Investology: Investment Management Intelligence
00:07:44 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:07:44] S0: the end of the month. [00:07:45] S1: oh it's a quarterly report okay so let me actually capture the data about the underlying company so i understand how this fund is building up exposures and risk all of that process is typically very manual and the outcome of that process is after you have checked down the notification document you have extracted the data [00:08:06] S0: you know, [00:08:07] S1: put it on to a sort of standardized format is to actually then do everything you [00:08:11] S0: need to do afterwards. [00:08:12] S1: The monitoring, the reporting, all of those processes I was talking about before. [00:08:17] S0: And that's usually done manually and on Excel. [00:08:19] S2: So at this stage, we haven't yet spoken about doing any due diligence, performing analysis, applying some judgment. [00:08:27] S1: We're just talking about inputting in the system, but then the interesting part comes, right? [00:08:34] S0: Correct. Correct. Then you will need to do the performance calculations, make sure the data is structured in a way that is accessible, run forecasting of it. You know, that's where all the fun starts. [00:08:46] S2: And that's not finished, right? You probably also need to report your investors, deliver reports for the compliance, all of that and more. [00:08:55] S0: Correct. That's the mix. That's the mix. And it's all about, it all starts with data. So that's the core of what we focus on. [00:09:02] S2: So what is the traditional approach to that? How have helpies dealt with it so far before the introduction of Tamarix?
00:09:02 · George Aliferis, Andrea Carnelli Dompé, Andrea Carnelli Dompé
[00:09:02] S2: So what is the traditional approach to that? How have helpies dealt with it so far before the introduction of Tamarix? [00:09:10] S0: Yeah, in most cases, it's manpower. The issue is that the data is so unstructured that until recently, it was just very hard to handle automatically. So it was manpower being thrown at a problem. As the industry has evolved, people started saying, okay, [00:09:24] S1: but let me just stick that data into a SaaS solution that allows me at least to [00:09:28] S0: query it afterwards in an efficient way. [00:09:31] S1: So then you had the next wave of evolution that was manpower feeding SAS software. [00:09:37] S0: Now, thanks to AI agents, we are trying to automate also the manpower part feeding into the system. [00:09:44] S1: So we've only spoken about the problem without speaking about the solution so far. Can you tell us where does Tamarix intervene and is it as early as this phase of [00:09:55] S2: ingestion of data? And then how far do you go in the process? [00:09:59] S0: We can support any kind of intervention in the value chain, but typically people will have preferences as to where we intervene. [00:10:06] S1: In the broadest sense, we can intervene already at the notification receipt level. [00:10:12] S0: So we can track notifications, recover the documents, extract the data, normalize it, [00:10:18] S1: and make it available on a software that also generates all the downstream
00:10:18 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:10:18] S1: and make it available on a software that also generates all the downstream [00:10:22] S0: analytics. All of that we can support. Some people will open the menu and say, actually, I just need you here. Yeah, we can support that end-to-end. [00:10:29] S2: And in practical terms, how do you insert yourself in this structure, in this process of communication between GPs and LPs? [00:10:39] S0: Well, the source of information is GP portals. [00:10:44] S1: And GP portals tend to run on a few systems that are quite common across the industry. [00:10:50] S0: But there's loads of exceptions. [00:10:53] S1: And those systems are not necessarily known for being very tech-friendly or open to [00:10:58] S0: third-party systems. So unfortunately, it's a wide range of techniques from having simulated logins to automated [00:11:10] S1: attachment extractions in the case of the notifications or sending the documents as [00:11:14] S0: attachments. [00:11:15] S1: There's loads of different strategies that are very specific to the GP portions. [00:11:20] S2: And how do I implement this as an LP? [00:11:22] S1: What is the first step in order to move from my traditional approach to your AI [00:11:28] S2: agent-driven approach? [00:11:30] S0: Typically, when somebody uses us for the end-to-end portfolio monitoring approach, [00:11:35] S1: it would all start by authorizing us to receive documents from GPs on their behalf. [00:11:42] S0: So that's step number one. [00:11:43] S1: Step number two would be tell us what you have so we can map out your portfolio.
00:11:43 · Andrea Carnelli Dompé, Andrea Carnelli Dompé
[00:11:43] S1: Step number two would be tell us what you have so we can map out your portfolio. [00:11:47] S0: Step number three is [00:11:49] S1: explain to us what kind of accounting monitoring reporting laws you have so that we can design the system in a very scalable way that goes and ticks all those boxes so you know we understand what you need based on that we work out what kind of raw data we need to extract and normalize and on top of that we work out also what kind of derived analytics you need so we really start with the clients and end goals And is this a high-touch sort of consulting process, or do you have a way to automate it? [00:12:27] S0: It sounds like a consultancy-like, but actually it's not. [00:12:29] S1: So we designed the system to be incredibly general and adaptable to different use cases. [00:12:35] S0: We are able to fine-tune it very well, [00:12:38] S1: but we are always using the same generalizable framework that we just roll out [00:12:42] S0: between new cases, which is one of our strengths. So it feels like it's just done for you, but it's not. [00:12:50] S1: And how are your clients able to measure the increase in efficiency and improvement? [00:12:57] S0: I think we have three main areas of impact. The first one is we reduce the amount of labor required for these tedious day-to-day tasks, [00:13:05] S1: which typically means that the people who used to do that are freed up to do
00:13:05 · Andrea Carnelli Dompé, Andrea Carnelli Dompé
[00:13:05] S1: which typically means that the people who used to do that are freed up to do [00:13:09] S0: something that is more value additive and value generated. So that's benefit number one. And that's in the order of, you know, for say a portfolio of 50, 200 funds, it's in the order of hundreds of hours per quarter. So that's a lot of the time. Secondly, we reduce the risks of missed data or incorrectly extracted data, and that, depending on the downstream implications, can save a lot of money and effort. And thirdly, more broadly, we make data more available, more quickly, more accurately, in general. So typically, clients not only automate what they have already, but are able through us to extract more data more promptly, and this is unlocking loads of new use cases. Classic example is look through data. [00:13:57] S1: Some LPs are so sort of stretched to simply process the minimum they're necessary things such as processing calls and working out valuations that they kind of don't have the time and bandwidth to extract look through information from their [00:14:13] S0: investments. But that's key to understand how your exposures are building up, where your performance is coming from, whether the GP is doing exactly what he said he would do. So there's loads of value that can be generated there. And typically when people come to us, since we make that information easily available, that's an extra edge that they gain to us.
00:14:13 · Andrea Carnelli Dompé, George Aliferis, Andrea Carnelli Dompé
[00:14:13] S0: investments. But that's key to understand how your exposures are building up, where your performance is coming from, whether the GP is doing exactly what he said he would do. So there's loads of value that can be generated there. And typically when people come to us, since we make that information easily available, that's an extra edge that they gain to us. [00:14:32] S2: So the conversation got a bit fixated on the process and the pipes. Ultimately, it's about spending more time on the things that matter the most, making better investment decision, having clarity and so on. [00:14:45] S1: And I would like to understand more the technology that you're using in order to deliver that. [00:14:50] S0: First of all, I mean, there's lots of buzz around AI, of course. [00:14:53] S1: I like to always remind people why we think it's transformational for private [00:14:57] S0: markets beyond the buzzword. [00:15:00] S1: And the problem that AI solves specifically for private markets is exactly the problem of data being so siloed on different sources and being so inconsistent [00:15:10] S0: across formats. [00:15:11] S1: And AI is particularly well suited at dealing with this messiness of sources and formats. [00:15:17] S0: So that's the kind of real big reason why we're investing in us in that direction. When it comes to agents specifically, It all boils down to the definition. [00:15:30] S1: So we kind of think of agents as being software systems that are smart enough to think for themselves and to make actions with some degree of independence and