AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace

It's great to be here. Uh, I'm Michael Manapat. I'm a co-founder of Rowspace, and Rowspace is an agentic platform that uses, uh, AI to help financial services partners like Apollo deal with messy, fragmented data. We essentially use agents to do the work of data engineering to wreck information or reconcile data from across a variety of systems so that portfolio managers, traders, and so forth can make decisions faster and more accurately. I'm joined here

with Jack McDonald, who is Chief Operating Officer of Redding Ridge, one of Apollo's credit units. And maybe to kick it off, Jack, I'd love to hear a little bit about you and what your business does.

Yeah. Thank you, Michael. Uh, I'm Jack McDonald. I am a principal at Apollo and COO of Redding Ridge Capital, which is our structured credit and CLO manager that is captive to Apollo. Uh, we're a separate, fully separate entity operating company that manages about fifty billion in assets and, uh, all across kind of the structured credit ecosystem, uh, primarily CLOs. Uh, I was-- been at Apollo since 2020, wear various different hats

throughout the, uh, my time there, uh, across our financial institutions group, our asset-backed group, and now, uh, currently at Redding. Uh, so yeah, happy to be here.

Awesome. So something that we've noticed in talking to a huge number of financial firms is that there's a pretty wide gap between when AI is nice to have and when AI can actually become mission-critical to a business. And I think in Apollo's case, we're seeing that AI can really be transformative in how the firm operates. I'd love to hear some examples from you on the kinds of work your part of the business was doing, what made it complex, and what you're seeing

now, uh, with AI.

Yeah. Um, so at Apollo and specifically Redding Ridge, you know, we manage many different fund entities, investments. We're getting, uh, thousands and thousands of data points on a, like, daily basis coming in across all of our investments. We manage probably, you know, a thousand named loan pools and have credits across multiple different businesses that we're assessing on a daily basis and investing in. Um, CLO management is very active, and it's traded in a broadly syndicated loan market, so having all that

information at your fingertips is very, very important. So at Apollo, what we're doing is, and at Redding specifically, is trying to enhance and en-ensure that all of our data and our data quality is accurate, consistent, and in a format that we can utilize, um, and make us much more practical and thoughtful when we're making decisions in a much fast-paced environment. So all of this information is privatized and is not very,

what I'll say, widely in the marketplace, like how public equities or public fixed income trades. It's very, uh, fragmented in various documents. So we've looked to companies like Rowspace to partner with to come in and actually sit with our teams, work with us, and understand the actual calculations and information that we're looking to and what's actually the golden source of truth, and then continuing to enhance the AI engines to understand that and

repeat that and just make us that much more efficient.

I think we have a bunch of engineers in the audience, and we have spoken a lot, just you and I, on fire drills that the firm does that used to take a team working a week, uh, if n-if not more. Uh, can you make it concrete for us and maybe walk us through an example of something that was painful or time-consuming, hard to do before that now you can do more easily with Rowspace and, and, and AI?

Yeah. Happy to, and I'll give kind of two examples that we, uh, that I like to look to. Um, the first being is just on the portfolio construction side. So CLOs are very test-specific. They have a bunch of-- They're very rules-based. They have, like, a very specific waterfall of where money flows. Um, so those all live in deep indenture documents and multiple different legal, uh, agreements that-- So sitting from, like, a portfolio manager's perspective in portfolio construction, they are reviewing those

documents, building Excel-based models, and building mock portfolios with, call it, anywhere from two hundred to four hundred single credit names to create a portfolio, which would typically take a PM, before kind of adopting Rowspace, anywhere from two to three days in a manual Excel. Today, we're able to kind of get the latest and greatest information that's real-time, create a portfolio that would take somebody days to now do it in a matter of minutes. And then that allows these PMs to kind of

build something to, call it, eighty, ninety percent completion and then kind of use their judgment and their thought to further refine that portfolio, that last ten percent to make it theirs. Um, that's one of the most tangible examples that's kind of pulling in various forms of data and, you know, information from across multiple different things, whether it's in doc libraries, you know, our trade, uh, and compliance systems and real-time pricing and stuff like that. Um, the other one that I think I would look

to is our kind of, like, warehouse purchase activity. So how we run our, our business operates with having a bunch of bank financing, and we can go out and buy loans in, in the fixed income market at any given point. Um, historically, we would kind of do a trade rec and trade activity on a weekly basis that would go around of, "Here's your sale purchase activity from the week. Here's what we bought. Here's what we sold." And it was very manual. We were going into various-- like, literally going into bank accounts to

understand, uh, statements where things actually settled. And it was an individual team that was going and rec-ing that out and then sending a reported email to, uh, the management team to say, "Here's the recap." Um, today, what we're doing now is we've been able to take that exact same report, drop it into Rowspace from a visualization perspective. That report was then created and now repeatable in the span of A minute. And now all of that is automated, and then from

our team's perspective, we are just QA-ing that and checking it each day to make sure that that looks right. And now it's a much more rinse and routine, uh, type process, which historically has taken many hours of the week for two to three individuals on our team to actually rec that out.

Yeah. I love both of those, those examples. So, you know, you aren't just reading the output of a chatbot to be informed. I think your team are making trading decisions, portfolio management decisions, so accuracy and quality are high. This is a two, a two-part question. I think a lot of people will ask us or ask generally, why can't you just plug this stuff into Claude and or ChatGPT and see what happens? And for someone like you, how does your team get the confidence in a product like

Rowspace or other AI tools to say, "We are going to make business decisions on this, make trades, build portfolios," all of that. How do you get to that level of, of trust?

Yeah. I would say Claude and the OpenAI's of the world and like the ChatGPTs, all the LLMs are, uh, very beneficial, but it's all general information that that's being worked off of and none of your own actual data. So it's great for research or kind of building new ideas or getting up to speed on some type of, uh, product or investment quite quickly. Where Rowspace is different and what we've kind of taken our approach to this, the, the AI build-out at Redding has

been around owning our own data and having some type of platform and technology that sits on top of that that we know is to be true. So the Rowspace team, which has been much different from what we've seen from other, uh, technology companies that we've worked with, is they're boots on the ground. So like Michael sits with our team on a daily basis learning and understanding our information flow. 'Cause for one piece of data, that might come from 20 different sources,

and there's only one that's actually right. So the Rowspace team is spending the time with our portfolio management team, with our finance teams to understand the calculations, understand what data is actually correct, and override other sources of information and kind of aggregate that. So to that end, we're able to kind of have trust in the system and, you know, appreciate that, okay, we've had our team go in and validate that this information is correct and we can rely on it, and it's coming from all of our, whether it's our

trade order management system, whether it's from our performance system, whether it's from Bloomberg or any pricing index that we're pulling in, and know that it's accurate. Whereas in a lot of other, you know, call it systems throughout even our own company, it's very fragmented and we have to kind of lift one piece of information from over here and bring it back over here and kind of pull that together manually, whereas Rowspace is now just automating that all for us.

Glad, glad to hear that. Need to talk about the firm at, at a higher level. Uh, it seemed to me for a long time, even early last year before Claude Code became a thing, when Cursor was still in the tab completion era, that Apollo was among the most AI-pilled of the kind of firms we spoke to. Uh, and that was everything from the firm's view on how to invest in an AI world. But operationally, uh, we began talking to you a long time ago, and you were in the,

"We must rebuild the firm in an AI native way, must have a platform, not point solutions." So I'm just curious, where did that come from, and what is it about, about, about Apollo's culture that made you so AI-pilled even before things have, have took off?

Yeah. So Apollo has always prided itself in being kind of the leader of the, uh, alternative asset management space and the financial services industry broadly. Uh, we pride ourselves in being very innovative and being at the forefront of all things change and what that looks like. So I think from the onset and the way our management team approaches how we operate is always being up to skill and enhancing our team's capabilities and being able to be nimble and adapt to where the market's

going. I think we appreciated that the AI world is here, and we need to adapt and adopt it, not, you know, discredit it or not utilize it. So we've been very forward and, you know, open-minded about adopting new technologies and bringing those into our workflows and how we can be more efficient and free up capacity for people to be more thoughtful and think and, you know, replace manual processes. So I think at the core of its level, we've always been a very, uh, innovative firm that's very flat and open culture to

be open to those types of change. And then I'm blanking on the second part of your question, sorry.

Well, maybe we can keep on going. Uh, so I think one thing that we've been thinking about is software engineering that has completely changed as a discipline. Like the world is completely different if you are a technology company like, like Rowspace. Uh, and maybe arguably law and customer service are almost there. Uh, where do you think finance is on the spectrum of AI is here, it's made a difference, or we haven't even, you know, gotten to the bottom of the, the first inning?

Yeah.

And what do you think is-- Two-part question again, so remember the second part. Uh, if we're early on, what, what's necessary to really change the shape of finance with AI?

Yeah. I think that's a great question. So I think finance broadly, these banks and large organizations are massive. They have a lot of, you know, back-end, what I'll call middle back office support, and I think there's a lot of room to grow and enhance there. I think probably as you appreciate now of working with us, that we are extremely complicated and have a lot of complexity in everything that we do. So it's not really straightforward. I think in finance broadly, especially in call it the asset management,

investment banking, the bank institution- institutional world, I still think it's very early. I think there's some value for some of the like, you know- Uh, model creation or getting up to speed on some company quite quickly and pulling and aggregating that type of research. But I think that's lower value add at least of where we stand today, and that's going to evolve. I think like, you know, there's probably a need where there's less junior support over time for certain functions

like that. But I think from where we are today and what we see from, from at least my perspective at Redding, I think we're just trying to adopt this technology and make it, you know, as efficient as possible for, you know, routine tasks that frees up capacity for, you know, other projects because we kind of have like endless things to do and enhance at our company, and that's just giving us a lot more flexibility to adopt that.

Yeah. Yeah. Uh, so maybe continuing on that note, I- I'm curious, uh, there's a world in which AI just becomes something that helps speed up tasks, uh, saves you on OpEx, things like that. And maybe in the same way, no given firm now has an advantage because they use Excel, because everyone uses Excel, and it's just a productivity tool. There's also a world in which AI actually materially makes a competitive advantage or helps reify Apollo's lead

in structuring and creative deals and so forth. Do you have a take on whether AI is going to be just a useful but a general commodity, or it's going to be something that can actually structurally make a business more competitive i- in a durable way?

I think if it's adapted and utilized properly, it will make you more competitive. I think at, at its core, and especially around asset management and investing type companies that are m- in the actively traded markets, like it comes down to your process and the information at your fingertips. So knowing that you have a strong process in place to go out and execute and knowing your available liquidity or, you know, where things are pricing and what the value is at any given point is crucial. So that just gives you, you know, more competitive edge, and

I think like that exists today in the, you know, actively traded markets, but in the private markets it's evolving, um, which we've kind of been at the forefront of trying to, to move on the broad Apollo machine. And I think having that will just turn into a competitive advantage over time where firms will be able to get further ahead as they're, you know, having access to better information. They, they know-- their teams know that it's accurate and like stuff is not misaligned because

the last thing we want to do is, you know, do something or make a decision that was misinformed or had misinformation and then that streamlines down the chain to both the enterprise and to the, you know, our investor base at the end of the day.

Yeah. Yeah, that, that makes sense. So I wanted to ask you what your prediction was for what Apollo would be like in-- when we're talking five years observing all the change. I think that's unfair. Uh, I can't even predict if my team's gonna use Codex or Claude Code next week, so I won't ask-- I won't ask you about five years in, in the future. But I'm curious for you personally, what are you hoping to see in terms of your job, team, how AI use at Apollo evolves, what conditions have to hold for

the technology to actually be truly valuable? You know, what are you-- h- how are you thinking about things over the next, you know, next few months?

Yeah. I think it comes back to some of the stuff I touched on before of just one, having just, you know, full confidence in any type of information that's flowing out at the end of the day to our investor base that's accurate, correct, and we don't have to spend hours manually checking that to make sure that that's validated and having the confidence that we're sharing, you know, no issues and/or mistakes, um, because we do have just a wide investor base, you know, over four hundred global investors. Um, I would also say

being much more tech-enabled in mindset. I think our team has not lacked that to date, but it's kind of been an afterthought around the use of technology. And I think the more that we can develop and enhance these tools that make, you know, routine tasks, you know, more efficient, it frees up capacity to add more thought or think more creatively around why does this process exist and how does it work today, and how can we actually enhance that, or should we change that, or should we bring in a new type of

technology and adopt that going forward? And our team has not lacked that to date, but it's kind of been a secondary thought where I, I think ideally our workforce is positioned to think about that more on a go-forward basis, and that's kind of their primary thought as they're tackling different problems. Specifically-- this is specifically at Redding Ridge day-to-day and kind of scaling our organization.

Uh, I think we have time for one more. I think one of my learnings from working with you has just been how unimaginably complex and creative the work is and how it takes a lot of effort, both human and AI, to get things to work. A lot of people in this room who either want to work on the technology side of AI and finance or perhaps finance side, is there anything that you would mention to them from a, "Here's something that you should know or keep in mind when trying to apply AI to domains like yours"?

Yeah. I would say it, it comes down to we are extremely complex organizations, and it's not as straightforward. And typically things might look a little bit novel or strange and out of place, but it's probably actually there for a reason or structured in a specific way. I'm sure as you guys have come in, you appreciate that we're super nuanced and complicated. So spending the time and really putting boots on the ground to sit with whatever team or front office professional that you're working to build a

tool for or enhance is time well spent. And I think the success and, you know, where we are today with the Rowspace products and kind of being ahead of our roadmap and expectations has been, you know, due to the fact that Michael and the team have spent ample time sitting on the ground with our team like pretty much every single day and, you know, understanding our workflows, which I think is crucial.

Awesome. Well, I guess last thing I'll say is that shameless plug, both Rowspace and Apollo Technology are hiring people who want to apply AI to finance. So please reach out to either of us if you are interested. On that note, Jack, thanks so much and-

Yeah

... great chatting.

Thank you. This was great. Thank you, guys.

Yeah.