Catch knowledge drift early
Surface contradictions, missing coverage, duplication, and weak structure before those defects compound in AI answers and support macros.
Third-party software for knowledge quality, support operations, and measurable self-service outcomes.
KB Sentinel is built for Jira Service Management and Confluence teams that need better AI answers, stronger self-service, and fewer preventable escalations. It detects drift, contradictions, duplication, missing coverage, and weak structure, then ranks what to fix first by support impact.
The product is not a generic doc assistant. It is a continuous QA layer for service knowledge that helps teams improve AI answers, self-service outcomes, and escalation behavior.
Surface contradictions, missing coverage, duplication, and weak structure before those defects compound in AI answers and support macros.
Focus teams on the fixes that move deflection, first-response quality, and escalation reduction instead of editing by article age alone.
Show teams a repair draft, supporting evidence, and the decision logic behind the recommendation without replacing human review.
Keep the top support intents honest with regression questions that reflect the real request patterns the desk sees every week.
Improve answer quality by tightening the knowledge base behind retrieval and self-service flows.
Fix the unclear or contradictory articles that cause agents and requesters to route work the wrong way.
Give operations teams a continuous score, prioritized findings, and a clear before-and-after pilot story.
Each pack is designed to help a buyer see the outcome quickly: fewer escalations, better answers, and a tighter set of canonical articles for one service lane.
Use KB Sentinel to clean up MFA resets, temporary admin access, VPN expectations, and passwordless recovery guidance.
Keep onboarding, leave, payroll, and policy guidance aligned so employee requests stop bouncing between HR, IT, and managers.
Use repeatable QA to keep badge access, visitor, AV support, and workplace request articles aligned with actual operations.
These images are the first listing asset set: they show the core dashboard, repair workbench, regression-suite view, and scan operations.
Dashboard overview
Knowledge Health, top findings, and launch posture in one view.
Repair workbench
Evidence-backed repair draft for the top-priority knowledge defect.
Regression suite
High-value questions tied to the pilot lane instead of generic QA checks.
Operations view
Knowledge assets, support demand, and recent scan operations for buyer walkthroughs.
The operational pages below are ready for draft Marketplace review and can move onto the company domain cleanly. Legal-sensitive documents stay clearly labeled until final review is complete.
Launch operating hours, severity targets, and what buyers should send with a support request.
Security contact, disclosure expectations, architecture summary, and current subprocessor posture.
Draft privacy notice covering data categories, model egress, retention, and customer request routing.
Draft launch terms that define service scope and support boundaries while awaiting final counsel review.
Minimal launch footprint with Atlassian Forge hosting and optional OpenAI repair drafting.