Proofsheet

Proofsheet is the analyst that shows its work. Every AI data tool can make a chart. The hard part is defending the number: which definition was used, which rows were dropped, whether the model invented a figure. Proofsheet turns a messy CSV into an audit-ready report. The user uploads an export, asks a question in plain English, and approves the business definition before any code runs: which orders count, whether shipping is revenue, how missing values, discounts, and ambiguous dates are handled. The definition is stored with the run, shown on every report, and reused on replay. A Rust control plane profiles the file, calls Vultr Serverless Inference for a plan and a pandas script, and executes it in a disposable gVisor sandbox with no network, no secrets, a read-only root, and hard caps. A second model, which never sees the first script, recomputes the same metrics in a separate sandbox; both import the same deterministic parsing contract. Agreement produces the two-execution stamp. The note may only cite placeholders resolved from the execution ledger, and a chart audit checks the series, top category, peak month, assumptions and rows used. A live “try to make it lie” check injects a fake claim, and the stamp is withheld. The result: every claim opens the exact code, stdout, sandbox and isolation level that produced it. A saved pipeline replays on a new export with no model in the loop, and Rust computes the period comparison. The production run: 522 rows in, 506 out, 333 used, 6/6 claims receipted, 5/5 independent agreement, +3.0% net revenue on replay. Stack: Rust (Axum, Tokio, SQLx) on a Vultr Cloud Compute VM, Vultr Serverless Inference, gVisor, React + TypeScript. Track: Blast Radius Zero, Pattern A. Live: http://149.28.225.246 Report: http://149.28.225.246/r/7263a5f0-e0be-47e1-9f6c-f29e23d3ffd8 Source: https://github.com/PhiBao/proofsheet Demo: https://youtu.be/bdNI-FLgZxI