The battle to tame spreadsheets with AI is far from over. A new company called Meridian has emerged from stealth with a more comprehensive IDE-based approach to agent financial modeling and deep pockets to build it. On Wednesday, the company announced it had raised $17 million in seed funding at a post-money valuation of $100 million.
“Our goal is to make financial modeling and spreadsheets more predictable and auditable,” CEO and co-founder John Lin told TechCrunch. “How can we condense a process that traditionally took hours into about 10 minutes?”
The round was led by Andreessen Horowitz and The General Partnership, with participation from QED Investors, FPV Ventures, and Litquidity Ventures. The company said it is currently working with Decagon and its off-deal team and signed a $5 million deal in December alone.
Excel agents are a popular target for AI startups, in part because of the high cost of human financial analysis. However, whereas previous Excel agents such as Shortcut AI built the agent into Excel, Meridian operates as a standalone workspace and is more like a Cursor. This allows your app to behave like an IDE and integrate data sources and other external references that can cause friction.
Based in New York, Meridian’s team includes alumni from AI companies such as Scale AI and Anthropic, as well as financial industry veterans such as Goldman Sachs.
As Ling explains, Meridian’s biggest challenge is the demanding requirements of financial customers, which often conflict with the non-deterministic nature of AI models.
“If you go to 10 different software engineers at Google and want to add a new feature to your app, you’ll probably get 10 completely different implementations, and that’s totally fine,” Ling says. “But if you go to 10 bank analysts at Goldman Sachs and ask them for 10 valuation models for companies, you’re probably going to receive 10 workbooks that are nearly identical.”
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As a result, the Meridian team has done significant work to maintain the flexibility of LLM-based tools while making the output more auditable and conclusive. The result is a combination of agent AI and traditional tools that minimizes the hallucinations that slow adoption for many companies.
“Our goal is to completely remove the layer of doubt from the LLM process,” says Ling. “We know exactly how the logic flows, and all these assumptions and everything that goes into the model, we know exactly where they’re coming from.”
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