Invest with AI
00:17:15 · Thomas Li, Brett Caughran
Moreover, if I gave you that skill, I'm making it even easier for you to go and view my data in Claude and not on my website. So I think for organizations where they look at that setup as a loss of adoption, then it's rightly so you get concerned, right? I have a different view. My view is so long as I am the reason a customer's problem gets solved, I don't really care if you're solving it in my system or, you know, in someone else's system, long as I am solving your problem. So I'm willing to put every single asset I have into the MCP, but you can see how at different organizations you end up at different conclusions. [00:17:53] S1: Yeah, I've noticed that I'm a FactSet subscriber and I've found that the time I spent in the FactSet terminal is down materially, which is sort of nice because the UI is not great. But I'm accessing a lot of that data via an agentic workspace. How do you think about like one the things I'm struggling with is and I get here, you know, sort of get this question from my students and clients is like, what's the right MCP stack within an agentic workspace? Because DeLuca was early as a connector into many of these workspaces, but now I could get fundamental data from six or seven different players in the same agentic workspace. How do you think about that competitive environment where more fundamental data pipes, KPI pipes are coming in into the agent? How you would counsel an investor to decide on that stack and how you think about Dilupa as being differentiated within that stack?
00:18:50 · Thomas Li
[00:18:50] S0: Yeah, so I love the way you frame it, which is like pipelines, right? So yes, we were the first to build this pipeline, but, you know, anyone can build a pipeline and ultimately, if you're an investor, you should have hundreds of these pipelines coming into your firm. But just like a pipeline, if let's say every pipeline is carrying water, right? What you really want is to get the pipeline with the highest quality water. Usually, the highest quality water tends to also come at a premium. And you might say, hey, I am a science lab, and this water needs to be so pure, it doesn't even conduct electricity, right? Pure water. Or you could be a household where you're like, that doesn't really matter, it just needs to be potable, I just need to be able to drink it. Or you could be a factory where it's like it doesn't even need to be like potable, I just need it to cool a machinery, who cares? So depending on your needs, you probably have different types, you could connect to all of those pipes, but you should only turn on the pipe that solves your problem. So I don't pretend that the lupus data and content can solve everybody's problem at their perceived price to value, right? What we do is we go for the extreme quality, the low latency, the high coverage, right? No fundamental data set out there has deeper data than we do, has more accuracy. So we are the most accurate, we have the lowest latency, and we are probably by far the most comprehensive, right? We cover data points that other firms just simply don't cover. We have, you know, guidance and adjustments and KPIs and guarantees and enterprise SLAs. So we're sort of like the pure water that goes into a lab, right? It's truly enterprise grade, targeting some of the largest institutional investors, and today serving some of the largest institutional investors, right? But I will argue, you know, if you're a smaller firm, and the quality doesn't matter, because your investment style and process just doesn't require like extreme high quality, like this is company dropped earnings, I need to know everything right away, then we might not be at the right price point, because your perceived value of our data will be lower. And there are, as you pointed out, a lot of other sources.
00:21:02 · Brett Caughran
[00:21:02] S1: Yeah, how do you think about a sort of, you know, debate with myself both sides of this sort of like best of breed stack within an MCP pathway of like, let me go pull in the six highest quality stack, the sort of pieces of the stack, which makes sense. I sort of agree with you certainly for the tier one client. Although the MCP pricing for that stack now can be like an aggregate, like $60.70 ks, like the numbers get pretty high when you start to add up the MCP price versus a number of vendors chasing this one pipe strategy where they're trying to be very good, but with one MCP price and there's some creative sort of, you know, vendor licensing strategies. How do you think about where does Dilupo want to play in that? And how do you think about the juxtaposition of those two MCP strategies?
00:21:53 · Thomas Li
[00:21:53] S0: Yeah, so because the beauty of a large language model is that whether you have one pipe or three pipes is fundamentally the same to the large language model, right? Because if, let's say, I'm connected to three MCPs, which basically means the model is connected to three APIs, And if behind every API is three other APIs, the model is now connected to nine endpoints, right? Versus if I had nine MCPs with only one endpoint each, the model at the very core is actually connected to the same amount of resources. So it doesn't really matter. From a buyer's perspective, yeah, you have to deal with more vendors and contracts and like people now, but at least from a product angle, you should be getting the same outcome. Now from a vendor perspective, obviously, if you have more content, you can make the argument I can sell to more people and therefore I can build a bigger business. But I think that the core to whether or not you want data into your pipe or not, is whether there are synergies between the data assets. So one example is, the clearest example is data and documents, right? If all I had was numbers, but I don't have the documents, then you are losing the context for the numbers, because the number 27 in itself means nothing until I can describe what it is. I can describe the history, can describe the context, and how the context has shifted. So having a pipe of just documents and having a pipe of just data will not get you the same output as having one pipe where they are interconnected and woven together where taxonomy has been built. But said differently, let's use like stock price as an example, right? If I have a pipe of just end of day stock price, and I have another pipe of just fundamental data, like that map is relatively simple, because end of day stock price doesn't have a lot of like you don't have a security master issue with end of day stock price, for let's say the New York Stock Exchange tickers. So then it works well with a large language model. However, if you say, hey, NFT stock price is not enough, I need tick by tick, you know, L2, L3 stock price data. Now all of a sudden, you have a security master issue, right? So now you need all of your security pricings to get mapped. If you throw in like option prices into that, now you have a real security master issue.
00:24:05 · Thomas Li, Brett Caughran
So trying to get the LLM to say, Hey, I'm going to buy one L1 panel, one L2 panel, one L3 panel, I'm going to buy a fundamental data panel, and magically assume that LLM can do the mapping in real time, or in runtime, like that's not, just not possible, right? You will end up with a lot of hallucination. So there are data sets that require the vendor to build taxonomies and mapping beforehand, and there are data sets that don't. [00:24:30] S1: Yeah. And how do you think about the moat of your of your data set? I mean, I sort of would seem that armed with agents, it would be easier for a competitor to recreate what you've built. But what are the sort of defensible elements of the moat of your high quality data?
00:24:48 · Thomas Li
[00:24:48] S0: Yeah, so the moat ironically itself is high quality, right? Because being able to collect data and being able to collect data at like a 99.9% accuracy are wholly different problems. I'm going to use the car factory example. So when you build a car, and Toyota is famous for like six sigma, right? When they build a car, the car has the same quality as every other car down to, you know, six decimal points of nine. That is really, really hard to do. And even if someone else has access to all the engineers to build the factory, like you cannot replicate the quality of the Toyota factory. Now why is that? It's because it's never about just a tool. Giving someone a really good hammer doesn't make him a good carpenter. It's everything else. It's how do you set up your process? How do you set up your controls? How do you set up internal SLAs? Down to how is your company culture? How do you incentivize and reward people? How do you hire? Right? Every single thing that we care about has an angle towards accuracy. Within ZALUPA, we even talk about what are the personality traits that lend to higher quality output versus what are the personality traits that lend to faster output, and are they the same personality traits? So we care about like microscopic little decision making. We care about how we get incentives aligned at the fifteen minute mark of earnings, the thirty minute mark, the forty five, the one hour mark. And how do you think those incentives translate into actual human outcomes, and the engineers outcomes? So you can't just go into just like how you can't go into cloud and collect all the data for me. It's about how you set up your entire factory with like very clear goals in mind. And for us, we have three things we care about. We care about completeness, we care about accuracy, and we care about speed. Right? That's like, frankly, the only three things we care about. And you can't just go to Claude and say, and maybe you could in five years, who knows, right? But you can't go to Claude and say, I want perfect data extracted from every document in a database in real time, go.