Y Combinator Startup Podcast
00:00:00 · Chaz Englander, Arnie Englander, Gustaf Alstromer
[00:00:00] S0: In the last seven days, we've signed the same number of contracts as we signed in the whole of q four. There is clear tangible value being driven by these products, and it's only gonna get better and quickly. [00:00:10] S1: Ultimately, you've gotta be very passionate about what you're building. You've got to have that perseverance. And if that sounds good to you, then then build a startup. If something [00:00:18] S0: logically makes sense, you should probably continue doing that thing. Right? And not let anything stop you. And I think, like, the consistency that we've noticed of founders that we've... Some of... That we've invested in or worked with is, the ones that kind of do that and really persevere tend to win. [00:00:39] S2: Today, we're here with Arne and Chas Englander. They are the founders of ModelML from '1 to '24. Prior to ModelML, they started two other YC companies that both were successful and sold, Fancy and Fat Lama. And this is probably the first time I've worked with a company where both of the founders had had a previous successful YC company before. So I'm super excited to welcome Chas and Arne here to YC. We'll come back. [00:01:03] S0: Thanks for having us. Thanks very much. Thanks for having us. [00:01:05] S2: Tell us what you guys are building.
00:01:05 · Gustaf Alstromer, Chaz Englander
[00:01:05] S2: Tell us what you guys are building. [00:01:06] S0: So MonoMel is an AI workspace for financial services. So that's our one liner. What that actually means in practice is we've... So we 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 data sets, etcetera. 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. [00:02:10] S2: 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?
00:02:10 · Gustaf Alstromer, Chaz Englander
[00:02:10] S2: 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? [00:02:15] S0: 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. Wow, congratulations. Thanks very much. And I think it's really just the turning point, I think, in the sector where, 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. [00:02:38] S2: What were people using ModelML using before? What were the tools that they were using in their daily work? [00:02:44] S0: So they would have their data sets, and then they spend a lot of time in the Office Suite or in Outlook, right? And 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. [00:03:16] S2: Right. And I remember from, I think maybe from our interview or sometime in the first office hour, you had a unique story on why you wanted to solve this problem. Remember what that was?
00:03:24 · Chaz Englander, Gustaf Alstromer
[00:03:24] S0: Yeah, so 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, right? And 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 agencik system that, to be honest, we were just building for fun and produced 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% 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, This thing will go off and look at their LinkedIn and their background and then look at comparable companies on Crunchbase or S and P and so on, and kind of 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, would go and look at review websites, just as an example. Yeah, we thought it was pretty cool, then people became interested in it. And then we kind of agreed that we were quite bad investors and okay at building stuff. And that's how it started. [00:04:46] S2: And that then turned into you selling this product or a new version of that product to the top financial firms in the world, basically.
00:04:46 · Gustaf Alstromer, Chaz Englander
[00:04:46] S2: And that then turned into you selling this product or a new version of that product to the top financial firms in the world, basically. [00:04:53] S0: Yeah. So we can say now that we're about 10% of the largest private equity firms and investment banks in the world use our product. We have other customers, asset managers, sovereign wealth funds, a bunch of venture firms. But yeah, it's been pretty exciting. [00:05:11] S2: And how were they doing their work before you started using ModelML? [00:05:15] S0: So as crazy as it sounds now, right, so let me use an example whereby, let's say you're tracking a public company, right? And every quarter, every time there's a release, maybe as part of your job as an analyst or associate, you'll go to the release, the filings, and you'll put together what's called an earnings summary. So think about it like a beautifully present slide, effectively a single slide, with a bunch of information. But all that information is coming from the same piece of information. So for example, you might always get certain numbers from the filing itself. You might get other numbers like consensus or similar from a fact set and so on, and they're put into these slides. And these things take ages. We're talking probably like days [00:05:57] S2: to a
00:05:57 · Gustaf Alstromer, Chaz Englander
[00:05:57] S2: to a [00:05:58] S0: company, just to pull together these slides, right? Because everything has to be linked back to source and checked and so on. And what you can do in Model ML is to think of Model ML as being used as the building blocks for this. So you have an Excel spreadsheet that's already connected into these data sources, and you might want to export that into designs, right? And so rather than having to go and gather that information, once that release happens, this one pager effect, it was actually three pages, a cover page, your main page, a legal page, just appears in your SharePoint or Google Drive, right? And it's kind of 90%, 95% of the way there. Wow. And we think, in some cases, more accurate because actually, trawling through these filings as a human, because you can also pull from multiple data sets for the same figure, yeah, can be more accurate. [00:06:43] S2: I'm assuming that you built something in the beginning, and there was a model that could do some amount of this work. Did YC how long ago? [00:06:51] S0: A year ago. [00:06:52] S2: A year ago. So the models have probably progressed a lot in that year. What were things the models could do a year ago that were impressive, and what can they do today, and what are they gonna be able to do in the future?