What It Means
Not 'probably fine' — safe_to_deploy means a human with authority reviewed it against your rubric and approved it.
safe_to_deploy is the green light. It's the first of three verdict lanes in the AI QA & Evaluation Platform, and it means: this message passed every check in your rubric and can ship without a human touching it. No queue. No delay. But here's what matters — it's not unreviewed. It's evaluated against your configured rubric and logged with a verdict, timestamp, and rubric version. If an auditor asks about it six months later, you can show exactly what standard it was measured against and when. The goal of the whole system is to maximize safe_to_deploy rates through better AI, better rubrics, and better training data — because every safe message is throughput you don't have to pay a human to touch.
Why It Matters
safe_to_deploy rates are your north star metric for AI outbound efficiency. Low safe rates mean your review system is a bottleneck — too many messages need human review. High safe rates mean your AI is well-calibrated, your rubrics are dialed in, and your review costs are dropping. Teams typically see safe rates climb steadily after calibrating with real correction data. That's the flywheel working.
How Bookbag Helps
Zero-delay clearance
Messages meeting all rubric criteria get safe_to_deploy instantly — no queue, no human wait time, no bottleneck.
Full audit logging
Every safe message is logged with its verdict, rubric version, and timestamp. Auditable anytime, even months later.
Calibration sampling
QA reviewers can sample safe messages to verify the rubric isn't letting bad content through. Catches drift before it becomes a problem.
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