Service
Run golden-path test suites on every release and monitor production conversations for hallucinations, drift, and policy regressions.
AI systems change often: prompts evolve, retrieval content shifts, tools get updated, and model providers quietly improve or degrade behaviors over time. Without a stable regression layer, teams are forced to rely on anecdote. That usually means they learn about a serious defect from customers instead of from their own release process.
Hallucination is only one failure mode. Teams should also track policy adherence, escalation quality, routing accuracy, data leakage, and whether the agent uses tools correctly. A monitoring system becomes much more useful when it reports behavior by scenario family, intent, severity, and operational impact instead of only producing a generic error rate.
GraiBot keeps golden-path and high-risk scenarios in a versioned library, reruns them on every meaningful change, and surfaces evidence when the output drifts. That allows the same test assets to support pre-release QA, post-release monitoring, and remediation verification. It connects naturally with AI chatbot QA testing and adversarial testing, giving teams one feedback loop instead of three disconnected processes.
It should measure answer quality, routing correctness, policy adherence, leakage risk, tool behavior, and escalation outcomes across the same versioned scenarios over time.
Post-launch monitoring catches drift from model updates, retrieval changes, traffic shifts, and real-world edge cases that were not visible during pre-release testing.
