Rogo(opens in a new window), an enterprise-grade AI finance platform, is changing how investment banks, private equity firms, and asset managers work.
By fine-tuning OpenAI’s models and integrating a broad set of financial datasets such as S&P Global, Crunchbase, and FactSet, Rogo delivers real-time financial intelligence to thousands of financial professionals, shifting their focus from manual work to high-value decision making.
Since emerging from stealth in 2024, Rogo has:
- Served over 5,000 bankers across publicly-traded investment banks and mega cap private equity firms.
- Saved analysts 10+ hours per week on tasks like meeting prep, company profiling, and market research.
- Grown their Annual Recurring Revenue (ARR) 27x, using OpenAI’s models to power their solutions.
“There hasn’t been a company upending finance workflows for decades,” says Tumas Rackaitis, co-founder and CTO of Rogo. “We built Rogo to be the essential tool every investment banker relies on daily—just like Bloomberg but for deep financial insights.”
Unlocking time and insights for modern finance firms
Rogo uses a variety of OpenAI’s models to deliver a secure solution that integrates directly with finance professionals’ daily workflows, unlocking time to focus on revenue-generating, high-leverage work.
Among dozens of other use cases, OpenAI’s API enabled Rogo to create a platform that delivers:
- Accurate, real-time insights: Users can pull actionable insights from filings, transcripts, and decks in seconds, and generate end-to-end, presentation-ready materials.
- Automated diligence: Rogo integrates private data rooms, generates tailored question lists, offers writing assistance, and helps track client interactions, saving analysts up to 10 hours each week and helping teams prepare faster for meetings.
- Collaborative workflows: Both junior and senior professionals rely on Rogo to streamline tasks like building market maps or doing competitive analyses, making it a shared resource across partners and analysts.
“Before Rogo, I’d spend hours pouring over dozens of documents and files. With Rogo, I get all the data I need instantly,” says an analyst at a leading public investment bank.
Fine-tuning OpenAI’s models to automate financial work
Rogo adapted OpenAI’s secure models to tackle the unique challenges of financial work. By fine-tuning OpenAI’s models and integrating vast datasets such as S&P Global, Crunchbase, and FactSet, the company is able to search and analyze over 50 million financial documents.
Their model architecture is layered to balance performance, cost, and use case requirements:
- GPT-4o powers Rogo’s chat-based Q&A and handles in-depth financial analysis.
- o1-mini is used to contextualize and structure financial data for effective search.
- o1 is reserved for evaluations, synthetic data generation, and advanced reasoning workflows.
This approach allows Rogo to optimize for cost by assigning routine tasks to smaller models while reserving the most advanced capabilities for high-stakes scenarios. A team of former bankers and investors also reviews and labels the datasets to ensure accuracy and relevance for financial end users.
At the core of Rogo’s machine learning (ML) engine is an agent framework designed to handle complex financial workflows, including multi-step query planning and comprehension, context management, and efficient search. The outputs are accessible across desktop, mobile, and tablet, making it easy for users to access insights whenever needed.
“We use OpenAI’s newest models for uniquely deep and complex financial insights, which are more relevant for investment funds like private equity firms and hedge funds,” says Rackaitis. “These insights can then be distilled into smaller models that offer superior speed and efficiency, which is essential for high-velocity environments like investment banks.”
Fueling growth with OpenAI’s evolving models
The Rogo team chose OpenAI for its superior reasoning capabilities, robust APIs, and flexibility to handle both broad and domain-specific tasks.
“The ecosystem of tools OpenAI provides – fine-tuning, function calling, and multimodal capabilities – ensures we can meet our clients’ complex demands now and in the future,” says Rackaitis.
The teams also share a culture of experimentation. Rogo’s deployment team of ex-bankers and investors work with customers to refine features in real time, and OpenAI’s consistent model advancements enable Rogo to iterate rapidly.
Rogo continues to expand its platform’s capabilities. In December, Rogo hired Joseph Kim as their Head of AI. Joseph joined Rogo from Google’s Gemini, where he focused on reinforcement learning with human and machine feedback for improving generative language models.
