HavenEDGE delivers Multi-Threaded, Five Whys, and Fishbone root cause 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 specific incident and AI reasoning rooted in industry knowledge.
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 evidence is collected, Haven performs AI 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 Actions Recommendation
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
Multi-Threaded RCA
AI inferred causal pathways for complex events.
Serious incidents rarely result from a single cause. HavenEDGE identifies and traces multiple AI-inferred causal threads that unfold in parallel across. These threads are reinforced and validated against historical incident patterns encoded in the Haven Industry Knowledge Graph, revealing systemic pathways rather than isolated failures.
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
Full human-in-the-loop control of the analysis
Five Whys Analysis
Five Whys with AI guardrails and knowledge graph context
HavenEDGE ensures each Why is explicitly grounded in evidence and anchored to the Industry Knowledge Graph. The system detects circular reasoning, vague causal statements, and breaks in logic as the analysis progresses. At every Why step, Haven proactively recommends deeper, more defensible causes by drawing on the current incident context and analogous historical cases with shared contributing 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
Additional evidence recommendation
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 evidence 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
Full human-in-the-loop control over the analysis
AI generated Fishbone diagrams
Output and Documentation Quality
HavenEDGE produces high quality, comprehensive, and consistent RCA outputs that safety teams can use internally or share with regulators, auditors, and insurers. Each AI output is linked to source evidence and causal logic.
Capabilities
Multi-threaded causal diagrams
Five Whys chains
Fishbone diagram
Corrective actions plans
Executive investigation summary
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.