Your financial model has answers it is not showing you.
Model Maestro does not read your Excel file like a document. It traces the logic inside it. Every formula. Every tab. Every assumption that someone entered as a number rather than built from first principles.
In M&A due diligence, that distinction — between a calculated output and a hardcoded guess dressed up to look like one — is often the difference between a deal that closes cleanly and one that surfaces a problem six months later.
Standard AI tools read the surface. The risk lives underneath.
When you feed a financial model into most AI tools, they see text. They read the values in cells, extract the headings, summarise what is visible.
What they cannot see: how a number was derived. Whether a revenue projection was hardcoded directly into a cell or built from a formula chain. Whether the working capital assumption on tab 3 is consistent with the cash flow logic on tab 11. Whether the EBITDA figure anyone is looking at is real or a plug.
These are not edge cases. They are the things that matter most in an acquisition.
Formula auditing at structural depth.
The VectorSheet Framework is the technical foundation of Model Maestro. It treats a financial model the way an experienced analyst would — as a network of logic, not a collection of numbers.
What Model Maestro finds that manual review misses.
Revenue projections. Cost assumptions. Working capital figures. Model Maestro flags every instance where a number has been entered directly rather than derived — and shows you where it sits in the logic chain.
A real acquisition model spans 15, 20, sometimes 30 tabs. Model Maestro follows the logic across all of them, mapping dependencies and flagging where the chain breaks.
Enterprise-grade Excel files exceed the context window of standard AI tools. The VectorSheet Framework compresses at scale — the model's structure is preserved, not truncated.
Every flagged item comes with a full trace: where the assumption originated, what it feeds into, and what changes if the number turns out to be wrong.
What this looks like in a real deal.
A PE analyst is reviewing a target company's three-statement model ahead of an LOI. The model spans 22 tabs and the revenue projection shows 18% CAGR for the next five years.
Model Maestro traces the formula chain behind that 18% figure and finds that three of the five revenue line items are hardcoded — not derived from any underlying volume or pricing assumption.
The model looks sophisticated. The assumptions underneath it are not.
This is what Model Maestro surfaces. Not a summary of what the model says. The truth of how it was built.
See the Logic Layer in Action
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