Rowspace has officially emerged from stealth with $50 million in funding, aiming to redefine how financial institutions apply their own institutional knowledge at scale. The company closed a Series A round co-led by Sequoia Capital and Emergence Capital, following a seed round led by Sequoia. Investors including Stripe, Conviction, Basis Set, Twine, and several prominent finance-focused angels also participated across both rounds.
Rowspace is tackling a problem that has quietly shaped financial decision-making for decades. Inside major investment firms, institutional judgment lives across fragmented systems buried in deal memos, financial models, email threads, portfolio monitoring tools, and legacy infrastructure. The experienced partner who has evaluated hundreds of deals carries pattern recognition that cannot be replicated easily. The credit analyst who has navigated multiple market cycles understands risks that never appear neatly in dashboards. Until now, that knowledge has remained scattered and difficult to operationalize.
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Rowspace connects both structured and unstructured data across a firm’s historical systems, from accounting platforms to document repositories and proprietary data infrastructure. It then applies what the company describes as a finance-native reasoning layer — one that mirrors how firms reconcile discrepancies, interpret nuance, and reach decisions. The result is an AI platform designed not to replace judgment, but to scale it. Teams can access insights through Rowspace’s own interface, within tools such as Excel and Microsoft Teams, or directly inside their existing data environments.
Co-founder and CEO Michael Manapat says finance has long faced a tradeoff between speed and rigor. High-stakes decisions often require deep analysis, but pulling together complete information can take weeks. Rowspace’s platform aims to eliminate that compromise by transforming proprietary firm data into what he calls “scalable judgment,” delivering answers with the level of precision institutional finance demands.
The platform is already being used by firms managing hundreds of billions — and in some cases nearly a trillion — dollars in assets. These institutions rely on Rowspace for portfolio monitoring, multi-decade deal analysis, and credit portfolio optimization. According to the founders, many turned to the company after finding that generic AI tools lacked the specificity, transparency, and accuracy required for complex financial workflows.
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Co-founder and COO Yibo Ling brings firsthand experience to the problem. As a former CFO overseeing major investment portfolios, Ling frequently had to synthesize fragmented information across systems to make informed capital allocation decisions. He believes most technology tools lack financial nuance, while traditional finance tools often lack technical depth. Rowspace, he says, aims to bridge that gap.
Investors backing the company point to the complementary strengths of the founding team. Manapat previously built machine learning systems at Stripe that processed billions of transactions and helped drive AI expansion efforts at Notion. Ling combines operational finance leadership with investing experience, offering a practitioner’s perspective on where existing systems fall short. Sequoia partner Alfred Lin noted that the founders have experienced the problem from both the technical and customer sides — a combination that is relatively rare in enterprise software.
Security and data control are central to Rowspace’s approach. The platform deploys directly within customer environments, ensuring sensitive financial data never leaves a firm’s control. In an industry where confidentiality is paramount, this architecture is designed to provide the trust necessary for widespread adoption.
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As financial institutions increasingly look to AI to sharpen their competitive edge, Rowspace is positioning itself as infrastructure rather than overlay — a foundational layer that embeds intelligence directly into how firms already operate. By codifying institutional workflows and reasoning patterns, the company envisions a future where judgment compounds over time instead of becoming diluted as organizations scale.
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