Proof of Work: AI Value Creation
00:25:22 · Joe Zein, Stuart Willson
Each row, so so now you have uh that library is all of a sudden organized and it has some rules and assumptions that you can trust to go on and build your analysis. So, for example, if I tell you that in this library, in that section, that's the health section, on that row, you're only gonna see books, books about the heart, then I can, you know, I know that I can only go that I only go need to read the books that are on that shelf and not read all of the books because there's not gonna be one misclassified book that is not in the right section. [00:25:55] S0: And so there's a real difference between AI doing search over a pile of documents versus querying governed structured data. How do you actually move a firm from one to the other? And how long does it take?
00:26:15 · Joe Zein
[00:26:15] S2: So tools like what Yann's building, like CRMs, those are uh a big step in one of those directions, right? And in that direction, where people have adopted at the end of the day, if you're using specific SaaS tools, those are already governed models, right? Like they they're using databases under the hood, they've gone through the job of abstracting that world, trying to design it in tables that are referencing each other. So those are you know a big, you know, help us bootstrap that implementation. But then you you will always have other than your CRM, your portfolio monitoring, the different SaaS tools that you're using that you can pull tables from using the API, you still have a bunch of files living in the file system. And there's a lot of techniques that we use that we go and pipelines that we run where we're you know reducing the set of all of the data into a canonical set of data, and then you know, removing all of the duplicates, and then removing all of the files that were that we think are not useful. And then we're using AI on top of that to be able to classify files. Is this a SIM? Is this a um you know, what kind of document is that? Is it a presentation about a portfolio company or is it an IC deck? And once we were able to process all of these, we're able to look into those documents, do extractions of things that would have not gotten extracted by the portfolio monitoring system, right? Because at the end of the day, the portfolio monitoring and valuation system is for anything post-closed. But then you also have all this material about all the deals that were looked at. Those never made it into something like 73 strings, right? They may have made it to some extent into the CRM, but not fully, right? And so capturing, looking back into those files, first selecting that subset and two looking through those files in a cost-efficient way and extracting those records and putting them in that database where we would have already pulled the records from the databases from 73 strings and from Salesforce and from different places. And then on top of all of that, deduplicating entities, because the last thing you want is to have a reference to Goldman Sachs that is GS somewhere and have it in you know 73 strings as Goldman Sachs Inc., you want to know that these two are kind of the same entity.
00:28:29 · Joe Zein
And it's this multi-step process that we usually take to move all of the unstructured data of a company into that structured database that is yet that is then used, that is very well documented to be used by AI.
00:28:43 · Yann Magnan
[00:28:43] S1: Don't have uh much to uh to add to uh to what Joe Joe said. I mean, I think you covered it all at the end of the day. Uh what I would add is uh that um it's a lot of a change management uh type of process. Um you whether you start from a blank sheet of paper, like there's a lot of uh change management at that point in time, or whether you start from uh um uh another system, legacy system, and you want to move to something that is more um AI native and that will help you uh uh generate more value out of the uh LLMs uh algos that are uh out there. Uh at the end of the day, you have to change a lot of things. Uh you have to change uh how people think about uh data, how they think about processes. You have to spend time understanding what's going to be the correct data model that's going to uh that's going to help you monitor your assets. But I think the opportunity is much bigger than that, is actually to be able to extract intelligence, insights across the portfolio. So a lot of a lot of thought has has to be put into that to make sure that you put in place the solution that really makes sense and that will really take you to the next level. So change management is something critical in terms of timing. It's not fast, it's not something you're going to do in a couple of weeks. You know, most often would be a quarter, two quarters, three quarters, depending on the size of the client and on the size of the portfolio and whether portfolio covers multiple strategies, multiple geographies, multiple sub-businesses. As again, you want to be able to uh you want to make sure that you are able at the end of the day, once you've done that work, that you extract intelligence and value not just at the portfolio company level, but across the portfolio. So that requires a lot of uh a lot of time. Yeah. Some time.
00:30:25 · Joe Zein
[00:30:25] S2: In an ideal world, too, right? You have different tools that a specific fund manager is using, right? They're using the portfolio monitoring system, they're using their CRM, they're using all sorts of different tools, right? And you want to go with the, I'm going with an ideal world that is still not completely ideal, where they don't have files, everything is living in some sort of system, right? So you would think that, okay, we have the model, but in reality you don't. You have parts of the model, you know, some subsets of models living each in their own SaaS. And the exercise is so, well, how can I, and this is the whole, you know, like 2020 data warehouse, bringing everything into one place and joining them. How do I make sure that this entity that is in 73 strings is the same as that entity that I have in uh Salesforce? And this is the whole practice of MDM or data management that has then been rebranded as analytics engineer, that is now being rebranded as AI infrastructure, of basically matching those records across different systems and putting them into one place. Because at the end of the day, 73 strings is looking at your data from the lens of the portfolio. Something like Salesforce is looking at your data from the lens of like BD and underwriting, right? And so you want to bring those lenses into one lens, which is the representation of how you think about your own company.
00:31:44 · Stuart Willson, Yann Magnan
[00:31:44] S0: Yeah, I'm glad you went there because I was going to ask Yann as a next question. How does 73 strings handle the entity resolution problem when the same company has three different names or four different names across three or four different source systems? How do you go about unifying that? And one could what can people learn from that approach? [00:32:06] S1: So uh I think we used to do that when we were uh back uh back a couple of years ago. Uh most of that uh issue was basically handled uh at uh the uh implementation point of the platform when we are implementing the platform. Well, basically, again, you you have to develop anyway that uh consistent data model across the board. Uh so one of the work that you do is working with the with the client, identifying all these types of issues, and so identifying the uh correct uh legal entities were uh one of this uh topic. What we're seeing happening is that actually that's typically the area where LLMs can help because they can make this type of connections. So you can accelerate that work now with with LLMs. Again, as long as you have put the data in the relevant place. So that's what we're seeing happening.