Rowspace raises $50M to make AI actually work for private equity

Private equity relies on judgment—and judgment, as it turns out, is notoriously difficult to scale. Decades of deal memos, underwriting models, partner notes, and portfolio data are scattered across systems that were never intended to communicate with one another.

Each time a new deal crosses a firm’s desk, analysts start from scratch, even when the answers to their most pressing questions are buried somewhere in the firm’s own history.

That’s the problem Rowspace was built to solve, and it’s why the San Francisco startup is emerging from stealth with US$50 million in funding and a bold claim: AI for private equity that doesn’t just assist decision-making, but actually learns how a firm thinks.

The company launched publicly with a seed round led by Sequoia and a Series A co-led by Sequoia and Emergence Capital, with participation from Stripe, Conviction, Basis Set, Twine, and a group of finance-focused angel investors.

Early customers—unnamed, but described as well-known private equity and credit firms managing hundreds of billions to nearly a trillion dollars in assets—are already on the platform, with about ten top firms on seven-figure annual contract values.

Two MIT graduates, one stubborn problem

Rowspace was founded by Michael Manapat and Yibo Ling, who met as graduate students at MIT before pursuing very different careers. Manapat went on to build the machine learning systems at Stripe that process billions of transactions, then helped drive Notion’s expansion into AI as its CTO.

Ling took the finance route—a two-time CFO who led finance teams at Uber and Binance, and spent years making investment decisions by manually synthesizing data across fragmented systems. When ChatGPT launched in late 2022, Ling tested it on due diligence tasks and immediately hit the same wall.

“Clearly there was a lot of promise, but it just wasn’t working,” he told Fortune. “You need the right information in the right context.” That gap—between AI’s potential and the messy, proprietary, institution-specific data reality of finance—became the founding thesis.

Ling, Co-founder and COO, put it plainly: “Most tech tools aren’t comprehensive or nuanced enough for finance. And most finance tools need to raise their technical ceiling. We intend to do both.”

What AI for private equity actually looks like

Rowspace’s platform connects structured and unstructured data across a firm’s entire history—document repositories, investment and accounting systems, old PowerPoints, deal memos—and applies what Manapat calls a finance-native lens: one that reflects how a firm actually reconciles information, interprets discrepancies, and makes decisions. Crucially, it processes all of this inside a client’s own cloud environment. The firm’s data never leaves its control.

The result is accessible through Rowspace’s own interface, within tools like Excel and Microsoft Teams, or directly into a firm’s existing data infrastructure. A first-year analyst reviewing a new deal can surface decades of prior decisions, comparable transactions, and internal underwriting patterns without picking up the phone or hunting through shared drives.

“Finance is full of high-stakes decisions. There used to be a tradeoff between moving quickly and making fully informed, nuanced decisions using all the possible data at a firm’s disposal. Our AI platform eliminates that tradeoff,” said Michael Manapat, Co-founder and CEO of Rowspace. “We’re building specialized intelligence that turns a firm’s data into scalable judgment with the rigor finance demands.”

The ambition is captured in a line Manapat uses internally: “Imagine a firm that never forgets. Where an experienced investor’s workflows—touching many different tools in specific ways—can be codified and multiplied. When that’s possible, a first-year analyst can tap into decades of institutional knowledge, and judgment scales with a firm instead of being diluted.”

Why Sequoia and Emergence are betting on vertical AI

The investor conviction behind this raise is itself a signal worth reading. Alfred Lin, the Sequoia partner who led the investment, positioned Rowspace as a direct answer to the question of which AI applications will survive the rise of increasingly capable foundation models.

“Michael built the machine learning systems at Stripe that process billions of transactions and helped drive Notion’s expansion into AI. Yibo has been a finance leader and investor who’s wrestled with the exact challenges Rowspace is solving,” Lin said, adding that both Michael and Yibo have seen the problem from both sides, pairing technical depth with firsthand understanding of what customers actually need.

Jake Saper, General Partner at Emergence Capital, went further on the data infrastructure thesis: “They’re doing the previously impossible work of connecting proprietary data, and reconciling and reasoning over it with real rigor. Without this foundation, it doesn’t matter what other AI tools you’re using.”

The argument is a neat inversion of the fear gripping much of the software industry right now: that foundation models will eventually commoditize applications. Lin’s view is the opposite—that vertical AI systems built on deep, proprietary data layers are precisely where durable competitive advantage will compound.

For AI for private equity specifically, where alpha is by definition firm-specific and non-replicable, that logic is particularly hard to argue with. The back office of investment management has quietly been one of the last frontiers general AI has struggled to crack. Rowspace just raised $50 million on the premise that it knows why—and what to do about it.

See also: Santander and Mastercard run Europe’s first AI-executed payment pilot

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