00:24:11 · Kris Bennatti
[00:24:11] S0: Yeah. My perspective is in 2026, integrate or die. You have to be the future analyst who's coding. They're building their own tools. I'm relatively technical. I would never buy a product that doesn't have an API. That's gonna become everyone going forward. So you have to offer integrations. You have to offer a product that works for people who are building their own dashboards, building their own skills, and we were doing that. We're we're improving all of that functionality and making sure that we have connectors that work for a variety of of use cases. The other way we're thinking about it is we have this bifurcation in the market right now where there's a lot of users like you, Bret, who are really excited about their skills, being able to pull things from other, all these different places. They're using Perplexity Computer, doing all of the cool stuff. They love the MCP experience. And we have these big enterprise contracts with people who can only use Copilot. They've never tried any of this before, and they are looking to us to become the the place where where they collect information and bring bring information into us and and are thinking about us as that place where you go to collect everything and almost be the server itself. So we're prioritizing integrating with other people right now, but also thinking about how do we make sure that our users who don't have access to these more powerful tools can take advantage of Hudson Labs in a more extensible way so that we also have connectors coming in, not just going out.
00:26:21 · Brett Caughran, Kris Bennatti
[00:26:21] S1: Gotcha. So almost sort of a bifurcation where in some, like I think everyone's searching for a single pane of glass right now. So in some instances you would be that single pane of glass and you would have sort of ingress MCP connectors in that. Is that right? And then you would also sort of have egress MCP into another single pane of glass? [00:26:43] S0: Yeah. And not just MCP, API, everything. All of the integrations. Yeah. [00:26:49] S1: How do you sort of think about the durable advantage of Hudson Labs in that sort of single pane of glass elsewhere? Like what are the key, we could talk about forensic risk score obviously, which sort of a differentiated data. What do you sort of like, I'm trying to sort of figure out what the institutional grade stack of connectors looks like, how many that is, where do I get my all data transcripts, etcetera? Like where do you think, what do you see as the TAM or the market share of Hudson Labs in that institutional grade fundamental agent stack?
00:27:25 · Kris Bennatti
[00:27:25] S0: We are gaining popularity in model building and hope to corner that market with the no hallucination guarantee being able to pull all of the information you actually need. Obviously, there's lots of tools like DALUBA that do more out of the box modeling for people who are doing it themselves. But generally, where we think about our moat versus others, so we've got the no hallucination elements, which is a or precision element, which is something institutional users really care about, whether that's single company or multi company. But we do expect eventually generalist models in the next five years to catch up from a hallucination perspective, and we don't think that, you know, ten years from now we can still be competing on a precision element, particularly from a single company perspective. So, really where we provide a lot of value is in situations where you need to be doing really good search and retrieval. Right now that does apply to single company workflows because you can't get good results over, you know, a longer time period and pull those numbers accurately if you want the restatement adjusted, if you don't want them to be full of junk. But where we offer a ton of value now and in the future is being able to retrieve information across 10,000, 20,000 companies. I think we have 52,000,000 data points in our vector database at this point. And you can do things with Hudson Labs that you literally cannot do with any other platform like pull out cool sentiment based elements, be able to find the things you're looking for quickly using materiality ranking, using our declarations around metadata, etc. So, to some extent, mostly right now, big firms don't have sophisticated search and retrieval. They're using the out of the box brute force methodology and you mentioned the fact that they don't care about spend and it's okay that these big firms are spending like $30,000 every couple of weeks trying to create information without a back end. But the problem is that it's not just about cost when we're thinking about search and retrieval and using a brute force mechanism across a large data set. It's also about accuracy. You know, if you try to use a brute force methodology to, you know, I'm an accountant, look up accounting policy changes using Claude right now, you end up missing so much information because it's hard to find.
00:30:40 · Kris Bennatti, Khe Hy, Brett Caughran
So there's this big demand for how do we do not just single company deep dives, but how do we do AI based screening, data set creation, high precision data set creation, go the extra step in these multi company workflows. [00:30:58] S2: Is it fair to say that the brute force, because presumably like with every new model release, the brute force powers get better. If I'm hearing you correctly, brute force can give you like, it might improve your breadth capabilities, but you still have to manage the accuracy. Like, brute force is not going to be as helpful to maintain that accuracy component then. [00:31:31] S0: Brute force is so what's a topic you want to learn about right now, Kaye? [00:31:39] S2: What's a topic? Let's see. I'm trying to learn about data lakes and data warehouses, like the distillation of information across different structures. [00:31:54] S0: There's a lot of information out there on the web about data lakes. There's a lot of information in SEC filings. Let's say data lakes, there's 15 articles about not data lakes, but other types of databases that are competitors and don't have the word data lake in them. If you want to use the brute force approach right now, you're only going to be able to effectively search for things that are easily findable. [00:32:35] S1: Got it. [00:32:35] S0: Right? [00:32:36] S1: Yeah.
00:32:36 · Brett Caughran, Kris Bennatti
[00:32:36] S1: Yeah. [00:32:36] S0: So if you're thinking about getting a complete lay of the land using AI search, you need a way to discover information that is related but not easily searchable. And that's true for a lot of AI style search. One reason I often point to sentiment is because it becomes really clear in that example. If you go, okay, find frustrated managers or find managers that are, show evidence of deflection. That's not something that you can type in and search for. And if you think of the AI model, how is it going to find it if you can't search for it either? And you need some way of making that information findable so that when you pull that into the context window, the model has something to work with. [00:33:35] S1: Got it. Yeah. Talk to us a little bit about, you're an accountant, the forensic accounting score, which is part of the one of the delightful capabilities of AI is to take a previously highly cumbersome process, like doing a deep forensic accounting analysis and sort of distill that into a score, which has the quantitative elements, but also the unstructured data elements. So this was sort of something that caught my attention very early on, and I've been a big fan of the Hudson risk score approach. Can you sort of just crack that open for us and walk through what you've been doing there?
00:33:35 · Brett Caughran, Kris Bennatti
[00:33:35] S1: Got it. Yeah. Talk to us a little bit about, you're an accountant, the forensic accounting score, which is part of the one of the delightful capabilities of AI is to take a previously highly cumbersome process, like doing a deep forensic accounting analysis and sort of distill that into a score, which has the quantitative elements, but also the unstructured data elements. So this was sort of something that caught my attention very early on, and I've been a big fan of the Hudson risk score approach. Can you sort of just crack that open for us and walk through what you've been doing there? [00:34:18] S0: Yeah. So, to take a quick step back, people have been trying to predict securities fraud since the beginning of time. And historically, when we used to try to do fraud projection, we would use generally things like days sales outstanding, how quickly is revenue growing, accruals, these financial metrics. Have you ever tried doing that, Brett? [00:34:51] S1: Oh, yeah.