After meeting in graduate school at MIT, Michael Manapat and Yibo Ling embarked on different career paths. Manapat held chief technical roles at Stripe and Notion, while Ling led finance teams at Uber and Binance. Still, they both confronted a similar challenge: how to assemble fragmented data to make important decisions about capital allocation, workflows, and more.
When OpenAI released ChatGPT in November 2022, Ling tested the chatbot to see how well it could carry out basic due diligence tasks. He quickly found the new AI tool was hampered by a familiar problem: data. “Clearly there was a lot of promise, but it just wasn’t working. You need the right information in the right context,” he told Fortune.
That realization motivated Manapat and Ling to join forces to build Rowspace, an AI platform that allows financial outfits like private equity firms and hedge funds to turn their years of proprietary data into alpha. The company is publicly launching today with $50 million in funding across a previously unannounced seed round led by Sequoia and a Series A co-led by Sequoia and Emergence Capital, with participation from Basis Set Ventures, Stripe, and Conviction, along with other firms and angel investors.
At a time of pearl-clutching and market turmoil on whether large language models and foundation models will render software obsolete, Sequoia partner and co-steward Alfred Lin told Fortune that Rowspace is a prime example of the type of application that will thrive in the brave new AI-empowered world.
“The thing that people are talking about is the marginal line of code is very cheap to produce,” Lin said. “What we’re looking for now in almost every single company is product velocity, and how fast product velocity generates other things that become moats, which are like network effects and people using your product on a daily basis.”
Finding alpha
Manapat described Rowspace as the intelligence layer that sits on top of a firm’s data. The platform integrates all of an institution’s structured and unstructured data, whether in the form of documents or accounting systems or old PowerPoints, and performs reasoning in advance. “We’re focused on how we make sure we understand all of the underlying data to drive actual decision-making,” he said.
Rowspace’s approach to data sounds a lot like the one used by popular new consumer tools such as Claude Cowork, which can query a computer’s files and create presentations or research memos. Manapat said that Rowspace is different in crucial ways. For one, it doesn’t take possession of a firm’s data, instead doing processing inside its customers’ own cloud systems.
On a deeper level, Manapat said that foundation models like Anthropic’s are good at last-mile tasks, like formatting a pitchbook in PowerPoint or building a cash flow model, which are generally completed with a real-time search approach.
“That’s not where our focus is,” Manapat said. As he explained, there are no ways to ensure the agent looked at all available information or took the time to reason in advance of making a conclusion, which is time-consuming and expensive. Instead, Rowspace is tasked with deeper analysis of data, such as being able to notice minute details from years of a company’s finances. That will always give the platform an advantage over the more general purpose Anthropics of the world.
“The foundation model is not going to be able to cater to every single [thing] that someone wants to do in all these different industries,” said Lin. “That is going to be left to players like Rowspace, specifically for the vertical they’re focused on.”
Manapat admitted that pure software or user interfaces are going to be hard to defend, especially as foundation models rapidly advance. But he said that’s why Rowspace’s focus is more on compiling and synthesizing a firm’s data in a secure way, and doing so with a financially literate team. The engineering corps comes both from tech-first companies like Notion and Stripe as well as private equity and credit. “There’s no one-size-fits-all solution in financial services, because in some sense, each firm’s alpha comes from their approach,” Manapat said. “We’re trying to help you learn from your own data and knowledge and approach and amplify that.”
While Rowspace declined to name its valuation or early customers, Manapat said that they include long-standing and name-brand private equity and credit firms, as well as crossover firms that work in both public and private markets. He added that Rowspace is working with about 10 top firms with seven-figure annual contract values.
“Customers use this tool to make money, and that’s where the rubber meets the road,” Lin said. “If we consistently, with our tool, help people use AI to make better decisions, they will make money, and they’ll do it better than others.”
