Rowspace raises $50M to help asset managers unlock proprietary data advantage before public data is fully commoditized by AI

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Hey guys, how's it going?

We are here.

Welcome to the show.

Tough act to follow with uh with Benny. It up to 11 every time.

Can you do any animal sounds? He's got the He's got the dolphin down. He's got the whale down. This is a unique ability I didn't know was necessary.

We're not going to see you make any uh any any whale sounds. Uh but uh it's great to meet you.

Great to meet you. First time on the show. Please introduce yourself in the company.

Great to Great to be here. So my name is Michael Manipath. I'm the co-founder of company called RSpace.

And RSpace is an AI platform for asset managers. We help our customers use their institutional memory. So that means all of their proprietary data, their accumulated judgment to make decisions faster. Okay.

So what that looks like is we actually plug into all of their internal systems. This is not just documents,

but it's databases, CRM, accounting, and trade information and we use agents to understand all the connections in that data, the inconsistencies, the conflicts. So agents can reason over it holistically. The idea here is that data is fragmented and is siloed and it's hard to make sense of in a single context window. But using this or having us work on this in advance is very helpful.

Yeah. Uh how jealous should every asset manager be of Bridgewater for collecting so much data over so many years? Uh do you have a view on the Bridgewater strategy? Can you explain what they actually do and whether or not you're a fan of it?

I I can't speak to to Bridgewater specifically, but I think you land on our key point here, which is that public data

is getting increasingly commoditized and actually AI is accelerating the commoditization of public data. The more you have tools like claw that can synthesize anything from the web or from public tools, less edge there is there. So the best edge firms have now is decades of data accumulation and their own insight and judgment as encoded in their data. And our whole goal is to help our customers tap that.

In almost every customer conversation we have, there's a line that's something like we are sitting on enormous value in our data. If only we could get at it or only we could find it. And and that's what we're trying to do. So I think AI will accelerate this dependence on your internal data and proprietary data as public data becomes less and less you know valuable.

Yeah. Uh talk to me about what uh what your product actually looks like once you roll it out. I because you know a lot of hedge funds and asset managers they do a lot of back testing and I could imagine going back and running a report like back test the thesis that we talked about but maybe we didn't actually implement. There's a whole bunch of ways that you could just do reporting, but then you could also go and say, "Hey, you know, just turn this thing loose. Go trade for us." Like, where are we on that continuum?

Oh, there there are a lot of great points there. So, the first thing is we are not trading or taking action automatically. The idea here is that we will help these firms consider the full amount of data that's possible and then they make the decision. Mhm.

But our goal really is it used to not be possible to say review every single name in the universe if you are about to consider a trade to rebalance your credit portfolio. Now you can with with ropace. Uh but it's still the human who makes the ultimate call varying. So that's one thing. The second thing is on on back testing. I love that example because it's actually been surprising how little post hawk analysis people do of their investment or trading decisions.

Just what was our thesis a year ago, two years ago, 10 years ago, what actually happened? How does that inform what we're doing in the future? That was just a timeconuming thing to do and people don't do it very much. But we work with a private equity firm now uh who's been in business for 50 years, a pioneer of the field and They can actually run every deal through space

along the lines of given our 50 years of history, what should we do here? What have we done? What are the risks? And that kind of analysis could not have been possible before.

Yeah.

What uh what is your competitive positioning like with the labs? They're all hiring consulting firms. They have forward deployed engineers. I'm sure they're pitching a lot of the same companies. And I can imagine how you would position Rrowspace against uh building something internal or just leveraging uh leveraging the the the applications that the labs are building. But but how do you sell it?

So on the apps that are out there now from labs and from other players in market, what we see is a focus on time savings. So faster models, faster decks, faster summarization of meetings and research and that's obviously extremely valuable. But our focus has been on decision-m. So what are the things you should be looking at which are impo would have been impossible to consider before that space can help you with. So uh in some sense there are things that our customers do with the ropace today which they did not do before. It's not about saving them time doing work they've done in the past. So that's one chunk of it. The other chunk is we are talking about in some sense the crown jewels for our customers. All of this trading data, position data, their thinking of thesis. This is extremely sensitive. So we only deploy in our customers environment. We never as a company actually take possession of their data and we do all this processing in their environment. So we have a security challenge, an infra challenge, an AI one and I think the sensitivity to the security compliance and other constraints of finance is a big differentiator for us.

Mhm. Take us through the fundraising round. What happened?

Uh so we've done two rounds over the past 18 months. uh 50 million in total led by both times and capital join in in the uh and we've had uh my former bosses were on your show on Monday and they were a big player in both the seed and the uh at Stripe. So uh it's been really fun because uh every major investor on our cap table I've been close to for at least half a decade. So Rose B gets to be a bit of this uh bring back the old gang together.

That's amazing. Well, congratulations. Where's the company based?

Oh, we are based in San Francisco, but a big push for the funding this year is to expand our New York presence.

Of course. Yeah, that makes a ton of sense. Well, have a great rest of your day. Thanks so much for popping by to tell us about the business. Very fascinating. Good luck.

Thanks for having me.

We'll talk to you soon.

Cheers.

Goodbye. Let me tell you about Gemini