00:06:38 · Joe O'Donnell
[00:06:38] S0: Yeah, I think we do hundreds of microservices that interconnect in various ways. I think there's different, I mean, there's a few different orthogonal ways that I think about at least, but one is just more obvious, which is we build data sets that don't exist. So just starting from that. I think, you know, the investment world, I would say, if if there's still a data advantage that exists in the investment world across the mainstream datasets, it's limited and declining. But there's a lot of niche datasets that don't exist. And by virtue of limited distribution, it can still maintain enduring value for investors for at least as long as they have limited distribution. And so we build as many of those as we can think of. And some of those are horizontal in nature and apply to all industries and some are vertical specific. And a lot of them tend to be needle in a haystack type data sets where we might look at millions of data points and find three cool things per year. And that's wildly inefficient for an investment firm to do or for anyone trying to build a data business around that particular thing, but it's extremely inefficient for us to do, especially at scale. And so that's one thing we do. The next thing that we do is we build intelligence on top of those datasets and the more mainstream datasets. Basically, the sort of... The more basic way to think about would be given data point x, what would a talented investor think about it? I think that context layer is incredibly important and non intuitive in a lot of different ways. And so that applies to small categories, but it also applies to big categories like an annual report. You know, there's the... So the old school way to look at an annual report, we just look at the annual report. Then there was, you know, blacklining and stuff like that. And then there's... Today, you know, there's Claude, tell me all the interesting stuff in these two reports, make no mistakes or whatever. Something worked elegant in that, obviously, but something along those lines. And then there, I think there are some people that are trying to build models around that. But getting that right at the level of a world class investor is super important. And there's a lot of big sort of more mainstream datasets where we build intelligence layers on top of that.
00:08:51 · Joe O'Donnell
And only then after those first two layers are built, can you build useful services and tools that sit on top of it. And so what we do on top of that is sometimes we'll build vertical specific things. So for example, we have a AI forensic accountant that can unpack and does unpack across 10,000 companies or so all of the various accounting manipulations that certain companies engage in and provides a clean P and L versus the company's reported results versus consensus, etcetera, stuff like that. So these are specialized roles. We build AI that just does those roles at the level of a world class practitioner. And then we also build increasingly just investment analysts that take all that information in and help people do research, help people generate investment ideas in increasingly specialized ways. And so, you know, it's sort of a layer cake where all of these things are necessary results that are actually useful for investors in terms of bending the curve on their results. I think one thing... On the other side, one thing that we shy away from is doing things that we think are just going to be... They're either already are or are very likely to be subsumed by foundation models. You know, I think, you know, we're we're a startup. It's... It'd be foolish for us to try to compete with Claude, for example, on anything that Claude is likely to do very well. So what I would put in that category would be everything, you know, broadly speaking, productivity tools. So things that are not bending the curve on revenue, but they're certainly bending the curve on cost or, you know, human cost or just actual cost. You know, build me a Excel model with one click or something like that. Cloud is wonderful at that with no plug ins at all. And fast forward twelve months, it'll be even better. In twelve months from there, I think it will be perfect. Maybe maybe even sooner.
00:10:41 · Khe Hy
[00:10:41] S2: Could I add the layer cake analogy is powerful one. When you go from like an Opus 4.6 to an Opus 4.8, like which parts of the layer cake become more powerful? Like, how has your product changed as the models have, like, step functioned?
00:10:59 · Joe O'Donnell
[00:10:59] S0: It's so it's a it's sensor in some ways, and it requires deep analysis and QA at every single layer of all of those things, which is an unfortunate reality of what we do. But what I'd say is that the scope of what's possible in AI increases, and sometimes it's a step function large increase. I would say the first time we really saw that was was way back for... With Gemini 2.5 where we started seeing that that level of functionality where we could build sort of more performant, more horizontal investment tools. And it really depends. So it's model specific and we don't often use off the shelf models for the stuff that we expect world class results from. Because as I was saying before, they just don't have the ability to deliver that last few percentile of results without a lot of fine tuning. And so we're finding just that fine tune models that we build ourselves, not because of anything related to cost, which I think is a big topic these days, but for performance purposes. You know, our goal is to deliver the absolute best results and the costs are just what they are. And so, you know, there's no trade off that we think we can participate in with respect to that. And so sometimes a new model will come out that a lot of people will be excited about in different industries and maybe sometimes in financial services as well that... Where we'll see literally no improvement at all and sometimes a degradation. In other times, it will it will be different. But it's really nuanced, and and I would say increasingly only applies to certain things that we do.
00:12:38 · Brett Caughran
[00:12:38] S1: Joe, one of the things I see a lot of AI finance firms run into is the sort of dichotomy of what to build and how to build it. And the how to build it is sort of getting a little bit more, not simple, but more simple than it was from an infrastructure perspective. I'm curious to understand how you think about the how to build, and now you have that advantage coming from the seat. How do you map out products that you think will be institutionally useful, particularly when public equities is a really sort of combinatorial complex practice. What one investor does from an investment process can vary quite differently from another.
00:13:26 · Joe O'Donnell
[00:13:26] S0: Yeah. I mean, I think it's, we basically try to meet investors wherever they are. So we're increasingly building flexibility in our platform. There's a headless version of Canary that we have an API, have MCP, for example, where if people want to integrate what we do into their own platforms and their own processes, we're happy to facilitate that. And we explicitly don't track what any of our users do at all. But from conversations with them, I think some of them will take certain parts of our product because they value those things and use them. And some people will take those exact same parts and use them in a different way. And some people will take just the datasets, and that's all they want from us. Sometimes they want the intelligence layer on top of that. Sometimes they want the entire platform. And we're happy to be flexible in terms of how we deploy that and or how people use us. And I mean, every company, I presume, could say this to a certain degree, but if we help people make a few better investment decisions per year, it's pretty easy to justify having a Canary subscription. But the other side of it, I would say, know, to maybe answer your question in a different way is, one is just the obvious thing, which is just I've faced these problems a lot and and my teammates as well have faced these sorts of problems a lot in their investment careers. And we know the landscape of what exists. And so we're just basically trying to fill in the blanks. And there's an enormous amount of work to do there, an enormous, enormous amount. I think we have a lot of great engineers and we build product and ship very fast, but the degree that at which to which our backlog builds is is is still greater than the rate at which we ship. And so there's a lot of things. And sometimes it's just, like I was saying before, sometimes it's just a very specific dataset around something that maybe two years ago would have been totally inefficient to ever try to productize. And sometimes it'll be, you know, sort of a specific tool around something like forensic accounting, like I mentioned before. And then from there, I mean, I think there's... Trying to think about the best way to put this. But there's a lot of stuff that sits outside of the scope of what investors demand.