Private equity runs on judgment\u2013and judgment, it turns out, is extraordinarily hard to scale. Decades of deal memos, underwriting models, partner notes, and portfolio data are scattered across systems that were never designed to communicate with each other. <\/p>\n\n\n\n
Every time a new deal crosses a firm\u2019s desk, analysts start from scratch, even when the answers to their most pressing questions are buried somewhere in the firm\u2019s own history.\u00a0<\/p>\n\n\n\n
That is the problem Rowspace was built to solve, and it\u2019s why the San Francisco startup is emerging from stealth with US$50 million in funding<\/a> and a bold pitch: AI for private equity that doesn\u2019t just assist decision-making, but actually learns how a firm thinks.<\/p>\n\n\n\n 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. <\/p>\n\n\n\n Early customers\u2013unnamed, but described as name-brand private equity and credit firms managing hundreds of billions to nearly a trillion dollars in assets\u2013are already living on the platform, with about ten top firms on seven-figure annual contract values.<\/p>\n\n\n\n Rowspace was founded by Michael Manapat and Yibo Ling, who met as graduate students at MIT before diverging into very different careers. Manapat went on to build the machine learning systems at Stripe that process billions of transactions, then helped drive Notion\u2019s expansion into AI as its CTO. <\/p>\n\n\n\n Ling took the finance route\u2013a two-time CFO who led finance teams at Uber and Binance, and spent years making investment decisions by manually synthesising data across fragmented systems. When ChatGPT launched in late 2022, Ling tested it on due diligence tasks and ran straight into the same wall. <\/p>\n\n\n\n \u201cClearly there was a lot of promise, but it just wasn\u2019t working,\u201d he told Fortune<\/a><\/em>. \u201cYou need the right information in the right context.\u201d That gap \u2014 between AI\u2019s potential and the messy, proprietary, institution-specific data reality of finance\u2014became the founding thesis.<\/p>\n\n\n\n Ling, Co-founder and COO, put it plainly: \u201cMost tech tools aren\u2019t comprehensive or nuanced enough for finance. And most finance tools need to raise their technical ceiling. We intend to do both.\u201d<\/p>\n\n\n\nTwo MIT graduates, one stubborn problem<\/strong><\/h3>\n\n\n\n