The Finance Startup Bringing Agentic AI to Wall Street

**Chaz Englander** (0:00)
In the last seven days, we've signed the same number of contracts as we signed in the whole of Q4. There is clear, tangible value being driven by these products, and it's only going to get better, and quickly.

**Arnie Englander** (0:10)
Ultimately, you've got to be very passionate about what you're building. You've got to have that perseverance. And if that sounds good to you, then build a startup.

**Chaz Englander** (0:17)
If something logically makes sense, you should probably continue doing that thing, right? And not let anything stop you. And I think the consistency that we've noticed of founders that we've invested in or work with is like, the ones that kind of do that and really persevere tend to win.

**Gustaf Alstromer** (0:39)
Today, we're here with Arnie and Chaz Englander. They are the founders of Model ML from 1 to 24 Prior to Model ML, they started two other YC companies that both were successful and sold, Fancy and Fat Llama. And this is probably the first time I've worked with a company where both of the founders had a previous successful YC company before. So I'm super excited to welcome Chaz and Arnie here to YC. Welcome back.

**Chaz Englander** (1:03)
Thanks for having us.

**Gustaf Alstromer** (1:05)
Tell us what you guys are building.

**Chaz Englander** (1:06)
So Model ML is an AI workspace for financial services. So that's our one-liner. What that actually means in practice is we've built a workspace that's akin to kind of the Office Suite. So our own version of Word, PowerPoint and Excel with the major difference that it's built on top of an agentic system that kind of mirrors what a human has access to at the firms we work with. So quite specifically, if you're a human at Firm X, you will have access to your files and folder systems, your emails, your CRM, any data vendors that you might use and pay for, real-time publicly available information, public filings, your internal custom datasets, etc. So then we kind of build this, we call it a cognitive architecture. It's a fancy word of saying, kind of like a brain that mimics what you have access to digitally.
And we overlay that with our user interface. The general idea being, well, if you had an Excel spreadsheet that was already connected into those data sources, you'd probably spend less time going and gathering information and analyzing it.

**Gustaf Alstromer** (2:10)
I can tell that you guys are excited about how things are going right now. Would you put some words on how things are going?

**Chaz Englander** (2:15)
Vertical. Look, I mean, in the last seven days, we've signed the same number of contracts as we signed in the whole of Q4.

**Gustaf Alstromer** (2:24)
Wow. Congratulations.

**Chaz Englander** (2:25)
Thanks very much. I think it's really just the turning point, I think, in the sector, whereas as we keep saying, it's like there is clear, tangible value being driven by these products, and it's only going to get better and quickly.

**Gustaf Alstromer** (2:39)
What were people that are using Model ML using before? What were the tools that they were using in the daily work?

**Chaz Englander** (2:44)
So they would have their data sets, and then they would spend a lot of time in the Office Suite or in Outlook. What that meant was, a lot of that process is super manual and super repetitive. I think the key here is, we're definitely not saying that humans should never do these tasks, but if you're organizing logos in a PowerPoint presentation, and you've done that hundreds of times before, and you're a very well-educated analyst or associate, it's probably not a great use of your time.

**Gustaf Alstromer** (3:17)
I remember from, I think maybe from our interview or sometime in the first office hour, you had a unique story on why you want to solve this problem. Remember what that was?

**Chaz Englander** (3:24)
Yeah. We sold our first two companies, and after we sold our second company, we did a bunch of investing ourselves, which I think on the whole we were pretty bad at, but we became very interested in automating as much of those processes as you possibly could.
So it started whereby we literally were, when we would receive an opportunity via email and having sold a company, you tend to receive a bunch of opportunities every day. It would then enter into this like a genetic system that, to be honest, we were just building for fun, and produced like a one-pager for us. But the interesting thing about the one-pager is, whilst we received some information via email, what was in the one-page, probably 90 percent of that information was unrelated to what we would have received via email. In other words, let's say we got an opportunity to invest in a startup, right? This thing will go off and look at their LinkedIn and their background, and then look at comparable companies on Crunchbase or S&P and so on, and produce this one-pager that you would probably go and do as a human, as your first port of call. Little things like, if it was a consumer company, it would go and look at review websites just as an example. And yeah, we thought it was pretty cool, and then people became interested in it, and then we agreed that we were quite bad investors, and okay at building stuff. And that's how it started.