Michael Sakowski on Crunched: Excel-native AI analyst for finance power users that caught a £10M valuation error

Summary

Crunched is an Excel-native AI analyst built specifically for finance power users — investment bankers, private equity associates, and management consultants. Co-founder Michael Sakowski describes it as a side-panel chat inside Excel, similar to Cursor but for what he calls the world's most popular programming language. The target market is roughly 5 million users, the top 1% of Excel's 2 billion-strong base.

Microsoft positioning

Sakowski is direct about the competitive dynamic: Microsoft is building Copilot for the full 2 billion-user base and is constrained by its competition with Google Sheets. Crunched is building for a narrower, higher-value slice where generic tooling falls short. The differentiation argument rests on depth of domain expertise — Sakowski and his co-founder each have more than 10,000 real Excel hours from prior roles at McKinsey and in finance, and the product is designed around workflows that analysts actually run: error detection, template augmentation, and model review, not just building basic analysis from scratch.

The data flywheel problem

The customer base creates an immediate tension: enterprise finance clients won't allow training on their data, which is live deal information rather than open-source code. Crunched's answer is that it does not train on customer data and cannot see what users prompt. The longer-term path is firm-level fine-tuning where large organizations do enough global modeling work to justify it, combined with forward-deployed customization — linking outputs into clients' specific LBO templates, formatting standards, and internal workflows.

Error detection in practice

The clearest proof point Sakowski offers is a working capital error caught by Crunched's detection system on a live transaction for a private equity client in London. The mistake overvalued the deal by £10 million. The deal itself involved a company described only as a $500 billion firm working on a $1.4 trillion transaction with roughly $20 billion in revenue — no names given.

The commercial logic is straightforward: if a single error catch saves £10 million, the ROI conversation with a finance team is easy. Sakowski notes the fundraise closed in record time, though no specific amount or lead investor is disclosed in the conversation.