Rowspace Raises $50M to Build Effective AI Solutions for Private Equity Firms

Private equity relies heavily on expert judgment — yet cultivating consistent, scalable judgment remains an extraordinary challenge. For decades, critical information such as deal memos, underwriting models, partner notes, and portfolio data have been dispersed across multiple disconnected systems, none originally designed to share data effectively.

As a result, analysts face the exhausting task of starting fresh with every new deal. Even though essential answers often lie hidden in a firm’s own archives, the fragmented infrastructure prevents efficient knowledge reuse.

This is precisely the problem Rowspace was created to solve.
The San Francisco-based startup has just emerged from stealth with $50 million in funding and an innovative vision:
AI-powered private equity tools that do more than assist decisions—they learn a firm’s unique thought process.

Rowspace publicly launched following a seed round led by Sequoia, accompanied by a Series A co-led by Sequoia and Emergence Capital. Additional investors include Stripe, Conviction, Basis Set, Twine, and a select group of finance-focused angel investors.

Early adopters — unnamed for privacy but described as top-tier private equity and credit firms managing assets ranging up to nearly one trillion dollars — are already deeply engaged with the platform, securing seven-figure annual contracts with about ten leading firms onboard.

Two MIT Graduates Tackle a Stubborn Industry Problem

Rowspace was co-founded by Michael Manapat and Yibo Ling, who met at MIT as graduate students but pursued different career paths afterwards.

• Michael Manapat developed machine learning systems at Stripe handling billions of transactions, and later served as CTO at Notion, driving their AI initiatives.
• Yibo Ling took the financial route, serving as CFO at Uber and Binance. He spent years manually synthesizing fragmented data for investment decisions.

When ChatGPT debuted in late 2022, Ling tested it for due diligence workflows but quickly encountered familiar limitations.

“Clearly there was a lot of promise, but it just wasn’t working,” Ling told Fortune.