UC-128
Chatbot Quality Monitoring & Eval Spine
Scores every chatbot's answer quality daily, gates model and knowledge-base changes, and flags any answer without a traceable source.
10-14Build Duration
10-25xIndicative ROI
The Challenge
Internal and external chatbots ship without a measurable quality bar, so regressions and drift are found by users. Hallucinated answers to teachers, investors or staff are a brand and compliance risk.
How It Works
- Ingests chatbot conversations, knowledge-base versions and model-change events.
- LLM-as-judge scores correctness and grounding, human-calibrated; drift is watched daily.
- Gates every model or KB change on regression tests and mines unmet intents into a gap backlog.
What It Removes
- Regressions found by users, not by a gate
- Drift felt in production before it is seen
- Answers with no traceable source
Input Data RequirementsChatbot conversation logs, knowledge-base and model versions, evaluation rubrics
Output FormatDaily quality scores per bot, regression gates, hallucination-risk flags, gap backlog
“A measurable quality score for every bot, watched daily - regressions caught before users find them.”