Rogo AI

Rogo was founded in 2022 by Princeton classmates Gabriel Stengel (CEO, ex-J.P. Morgan), John Willett (COO), and Tumas Rackaitis (CTO, ex-Lazard). That pedigree shows in every product decision.

This isn’t a general-purpose AI tool that happens to work in finance.

It’s built from the ground up around the specific workflows, data sources, compliance requirements, and output formats that investment banks and asset managers actually use.

The company raised $160 million in its Series D in April 2026, pushing its valuation to $2 billion (up from $750 million after its Series C just three months earlier in January 2026).

That funding velocity, combined with a Forbes AI 50 2026 selection, makes Rogo one of the fastest-growing vertical AI companies in the market.

As of mid-2026, over 35,000 finance professionals across 250+ firms use Rogo. The client profile leans heavily toward bulge bracket and elite boutique investment banks, major asset managers, and large private equity firms.

The platform runs on Amazon Bedrock infrastructure and supports frontier models including GPT 5.5 and Anthropic Opus 4.7, with outputs designed to be auditable and compliant with SOC 2, ISO 27001, GDPR, and EU AI Act requirements.

I was not able to get hands-on access to Rogo for testing (the platform requires institutional onboarding), so this review is built from published case studies, third-party hands-on reviews, user interviews, analyst reports, and the company’s own documentation.

I’ll be transparent throughout about what’s verified from direct testing versus what’s sourced from secondary research.

Key Features

Felix: The AI Analyst Agent

Felix is Rogo’s core product, positioned as an AI agent that handles the work junior investment banking analysts do manually.

You give Felix a task (“build a comparable companies analysis for mid-cap US SaaS companies,” “summarize the last four earnings calls for Company X,” “draft the market overview section of this pitch book”) and it searches across financial data sources, synthesizes the information, and produces a structured, cited output.

According to AWS’s case study with Rogo, the platform “enables finance professionals to process more deals, expand coverage, and get smart faster” in a secure environment.

The practical claim: tasks that take a junior analyst 4 to 6 hours (pulling comps, reading filings, drafting memo sections) compress into 15 to 30 minutes with Felix handling the first pass.

Multiple users report that Felix handles standard analyses reliably but requires human review for high-stakes decisions.

Generated investment memos save time but still need senior judgment about deal merit. Financial models built with AI assistance need manual verification before going into actual decision-making.

Financial Data Integration

Rogo connects to the data sources investment professionals actually rely on:

• PitchBook for private market data, deal comps, and investor profiles
• LSEG (London Stock Exchange Group) for market data, pricing, and indices
• Fitch Solutions for credit research and country risk data
• Third Bridge for expert network transcripts and industry insights
• SEC EDGAR for public filings (10-K, 10-Q, 8-K, proxy statements)
• Earnings call transcripts across public companies
• Internal firm documents (research reports, prior pitch books, proprietary databases)

The internal document integration is what separates Rogo from general-purpose AI tools.

When an analyst asks Felix to draft a market overview, it pulls from both public data and the firm’s own prior work, maintaining consistency with how the firm has positioned similar analyses before.

Document Generation and Automation

Rogo generates formatted financial documents: pitch book pages, investment memos, market overviews, company profiles, and comparable company analyses.

The output isn’t raw text. It’s structured in the formats that investment banks actually use, with data tables, charts, and sourced citations.

According to SwitchTools’ review, due diligence teams use Rogo to process data room documents and management presentations at deal speed, with the AI flagging inconsistencies across documents rather than requiring analysts to cross-reference hundreds of files manually.

This is particularly valuable in competitive M&A processes where multiple bidders are reviewing the same data room under tight deadlines.

Company and Market Analysis

Ask Rogo about a company, a sector, or a market trend, and it synthesizes information from SEC filings, earnings transcripts, research reports, and news sources into a structured analysis with cited sources.

The output includes revenue breakdowns, margin trends, competitive positioning, and relevant market dynamics.

AIApps’ review notes that Rogo “enables quick access to actionable insights by efficiently processing large volumes of information,” which is the core value for coverage bankers who follow 20 to 30 companies and need to stay current across all of them simultaneously.

Security and Compliance

This is where Rogo’s enterprise focus pays off. The platform holds:

• SOC 2 Type II certification
• ISO 27001 certification
• GDPR compliance
• EU AI Act compliance
• Auditable outputs with source citations
• Data segregation between client firms
• Deployment on secure cloud infrastructure (Amazon Bedrock)

For regulated financial institutions where data handling, model governance, and audit trails are non-negotiable, Rogo’s compliance stack is one of the most comprehensive among AI financial tools.

AlphaSense and Hebbia have comparable security postures.

Most general-purpose AI tools (ChatGPT, Claude, Gemini) do not meet these standards for financial services use without enterprise agreements.

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