Rowspace
Introducing Rowspace — The best way for investors to scale their edge with AI
Date
FEB 2026
Authors
Michael Manapat
Yibo Ling
The best investors we know have built something over years and decades that's impossible to replicate: institutional memory of how they think and operate. It lives in memos, models, investment committee notes, emails, deal history, and trading data — scattered across systems that hold the patterns of what worked, what didn't, and why. That accumulated knowledge and how they apply it is their edge.
The problem is, it's trapped. It's messy and conflicting. It's locked behind stringent security and compliance controls. And most AI tools can't extract its meaning, much less scale it. That's the challenge we're taking on at Rowspace.
The firms we speak to know they need to drive AI transformation on an aggressive timeline. We've seen close up that AI is useless without the right data to act on. With public data increasingly commoditized, maintaining your edge demands a way to scale the judgment and insight latent in all the proprietary data your firm possesses.
Rowspace is launching today to provide exactly this: specialized intelligence for investors to make faster, sharper decisions. We're thrilled to do it with $50M in backing from Sequoia, Emergence, Stripe, Conviction, Basis Set, Twine, and angels from across finance.
We set out on this mission knowing we'd need to do three things. First, comprehensively map all of a firm's siloed, often chaotic data — memos, decks, trade, position, and GL data, internal communications, and more. Second, use this data to model how they work, what to trust, what matters, and reconcile it all into a bespoke layer of judgment. And third, feed this intelligence into critical analytical and decision-making workflows.
As good as frontier models have gotten, finance isn't a domain where "mostly right" is good enough — the stakes of even one wrong number are too high. It's one thing to find a plausible answer in a document. It's another to know which addbacks apply to an EBITDA calculation based on a particular credit covenant.
How Rowspace drives better decisions
We've been working with some of the largest and most exacting financial institutions in the world. With Rowspace, they leverage their institutional knowledge to answer complex questions and build AI workflows they can trust. Just a few examples:
One of the world's largest credit originators monitors portfolio health in real time. Rowspace continuously reconciles position files, rating actions, indentures, and credit agreements — flagging eroding covenant headroom, OC test pressure, and negative credit migration immediately. When a trigger is hit, Rowspace recommends action based on all the available data.
A private equity pioneer builds conviction faster by learning from every transaction they've ever done. Before Rowspace, comparing a new opportunity to the firm's fifty-year track record meant hunting through file stores, databases, and the memories of senior partners. Now Rowspace synthesizes the case for and against each deal — grounded in which underwriting assumptions proved right, which risks keep recurring, and what patterns actually predict outcomes.
A leading crossover fund sees one trusted view across hundreds of names. Each company reports differently. Investments span years and funds. Rowspace reconciles company metrics and commentary with internal investment data — so teams understand exactly what happened, when, and what they should do next.
How Rowspace works
Rowspace deploys into your environment: We plug into your data where it lives, understand the connections and inconsistencies, and restructure it for your core workflows. Your data always remains in your control.
We connect to systems you already use: Data stores like Snowflake and AzureSQL, CRMs like Salesforce and DealCloud, document stores like VDRs and SharePoint, fund systems like Allvue and WSO.
We create structure: Processing covenant terms from credit agreements, building clean timelines from company reporting, and helping you refine the definitions and schemas that clarify your decisions.
We track lineage, source everything, and resolve conflicts: Every output traces back to its sources, with a clear view of how Rowspace chose among candidates. With your team in the loop, our understanding of how you interpret data increases over time.
Results flow wherever you work: Chat, dashboards, Excel, Slack, Teams, or agents customers build. The goal isn't to change how you work. It's to make it more powerful.
Why we're doing this
We've both spent time in enterprise environments and seen the same thing: AI is a powerful brain but requires a nervous system that understands and pulls signals from every part of the organization in order to be transformative.
Michael has spent his career building systems that reason over messy data in high-stakes settings (fraud and credit underwriting at Stripe) and systems that unify context across tools (AI at Notion). Rowspace combines those threads and applies them to the strictest domain for correctness: finance.
Yibo brings expertise from the other side — a former CFO who's run an investment organization himself. Few people have operated at the intersection of technology and finance, understanding the constraints and possibilities on all sides.
We feel lucky to be building this together, and believe we're at the doorstep of massive transformation. Book a demo. Or come build with us.