When AI affects retention and deflection, “it seems fine” isn’t enough. We use this framework to help companies detect issues, trace root causes, and connect evaluations to business outcomes.

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Production AI assistants fail silently when evaluation focuses only on individual responses rather than full user sessions and system behavior. A comprehensive framework evaluates conversational AI at three levels (turn, session, cohort), measures quality through core and custom dimensions with weighted scoring, connects evaluation to observability telemetry for root cause tracing, and ties metrics to business outcomes like retention and deflection. This systematic approach helps teams detect issues, trace failures to specific components (retrieval timeouts, tool failures, escalation logic), and iterate with confidence by treating AI assistants as observable systems rather than isolated models.

Your AI Assistant has an observability problem Evaluating AI Assistants as Systems, Not Models: A Production Framework for Conversational Interfaces