He taught machines to judge billions of payments at Stripe. Now he is teaching finance firms to remember - and calling the company Rowspace.
Co-founder & CEO, Rowspace. The math checks out.
In February 2026, Michael Manapat ended the stealth and said the quiet part out loud: most finance firms forget almost everything. Decades of deal memos, the instinct of the partner who has seen three cycles, the reason a credit bet went sideways in 2014 - all of it scattered across legacy systems and human memory. Rowspace is his fix.
The pitch arrives as a single sentence, the kind that survives a board meeting and a dinner party alike: "Imagine a firm that never forgets. Where an experienced investor's workflows can be codified and multiplied." Rowspace connects the structured and the unstructured - documents, investment systems, accounting records, deal memos - across a firm's entire history, and it does this without the data ever leaving the client's own cloud. The interface meets investors where they already live: Excel, Microsoft Teams, the spreadsheets and infrastructure they refuse to abandon.
The early customer list is short and heavy. Roughly ten name-brand private equity and credit firms, managing anywhere from hundreds of billions to nearly a trillion dollars in assets, signed on before the public knew Rowspace existed - on seven-figure annual contracts. They use it for portfolio monitoring, for combing decades of deal data, for credit portfolio optimization. The unglamorous back-office work that, done well, becomes an edge.
Before Rowspace, there was a sabbatical, and before that, a confession posted to the internet: "After 3.5 amazing years at Notion and some time off, I've started a company that's at the intersection of everything I learned at Stripe, Notion, and Google - AI, fintech, productivity, and search." Four obsessions, one company. It reads like a man finally allowed to stop choosing.
At Stripe, Manapat was the engineering manager for Radar and machine learning - the brain behind the fraud system that scans every card payment across more than 100,000 businesses and decides, in the time it takes a page to load, whether to let the charge through. By 2018 that engine had blocked some $4 billion in fraud. His models learned to cut fraud rates by up to a quarter while keeping legitimate customers from getting wrongly rejected, the eternal tightrope of the trade. His work became the basis for a primer on machine-learning fraud detection that the rest of the industry still reads.
Then Notion, where he served as CTO for about three and a half years and helped push the productivity darling into AI. He once described his small product team there as a unit that "moonlights as a brigade de cuisine" - a recruiting line that tells you he hires for taste, in more than one sense. Before all of it, he was a software engineer at Google, and before that, an academic: a Ph.D. in mathematics from MIT and a postdoctoral fellowship in applied mathematics at Harvard.
The Co-FounderRowspace is not a solo act. Manapat's co-founder is Yibo Ling, the company's COO and a two-time CFO who ran finance teams at Uber and Binance and spent years wrestling order out of fragmented data systems. The two met as graduate students at MIT, scattered into wildly different careers - fraud models and balance sheets, search and accounting - and circled back to build the thing that needed both of their pasts. One man knows how machines should decide; the other knows what a finance team actually does at 11pm. The seam between those two is the product.
Why It MattersThe clever, almost contrarian bet inside Rowspace is restraint. The platform runs inside the client's own cloud environment, and the data never leaves. For firms that treat their deal history as their crown jewels - and are bound by compliance regimes that treat it the same way - that architecture is not a feature, it is the entire permission slip. Manapat spent a career at companies where trust was the product: a payment that must not be fraud, a workspace that must not lose your notes. Rowspace inherits the lesson. Make the machine smarter, but never make the customer nervous.
It is a tidy through-line for someone who started in pure mathematics and kept finding bigger rooms to apply it. Fraud was a decision problem. Productivity was a memory problem. Finance, it turns out, is both at once - and that is exactly the intersection he said he was chasing.
"Imagine a firm that never forgets. Where an experienced investor's workflows can be codified and multiplied."
"A company at the intersection of everything I learned at Stripe, Notion, and Google - AI, fintech, productivity, and search."