Rowspace emerges from stealth with $50M led by Sequoia to tame messy data for investment firms using AI

Rowspace is stepping out of stealth with fresh capital and a clear pitch to Wall Street: your firm’s most valuable asset isn’t another model. It’s the judgment buried in decades of internal data.

The San Francisco AI startup announced $50 million in combined seed and Series A funding, with the Series A co-led by Sequoia and Emergence Capital. Stripe, Conviction, Basis Set, Twine, and a group of finance-focused angels joined both rounds. The company says the backing will support hiring across engineering and research as it builds out teams in San Francisco and New York.

Rowspace is targeting a stubborn problem inside financial institutions. Over years of dealmaking, firms accumulate memos, models, emails, and system records that capture how experienced investors think. That institutional memory rarely resides in a single place, and most AI tools struggle to interpret it in context. Rowspace’s bet is that firms want a way to operationalize that judgment at scale rather than start from scratch with generic models.

Sequoia backs Fintech startup Rowspace with $50M to unlock institutional knowledge across Wall Street

The platform connects structured and unstructured data across a firm’s internal systems, from document repositories to accounting platforms. It then applies a finance-native layer that mirrors how each firm reconciles information and makes decisions. The output is delivered through Rowspace’s interface or embedded inside tools teams already use, including Excel and Teams.

“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,” says 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.”

Early customers include firms managing hundreds of billions of dollars in assets, according to the company. Rowspace says those clients are using the system for portfolio monitoring, cross-cycle deal analysis, and credit portfolio optimization. The pitch is straightforward: instead of analysts hunting across fragmented systems, the firm’s historical knowledge becomes queryable and reusable.

“I’ve lived this problem,” says Yibo Ling, Co-founder and COO of Rowspace. “As a former CFO who’s managed a major investment portfolio, I’ve made decisions by synthesizing data across fragmented systems. 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.”

Investors are leaning into the founders’ background as part of the thesis. Manapat previously built machine learning systems at Stripe that process billions of transactions and later helped drive Notion’s move into AI. Ling brings experience from the finance side, having served as a CFO and investor.

“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,” says Alfred Lin, who led the investment for Sequoia. “They’ve seen the problem from both sides, pairing technical depth with firsthand understanding of what customers actually need. That combination is rare.”

Emergence Capital echoed that view, citing the team’s direct exposure to workflow gaps within financial firms.

“We back founders who bring lived experience to big, enterprise goals—basically the definition of the Rowspace team,” says Jake Saper, General Partner at Emergence Capital. “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.”

Rowspace positions its system as a memory layer for investment organizations. A private equity team reviewing a new deal can pull from years of prior decisions. A growth investor allocating capital can work from current reconciled data rather than waiting weeks for manual updates. Credit teams can surface opportunities aligned with their macro view while maintaining compliance checks at both the loan and portfolio levels.

“Imagine a firm that never forgets,” says Manapat. “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. That’s what we’re building.”

Security remains central to the pitch. Rowspace deploys directly inside customer environments so data stays under the firm’s control, a design choice meant to address concerns from large financial institutions wary of sending sensitive information to external AI systems.

The broader opportunity is significant. Financial firms are under pressure to extract more value from proprietary data, yet many still rely on manual workflows stitched across legacy platforms. Rowspace is betting that the next wave of advantage in finance will come from systems that preserve and compound institutional judgment rather than replace it.

The company is based in San Francisco and led by former Notion CTO Michael Manapat and two-time CFO Yibo Ling. With fresh funding in place, Rowspace is moving to prove that firm memory, once unlocked, can become a durable edge.