Meridian raises $17 million to remake the agentic spreadsheet

Source: TechCrunch

The fight to tame spreadsheets with AI isn’t over yet. A new company called Meridian has emerged from stealth with a more comprehensive IDE‑based approach to agentic financial modeling — and plenty of funding to build it.

Funding round

On Wednesday, Meridian announced $17 million in seed funding at a $100 million post‑money valuation. The round was led by Andreessen Horowitz and the General Partnership, with participation from QED Investors, FPV Ventures, and Litquidity Ventures. The company says it is currently working with teams at Decagon and OffDeal, and signed $5 million of contracts in December alone.

Product approach

Excel agents have been a popular target for AI startups, due in part to the high cost of human‑led financial analysis. While previous Excel agents such as Shortcut AI built agents directly into Excel, Meridian operates as a stand‑alone workspace, more akin to Cursor. This IDE‑like environment lets the app integrate data sources and external references that would otherwise create friction.

Team

Based in New York, the Meridian team includes alumni of AI firms like Scale AI and Anthropic, as well as financial veterans from institutions such as Goldman Sachs.

Challenges and auditable AI

As CEO and co‑founder John Ling explains, the strict requirements of financial clients often clash with the non‑deterministic 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 ten completely different implementations. That’s fine. But if you go to ten banking analysts at Goldman Sachs and you ask for ten valuation models for a company, you would probably get ten almost identical workbooks.”

To address this, the Meridian team has focused on making outputs more auditable and deterministic while preserving the flexibility of LLM‑based tools. The result is a blend of agentic AI and conventional tooling that minimizes hallucinations and speeds up enterprise deployments.

“Our goal is to really remove the doubt layer right from the LLM process,” Ling adds. “You know exactly how the logic flows, and all of the assumptions that go into the model—you can see exactly where they’re coming from.”