Rowspace has launched with $50 million in total funding, split across a seed round and a Series A. The Series A was co-led by Sequoia and Emergence Capital, while Sequoia also led the seed. Additional participants include Stripe, Conviction, Basis Set, and Twine, along with a number of angels from across the finance industry. No valuation was disclosed at launch.
The backing reflects strong early conviction in the company's approach. Many of Rowspace's investors had been connected to the founders for years before the company was formed, a fact that speaks to the depth of trust built around the founding team's vision and technical credentials.
Financial firms generate and accumulate enormous amounts of proprietary data: investment memos, deal files, credit models, email threads, CRM records, and legacy systems that rarely talk to each other. The knowledge is there. The problem is access. A partner who has evaluated five hundred deals carries judgment that simply cannot be found in a spreadsheet. A credit analyst who has managed through multiple economic cycles knows what signals actually matter. But when that knowledge lives across disconnected repositories, it is effectively invisible to the firm as a whole.
Generic AI tools have not solved this. They were not built for the specificity that finance requires — the ability to reconcile conflicting data sources, interpret a firm's own conventions, and deliver results that hold up to scrutiny. That gap is what Rowspace was built to close.
"Finance is full of high-stakes decisions. There used to be a tradeoff between moving quickly and making fully informed, nuanced decisions using all the possible data at a firm's disposal. Our AI platform eliminates that tradeoff," says Michael Manapat, Co-founder and CEO of Rowspace. "We're building specialized intelligence that turns a firm's data into scalable judgment with the rigor finance demands."
Rowspace plans to move quickly this year, with a focus on hiring engineering and research talent across its San Francisco and New York offices. The company is specifically looking for people drawn to technically demanding problems with real economic consequences.
Beyond headcount, the funds will support continued product development and deeper integration with the systems financial firms already use. Rowspace currently delivers its intelligence through its own interface, within tools like Excel and Microsoft Teams, and directly into a firm's existing data infrastructure. The goal is to make the platform useful wherever work actually happens, without requiring firms to change how they operate.
Rowspace was founded by Michael Manapat and Yibo Ling, two operators who arrived at the same problem from very different directions. Manapat was previously CTO at Notion and led machine learning at Stripe, where he built the systems that process billions of transactions. Ling is a two-time CFO who headed corporate development at Uber and spent years making investment decisions across fragmented data environments.
"I've lived this problem," says Ling. "As a former CFO who's managed a major investment portfolio, I've made decisions by synthesizing data across fragmented systems. Most tech tools aren't comprehensive or nuanced enough for finance. And most finance tools need to raise their technical ceiling. We intend to do both."
The platform connects structured and unstructured data across a firm's full history — document repositories, investment and accounting systems, data infrastructure, and more. It then applies a finance-native lens that reflects how that specific firm interprets information and makes decisions. Crucially, Rowspace deploys directly into customer environments, meaning data never leaves a firm's own control. Security has been a design principle from day one, not an afterthought.
Firms managing assets ranging from tens of billions to almost a trillion dollars are already using the platform for portfolio monitoring, credit portfolio optimization, and complex analysis spanning decades of deal data. They chose Rowspace because off-the-shelf AI tools could not deliver the accuracy their decisions demand.
"Imagine a firm that never forgets," says Manapat. "Where an experienced investor's workflows — touching many different tools in specific ways — can be codified and multiplied. When that's possible, a first-year analyst can tap into decades of institutional knowledge, and judgment scales with a firm instead of being diluted. That's what we're building."
Sequoia led both rounds, with Alfred Lin heading the investment — a signal of serious conviction from one of the most recognised names in venture. Emergence Capital co-led the Series A, bringing its track record in enterprise software and a clear view on why the technical difficulty of Rowspace's core problem makes it worth backing early. Stripe's participation adds a different kind of weight, given Manapat's history there building the machine learning infrastructure that now processes billions of transactions. Conviction, Basis Set, Twine, and a group of finance industry angels round out a syndicate that brings domain knowledge well beyond capital.
