A new generation of AI platforms is beginning to reshape the way investment banks operate behind the scenes. New York-based Rogo has secured $160 million in new funding as financial institutions increasingly look to automate research, analysis, and deal preparation workflows that have historically required large teams of analysts.
AI platform built specifically for financial services
Rogo, an AI platform focused on financial services, has raised $160 million in a Series D funding round led by Kleiner Perkins. The investment brings the company’s total funding to more than $300 million.
The round also included participation from Sequoia Capital, Thrive Capital, Khosla Ventures, J.P. Morgan Growth Equity Partners, BoxGroup, Mantis VC, Jack Altman, Evantic, and Positive Sum.
Founded in 2022 by Gabriel Stengel, John Willett, and Tumas Rackaitis, Rogo was created to address one of investment banking’s most labour-intensive challenges: the massive amount of manual research and document preparation required for deals and financial analysis. The founders, who previously worked at J.P. Morgan and Lazard, began developing the technology after experimenting with early generative AI models and building prototypes focused on financial data analysis.
Supporting thousands of financial professionals
The platform is now used by more than 35,000 financial professionals across over 250 financial institutions worldwide, including firms such as Rothschild & Co, Jefferies, Lazard, Moelis, and Nomura.
Rogo combines specialised financial reasoning models with both internal and external data sources. These include deal databases, market data providers, regulatory filings, and CRM systems. By integrating these sources into a single AI environment, the platform automates research tasks and accelerates workflows typically handled by junior banking teams.
Automating complex deal workflows
A key component of the platform is Felix, Rogo’s AI system designed to handle complex, multi-step financial workflows.
Felix can automatically generate presentations, financial models, and deal documentation. It also supports processes such as deal screening, preparation of Confidential Information Memorandums (CIMs), buyer outreach coordination, and data room diligence. Tasks that previously required teams of analysts working for days can now be completed in minutes.
To help financial institutions adopt the technology, Rogo deploys experienced finance professionals directly within partner organisations. These specialists work alongside banking teams, helping integrate the platform into daily workflows and ensuring adoption across roles ranging from analysts to senior managing directors.
Expanding globally through acquisitions
Rogo has also been expanding its capabilities through acquisitions and international growth.
The company acquired UK-based Plux AI, which specialises in tracking complex financial market developments. Its founders, Deepak Guneja and Pratyush Kamal Chaudhary, joined Rogo to help strengthen the company’s European operations.
Rogo also purchased Offset, an AI agent company founded by Raj Khare. The acquisition enhances Rogo’s ability to deploy agent-based automation across financial processes.
Competition in AI-powered financial intelligence
The company operates in an increasingly competitive market for AI-driven professional tools.
Startups such as Harvey are applying a similar approach in the legal sector, while platforms like Visible Alpha, Tegus, and Bloomberg are introducing AI-powered financial research capabilities.
Rogo’s strategy focuses on combining AI technology with deep institutional integrations and human financial expertise to build what it describes as a dedicated operating system for investment banking.
What this means for the industry
- Investment banking workflows such as deal screening, research, and pitch preparation are becoming increasingly automated through specialised AI platforms.
- Financial institutions are beginning to adopt AI tools that integrate directly with proprietary datasets and internal systems rather than relying on generic AI models.
- The emergence of AI systems designed specifically for finance signals a shift toward industry-specific “AI operating systems” tailored to complex professional workflows.
- As adoption grows, AI could significantly reduce the time and cost required to prepare deals, potentially reshaping the role of junior analysts in investment banks.
