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AI-Powered Predictive Routing

Description

Uses machine-learning models trained on historical interaction outcomes to predict which agent is most likely to achieve the desired result for an incoming contact. Routing decisions are optimised for metrics such as CSAT, first-contact resolution, or conversion rate.

Canonical use case

A subscription service uses predictive routing to match at-risk customers identified by a churn model with retention-specialist agents who have the highest historical save rate for that segment.

Open Items

  • [ ] Canon alignment — populate canon_axiom_refs or confirm no existing axiom applies
  • [ ] Dependency assessment — set dependencies_assessed: true once SA has reviewed the full chain
  • [ ] effort_estimate — replace 0 with rough engineering days (order of magnitude)
  • [ ] public_description — write the public-facing description before publishing