Private equity operates on judgment, and it turns out that judgment is very difficult to scale. Decades of deal notes, underwriting models, partner memos, and portfolio data are scattered across systems that weren’t designed to communicate with each other.
Every time a new deal hits a company’s desk, analysts start from scratch, even if the answers to their most pressing questions are buried somewhere in the company’s history.
That’s the problem Rowspace was built to solve, and it’s why the San Francisco startup is emerging from stealth with $50 million in funding and a bold pitch: an AI for private equity that not only helps with decision-making but actually learns how companies think.
The company went public 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.
Early customers (whose names were not disclosed but are said to be name-brand private equity and credit firms with assets under management ranging from hundreds of billions of dollars to nearly $1 trillion) are already on the platform, including about 10 top companies with seven-figure annual commitments.
Two MIT graduates, one stubborn problem
Rowspace was founded by Michael Manapat and Yibo Ling. They met as graduate students at MIT and later diverged into very different careers. Manapat built machine learning systems that processed billions of transactions at Stripe, and later drove Notion’s expansion into AI as CTO.
Ling chose a career in finance, leading finance teams at Uber and Binance, twice serving as CFO, and spending years making investment decisions by manually integrating data across fragmented systems. When ChatGPT launched in late 2022, Ling tested it on due diligence tasks and ran straight into the same wall.
“There were obviously a lot of expectations, but it didn’t work out,” he told Fortune magazine. “We need the right information in the right context.” The gap between the promise of AI and the reality of finance’s messy, unique, and organization-specific data became a foundational theme.
Lin, co-founder and chief operating officer, declared: “Most technology tools are not comprehensive or nuanced enough for finance. And most financial tools need to raise the technical ceiling. We intend to do both.”
What does AI for private equity actually look like?
Rowspace’s platform connects structured and unstructured data across a company’s history, including document repositories, investment and accounting systems, old PowerPoints, and transaction memos, and applies what Manapat calls a finance-native lens: one that reflects how companies actually reconcile information, interpret contradictions, and make decisions. The key is to handle all of this within the client’s own cloud environment. A company’s data never leaves its control.
Results can be accessed through Rowspace’s proprietary interface, within tools such as Excel or Microsoft Teams, or directly into a company’s existing data infrastructure. First-year analysts considering new deals can uncover decades of past decisions, comparable deals, and internal underwriting patterns without picking up the phone or digging through shared drives.
“The financial industry is full of high-stakes decisions. There used to be a trade-off between acting quickly and using all the data at a company’s disposal to make informed, nuanced decisions. Our AI platform eliminates that trade-off,” said Michael Manapat, co-founder and CEO of Rowspace. “We build specialized intelligence that turns enterprise data into scalable decisions that meet demanding financial requirements.”
That ambition is reflected in the lines Manapat uses internally. “Imagine a company you’ll never forget, where an experienced investor’s workflow (exposure to different tools in a specific way) can be codified and expanded. If that’s possible, a first-year analyst can leverage decades of institutional knowledge, and their judgment can be tailored to the company without diluting it.”
Why Sequoia and Emergence are betting on vertical AI
The investor confidence behind this hike is a signal worth reading in its own right. Sequoia partner Alfred Lin, who led the investment, positioned Rowspace as a direct answer to the question of what AI applications can survive in the face of the rise of increasingly capable underlying models.
“Michael built a machine learning system with Stripe that processes billions of transactions and drove Notion’s expansion into AI. Yibo is a financial leader and investor who has taken on the very challenges that Rowspace is solving,” Lin said, adding that both Michael and Yibo have seen problems from both sides, combining technical depth with a first-hand understanding of what customers actually need.
Jake Saper, general partner at Emergence Capital, expands on the data infrastructure theme, saying, “They’re doing things that weren’t possible before: connecting their own data and tightly orchestrating and inferring it. Without this foundation, it doesn’t matter what other AI tools you have.”
This argument is a nice reversal of the fear currently gripping much of the software industry: that the underlying model will ultimately commoditize applications. Lin’s view is the opposite: Vertical AI systems built on deep proprietary data layers will provide even more lasting competitive advantage.
Especially in the case of AI for private equity, that logic is particularly difficult to refute because alpha is, by definition, company-specific and non-replicable. The back office of investment management is one of the last frontiers that general AI is struggling to break through. Rowspace just raised $50 million, assuming we know why and how to deal with it.
(Photo provided by Rowspace)
See also: Santander and Mastercard launch Europe’s first AI-powered payments pilot
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