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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AI assistants require evaluation at three levels: individual turns, full sessions, and cohort trends. A framework combines evaluation (defining quality metrics), observability (capturing system behavior), and traceability (connecting failures to causes). Quality assessment uses CORE dimensions (clarity, relevance, tone, accuracy) plus CUSTOM product-specific metrics, weighted by importance. Hybrid review pipelines combine manual calibration with automated LLM-as-judge scaling. Success requires instrumenting telemetry across model, retrieval, and tooling layers, then connecting conversation quality to business outcomes like retention and deflection.

You launched an AI assistant. Do you really know how it's performing?