Support policy conflict audit
Policy Conflict Detection for AI Support
A workflow for finding policy contradictions that make AI support agents answer confidently from the wrong source.
Buyer problem
Support operations and knowledge owners cleaning AI source risk
AI agents do not know which policy is canonical when help articles, macros, SOPs, tickets, and internal notes disagree.
Readiness workflow
Make the launch decision from evidence.
Group sources by customer intent
Compare every source that could answer the same customer question instead of reviewing documents one by one.
Flag contradictory conditions
Look for conflicting timing, eligibility, plan, region, proof, approval, or escalation language.
Approve one canonical answer
Choose the source-backed answer the AI is allowed to use and block the intent until the source set agrees.
Evidence checks
What the audit needs to prove.
Outputs
What the team should have after the review.
Example rollout patterns
Two ways this use case shows up.
Refund policy cleanup
Resolve a public 30-day return article, an agent macro with 45-day exceptions, and a finance SOP that says manager approval is required.
Account access policy
Block admin-change automation until security, support, and help-center sources agree on identity verification.
FAQ
Questions before the audit.
Why do policy conflicts matter for AI support?
When sources disagree, the AI may retrieve the wrong rule and produce a confident answer that violates the actual operating policy.
What sources should be compared?
Compare help articles, macros, snippets, SOPs, internal notes, tickets, pricing pages, product docs, and escalation playbooks.
Should conflicted intents be automated?
No. Conflicted intents should stay blocked or human-owned until the team chooses and publishes a canonical source.
Related use cases
Compare adjacent readiness work.
Vendor and templates
Use the matching rollout assets.
Launch boundary
Turn this use case into approved AI support scope.
Meihaku maps customer intents to source evidence, readiness blockers, and the answers your team approves.