00:13:54 · Brett Caughran, Kris Bennatti
[00:13:54] S1: Got it. [00:13:54] S0: Which is why I believe we're the only platform where you can do sentiment based screening. So ask like who are the most stressed out CEOs? Those types of queries. Because if you're using like LM and it's just using search, how does it search for stress? There's no keyword that you can use there. But if you have embeddings that have information about sentiment, you can do really cool searches. [00:14:17] S1: This was a huge problem for the institutional finance use cases of chatbots in default 25. And in all of my experimentation going into ChatGPT or Claude, almost anything quantitative was not helpful due to this sort of accuracy, like 60, 70% benchmark. Part of the way the ecosystem is aimed to solve that is through MCP and connectors into an agentic workspace. And so now I can connect into, rather than web searching for a number, I can connect into the MCP of a Dilupa or a FactSet, etcetera, through my agentic workspace. Is that, how do you think about sort of what you're doing in sort of you've aimed to solve this problem in the way you just explained? How would you evaluate the way that the industry has tried to solve this problem with that MCP and connector movement into agents?
00:15:13 · Kris Bennatti
[00:15:13] S0: So I know the last time we talked about this, I had complained about the Claude MCP introducing hallucinations into Hudson Labs results, which had been very frustrating. And I would say I've been very, very impressed with how MCPs have evolved since. They're a lot less brittle than they used to be. I, you know, I think I think it's a good approach. Think having connectors that deal with the vectorization, the preprocessing, doing all of this back end work for you and pulling that into your own universe is a good idea. There's definitely some trade offs if you're using MCP. If you're using Hudson Labs via MCP, it's gonna be a lot less efficient. There's sort of a... One of the ways that Claude has become less brittle is by using more calls. So it so it is a much less efficient way of using Hudson Labs, but it is much better than it... I don't know the last time we talked about this, Brett, but it's it's a lot better than it was before. I mean, there's still some situations where it becomes harder to control. We don't offer a no hallucination guarantee if you are using Hudson Labs through MCP because it's not deterministic, right? It's using another model on top of ours, and sometimes it can introduce things that we didn't expect or you didn't expect. But at this point, we feel that the benefits outweigh the risks, which wasn't true a year ago.
00:17:07 · Brett Caughran, Kris Bennatti
[00:17:07] S1: Interesting. Interesting. Yeah. And that's principally like the evolution has principally been what? Just the model's getting better, the engineering around it? Like how has that brittleness problem been mitigated? [00:17:21] S0: Model is getting better, more guidance around how to set up an MCP. I would say a lot of issues right now with usage of, say, any connector, they're not... They're mostly solvable through better API documentation, better MCP documentation, facilitating more endpoints. So, it's because there's a little bit more documentation and more flexibility, it becomes easier to guide these servers to make better use of your own data. [00:18:01] S1: And I hear, and I don't fully understand this, I hear some people say like, Oh, XYZ vendor has a, their API is great, or their MCP is great, their MCP is not so good. Like what is the skill in sort of like for a neophyte, what's the process of taking your data into an MCP and where is the skill in that? How do some vendors sort of do that well? How do some vendors fail in your assessment?
00:18:01 · Brett Caughran, Kris Bennatti
[00:18:01] S1: And I hear, and I don't fully understand this, I hear some people say like, Oh, XYZ vendor has a, their API is great, or their MCP is great, their MCP is not so good. Like what is the skill in sort of like for a neophyte, what's the process of taking your data into an MCP and where is the skill in that? How do some vendors sort of do that well? How do some vendors fail in your assessment? [00:18:33] S0: Like many things, I think it's a function of effort and attention to detail because it does take effort to break down your products and make it identically usable. You're translating essentially what is... Was originally a user interface into something that makes the most sense to an LLM. And what makes sense to an LLM doesn't always make sense to a human being. I think, you know, because we have Suhas, we're particularly good at making our tools LLM readable. That is another thing we talked a little bit about, you know, what's different between Hudson Labs, what's going on in the back end. And one of the things that we do do differently is when you ask a question as a human being, we have a translation step and make your prompt more LLM friendly, particularly when we're talking about market search where those search and retrieval steps and how you ask that question really, really matters. And that applies to MCP as well.
00:18:33 · Kris Bennatti, Brett Caughran
[00:18:33] S0: Like many things, I think it's a function of effort and attention to detail because it does take effort to break down your products and make it identically usable. You're translating essentially what is... Was originally a user interface into something that makes the most sense to an LLM. And what makes sense to an LLM doesn't always make sense to a human being. I think, you know, because we have Suhas, we're particularly good at making our tools LLM readable. That is another thing we talked a little bit about, you know, what's different between Hudson Labs, what's going on in the back end. And one of the things that we do do differently is when you ask a question as a human being, we have a translation step and make your prompt more LLM friendly, particularly when we're talking about market search where those search and retrieval steps and how you ask that question really, really matters. And that applies to MCP as well. [00:19:50] S1: Yeah. How have you thought about the evolution of prompts to skills? I find myself really not prompting much anymore. I have a few sort of conversion prompts, but I find my day to day investment process has really centered around skills into an agentic workspace. How have you thought about that transition of the instructions from the investor to the portal?
00:19:50 · Brett Caughran, Kris Bennatti
[00:19:50] S1: Yeah. How have you thought about the evolution of prompts to skills? I find myself really not prompting much anymore. I have a few sort of conversion prompts, but I find my day to day investment process has really centered around skills into an agentic workspace. How have you thought about that transition of the instructions from the investor to the portal? [00:20:20] S0: Yeah. The goal as a provider is provide the thing that the customer wants with as little instruction as possible. So we think about that a lot. You know, if you just type in SSS, we will guess that you want same store sales and bring that back to you. And that's not something that we were able to do or doing two years ago. So you're right. You know, prompting is becoming a lot less important.