outputs to ensure accuracy, but that's never been done before. Most people, I think in 2018, would be selling software to a fund admin instead of saying, I'm actually going to be the full-stack fund admin.

Yeah, because a lot of people then would say, what's the size of the fund admin software market? And it's like—

Tiny.

Yeah, it's not existent. I mean, PayForBill.com and QuickBooks maybe. Intuit is a big company, but like, you know,

it's tiny.

And so what kind of changed then over the past couple of years to just make this possible? And then you talked about how you built your own general ledger. Why did you need to build a general ledger? So like what's kind of changed and then why did you do what you did?

Yeah, I think the big why now about this market is like it's possible now with AI to act as a fund accountant. We call it like an AI fund accountant. And so it's like, okay, we can take this thing that used to be really disconnected toolset with a bunch of humans in a room trying to click buttons and do work, to agent can actually, like, click the buttons and do the preparation work for our team to review. And so I think it was a lot about what does AI unlock? But what I tell the team all day long is like, if you don't have the right bedrock, the right, like, core foundational system of record, it doesn't matter what AI is doing on top. Um, AI is not going to figure out bill.com, which randomly isn't going to give access unless you're doing some computer use agent. AI is not going to figure out Excel because there's a lot of limitations. AI's not going to figure out QuickBooks and other things. And so we said the 80% is building the general ledger from scratch, and then on top of that, having agents action on it. So I think the big change here was it's now possible to have agents doing work that would require an army of people. And now our fund accounting team can just review work.

And it sounds like really what a fund administrator is, is basically an accounting firm for an investment fund. Like in the most simple terms.

Simple terms, financial reporting. You know, they also do like investor services.

And you kind of talked about how we're entering this era of you can build an AI-native services company. So traditionally, if I'm thinking about from like a technology business perspective, I'd look at a fund administrator and say, you know, they're not building any technology. It's just mostly people. It doesn't scale in the most efficient way. So what's kind of happening in this sort of like broader AI-native services? Like, are there certain things that's that needs to be true in order for that to, like, unlock, like, this massive opportunity?

Yeah, I think we think a lot, and this is something we've spoken a lot at the board level in the past, like, quarter or so, which is like, how do we productize the services layer? Which is like, we are tracking key metrics on things like what percent of cash is auto-categorized, um, what percent of times when a customer sends us an email with a request that AI agent can action on it with no edits, right, by the, by the human team.

This is sort of like a customer service type of automation as well. And it's like, so you're automating like the accounting, you're automating the customer service, you're automating like other software that you're kind of starting to build on top of things.

Yeah, a lot, a lot of it is like, like productizing these like core services delivery metrics, we call it, which are things like cash categorization, email agents that are acting on like customer requests and other stuff. And so that's been like the key part. Without that, you are just— and I think the reckoning will happen in 2026 for AI-native services businesses. that are growing revenue but not actually productizing the services part.

Oh, wow. Is that a common thing?

Very common. I think a lot of people are doing that.

Are there certain categories you think it works best in? Fund admin? I'm assuming that's one of them.

Well, I'm talking about my own book now. This is talking about my own book. I think there's, there's a lot around like, what is the data layer that we have access to that can enable us to deliver a better service? And so part of what we've done is said like, how do we build wedge products around the core fund admin? Because if I go to someone and be like, I'm going to give you slightly better fund accounting, or I'm going to give you 10 times better fund, that's still kind of interesting, but not the most compelling. And so we, we built a lot around AI-native waterfall modeling, which is adjacent, or portfolio management, which is adjacent, or portfolio monitoring, which is adjacent. So it's like, what does the full bundle look like? That's not just the core fund admin.

And so the kind of like the core customer or core user buyer decision maker of this is a CFO of an investment firm, like the finance leader in an investment firm.

The CFOs are my heroes.

Yeah, I know. I know you love, you love CFOs. So what, what can they use AI for? 'Cause you just mentioned so many things like a waterfall model. Maybe you can explain what that is. Maybe it's not worth explaining, but you can just like make that in Excel kind of, right? Like, so what's the importance of all this stuff?

So I think there's like, our goal is to become the default for how fund CFOs adopt AI. I think there's a lot of noise in the market. There's a bunch of providers. I call it the army of overpriced SaaS tools that are offering like, here's what AI is. And the CFO is like, this doesn't actually help me. And so part of our goal was we're launching an MCP server, which you can think about that as an API for AI, right? Which is like, I can have all of this data that Hanover Park has inside, which is your LP data, your, your portfolio data, your fund performance data, all the data that might be in disconnected systems is now in one place. And now you can give that and feed that directly into Claude. You can feed it into ChatGPT or Gemini, and now you can instantly access and say like, generate me a report with Yale Endowments commitments across our 5 funds with a pretty graph with my logo on it, with our top 5 performing portfolio companies by ownership percentage, MOIC, and gross IRR. That is like literally 100 hours of work. to do across a bunch of random tools today. And now you can instantly do that through Hanover Park. And so I think that's some of the power is being able to like feed this into the tool you're already using as well.

And that's even something that's something like the IR team might make. So it sounds like it's not even just CFOs, it's like other people at the firm can maybe start using the tool too.

What we realized is there's so many like CFOs and finance teams are like drowning because they get like hundreds of data requests. are like, hey, can you pull this random initial versus follow-on thing of my venture fund portfolio companies that we have no idea what the stat is? And the CFO's like, oh my God, what do I do? They're like, do I just run around and try to go into 17 spreadsheets that haven't been updated in 3 months to figure out where this is? Mm-hmm. This enables anyone to instantly access your data, as well as the managing partner or the founder of the firm. So they don't have to be begging their CFO to get back to them in 3 days. They can instantly access it, which gives a lot of credibility to the finance team.

There's a lot of these kind of like, I don't even know what you call it, like banker AI tools or like, you know, junior credit analyst type AI modeling tools. Does this start to kind of flow into that? Not really. Like, where's the delineation between all that stuff?

Yeah, I think like part of our— we call ourselves the anti-GPT wrapper. There's a lot of companies that are fine that are basically like, I am going to take GPT, I'm going to verticalize it for something specific. I'm going to enable it to be better for a lawyer, a banker, or whatever it is. Um, part of our goal was actually we're the opposite of that because we did the really hard thing first. Building a general ledger from scratch is one of the hardest technical challenges you have. So hard that our early investors were like, what is going on? This is crazy. And so we're like, once you do that though, you unlock all the ability for AI to action on data that you couldn't before. And so part of this was building the GL sets the stage to build the magical MCP server on top versus the opposite, which is I'm going to get data that everyone else has access to and then I'm going to make it slightly better on the AI front. So that's kind of how we thought about the paradigm, which was different.

So, and it's basically, you know, you're— people you might describe, if you're trying to like hype up this in the most simple terms possible, you'd say it's like an AI-native fund administrator.

Yeah, I think AI-native fund admin— strike that. Fund admin, most unsexy term in the world. I kind of think about it as like financial infrastructure for the investment firm. So Stripe did this for payments, Ramp did this for expenses, Hanover Park's doing it for investments. Okay. That's kind of how we think about it.

And like, you didn't even start with like, hey, we're building an AI model for your fund. You started with like, we're building a general ledger that— I mean, general ledgers have existed since like the invention of commerce, basically, like ancient Italy or whatever.

Yeah, I think, I think the vertical integration story is around like, okay, what is the core job to be done? Every single fund in the entire world needs to generate quarterly financials for their limited partners. Great. You're going to have to pay someone to do that. May as well be us, right? And kind of think about that as like the core layer. But on top of that, now it's like, okay, I need to help a CFO make better decisions. Great. All that data is now sitting in Hanover Park to help you make better decisions. On top of that, maybe in the long term, it's like, How can I unlock alpha for the investment firm? How can we have this investment firm make better decisions on top of all the source of truth data that Hanover Park has? And so that's kind of how I think about the layering from most unsexy to most sexy.

And so what makes it so hard to make a general ledger? Because it's just like some accounting, like it's like debits and credits. Like, I don't know, it just seems— it doesn't seem that hard.

Yeah, I know. So I think partnership accounting— and this is— tune out if this is the most boring thing you've ever heard in the English ever. So Um, part— there's a lot of complexity with fund accounting. And so we're kind of like, okay, build debits and credits for a simple small business is like table stakes, honestly. But saying, okay, I have all of these different limited partners in a fund, they have different allocation percentages, they have different side letters waving economic terms, they have blockers and splitters and aggregators, and they have 50 legal entities for one fund. All of that complexity of the interaction between all these legal entities and all of the partners in those entities Gets crazy.

And then there's multiple funds sometimes too, right?

Dozens of funds. And then you have a management company that needs to talk to the funds. And so I think the complexity lies in like, think about these investment firms as the most complex financial institutions in the world, and organizing that data is crazy, right? And so we kind of had to build a system that can handle the most complex investment firms in the world.

And is this sort of why it was just like a, let's just throw some bodies at this and people just manually do it?

Correct.

That's kind of how it works.

And I think a lot of the underlying tools that were built, there's some like specific fund accounting tools that people had tried to build, but it's like the problem is there was a disconnect between the people doing the work, the fund accountants, and the people building the product, random engineers at a different company. That disconnect created tons of problems because no one's actually talking to their quote unquote customer all day. And so what we've done is said, hey, we have this 60-person room in Flatiron where we all go to every day called an office. Let's all put the best fund— I call it the Navy SEALs of fund accounting alongside, you know, lead engineers to kind of like work in harmony.

And you— the last time you came on the podcast, we had an interesting conversation where the traditional fund admin sees like an engineer. They call them the IT department. Like your job is to like get people access to like the HR system or something or, or, you know, sign up for QuickBooks and generate someone an account on The different software you use.

I love you talking about accounting. This is hilarious.

Well, and so, so is— so just basically like who you're competing against. You're like, you are an engineering product-led company that is competing against people who think about it as an IT department.

Yes.

Basically, like it's not— it's like a back of house versus front of house thing.

Yeah, I think, I think I think about like, of like, if you're one of the greatest engineers in the world, and we've like built this hacker culture, you know, we had somebody dropped out of Harvard, you know, he was a sophomore in college. We have a Team USA drone racer. We have people that have multiple, multiple ex-founders, former and future founders, I call it. So we built this really talent-dense engine product team. Those people would never in their wildest, wildest considerations or nightmares join a legacy fund admin who doesn't care about them. And so our goal is how do we build this financial infrastructure for the most complex firms in the world? If Ramp can make expense management sexy, we can make investments sexy. Right. And so I kind of think about this as like the most elite engineers on the planet will never join a legacy fund admin because no one cares about them there. What we've done is say, how do we build this like world-class team of engineers, people that drop out of Harvard, Team USA drone racers, shout out JT, former founders, future founders, like people that are top 1% that might join an AI lab, say, hey, I have this like deep obsession with like building for fintech and building for the most complex investment firms in the world. This is a series of insanely hard technical challenges when you think about the data complexity that we have. Let me go do this here. That's very different, obviously, value prop than a legacy fund admin.

Yeah. Okay. So you've built this environment where the top 0.1% of engineers want to go. How do you convince people to join? And then maybe another interesting thing to hit on is how has that changed over time? If somebody listened to this first conversation, They can hear your original philosophy. What's changed over the past year?

Yeah, I think I want to— I kind of say like growth solves a lot of problems. We went, you know, $0 to $15 billion in 21 months since our first line of code. That level of growth proves to the most skeptical engineer on the planet like, hey, this is working. And if I have a deep interest in fintech, this could be compelling and interesting. And the second piece is like, you are what you prioritize. There's a lot of companies who have 100 people on the team and 80 salespeople. That is not the company that I'm building. I have still scaled us to $15 billion of assets with no other salespeople on the team, primarily because I'm like, I'm just a founder with a product and a plan. We don't need an army of salespeople. That resonates with the best engineering teams and the best engineers on the planet because they're thinking like, okay, what is prioritized? What is hired for? If this is an eng-first hacker culture, Yeah. That matters deeply when I think about like how I'm going to be prioritized, first-class citizen, etc., in the type of company I'm at versus an army of salespeople that are like pushing on engineering when it comes to the build, when it comes to building.

So then how do you guys figure out kind of what to build when you're talking about like the engineering being first class? Like, are you— and it sounds like it's literally just you, like, are you talking to customers? Are engineers joining calls? Are engineers like using, demoing the products? Like, I know I've seen— I think I've seen something around like when I was hanging out in the office one day, like everyone uploads stuff or something. I may be remembering the story wrong, but—

So we have demo night every Friday night where literally people are like showing what they're building, both to the fund accounting team as well as on the engineering side, which is really fun. Um, but kind of how we think about what to build is we have a Slack channel with every customer. I literally, I said I live in constant paranoia that my customer you know, is finding a better solution. Just like this Jeff Bezos quote of like, oh my God, live in fear that your— not that your customer's like unhappy, that they'll find something better. And so like, we constantly want to be like pushing the envelope to invent on our customer's behalf. And so we take that super seriously when it comes to feedback and being in a Slack channel with every customer. Our engineering team is like blown away by like how much this matters for the CFO. If we are their most important vendor, the things that we deliver for them can really change everything. And so like, The level of importance creates a sense of urgency, especially on the team. And then on what we're building, what we're working on, it's like we really said, start with the unsexy, automate core financial reporting, automate core fund admin. And then now we've built a series of portfolio management and portfolio monitoring and all the adjacencies that should live in one place that haven't, but stay tuned for more stuff.

So can you give me some examples if I'm not super familiar of what some of those tools might be and what I'm using them for?

Yeah. So say, you know, you invest in Hanover Park at the Series A, you know, you get a set of legal documents or cap table or something like that. Typically these legacy providers, all they do is like save that brand, those docs randomly.

It's in a random folder.

It's in randombox.com or Google Drive or something super random and be like, oh yeah, like, I guess I'll look at that if I need to look at it for audit at the end of year. And they'll like book some super boring accounting stuff that the CFO doesn't care about.

Sometimes there'll be, there will be like a shared drive though. You can access it from like your browser and you can download it.

You can download, you download the file. And so what we said is we were like, amazing, we have this leverage position because we're doing your fund accounting that we have all those docs in real time. And so you just forward that over email to us and say, hey, I don't need to click 50 buttons in a UI that was built in 2018 to create more work for me as the CFO. I can just delegate to a Hanover Park AI email agent. That email agent reads it, it uploads it, it extracts and processes like 150 key terms. And so now I've enriched and been like, hey, not just cost and fair value, I have how much do I own of this company? What's the ownership percentage from the cap table? What's the latest valuation, post-money valuation? Who are my co-investors in the deal? Right? Like all these other like important details that should live in one place that don't. And so we bundle this like quote unquote AI portfolio management tool in for free alongside the core fund admin services.

And I think you mentioned it took you about 9 months to kind of make this. Am I remembering that number right?

Boy, yeah. There's a— so this is a very complex technical— like, if you're an engineer listening to this, it's like you want the hardest technical challenges in the most complex industry. Like, this is the place for you. We've processed 200,000 documents at this point. I'm talking random Indian language docs where AI translating into English and like all this crazy complexity. In the pursuit of like, how does the customer lift zero fingers, just forward it over to us and let us take care of the rest?

And so like the, the, what was like the hardest part about doing all of this? Is it like natural language processing, like extraction? Is it like organizing it in like a sortable, efficient way?

The extraction is really complex because like, you know, it's really simple if you're doing a Series A in the United States like we did. But like, if you're doing a deal in Europe, if you're having a random, like, mistake made by the lawyers on the cap table, okay, if we have like a random Excel file that doesn't make it in that we don't get access. And so there's just like, I call it the land of infinite edge cases, which is just like highly complex documents that like, you know, kind of what the answer should be at the end potentially, but it's really, really hard to figure out with all the different legalese that's in there. And so that was part of the, part of the challenge.

So you walk into the office at Hanover Park in the morning. What's it like?

So at 9:00 AM exactly on the dot, I literally walk out of my office and we just have a speaker blasting music. My team will hate this, but it's literally one of 2 songs every single day for the past 21 months since we founded the company. It's One More Time by Daft Punk or it's Levels by Avicii. There was a period where we shifted a little bit, given literally my entire team now says to me, These are the songs that I never want to hear outside of work ever again, but we do that. And so it's a fun environment where we're blasting music. We go right into engineering standup. We then go right into the accounting team standup and we kind of get right into our day. But my goal is how do we create this overwhelming sense of energy and how much we're building and how fast things are moving?

Is there a gong in the office?

How did you hear about this, Tyler? You heard about the gong? So So we have this gong in the office. So we've moved offices 3 times in under a year. It's been very entertaining for everyone involved.

I actually haven't seen the new one yet.

You haven't seen the new one? How close is it? Yeah, it's right. It's right near here.

Okay.

Can I stop by like right after this? You can stop by after.

I want to see it right after this. I'll head to the airport.

So, so we have this, we like in our first office, which was this tiny little office in FiDi, we had this like gong. Anytime we would get a sale, anytime something great would happen, we did this. Now we've moved offices to much bigger offices and we still have a small gong. We've gotten people to say, why do you have this tiny gong? You need to have a bigger gong, Adam Neumann, WeCrashed style. We have not upgraded yet, but the gong is very— the only people allowed to ring the gong are one, customers. And so we literally had a customer in the office the other day and I told Johnny, I was like, Johnny, I know this sounds really weird. I'm going to now interrupt the entire team of what they're doing. And they have to ring, you have to ring the gong. So he comes over, he rings the gong, and the entire team goes wild. Like literally people are cheering, they're about, like, I think people are going to like jump on top of their desks at this point and people are getting fired up. Or we ring the gong when it comes to like we ship some massive product update or close a sale.

And so those are the only people that are allowed to ring it, right? No outsiders?

No outsiders.

So I technically cannot ring the gong.

No investors.

I'd have to be a customer.

You'd have to be a customer.

Which I need to hit that institutional scale. I don't have a CFO. It's just me.

We're just CFOs.

Soon, soon we'll be on that level soon. And so you had this one guy who I think he biked to work during a snowstorm one time or something. Like, what's the story with that?

So I got to give a shout out to Philip. We, we literally, it was like a Sunday at 2 p.m. There's a crazy blizzard happening in New York City. And of course, I don't expect people to be in the office. This is like hunker down and survive.

Yeah.

Somehow, Philip decided to go and rent a Citi Bike and bike in a 10-inch snowstorm to the office. And he shows up and he's like, I just biked here. I'm like, how are you alive right now? And he just goes right into coding and he's like, I'm good. I'm just biking. So I'm like, okay, this is what we screen for though. We have forces of nature that are figuring stuff out.

Was the subway down because there was so much snow? Or what was that?

I think he's anti-subway. That's what it is. He's not a subway guy. He's a bike guy.

Interesting. I mean, the streets would probably be open, very clear. You could probably just keep cruising. You probably wouldn't have to stop.

Yeah, maybe. Yeah, it's crazy. It's like if it even got plowed that day.

So then what do you guys do on Friday nights? I know you have a Friday night thing that you do.

So we have this thing where we love to celebrate Like our product velocity is everything, right? Like we're literally doing multiple releases a day. I would say compared to many legacy players who call engineering IT and don't actually ship net new product, our goal is like, how do we have the highest product velocity out there? And so we have multiple product releases a day. And what's really fun is we have engineers demo what they built like every few weeks on Friday at like 6:00 PM where they'll literally like everyone will huddle around what we call the podcasting couch. It's not actually for podcasting, but—

Podcasting couch. We should have done this later. Yeah, we should have.

All the podcasting guests. We have this massive couch, which people have gotten excited about, um, that literally everyone goes onto that couch and like we demo like the latest and greatest and people go crazy. We're playing music, people are getting fired up about what's going on. And so it's been a fun little ritual to inform the broader team as we've scaled a lot in terms of team in the past 6 months to like tell everyone what we're working on.

So this is not like a, like a hackathon, random like side project type of thing. It is, you're showing what, what you built in the product.

Yeah, exactly. Yeah.

Okay. And you said you're shipping multiple things per day. So is it Friday, you spend like an hour and just everything that was shipped, people just kind of go through and show what they shipped?

Yeah. And there's a lot of times when people get feedback. So like we'll ship something and it'll be like an iteration and the fund accounting team will be like, oh my God, this could be amazing with these 2 changes. Or hey, we get CFO feedback and people are excited. I actually want to start bringing on, bringing customers to the office for these Friday demos. To see the latest and greatest. Because like what we've had is like I had a customer call me and be like, hey, you're our most important vendor. I love Hanover Park. I don't think anyone's ever said that about a fund admin before. Or like, I love Hanover Park. Can we host a customer conference at the office and can everyone just like jam and huddle about like what the future of finance can look like in this space? And so we've not done that yet. Stay tuned. But it'd be great to get more like live feedback at these demo nights.

So when somebody comes across Hanover Park, what is like the reason that they choose Hanover Park versus— I mean, there's a couple other options that are out there. Like, what is usually the, the discovery process of finding it and then the decision-making process?

Usually all the problems come from as you're— if you're just, you know, a $50 million venture fund and it's super simple and it's one person in a room, like things are super simplistic, like there's not a lot of complexity. But as you scale, all of these manual processes break. And so you're like, it went from, oh, I did this one manual process for 2 hours a month to I'm doing 5 hours of manual processes a day just to tread water. And so therein lies the problem with all the legacy players where things are break— broken and held together by legacy human duct tape and Excel, right? And so that's problem one. It's like, oh my God, I can't scale. Problem 2 is like, I've heard of all these good things about AI. ChatGPT's out, Claude's out. But what has it even done for CFOs? Nothing, right? And people are frustrated by that. And so it's a combination of things are breaking while we scale and, oh my God, how can I adopt AI? And so we're kind of at the intersection where it's like, hey, instead of buying a random army of overpriced SaaS tools on one side to complement a broken system of record, which is your core fund admin today, why don't we marry that into a single source of truth? where you can expose all of your data to an MCP via, you know, LM to Claude or ChatGPT or Gemini and like be able to action on your data. And so it's this like, oh my God, my data's super stale, everything's broken as we're scaling, I have no idea what's going on, I have no visibility, to, oh my God, the only way to duct tape this together is buy 20 other SaaS tools. And we, we're— we solve both those problems.

Isn't it really hard to To switch a fund admin though? Like, it's got to be one of the most difficult problems in the history of humanity.

So I joke, I literally on like, I'll be on like a first call with, with a CFO and I'll be like, look, I know I'm asking you to marry me on the first date, but here's what we're doing, right? Because like it is a— and I take that like insanely seriously of like the level of trust that our customers put in us. Like, that's what keeps me up at night, right? Of like how much I care about, about their trust and And what that matters. And so one, it's a massive decision. 2 is, oh my God, Hanover Park's amazing, but isn't it such a pain in the ass to switch? That's literally what everyone says.

And so that seems like that would be the disqualifier. Like, seems cool, but like, I don't want to do this.

I don't want to deal with this.

I'm not going to put up with this.

And so I'm, I literally tell our team, like, we're building a one-click migration future. Today it's not one-click, obviously. We did migrate someone in, you know, only a handful of days, which was pretty crazy. Shout out Mackenzie at Asylum Ventures. She's amazing. And so we did that. But our goal is now, what's possible with AI is there's a lot of long horizon agents that can run for hours and hours and days and days, and we're experimenting right now on how that can supercharge migration. And so I see a future where we get to one-click migration.

Really? What needs to happen, you think, to get there? Just do the models have to get better? Does the context windows have to get longer?

I think it's just if you're a world-class engineer that's looking at this, this is an example of a really fun tech technical challenge to work on, which is super challenging technical problem wrangling the most complex financial data in the world. It's really just like eng bandwidth plus some pushes on the frontier of how do we think about these long-running agents that need to operate over days and weeks, not minutes.

So that is probably the big overarching problem that people are trying to solve right now is cracking how to speed this up.

Yeah, I think it's a massive problem that Traditionally, people have like hired an army of humans to solve it. And it's like, well, if that doesn't deliver a 10x better experience, like what would? And I think there's an opportunity to do that.

And I know there was a time where you— I think when you, when you first kind of started the company or getting customers, and it's even like harder ask back when you started it, you— I think you promised that you'd literally sleep in people's office until the migration was done. Is that, is that still a thing that you promise? Or—

So I, People have been like, don't sleep in my office. What are you talking about? I don't want you here, Chris. I don't want to spend more time with you. So that happened. It did. I'll never forget, though, one of our earliest customers. And like, by the way, I have like a crazy place in my heart for like our earliest customers that trusted us with their most important financial data. And I was one of our earlier customers and I'm on a call with him on like a Tuesday and he's like— and I'm like, hey, where do you— where do you live? Where are you? And he's like, why are you asking me where I live? And he's like, He's like, I'm in this random town in Florida. And I'm like, okay, I'm booking a flight right now. I'm coming down to Florida. This was the first call. He's like, who is this guy?

I'm like— So he wasn't a customer yet?

No, a prospective customer. And I literally booked a flight and I go and literally me and him spend 5 hours at his house till 2:00 in the morning, basically talking about how the future of finance is possible. They became one of our earlier key critical customers and has been like a massive advocate for us. So it's credit to them and kind of like what happened. But in the early days, like, do whatever it takes to win.

What did you talk about in that 5 hours?

Like,

what was the biggest takeaway?

Everything that was broken about the current system. He had been using a legacy fund admin. He had been duct taping additional tools on top. And because a lot of the problems is like, oh my God, this might be surprising for the audience, but like CFOs don't have visibility in real time of anything that's going on. That's crazy. Think about you're making $100 million investment decisions, but you don't know what's going on today in your, with your data. And so he was constantly struggling with, I have no idea what's going on. I'm asking these humans to book cash and do super basic things. It gets delayed by weeks and weeks and weeks and months. And I tried to buy another SaaS tool, but it had the same problem because it didn't talk to my underlying data, right? So it was just a frustration layer. And with what's possible with AI now, like I think now is the time where we should solve that problem.

Speaking of real-time data, I have another portfolio company. She was just on the podcast a couple of weeks ago, Arty. They basically enable real-time data, basically syncing between all your databases, the data warehouse. I don't know if you guys have tried it yet, but I'm giving a

plug. Oh, this is the plug. I'm giving a plug. This is a on-pod plug. Have not tried it.

You've not tried it. It might be interesting. We can, we can maybe talk about it after. I'll throw a link in the show notes if people want to check out that episode. But basically, like, their thesis is if you have AI agents that are like doing things automatically, you just need real-time data. Everything needs to be synced because if something's off by, let's, you know, let's say you have to wait for a sync or the data's off and the agent starts to make a decision like you're making decisions and it's just doing things with incorrect information.

100%.

So you mentioned getting on the plane. You jumped on this plane, went down and talked to the customer in Florida, spent 5 hours at his house. That's something, the first episode we spent a lot of time talking about what you had learned about sales before you started Handover. You spent some time at Meow. I don't know to what extent we should re-hit on that stuff and to what extent we should talk about how your thinking about understanding how to sell things, understanding customers has evolved over time. But it may be interesting for people hearing this for the first time. What was Meow and what was your journey like there?

Yeah. Ended up joining Series A fintech that was doing business banking, helped us scale from, call it, 10 to 1,000-ish customers, became chief revenue officer of the business. Brandon and Bryce, the founders of Meow, were the first checks almost pre-idea into Hanover Park today. And so ton of respect and credit for them, but it taught me a lot. It literally, SVB collapsed, we scaled exponentially, closed hundreds of customers myself personally. I wrote this almost manifesto, which I call Always Get on the Plane, that I think has become popular now. I think people are just stealing my tweet and retweeting it, but it's like—

You came up with this?

I came up with this. Yeah. So I wrote this thing of Always Get on the Plane, and the key principle's the following. If you are asking someone to trust you with something as important as what we build and what we sell, the fact that if I won't get on the plane to spend 4 hours in a room with them, This is a decade-long relationship.

Yeah. Can they really trust you if you're not going to do that?

And it's like, well, I can't build trust over a Zoom call, right? And so we— I like did this, I popularized it, and now like it is like one of the key philosophies, especially for us. And so, you know, I was in— there was a 10-person finance team at a massive private equity firm that we're talking to that like I literally told them, I was like, I'll just go to your office wherever in random place in California. And like, I'm going to be there for 5 hours with their whole finance team. After 5 hours, you really get a chance to know someone and you build that level of, let's just be honest with each other about what the trust and the blunt feedback is. And so I've obsessively been focused on this always get on the plane thing, and I'm glad to see people are taking it.

Yeah. So how do you manage that though? You're trying to run a company, you're trying to hire and recruit people, and then suddenly in 2 hours you got a flight you got to jump on and you're spending 2 days in a random town in California.

Yeah. So I look, time management, prioritization, there's just like, there has to be like a level of like, okay, how do we think about what is the highest ROI thing? I think a lot about like, I've taken a lot of inspo from Elon on this of like, what is the limiting factor in the company? And then how do I parachute myself into the biggest problem? And how do I take that and like obsess over that problem until it's solved? And so, you know, limiting factor is migration. We need to go like, parachute in, work with our head of ops, Emily, who joined, and obsess over what does AI-native migration look like? So at different times, whatever the limiting factor is in the company, my job is jump into battle and figure it out.

One of the things that you've done really well, people call it founder-led sales, where you're, I think you said you're the only person that's on the sales team for—

Still, I don't know how. I'm just a founder with a plan. I'm not even a sales guy.

Yeah. So maybe it's not sales, it's just trust building. You're trying to understand customers, trying to solve their problems. So what have you, what's been the biggest learnings you've had over the past 21, I was gonna say 21 years, 21 months since you, since you wrote the first line of code and started onboarding customers?

Yeah, I think for, and, and we're definitely in founder-led sales mode too long. Uh, we're at $15 billion of assets on platform. That's probably a little too long. And it's just been, it's been something I've held onto just given how critical and, and customer trust is and how that interacts with like product and eng on that side. But For me, I've really tried to transition from how do I get our first 5, 10, 15 customers into a position of how do I make sure that these people can become raving fans? Which I think is a very different philosophy. And all the worst parts about sales in general of I'm just trying to get them some sales guy with commission, obviously I'm a founder with no commission. And so what I think about is If our number one goal in 2026 is every customer's a raving fan, that is very, very hard in our industry. That's like the craziest thing ever, right? And so with how unhappy most customers are with their current providers, and so my goal is that how do we set and manage expectations prior to signing any contract to ensure that they can be successful in their first 100 days plus? And so it's a lot of been like, hey, we've proven we can do it with your peers, with your counterparts. Here's 5 other customers that are exactly like you guys that we can service. Yeah. How do we make sure that you're a raving fan in 12 months?

You mentioned it was different getting the first 5 to 10 customers and making people raving fans. How did you get the first couple people on board? Because you had built this general ledger from scratch, you had no customers, and then suddenly you're like, hey, trust us to manage your fund.

So I got to give a lot of credit. I'm going to give a shout-out to Chad at Suza, founder of Suza, Protush. Kenny, who's their CFO. I love you, Kenny. You're literally like, he was our, they were our first customer. And so I literally was like, hey, we're doing this. You're our first investor. They didn't want to move over everything to start. That's a crazy thing to be like, hey, move over all of your funds to an unknown provider called Hanover Park that you invested in a seed round for the most important thing for your firm, which is all of your LP relationships. And they were literally like, It took some convincing. It took some trust building, but they ended up doing it. And that was obviously a critical first customer. But the people after that, it was like, hey, I will literally do whatever it takes to ensure that this is successful. I don't care if I need to sleep at your office. I don't care if I need to book the entries myself. It's just all about, hey, we're taking this journey together. Here's why this can be a super compelling future. But look me in the eyes and say, I will deliver for you. And that's how we got the first 5 or 10.

And you had this concept of, I think it's called polite persistence. Is that the phrasing? Yeah. So was there a lot of that? You had to continue to be getting in front of people, continuing to— what was that process like?

I mean, text, call, carrier pigeon, email, LinkedIn DM, Twitter. It was really anything. And I kind of see like in the earliest days and even now it's like whatever it takes to win, do. And I think like people don't take that seriously enough of like, I think any company, it's like you should have a level of obsession and desperation where it's like, I will literally do whatever physically is possible to make this happen. And so we've like, I took that very literally, like 10 texting, calling, voicemails, whatever. And like, because the reason why I did that is like, I know that this is the right future that you should be moving to. I know this is scary, but we got you. We're going to execute well for you and we're going to make this happen. And so that was a lot of the early trust building, plus just obsessive persistence.

How do you avoid being annoying in that process though? Because when I have somebody that's constantly like, hey, did you see this message? Hey, I'm just following up, I usually kind of start to tune that out. So how do you not do that?

A lot of jokes. Yeah, yeah. If someone's calling you being like, Hey, it's me calling you. Hey, it's me randomly calling you, Turner. I'm sure you're really excited to hear from me right now. There's a little bit of humor and fun to it of like, hey, it comes off as very authentic if you actually believe in what you're building and selling versus some random sales guy who's sending friendly follow-up 58 times, which no one cares about. And so that's been a lot of the focus, which is how do I build trust while also being persistent?

And so now you're making this transition to making people raving fans. Like, how do you be a raving fan? Like, as a CFO, like, what are you happy about? Like, what are you, what are you telling your friends in the CFO group chat of like, Hanover Park is so

great? So there's the 3 elements to a raving fan that I think about. One is amazing onboarding, deployment, migration. That's the one-click migration thing we talked about earlier, which is like, how do we make this experience where literally You sign a contract, you give us data access, and you put your feet up on the table and never think about this again. That is incredibly challenging, right? And we've done this now for customers and people have been like, you told me migration wasn't going to be bad. It's amazing, right? So that's, that's the first piece. The second piece is taking the fund admin services from reactive to proactive. And so how do we use AI to supercharge services delivery by saying, no, I don't need to wait days or weeks or months to see this action happening. I can see things in almost real time, right? So that's the second piece. And then the third piece is jaw-dropping AI-native product experiences. And so it's not good enough to just deliver fund admin or the core service delivery. It's how do we unlock and build something magical on top of that data that no one else could offer, right? And so that's been the 3 elements, and hitting those are things that I like care deeply about tracking and monitoring and focusing on.

Was there a time when you kind of knew things were kind of working, like you were just getting like the scale sort of tipping towards more positive feedback and like momentum versus not?

Yeah, I think like after $5 billion of assets, now we're almost $15 billion. After $5 billion, things started moving downhill. Ecosystem started to pick up of people being like, have you heard of this Hanover Park thing? Despite us doing not a ton of marketing aside from me tweeting and posting on LinkedIn. Right. we started to get this like positive momentum in ecosystem of like, oh my God, this could be the future. The idea of vertically integrating the core accounting system and building all these magical moments on top could be possible. And so that's when things started to kind of snowball. And then now it's just been like, let's make sure we can catch the snowball rolling down the hill.

And kind of hitting on the, you know, the content that you post online, you are probably one of the— if somebody were to say who's a founder that's doing founder-led content well, I'd probably send them your profiles and just like, check out what Chris is doing, follow his stuff. What's been sort of your process for thinking about things? And then maybe even you started making content online before you even started Hanover Park. So what's that kind of like whole journey for you been like?

Yeah, so the quick primer and background on it is, was a college kid at Yale, literally was like, okay, I was reading it. And before Sam Altman was cool in 2020, he wrote this blog post.

This is pre-ChatGPT.

Pre-ChatGPT, pre-he was cool. He wrote this blog post, How to Be Successful, and I obsessively read that and was like, this is insanely cool. And I was like, okay, this startup thing might be interesting, but my dad sells commercial insurance, had no background, no ecosystem, nothing. And I was like, okay, there's really 2 ways you add value in early stage, writing code or selling code at the early stages. Wasn't a good eng, so I wanted to figure out distribution and sales and said, how do I write compelling content on the internet that people would actually click on, watch, listen to? And so I did this whole interview series where I interviewed Emmett Shear at Twitch and, you know, Michael Seibel at YC and, you know, Kevin Ryan, who founded MongoDB. And I turned all of that into Twitter content that went super viral. And so built the following to a few hundred thousand people over the past few years. And, but like when I started Hanover Park, I was like, I actually don't care about virality, which is a little bit contrarian. Like it doesn't matter if Elon retweets me, which he did one time, which got me like 50 million views or something. Mm-hmm. That doesn't matter. It's actually like If I'm trying to use this as a way in which CFOs find us, I need to write for them, which is obviously a much more niche audience than some random hustle tweet about whatever.

You're tweeting about the accrual-based accounting method, closing faster. The average person reads that,

it's like— They're like, what am I—

They'll block you.

Please block me. Yeah, yeah. They're like, block Chris. And it's really the problems that a CFO faces of You're up at 2 in the morning, your LP calls you and says like, this number's wrong. Do you ever want that problem? No, you don't. Right. So it's a lot about that in terms of like how I think about the positioning and how I think about what we're doing. And so like one of our largest customers came inbound off a LinkedIn post that had 10 likes.

So it's really not about going viral. Like going viral is not how you do founder-led sales. It's resonating with the problem that your customer is facing.

It's really just obsessive. Like all I do all day is talk to CFOs. They tell me their problems, they tell me some of the things that they're having issues with, and then we try to invent the future for them.

Do you then take that feedback? You take that feedback, put it in the product, and you also— I feel like a decent amount of your content is like, I just talked to a CFO at a billion-dollar private equity firm, they said this, and that's the content.

Yeah, I kind of think about it as any conversation that I'm having could turn into content. So the frame I have on this is with all the AI slop out there, You should only be writing and creating content of things that no one else could create. And a lot of that is personal conversations. And so I talked to the CFO about XYZ, here's what I learned. No one else had that conversation that day. So it's a lot about how do I, in a world of abundance of content, how do we have something scarce?

Do you keep some sort of document or note where you'll dump ideas in and you come back and visit it? Do you plan it out Sunday night, write some content for the week? What's your process?

Yeah, in real time, I'll jot down things as I'm having phone calls, conversations, et cetera. And that will be

And so I think we maybe hit on this a little bit earlier. I actually don't even remember if it was before we started recording or not, but you talked about there's a lot of the advice you kind of got or read about and heard about starting a company and building a company, I think you said 99% of it just ended up being wrong. What's kind of the advice that you found is most wrong?

So I think, yeah, my take on this is I think almost all advice, like 99% of advice on scaling a company is incorrect right now. And so the traditional advice is like, oh my God, delegate everything, hire this massive team, hire VPs to do different actions and whatever. And I'm like, a lot of that is noise and basically creates this hierarchical political system by which people do well by figuring out ways to take credit. My goal is How do we do the anti that? And I took a lot of inspiration from from Elon on this of like be at the bare metal as much as possible. Like literally, like don't talk to like three levels of whoever. It's like talk to the engineer writing the code on this like AI email agent and how this works, right? And like figure that out because the problem that creates like if you don't do that, you create frustration for your best performers. The best performers like want a hard problem. They want the room to run and like go cook on that problem. And so we've really been anti this hierarchical system by which information is filtered in different ways and say, let's just cut to the source and go direct.

Isn't there an argument though that you kind of quote unquote need a grownup or quote unquote need someone who's kind of done it before and knows the playbook? If you don't have the VP that's done this before and can share the knowledge with the team or knows what it looks like, do you miss out on that?

So I think what's changing a lot now with what does it mean to build a company in the AI era, which is very different than I think the SaaS era of the 2010s, which is like the playbooks are being rewritten. What's possible is different. It used to be you have a massive sales and marketing team. Now you have inference cost, right? And like that's your sales and marketing team. And so a lot of this is I think changing of like instead of being an army of GTM, you have like the product does the talking, right? I think it's changed a lot. And so you look at companies like Cursor that have scaled crazy with a very small go-to-market and sales team. And so I think a lot of the playbooks are changing, which means someone who's reusing a playbook from the last era is probably going to be not effective. However, that doesn't mean— we have multiple people that have been at companies that have exited before. They've been senior engineers there. There is some level of how do we counterbalance the hacker culture of the 20-year-old who drops out of Harvard to what I call the experience, like, I've seen all these infrastructure problems at scale on the engineering side. And then obviously on the fund accounting team, we are obsessively focused on bringing in expertise for the Navy SEALs of fund accounting. That obviously requires a different level of experience in terms of how situations have been adopted over time.

Are there any things that you try to do based on, someone said, this is how I've done this in the past, And it went horribly wrong that you feel like hopefully people can maybe learn from, and specifically with the way that building companies have kind of changed over the past couple years with kind of these AI-native businesses?

Yeah, I think honestly one of the things that we preach every single day is this idea of one-way doors versus two-way doors. Jeff Bezos invented this where it's like a one-way door is if you make a decision in this way, you literally can't go back. It's impossible. So an example would be, The investors you have on your cap table, you can't get rid of them. They're a one-way door. They're here for the life of the company. The two-way door is, hey, we can make a decision. It's easy to go back at any time. And those are almost all decisions in a startup. And so what we've done is drop your ego to the ground, be intellectually honest about truth-seeking of what's the right answer for the company, and make a fast decision because you can always revert back really easily at any point in time. And so empowering the team to say, make really fast two-way doors. Yeah. Without bogging it down in process to create a committee to figure out if that makes sense to make a decision. It's better to have iterated 5 times before the committee even gets out of bed.

How do you know if something's a one or two-way door? Is there a gut check on, hey, we know this is reversible or won't blow up the product or customer trust or something like that?

Examples would be shipping an internal accounting update for our fund accounting team is very two-way door. If it doesn't work, in 5 seconds we can update it and nothing changes. Right. Versus like, you know, one-way doors, like a massive change to our LP portal, right? Which is like what the massive endowments and banks in the world log into every day across our 10,000 different limited partners. And so I think it's more like that's on the product decisioning side of like, okay, great. You know, that is still a two-way door because you can revert quickly, but it's something that we'll take a little bit more seriously.

And so you started initially, you kind of mentioned Sousa Ventures was the first customer.

Customer one. Shout out.

Shout out to Chad and Pratish and Kenny.

And Kenny. Kenny's a legend. He's the one with all the credit.

Nice. I've never met Kenny before. Next time, next time I'm near the office, I should try to do it, try to meet all of them or try to meet Kenny. And so how did you kind of expand customer base over time? Because your first couple of customers were all VC firms. You seem like you kind of have like been like laddering up almost, I guess. I don't know how to describe this. Like, what's the process been like of expanding?

Part of our goal is like we want to build for complexity, which is like The most complex firms in the world have blockers and splitters and aggregators and triple parallel funds and UK subsidiaries and different currencies. There's all this noise, right? If you were just built for a $10 million venture fund, obviously you are not the right partner and customer for them, or customer fit. And so what we've done is we said, okay, we need to solve this cold start problem. We need to get our first 10 customers. We're going to do that in venture capital, but we've quickly expanded into other asset classes like private equity and soon private credit as well. where we knew the needs are much more sophisticated and we built for that future. And now it's been kind of a great opportunity to expand in these different types of asset classes. And so we launched a product recently, breaking news, on AI-native waterfall modeling. And so in natural language, I can run my deal-by-deal waterfall if I'm a CFO, which would take hours and hours and days and wrong, and it's not correct. And it's like 7 different system exports that we've made instantaneous in natural language to run these scenarios. And so That would be a product that a private equity firm would fall in love with.

So how do you know that something like that is accurate?

Yeah, great question.

Because I fuck it up even when I make it manually in Excel. I'll forget to drag a formula and I'll notice it later, error checking everything.

Yeah. One of the key product principles of the company is what I call trust but verify. And so it's not like if you just give someone a number, assume every single CFO, they're the most skeptical people on the planet. going to assume it's wrong. You need to be able to have them click into it, export, run their own calculations to verify the accuracy of a number. And so the trust but verify approach on the waterfall modeling, plus the explainability. The beautiful thing is AI can tell you where the work's coming from. It can say, hey, we went through these 3 different tiers of our waterfall and here's exactly what happened. And so that, plus the auditability and explainability, has been a critical pillar.

And it's all— when you export to Excel, It comes with like linked formulas and everything. Am I remembering this right?

Yeah. I mean, this is actually really fun. I think there's been 2 different approaches in the market when it comes to this. One is, hey, it's a bunch of manual, like humans running around with like really big Excel files. That's one. The problem with that approach is it's very error-prone and manual and slow, but it gives you some auditability because it has formulas. The other approach, which is like a very 2018 SaaS approach, was like, hey, I'm going to give you just a UI. Great, it's at least something, but it's just hardcoded export randomly with no connectivity. We don't think either of those is the right solution. So we actually built an Excel replacement in the browser similar to Ramp Sheets or Try Shortcut. There's these tools that do this generally. We built this specific for our industry, for fund CFOs, which enables us to instantly generate all the financial reporting without having to go into this crazy offline process. And so We think we've kind of married like best of both worlds when we think about the reporting aspect with the auditability of like formulas across the different Excel replacement that we built.

Can you still export it to Excel?

Always. Yeah, everything's exportable.

I was going to say, like, I just don't think if you told a CFO everything we've just described about Hanover Park, but you can't use Excel anymore,

it's over.

Yeah, like that's, that's even, even harder than the migration of a whole provider is like getting them off Excel. The industry runs on Excel. And I think you have a nickname with your friends. They call you Chip.

Oh boy.

What's going on with Chip?

Oh boy, this is coming up. This is coming up. So my friends will shout out Raj and my friends. But when I started the company, everyone was like, you're crazy. What are you doing? You just left this job and previously left Goldman Sachs. And this is insane. What are you working on? What are you doing? And I basically had this thing where I had this massive chip on my shoulder. I love this quote from Josh Wolfe at Lux Capital, which is, chips on shoulders put chips in pockets. And now that Peter and Lux have invested, it's kind of entertaining for it to be full circle, which is everyone started calling me Chip because they were like, wow, he has this massive chip on his shoulder, because everyone doubted that this would be successful. We still have a lot of work to prove it though.

Yeah. I think one thing That's been kind of interesting is despite you have this chip on your shoulder, you really want to be successful, there's been times that you've turned down customers, somebody you don't think that you can take them on and give them the right kind of service. How do you know when to do that?

Yeah, I think ideal customer profile, which is a common term, has become insanely important for us. I am deathly afraid of disappointment. Right, which is disappointing a customer that is really excited about Hanover Park that we can't deliver what we need to deliver for them. And a lot of the problems in that is like you take on too many customers that are not the right profile, that have different needs from who you're focusing on. And so you don't end up building for them. You end up with a bunch of people that are asking for things that don't match. Right. And that's a massive problem. So I'm turning away customers left and right to ensure the people we do partner with have the A+ raving fan experience that we want to deliver. Um, and so that's kind of been the focus of like, It sounds crazy, but we'll turn away customers all the time to ensure that the customer base we have can become raving fans in 2026.

But don't you want to grow super fast? That's kind of the point of a hypergrowth startup, right? Is grow as fast as possible. Isn't that what they say?

I think grow as fast as possible with the right constraints of who you're building for. And so obviously there's a massive market of opportunity, and we've just focused, I would say, on mid-market enterprise, which are larger firms with finance teams, more sophisticated CFOs than some of the smaller firms. That doesn't mean in the future we won't go there. I just think it is not a near term. It might be a long term as we are able to democratize the services delivery that we have and make it scalable.

Talking about longer term, what is kind of the longer term, 10 years from now? I don't know if we're going to do this every single year.

Yeah, let's do it every year for the next decade.

Yeah, but like, I mean, talking about 10 years from now, whatever, whatever the long term is, like I'm reading the S-1 for Hanover Park, you're like spilling it, spilling it all out there. Like, yeah, what am I going to be reading about?

Yeah, well, I think what's pretty exciting, pretty awesome is that when Jake and Emergence led our Series A, they wrote this memo and they said like, this, if executed incredibly well, we have a lot of work to do, is a Veeva-like opportunity. Veeva is the largest vertical software business in the world right now. It's around $50 billion market cap. Which is like, but what's funny about Veeva is they started as a CRM for life sciences. That is the most niche, boring thing of all time. Now they're the board-level vendor for Moderna and Pfizer and like all these different things, right?

Board-level vendor.

Board-level, meaning they're the most important vendor for like Moderna and Pfizer. Like Moderna and Pfizer pay them like tens of millions of dollars a year, right? And so our goal is to do that in finance, right? And so it's like, okay, We want to become the most important vendor for Blackstone and KKR and Vista Private Equity, and pick your name of really important investment firms, and not just do one siloed specific thing that they're used to. We want to own the core back office, own the middle office, and own all the software tool spend for the investment firm. And along the way, have the opportunity to bundle all these additional products and service offerings around it. And so And then in the super long term, if now all of this data that used to be in 50 different random tools is now in one centralized system of record, why can't we help people make better decisions and unlock alpha and do that for the investment firm? And so that's the super long term.

So I have one more thing I want to ask you, your kind of personal AI stack, what you use personally as Chris. So I think a year ago you told me you were using Claude artifacts and you were also using something called Hemingway for writing. What's it? Still using those?

What's— Number one tool right now, Granola.

Granola. Okay.

It's just like AI call, you know, call. So like what we do is a lot of our internal calls is like we'll basically record any meeting and then we have this like transcript repository that you can query and do a bunch of analysis on. And so like that really helps with like context sharing across teams, especially as we've scaled the team really quickly. And so that's been one. The other is like I am obsessed with Claude Cowork. Claude Cowork has changed everything. Claude Cowork is my ChatGPT moment, actually.

So not even ChatGPT, Claude Cowork was yours?

Claude Cowork is 10 times better for ChatGPT than me. It's crazy. What it's basically done is I used to be an investment banker. I used to have to put PowerPoints together. I used to put financial models together. I am now— I did this massively important presentation for a trillion dollars of assets and legitimately, Claude Cowork took call transcripts, notes, all this stuff, and I was like, here's the format of a different deck. Build me this 10-slide thing from scratch with all of these graphs. It one-shotted it. I made an hour of modifications of something that would've taken me 10 hours and I was done. And so it's been transformational when I think about all of the data analysis stuff as well as building presentations.

You said you built this kind of Central to-do list thing. I remember you were texting about like, what is it exactly?

Oh yeah.

Do you use it or just like a fun thing you try to do?

So, so I've literally like, as like the non-technical CEO that wants to be closer to the bare metal, I was like, AI coding tools are so much better. So I literally whipped up Claude Code this past weekend. I was like, I'm going to vibe code a like CEO command center basically to make decisions. And it was like pulling in all of Slack, all of email, all of Google Calendar, all of my granola notes, all of these different sources. To build a dashboard to help me be like, what are the decisions that I need to action on right now? And then a self-updating to-do list that's synced with my email and other things. And so it's been pretty fun. It's still super experimental. I did it for 2 hours on a Saturday, but it's been pretty fun to play around with.

Is it like you get a feed that's updating every minute synced with Slack and you're typing in and hitting enter to make it do things? How does it actually work?

Yeah, it's pretty crazy. It is. So it's updating, I think it's every hour or something. pulled together from any activity over Slack, over email, over calendar, over anything. Linear is another one. We pull in our product issues and stuff like that. And so all of that activity feeds in. And then if I want to update the dashboard, it's like I'm just connecting to a bunch of MCP servers and then I'm literally prompting Claude Code to do things. And so it'll just auto-update. Say it's like, hey, you need to respond to this email. It's super critical about this deal. Once I respond to the email, it just auto-updates.

In the, in the dashboard.

Yeah.

Yeah. Because that's the thing that I've always had, the, the issue I've had with these like to-do lists is like you make your to-do list and then you go and do the thing and you like go and change the to-do list. It's like,

what's going on? Yeah. Yeah.

So it's like, I, I've always had super simple to-do lists that are basically like high-level bang out this hour thing. And then you like, maybe it's worth updating, but like some people make these like super elaborate, like very minute to-do lists, it's like you're spending half your time on your to-do list. On your to-do list.

I mean, I think it's like this, like, you know, productivity optimization sometimes is procrastination, I think is like the piece of it of like, if you're doing too much, like the simplest execution should be like, I'm doing this thing today and here's the most important problem to solve and everything else is noise.

And you just announced, I think you mentioned earlier, the Handover MCP. So what is that and what can you do with it if you're a customer?

So Breaking news, Hanover Park MCP is live now. What this enables us to do is MCP, think about it as API for AI. And so instead of having many different disconnected data sources, the really cool thing about Hanover Park is because we are doing your core accounting execution, we have all of your system of record data. That's what gets audited. That's what gets sent to your limited partners. That's what you get your financials. On top of that data, we built some magical things like Now we have all your LP data organized, we have all your portfolio data extracted, we have all your KPIs collected, all this stuff. Now with our MCP, it can be like, I don't even need my GP to log into Hanover Park. I can just tag it right into Claude alongside our CRM from Adeo, and I can just query the full context of my data and generate PDFs on the fly. And so it's been a pretty magical opportunity for customers and excited to see where it goes.

You mentioned Attio. Did you switch over to Attio?

I didn't. I'm a HubSpot stan. Oh, okay. Look at me, old school. Use HubSpot for CRM right now. But I'm thinking about new things like Monaco and others.

Oh, nice.

Shout out Sam Blonde.

Yeah, he actually— I actually need to respond to my DM. I was like, hey, want to come on the podcast, talk about Monaco, everything you've learned about sales and growth? And he's like, yeah, let's do it. I think we're going to do it in a couple of weeks. So if you're listening to this, Look out for that episode. I think we have a couple, let's see, I think it's either right before this or right after, we have one with my friend Chetan at Benchmark. We're talking all about software. We have an episode with Scott Stevenson at Spellbook. It's the fastest-growing AI company in Canada. We think that that's a true statement based on— Is that true? We think, based on what he's heard from— Legal, right? Yeah. It's sort of like Cursor for contracts. So if you're a lawyer, a lot of the work you're doing is with Contracts. It's essentially like a Microsoft Word plugin where you can sort of edit contracts like you would code, essentially. And if you're a lawyer, a lot of your job is just writing legal documents and editing legal documents, and it just helps you do it faster. And they have a couple other— it's almost like Claude Cowork for doing legal things. So you can say, hey, write me this document based on these things, and just go and do it. It's pretty interesting. I'm trying to think of who else we have. We have Mike and Nikhil at Footwork. I don't know if you ever met them throughout the course of fundraising, but Nikhil was a seed investor in Canva, Farmer's Dog, probably missing a couple multibillion-dollar companies. And then Mike was the COO of Stitch Fix, zero to a billion in revenue, ran walmart.com, was in charge of all that, thousands of employees. Walmart.com is pretty big now. So anyways, a lot of good conversations coming up on the podcast that people should continue listening to.

Love it.

Subscribe to the show, subscribe to the newsletter. I might not even have to do an outro now. We can just do the outro right now. Also, thanks to Numeral and Flex for sponsoring the show. Check out numeral.com for sales tax. Do you guys have sales tax you have to collect? Are you using Numeral?

We're not using Numeral yet. What are you talking— You should use Numeral. Oh, whoa, whoa. That's good.

And then other sponsor is Flex, which I use for the fund. If you check out the link in the description, sign up for the waitlist, you'll get the Flex Elite personal card. It's like business and personal banking all in one.

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And they give you credit. So there's a lot of guys out there that'll say, oh, we'll give you a credit card, quote unquote, just based on your Your bank account and what kind of cash, they'll give you a percentage of it. Flex actually gives you like a credit card, underrates it like Amex. So worth checking all those out.

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