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Human-in-the-loop (HITL) systems can become bottlenecks if applied uniformly across all AI decisions. A tiered architecture approach handles 85% of predictions automatically while routing only uncertain or high-risk cases to human reviewers. This three-tier system uses a prediction router to classify decisions in under 1ms, applies confidence calibration and active learning for smart sampling, and implements multi-speed feedback loops from real-time to quarterly. Results show 73% reduction in review time, 75% drop in false negatives, and 67% cost savings while scaling to 14M predictions per second.