Rogo
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Last reviewed
Jun 8, 2026
Sources
13 citations
Review status
Source-backed
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v1 · 1,378 words
Add missing citations, update stale details, or suggest a clearer explanation.
Rogo is a New York City based artificial intelligence company that builds an agentic AI platform purpose-built for the financial services industry. Marketed as an "AI analyst" for investment banking, private equity, hedge funds, and asset management, the Rogo platform combines finance-tuned large language model reasoning with deep integrations across internal and external data sources to automate research, build financial models, draft memos and pitch materials, screen deals, and answer complex questions in seconds rather than days.[1][2] By 2026 the company reported more than 35,000 users across over 250 financial institutions and had raised more than $300 million in venture funding, including a $160 million Series D in April 2026 led by Kleiner Perkins at a $2 billion valuation.[1][3]
Rogo positions itself within the broader wave of AI in finance tools that aim to automate the labor-intensive analytical work historically performed by junior bankers and analysts. The company is frequently cited as a leading example of "agentic" AI applied to Wall Street workflows, where software agents execute multi-step processes autonomously instead of merely answering single questions.[2][4]
Rogo was founded around 2021 to 2022 in New York City by Gabriel Stengel, John Willett, and Tumas Rackaitis.[5][6] Stengel and Willett, both Princeton University graduates, developed the earliest version of the product while studying together, and later teamed with Rackaitis, a software engineer, to build the company.[6][7] Sources differ on the exact founding date, with some listing 2021 and others 2022.[5][8]
Gabriel Stengel serves as chief executive officer. He worked as an investment banking analyst at Lazard before founding Rogo and had earlier engineering experience at MongoDB.[6][8] John Willett, the chief operating officer, previously held finance roles at firms including J.P. Morgan and Barclays.[7][6] Tumas Rackaitis, the chief technology officer, holds a computer science degree from Oberlin College and had worked as an engineer and trader before joining the company.[7] The founders have said they started Rogo after recognizing how much of a banker's day was consumed by repetitive research, modeling, and document preparation that AI could accelerate.[6]
The company describes its offering as "the best AI analyst on Wall Street," built by former bankers, investors, and AI experts.[9] It maintains its headquarters in New York City and opened a London office in early 2026 as part of an international expansion.[5][10]
Rogo is a generative AI platform purpose-built for finance professionals. Users interact through a chat-style interface, issuing plain-English prompts to conduct research, run comparable-company analyses, build valuation and financial models, draft investment memos, prepare pitchbooks, screen deals, and analyze filings and market data.[5][2] Outputs are formatted to meet institutional auditability standards and delivered in clients' preferred templates across Excel, PowerPoint, and Word.[4]
Rather than relying solely on general-purpose foundation models, Rogo develops what it calls purpose-built financial reasoning models, tuned on financial data and workflows.[1][3] The platform ingests information from a firm's internal systems, such as customer relationship management software and data warehouses, as well as external market-data providers; reporting has identified integrations with vendors such as FactSet.[2] The company has emphasized security, deploying customer environments with audit trails and access controls; it has built internal tooling, including a security system it calls Sisyphus, to scan its infrastructure for vulnerabilities.[2]
In 2026 Rogo introduced Felix, an agentic AI system that executes complex, multi-step financial processes autonomously. According to the company, Felix can handle workflows such as deal screening, confidential information memorandum (CIM) generation, buyer outreach, and data-room diligence, and bankers can delegate tasks to it by email "the way they would to a junior analyst" and receive finished work around the clock.[1][4] The platform spans origination, execution, advisory, and portfolio-intelligence workflows.[3][4]
Rogo has reported strong early commercial traction, including reaching seven-figure annual recurring revenue within five months of launch using a single sales representative, and processing tens of thousands of queries per day.[6] Reporting has placed its enterprise subscriptions at roughly $3,300 per seat per year under a single-tenant deployment model.[5]
Rogo raised more than $300 million across several rounds between 2024 and 2026, with its valuation climbing rapidly. The company closed an early seed round in February 2024 led by AlleyCorp, an $18.5 million Series A in October 2024 led by Khosla Ventures, and a $50 million Series B in April 2025 led by Thrive Capital at a $350 million post-money valuation.[5][6][11] The Series B added J.P. Morgan Growth Equity Partners, Tiger Global, and Positive Sum Ventures as investors.[11]
In January 2026 Rogo raised a $75 million Series C led by Sequoia Capital at a $750 million post-money valuation, with new backers including KKR co-founder Henry Kravis and Wells Fargo, alongside returning investors.[5][12] Three months later, on April 29, 2026, the company announced a $160 million Series D led by Kleiner Perkins at a $2 billion valuation, more than doubling its January figure.[1][3] Participants in the Series D included Sequoia, Thrive Capital, Khosla Ventures, J.P. Morgan Growth Equity Partners, BoxGroup, Mantis VC (the venture firm of The Chainsmokers), Jack Altman, Evantic, and Positive Sum.[1][3] The round brought Rogo's total funding to more than $300 million.[1]
The following table summarizes Rogo's disclosed funding history. Some early figures vary slightly across sources.
| Round | Date | Amount | Lead investor | Post-money valuation |
|---|---|---|---|---|
| Seed | February 2024 | not disclosed | AlleyCorp | not disclosed |
| Series A | October 2024 | $18.5 million | Khosla Ventures | not disclosed |
| Series B | April 2025 | $50 million | Thrive Capital | $350 million |
| Series C | January 2026 | $75 million | Sequoia Capital | $750 million |
| Series D | April 2026 | $160 million | Kleiner Perkins | $2 billion |
Rogo has said it will use the Series D proceeds to accelerate global expansion, deepen partnerships with leading financial institutions, grow its forward-deployed engineering and banking teams, and scale Felix.[1][2]
Rogo reported more than 35,000 professionals at over 250 financial institutions using its platform by 2026, spanning junior analysts to senior partners.[1][4] Named customers include the investment banks and advisory firms Rothschild and Co, Jefferies, Lazard, Moelis and Company, Nomura, and Raymond James, as well as buy-side and asset-management clients such as Tiger Global, GTCR, and Siris Capital.[3][12] Several of Rogo's largest investors, including Tiger Global, Thrive Capital, and J.P. Morgan, are also customers, reflecting a pattern in which financial institutions both back and deploy the product.[12]
Rogo operates in a competitive market for AI tools aimed at finance and knowledge work. Direct competitors include Hebbia, which builds generative AI for document analysis and workflows, and AlphaSense, a market-intelligence platform that pairs proprietary content with natural-language search.[12][13] Industry analyses have also grouped Rogo alongside specialized finance-AI vendors such as Canoe Intelligence and Boosted.ai.[12] More broadly, Rogo competes with general-purpose enterprise AI assistants such as Microsoft Copilot and the enterprise offerings of OpenAI and Anthropic, as well as proprietary tools built in-house by large banks. Rogo argues that generic AI assistants fail to meet institutional finance's requirements for precision, auditability, and domain-specific reasoning, a gap it positions its finance-tuned models and agents to fill.[4][2]