The race to bring AI-powered control to spreadsheets continues with fresh momentum. Emerging from stealth, a new venture named Meridian is taking a more expansive, IDE-inspired approach to agent-driven financial modeling—backed by substantial investment. This Wednesday, the company revealed $17 million in seed funding, achieving a post-money valuation of $100 million. “How can you take a process that traditionally might have taken cool hours and condense it down into like 10 minutes,” the team notes.

This funding round was spearheaded by Andressen Horowitz and the General Partnership, with additional investment from QED Investors, FPV Ventures, and Litquidity Ventures. Meridian reports it is already collaborating with teams at Decagon and OffDeal, securing $5 million in contracts in December alone.

AI startups have frequently targeted Excel automation, attracted by the significant expense of human-conducted financial analysis. However, while earlier solutions like Shortcut AI embedded agents directly into Excel, Meridian functions as an independent workspace, closer in concept to platforms like Cursor. This standalone design allows the application to operate as an integrated development environment, smoothly incorporating data sources and external references that typically cause workflow interruptions.

Headquartered in New York, Meridian’s team combines talent from AI leaders such as Scale AI and Anthropic with finance specialists from institutions like Goldman Sachs. According to Ling, the primary hurdle is meeting the rigorous demands of financial clients, which often conflict with the inherently unpredictable nature of AI models. “If you go to ten different software engineers at Google, and you want to add some new feature into an app, you’ll probably get like 10 completely different implementations. And that’s totally fine,” Ling explains. “But if you go to 10 banking analysts at Goldman Sachs and you ask for 10 valuation models for a company, you would probably get 10 almost identical workbooks.”

In response, Meridian has focused intensely on making its outputs more auditable and consistent, without sacrificing the adaptability of LLM-based tools. The platform blends agentic AI with conventional software engineering, reducing the unreliable outputs that often hinder enterprise AI adoption. “Our goal is to really remove the doubt layer right from the LLM process,” Li states. “You know exactly how the logic flows, and all of these assumptions or whatever that go into the model, you can see exactly where they’re coming from.”