Rowspace, a San Francisco-based startup targeting the private equity industry, has emerged from stealth with $50 million in funding and a platform designed to transform decades of scattered institutional data into searchable, scalable judgment.
The raise spans two rounds: a seed 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. About ten name-brand private equity and credit firms, collectively managing hundreds of billions to nearly a trillion dollars in assets, are already paying seven-figure annual contract values to use the platform.
The Problem Private Equity Has Always Had
Private equity firms accumulate enormous amounts of institutional knowledge over decades: deal memos, underwriting models, partner notes, portfolio data. But that knowledge lives in disconnected systems, old PowerPoints, and shared drives that nobody designed to communicate with each other. When a new deal arrives, analysts effectively start from scratch, even when relevant answers are already buried inside the firm’s own history.
That inefficiency is what Rowspace co-founders Michael Manapat and Yibo Ling set out to fix. The two met as graduate students at MIT before splitting into different careers. Manapat built machine learning systems at Stripe that process billions of transactions, then served as CTO at Notion during its push into AI. Ling took the finance route, serving as a two-time CFO at Uber and Binance, spending years manually synthesizing investment data across fragmented tools.
When ChatGPT launched in late 2022, Ling tested it on due diligence tasks and hit an immediate ceiling. “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 became the founding thesis for Rowspace.
What the Platform Actually Does
Rowspace connects structured and unstructured data across a firm’s entire history, pulling from document repositories, investment and accounting systems, old presentations, and deal memos. It applies what Manapat describes as a finance-native lens, one that reflects how a firm actually reconciles information, interprets discrepancies, and reaches decisions.
Critically, all of this processing happens inside the client’s own cloud environment. The firm’s data never leaves its control. The platform surfaces through Rowspace’s own interface, within Excel and Microsoft Teams, or directly into a firm’s existing data infrastructure.
The practical effect: a first-year analyst reviewing a new deal can immediately access decades of prior decisions, comparable transactions, and internal underwriting patterns, without calling a senior partner or digging through shared drives.
Manapat frames the ambition directly: “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.”
Why the Investor Backing Matters
Ling, serving as co-founder and COO, put the market gap 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.”
Alfred Lin, the Sequoia partner who led the investment, positioned Rowspace as a direct answer to what AI applications will survive as foundation models grow more capable, pointing to Manapat’s track record building production-scale ML systems as central to that conviction.
Private equity has long resisted generic software. Rowspace is betting that the firms who figure out how to operationalize their own institutional memory will outcompete those who don’t, and that the window to build that infrastructure is now.
Photo by Sawyer Bergeron on Unsplash
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