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We present RouteNLP, a closed-loop framework that routes queries across a tiered model portfolio to reduce value whereas satisfying per-task quality constraints. The framework integrates three parts: a problem-conscious router with shared process-conditioned representations educated on desire information and quality alerts; confidence-calibrated cascading that makes use of conformal prediction for distribution-free threshold initialization; and a distillation-routing co-optimization loop that clusters escalation failures, applies focused knowledge distillation to cheaper fashions, and automatically retrains the router, yielding over twice the associated fee enchancment of untargeted distillation. 5K queries/day at an enterprise buyer-service division, RouteNLP lowered inference costs by 58% whereas maintaining 91% response acceptance and reducing p99 latency from 1,847 ms to 387 ms.
On a six-activity benchmark spanning finance, customer support, and legal domains, the framework achieves 40-85% value reduction whereas retaining 96-100% quality on structured tasks and 96-98% on technology duties, with human analysis confirming that 74.5% of routed era outputs match or exceed frontier-model high quality.
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