SolvNum

The mission you approved on the screen, the mission the platform actually flew, and the mission the incident-review team replays in the lab are three slightly different sets of numbers — because they ran on three different computers with three different math libraries.

Imagine a single deterministic arithmetic substrate where plan = flight = replay, by construction, on any hardware. One SHA-256 receipt proves it. A cross-target equivalence argument that fits on one line of a DO-178C certification package.

A content-moderation pipeline ingests model logits, runs calibration, applies policy rules, and emits a decision. The same logits fed into the same pipeline produce subtly different confidence scores on CPU vs GPU vs on-device — because platform math-library calls disagree in the last few ULP. At borderline cases, the decision flips.

Imagine a decision layer where every party — platform, regulator, researcher — re-derives the same decision from the same input. Zero receipt drifts across 1,000,000 decisions. EU AI Act Article 12 ready.

A fleet of 200 CNC machining centers runs a vibration model on the edge PLC and a matching twin in the cloud. When they diverge, maintenance is dispatched. Today, “diverge” usually means the two solvers disagreed on the last digits of an eigenvalue — not that a bearing is failing. The team learns to ignore alerts.

Imagine a twin where numerical divergence is zero by construction. Any divergence the maintenance system flags is a real physical change in the machine. 0% false-alarm rate from numerical mismatch. 100% fault-detection recall.