An AI gave me a confidently wrong spreadsheet total — so I built one where AI writes code, not guesses, and gets verified.

A developer built Sheet Analysis AI, an open-source tool that prevents AI from hallucinating spreadsheet totals by having the model write JavaScript code that runs locally in the browser, with a deterministic auditor verifying every figure before display. The tool, built with React 19, TypeScript, and Vite, is licensed under AGPL-3.0 and aims to establish a pattern for trustworthy AI data analysis.

A few months ago I asked an AI tool to total up a sales spreadsheet. It gave me a clean, confident number. It was wrong. Not "rounding error" wrong — it had quietly skipped rows and produced a total that just looked plausible. Nothing flagged it. Nothing hedged. It just said the number, like it was fact. That's the actual problem with LLMs on tabular data: they don't calculate, they estimate. Ask a model to sum a column and, under So I built Sheet Analysis AI https://github.com/Durlabhkumarjha/sheet-analysis-ai specifically so it can't do that. Here's the actual mechanism — not the marketing version. When you ask a question like "which region grew fastest?", the model doesn't see your data. It sees: region , revenue , date From that, it writes a small piece of JavaScript — actual code, not a natural-language answer. That code is then executed locally, in your browser , against your The model decides the approach group by region, sum revenue, sort descending . Your machine does the calculating . This alone kills the "confidently estimated" failure mode, because there's no estimation step left — it's just code execution. The deterministic dashboard KPIs, Mann-Kendall trend detection, ANOVA seasonality, Pareto/RFM segmentation, forecasting doesn't even involve the AI — it's plain statistical code that runs the instant you upload a file, no API key required at all. Even code-generated numbers can be wrong — bad logic, an edge case, a misread column. So before anything renders, a separate deterministic auditor — no AI involved — re-checks every figure against the source rows. A concrete example: Say your data is: | Region | Product | Revenue | |---|---|---| | North | Phone | 200 | | North | Laptop | 200 | | South | Phone | 100 | | South | Laptop | 500 | Total revenue is $1,000. The auditor checks this a few different ways: 400 / 1000 , or a plausible-sounding guess? 400 + 600 = 1000 . By product: 300 + 700 = 1000 . If those don't match, something's broken upstream and the number is blocked, not shown.Any single failed check blocks that figure. It doesn't get downgraded to "approximately" — it just doesn't render. To be upfront about scope: React 19 + TypeScript + Vite. No backend in this build — parsing, analysis, and the reconciliation gate all run client-side. Licensed AGPL-3.0. I don't think this needs to be a product. I think the pattern — AI proposes the method, deterministic code executes it, a separate auditor verifies it before display — is generally useful for anyone building "AI + your data" tools, and it's more useful to more people as a reference than as a SaaS with a handful of users. Repo: https://github.com/Durlabhkumarjha/sheet-analysis-ai https://github.com/Durlabhkumarjha/sheet-analysis-ai Genuinely curious if anyone's solved this "AI + real numbers" trust problem differently — would love to compare notes in the comments.