NEW YORK, March 13, 2026 /PRNewswire/ — Rogo, a prominent AI platform utilized by leading financial institutions, has announced its acquisition of Offset, an AI agent company founded by Raj Khare and Shiv Shrivastava. Offset specializes in developing learning agents designed to operate directly within financial workflows across sectors such as investment banking, private equity, hedge funds, and corporate finance.
Through this acquisition, Rogo plans to integrate Offset’s innovative technology into its platform, which is currently used by more than 25,000 finance professionals. This move aims to accelerate Rogo’s roadmap in delivering intelligent systems that are embedded directly into the everyday tools financial professionals rely on.
Financial institutions have traditionally relied on spreadsheets and presentations to build models, analyze investments, and communicate results. However, the process of maintaining and updating intricate financial models is often time-consuming and fraught with potential errors. Offset was created to address these challenges by developing AI agents that possess an understanding of financial models at a structural level and learn how financial workflows adapt across various assumptions, formulas, and outputs.
Offset’s platform is centered on agentic systems that can recall how financial models are constructed, updated, and maintained over time. These systems enhance the efficiency of financial workflows by allowing analysts and investors to interact with AI that comprehends the underlying structure and logic of financial models, rather than merely generating outputs externally.
“Raj, Shiv, and their team bring invaluable technical depth in agentic systems,” said Gabe Stengel, CEO and Co-Founder of Rogo. “The foundation of Offset reflects the same belief we have at Rogo. The future of financial workflows will be powered by intelligent systems embedded directly in the tools professionals use every day. We are excited to build together as we continue expanding our platform.”
Khare, Co-Founder of Offset, expressed similar sentiments, stating, “We built Offset around the idea that AI agents should operate directly inside financial workflows, not just generate outputs from the outside. Our focus has been on building an agentic platform that understands financial models structurally and can automate the workflows analysts rely on every day. Integrating this technology into the Rogo platform allows us to bring these systems to financial institutions at scale.”
The integration of Offset’s agentic architecture with Rogo’s platform and data integrations promises to enhance the efficiency of financial operations across global institutions. This acquisition is part of a broader growth strategy, following Rogo’s recent $75 million Series C financing led by Sequoia Capital, further solidifying the company’s position as a leading AI platform tailored for the finance sector.
Rogo serves as an AI platform for investment banks, private equity firms, and hedge funds, with notable clients including Lazard, Moelis, Nomura, and Tiger Global. The platform automates core financial workflows, surfaces market intelligence, and unifies financial data with auditable sourcing, integrating seamlessly with technology partners and industry data providers such as OpenAI, Google Gemini, and S&P Global. Rogo is backed by investors like Thrive Capital, Khosla Ventures, and J.P. Morgan Growth Equity Partners.
On the other hand, Offset, founded by Khare and Shiv Shrivastava, focuses on creating AI agents that operate within financial workflows. The company’s mission is to develop agentic systems capable of comprehending and maintaining complex financial models, thus enabling AI to adapt to the evolving landscape of financial analysis and decision-making.
As Rogo enhances its platform through this acquisition, the implications for financial institutions could be profound, potentially transforming how analysts interact with financial data and models. By embedding intelligent systems directly into the tools of the trade, Rogo aims to redefine efficiency and accuracy in financial workflows.
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