Corrections

AI-powered CAPA management for pharmaceutical manufacturing. Every corrective action recommended by AI. Every effectiveness check automated. Every resolution traced end-to-end.

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Built for Effective CAPA Resolution

AI recommends corrective actions based on what actually worked in similar past events, not just what was documented, but what prevented recurrence.

Leucine Corrections closes the loop between investigation and prevention. When a deviation or audit finding triggers a CAPA, AI analyses historical resolution data to recommend corrective actions with the highest likelihood of success. Effectiveness verification is scheduled automatically. And the full chain, from source event through investigation, corrective action, and verification, is linked end-to-end with complete traceability.

AI-Recommended Corrective Actions

AI analyses historical CAPA outcomes to recommend corrective actions that actually prevented recurrence, ranked by effectiveness, not just frequency.

Historical Pattern Matching.

AI matches the current issue against thousands of resolved CAPAs, identifying which corrective actions were most effective for similar root causes.

Effectiveness Ranking.

Recommended actions are ranked by historical success rate, showing which corrections prevented recurrence vs. which merely addressed the symptom.

Root Cause Alignment.

Recommendations are mapped to the specific root cause identified during investigation, ensuring corrective actions address the actual driver, not a proxy.

Cross-Site Learning.

Corrections that worked at one facility are surfaced as recommendations at other sites facing similar issues, building organisational knowledge.

Automated Effectiveness Verification

Effectiveness checks are scheduled automatically based on CAPA type and regulatory requirements, with re-occurrence monitoring that proves corrective actions actually worked.

Auto-Scheduled Verification.

Effectiveness checks are scheduled at configurable intervals after CAPA implementation, 30, 60, and 90 days, or per your SOPs.

Criteria Definition.

Measurable effectiveness criteria are defined at CAPA creation, what metrics must improve, by how much, and over what period.

Re-Occurrence Monitoring.

AI monitors deviation data after CAPA closure to detect whether the original issue recurs, automatically flagging ineffective corrections.

Statistical Verification.

Effectiveness is verified statistically, not anecdotally, using process capability data and deviation trending before and after implementation.

Cross-Functional Coordination

CAPAs that span departments are coordinated through shared workspaces, with task assignment, status tracking, and real-time visibility for every stakeholder.

Multi-Department Tasks.

CAPA implementation tasks are assigned across departments, production, QA, QC, engineering, and regulatory, with clear ownership and deadlines.

Shared Workspaces.

Investigation findings, proposed actions, and implementation evidence are collected in a shared workspace accessible to all contributors.

Status Visibility.

Real-time dashboards show CAPA status by owner, department, and due date, with colour-coded indicators for on-track, at-risk, and overdue items.

Escalation Management.

Automated escalation rules notify management when CAPAs miss deadlines or when effectiveness verification fails, ensuring nothing falls through.

End-to-End Traceability

Every CAPA links back to its source event and forward to its verification, creating a complete audit trail from initial detection through corrective action to proven effectiveness.

Source Event Linking.

Bidirectional links connect every CAPA to its source, deviations, audit findings, complaints, or risk assessments, with full context preserved.

Impact Chain Tracing.

The complete chain from detection through investigation, root cause, corrective action, implementation, and effectiveness verification is traceable in a single view.