Rogo's AI-banker bet: from a Princeton thesis to $2B automating juniors' grunt work

Agentic AI platform for investment banking and finance: automates research, Excel modeling, deal screening, CIM generation and due-diligence workflows, integrating banks' proprietary data with market databases and filings.

That junior bankers' grunt work - models, memos, due diligence - could be automated by finance-native AI agents, and banks would trust them with critical workflows.

$50M Series B (Apr 2025, total $75M); $160M Series D (Apr 2026, led by Kleiner Perkins, valuation ~$2B, total over $300M)

Founded in January 2022 by Princeton classmates Gabriel Stengel, John Willett and Tumas Rackaitis, who had worked at J.P. Morgan and Lazard. The release of GPT-3 for developers let them expand their senior thesis - a chatbot for econometrics - into a working prototype, and they left their finance jobs to build it.

Raised a $50M Series B in April 2025 (total $75M) to invest in financial reasoning models and autonomous agents. The platform automated analyst-level work - research, valuation analysis, Excel models, presentations - for investment banks, asset managers and private equity. By April 2026 it supported 35,000+ professionals at 250+ institutions, acquired UK-based Plux AI and the AI agent startup Offset, launched Felix (an end-to-end agent that handles deal screening, CIM generation, buyer outreach and data-room diligence), and closed a $160M Series D led by Kleiner Perkins with Sequoia, Thrive Capital, Khosla Ventures and J.P. Morgan Growth Equity participating.

Still scaling: the April 2026 Series D valued Rogo at roughly $2B, and the company is expanding globally, deepening bank partnerships and turning Felix into an end-to-end workflow system while banks debate what automation means for entry-level hiring.

Junior banking work is structured, high-stakes and repetitive - a perfect first wedge for automation that generates visible ROI for banks.

Founders who had done the job themselves knew exactly which workflows to target and how banks would buy.

Integrating proprietary firm data and embedding forward-deployed bankers made Rogo part of the bank's operating process, raising switching costs.

Client concentration in a few hundred institutions means each enterprise deal is large, and the category's growth shows in both revenue and branded search.

The wedge is the workflow, not the model: training finance-native agents on proprietary data and embedding them in banks' own systems made Rogo hard to displace even as general-purpose AI improved.

As of May 2026, Rogo was valued at roughly $2B after its April Series D, supported 35,000+ finance professionals at 250+ institutions, and was expanding its agentic platform (Felix) and global footprint while the banking industry debated how much AI adoption will reshape entry-level roles.