00:00:00 · Ben Haber
[00:00:00] S0: Welcome to the Capital Connect podcast. My name is Ben. I'm the cofounder and CEO of Monark. On Capital Connect, we take a deep dive into the most interesting and timely topics in private capital markets with the most informed and intelligent guests in the ecosystem. Today, I'm joined by Yann Magnan, the cofounder and CEO of seventy three Strings. 73 is an AI powered platform for data collection, portfolio monitoring, and valuation that powers the private capital industry, helping alternative investment funds streamline the middle office processes that have historically been defined by spreadsheets and manual work. The platform supports clients globally across private equity, growth equity, venture capital, and private credit, and is trusted by some of the world's largest asset managers. Yann brings more than twenty five years in valuation and corporate finance experience, including building the Paris office of Duff and Phelps and serving as its EMEA market leader. Seventy three strings is backed by Blackstone, Fidelity International Strategic Ventures, and Broadhaven Ventures. Well, Yann, thank you so much for being here. I really appreciate it. Thank you, Ben. It's good to see you. Yep. Maybe let's start with the founding moment for 73. What was that like? What problems did you see, and what really prompted you to start the company?
00:01:14 · Yann Magnan
[00:01:14] S1: Yeah, that's a very good question. So actually it's an interesting story or I think it's interesting. It's definitely interesting to me. As you can see, I'm not the typical young founder of a startup. I've had a twenty five years career in the financial advisory industry, and more specifically doing valuation for alternative assets and non listed assets. And with that as a background, twenty five years... And I have also a background as a... You know, I studied engineering, but I went straight into accounting and finance after after studying. After twenty five years of doing that, so that was getting us towards 2020. So now you have an an idea of what age I am. But towards twenty twenty, I started to think that realizing that everything that I was doing in terms of valuation of non listed assets, everything was manual, manual as in Excel, Google search, and email. Everything by my teams who are doing even worse was manual as well as in a lot of efficient... Inefficiencies, a lot of risk as well as in fat fingers, and everything that my clients were doing was percent, Excel, Google search, and email. And I started thinking, I mean, there must be ways to do to do things like differently. We could be using technology to create, you know, a platform that would create more structure to to a process. And on top of that, we should be able to use AI as well to, again, create efficiencies, do certain tasks that we were... That I was asking my my teams to do do them like more, you know, automate them. And that's basically the funding moment for us for for 73 Strings. So realizing that technology was mature enough that you could develop a platform at a reasonable cost for clients helps solve what I was seen thinking was their pain point was like totally disjointed valuation process.
00:03:04 · Ben Haber
[00:03:04] S0: I'm sure the rate of acceleration now has been exciting, but I'm curious maybe to go back from 1995 to 2020. Yeah. How would you describe the change in the processes that the industry followed in terms of data collection evaluation, if there was much change at all in the... In that twenty five year span?
00:03:23 · Yann Magnan
[00:03:23] S1: There has been massive changes and, yeah, getting back to 1995, it's like... It's definitely a jump back thing that... Look. When I started working at in that in that particular instance, we were sharing laptops like one analyst that three year laptops. And most of the work we are doing was actually writing down on the writing down on the piece of paper with which as you can imagine, you know, when it comes to valuation and you have calculations to do like DCF and these type of things, that was really like a nightmare. I mean, of course, that evolved like really, really fast. But the key thing was everything was you take data, try to structure that data manually, try to understand what are the nuances between EBITDA for this company, EBITDA for that company, put everything on the same, at the same, you know, level, standardize everything, get that into a model or on the model, and then run it again with different assumptions. And all of that was, you know, as I said, manual. So lots of time spent just getting data into into a system, into an Excel model with all the issues that goes with it, like, basically, again, fat fingers, you know, missing something somewhere. You have a... An intern, no disrespect to interns, but, you know, but that's something hard coded formula where it was he or she was not supposed to do that, then you can't uncover it. That happens to anybody. But the issue is how do you uncover that type of of of situation. So that's basically the world we lived in. And then we've seen two things happening in parallel. Number one, technology becoming more and more mature, and, of course, cloud has changed everything. And number two, what we've seen as well is the massive influx of capital towards private capital, which has led alternative investment funds to grow pretty fast to to invest more into portfolio companies and to be looking as well for more structure in the way they do things to be able to report to a piece better and faster. So those two trends were basically happening more or less at the same time and converging towards, you know, 2015, 2020. And more recently, what we have seen happen in the past two years. I mean, it's...
00:05:24 · Yann Magnan, Ben Haber
The trend that I'm only going to talk about is is older than that, but it's really started to pick up in the past two years is with trends towards alternative investment funds trying to raise capital from retail investors, evergreen type of structure. More recently in The US, we are talking about four zero one k plus that might be able to invest into alternative assets. So all of that is coming to a point... To an inflection point when where things are going much faster. [00:05:50] S0: We'll we'll definitely touch on Evergreen Funds. But but first, maybe just to speak to the power of the 73 strings platform today. So you you really set out to to try to automate both the data collection app, but then the ongoing valuation of portfolio companies specifically for alternative asset managers and and and private investment funds, which obviously has benefits for portfolio construction and reporting for LPs. I guess I'm curious, one, maybe just talk to what the platform does today, how you're using AI in managing the data across, alternative investment fund. And and, specifically, I'm curious how you think about how portfolio managers should really separate between what the platform should do versus what they should be doing and how much they can automate with with a platform like 73 strings?
00:06:38 · Yann Magnan
[00:06:38] S1: Yeah. That's a very good question. So maybe let me start with a statement. So I've always been saying, even when I was a corporate finance valuation consultant, I've always been saying to my clients at that point in time, valuation is art and science. And I still believe I'm still saying the same, but valuation is art and science. What does that mean? Art and science, that means that there's two pieces, two components into a valuation process. Number one, everything that's calculation, the evaluation models, the discounted cash flow multiples, a waterfall on the capital, OPM, CVM, all these type of calculations. That's the science piece. The other part of the science piece is, in particular, when it comes to the alternative investment fund that has to do the valuation of the same assets over and over again for reporting purposes is via workflow. So how do you get from a, I'm receiving data from my portfolio company, the borrower, to z, I'm reporting that valuation to to my LPs, to refund the funds, and so on. So that's the science piece. The art piece is actually everything that's human judgment. As of today, there's always in each and every valuation, there is a need for someone to say... To pick a number, pick to a judgment like, you know, basic example, a private equity backed company, typically valued based on the on the multiple of EBITDA. You have a peer sample, a sample of of comparable companies. You get to a range of multiple 12 to 14, whatever. And someone has to pick a number in there. It could be 13, the average could be 12.5, could be 14, whatever makes sense based on how the business you want to value really compares to that group of comparable companies that you have. That's what I call you managers with. What we are doing at seventy three Strings and what I think technology should cater to is everything that's the science piece. What we are not doing, we don't even want to touch that. I don't think that issue... It's a good idea to get technology touch that is the RPs for couple of reasons. Number one, it's very complex. Number two, clients want to be able to exert the judgment that they think is relevant. Number three, because evaluation we're talking about is post investment for reporting purposes. You want someone to be comfortable for evaluation.