Check whether your AI assistant is safe and accurate before customers use it.
Run quality checks across answer accuracy, source support, fallback behavior, tone, and sensitive data handling so your team can fix gaps before launch.
Seven checks teams can understand without reading model internals.
Answer accuracy
Check whether responses actually answer the user question correctly.
Source match
Measure whether the answer points back to the right source material.
Evidence support
See whether the answer is fully supported by the connected knowledge behind it.
Unsupported answer detection
Flag answers that go beyond the available evidence or invent missing details.
Safety checks
Review risky prompts, sensitive data handling, and response guardrails before launch.
Brand tone
Check whether the assistant sounds consistent with the experience your team wants to provide.
Fallback behavior
Verify that the assistant knows when to say it does not know or to escalate.
Test, review, fix, and rerun before launch.
Connect knowledge and prompts for the assistant you want to validate.
Run a readiness check against real or representative user questions.
Review weak answers, improve the setup, and rerun until the score is trustworthy.