HavenEDGE delivers multi-threaded RCA, Five Whys, and Fishbone analysis supported by the Haven Industry Knowledge Graph. It provides AI inferred causal pathways, AI generated root causes, and AI recommended corrective actions that reflect both the incident and patterns learned from prior events.
A complete RCA system grounded in intelligence, not templates
HavenEDGE uses the Haven Industry Knowledge Graph to understand hazards, controls, exposures, failure modes, and contributing factors. As investigators upload evidence, Haven performs AI inferred reasoning to identify causal patterns and connections the human eye may miss. The system continuously improves by learning from prior incidents, enabling investigators to generate faster, deeper, and more defensible analyses.
Corrective Action Generation
Corrective actions are not generic lists. HavenEDGE generates AI recommended corrective actions using causal reasoning, the knowledge graph, and patterns from prior incidents. Each recommendation is scored for effort and impact and mapped to the Hierarchy of Controls. Actions reflect proven interventions from similar events across the customer’s data and the broader knowledge base.
Capabilities
AI generated corrective actions tied to root causes
Scoring based on effort and estimated impact
Knowledge graph aligned control mapping
Learning from historical incidents
Traceability to RCA pathways
Outcome
Long-term, systemic corrective actions that prevent recurrence.
Multi-Threaded RCA
AI inferred causal pathways for complex events.
Real incidents involve multiple contributing actions and conditions. HavenEDGE uses graph-based reasoning to surface AI inferred causal threads that span people, process, equipment, and environmental factors. These patterns are strengthened by the system's understanding of historical incidents captured in the Haven Industry Knowledge Graph.
Capabilities
Parallel, AI inferred causal threads
Mapping interactions across conditions, actions, and failures
Automated detection of missing contributing factors
Timeline alignment using evidence from havenSIGHT
AI generated pathway diagrams for review
Outcome
A more complete and realistic understanding of why incidents occur.
Five Whys Analysis
Five Whys with AI guardrails and knowledge graph context
Five Whys is powerful when applied correctly. HavenEDGE ensures each step is grounded in evidence and supported by the knowledge graph. The system flags repetitive logic, vague statements, and missing links. When investigators enter a Why step, Haven provides AI recommended deeper causes based on both the current incident and prior cases with similar factors.
Capabilities
AI generated suggestions for deeper Why steps
Guardrails against repetition and abstraction
Links to prior incidents with similar cause patterns
Knowledge graph aligned progression
Automatic mapping to corrective actions
Outcome
Cleaner, more defensible Five Whys results backed by incident history.
Fishbone Analysis
AI assisted cause classification across standard domains
HavenEDGE performs Fishbone analysis using the Haven Industry Knowledge Graph to categorize causes under people, process, equipment, environment, materials, and management. The system highlights missing categories and suggests AI inferred factors that appear in similar incidents across the dataset.
Domains Supported
People
Process
Equipment
Environment
Materials
Management
Capabilities
AI recommended contributing factors by category
Automated identification of category gaps
Evidence anchored classification
Integration with multi-threaded RCA outputs
AI generated Fishbone diagrams
Outcome
A structured and comprehensive view of all contributing factors.
Failure Mode and Control Mapping
HavenEDGE maps each causal factor to related hazard controls and failure modes stored in the Haven Industry Knowledge Graph. It identifies whether a control was missing, weak, inadequate, or bypassed.
Capabilities
Control type identification
Control effectiveness scoring
Knowledge graph powered failure mode detection
AI inferred control recommendations
Integration with corrective action generation
Outcome
Clear insight into which controls failed and how they should be improved.
Evidence and Timeline Integration
Every causal step is linked back to specific evidence, including witness statements, documents, forms, images, or audio. Haven reconstructs timelines using AI inferred temporal reasoning and aligns each root cause with supporting facts.
Capabilities
Evidence-linked causal factors
AI generated timelines
Knowledge graph consistency checks
Identification of contradictions or missing data
Outcome
Defensible RCA supported by clear, traceable evidence.
Output and Documentation Quality
HavenEDGE produces clean and consistent RCA outputs that safety teams can use internally or share with regulators, auditors, and insurers. Each output is AI generated and linked to source evidence and causal logic.
Capabilities
Multi-threaded causal diagrams
Five Whys chains
Fishbone diagram
Control analysis summary
Corrective actions matrix
Executive RCA summary
Outcome
High-quality, standardized RCA across investigators and sites.
See Haven in Action
Schedule a personalized demonstration with our solution architects. See how Haven's AI-native platform transforms reactive reporting into proactive prevention.