AI review loop
Extraction output is scored against a held-out review set on every model change; low-confidence results are routed to human review before they reach an artifact.
Case notes
Short operational notes from private delivery.
Practical notes from running real systems. No client material — just the patterns and decisions that held up in production.
Deployment recovery
Every deploy carries written notes and a rollback path, so recovery is a procedure, not a scramble — the on-call reverts within a known, tested window.
Batch export hygiene
Export bundles are versioned, validated before delivery, and cleaned up on a schedule per their retention window. Stale artifacts do not accumulate.
Workspace status separation
Status and recovery tooling lives in its own workspace, away from production, so a noisy incident never blinds the monitoring meant to observe it.
Probe tuning
Synthetic probes are tuned to exercise critical paths often enough to catch real failures without generating noise. We trim probes that only confirm what we already know.
Retention policy
Each project gets an explicit retention window for uploads, artifacts, and exports, agreed up front and enforced by scheduled cleanup.