Rogo closes $160M Series D, passes $300M raised

Rogo, an AI agent vendor built for finance professionals working inside Excel, PowerPoint, and firm data warehouses, raised a $160M Series D in 2026, lifting total funding past $300M. The company reports more than 35,000 users across over 250 institutions, including Rothschild & Co, Jefferies, Lazard, Moelis, and Nomura.

The product category Rogo occupies, framed as research and analysis in Gradient Labs' agent-company map, is defined as pulling, modelling, and summarising market and portfolio data for investment banking, markets, and strategy teams. The compliance posture is marked medium, with information-barrier controls between deal teams the key constraint.

The client list skews toward independent and advisory-led houses rather than the full-service bulge bracket. Rothschild, Lazard, Moelis, and Jefferies all run large M&A franchises where associate and analyst time is the binding constraint on deal throughput. Nomura adds Asia-Pacific coverage. The common workflow across these firms is pitch and model production under deadline, which is the work Rogo targets at the desktop layer rather than through a separate interface.

The 35,000-user figure is a disclosed count rather than an audited active-user metric, and Gradient Labs cites it alongside the funding figure in the same reference entry. For comparison, the full population of front-office M&A, markets, and research professionals at the five named firms is in the same order of magnitude, which suggests meaningful but not yet universal penetration inside those accounts.

Watch for the next two data points. The first is whether Rogo's named clients expand from pilot deployments inside single coverage groups to firm-wide seats, which is the step that converts a logo list into a renewal base. The second is competitive response from the horizontal AI platforms that sell into the same banks on an enterprise agreement. If analyst-workflow spend stays with specialist vendors through 2026, the agent-company map will keep fragmenting by function rather than consolidating by platform.