Reason through incidents with greater depth and consistency.
Haven brings evidence, timelines, causal reasoning, failed controls, root cause analysis, corrective actions, and report development into one AI-guided investigation workflow.
Investigators stay in control of every conclusion. Haven organizes evidence, tests reasoning, and surfaces gaps a manual review might miss.
Depth and consistency are hard to guarantee at scale.
Complex incidents generate evidence from many sources: witness accounts, documents, photographs, sensor data, procedures, and prior records. Assembling that evidence into a coherent, defensible account takes significant time, and the depth of reasoning can vary widely from one investigator to the next.
Haven gives every investigator the same structured starting point: evidence organized, timelines built, causal threads tested, and gaps surfaced before the report is written.
One workflow, from raw evidence to a defensible report.
Evidence synthesis
Bring witness accounts, documents, images, and records into one organized evidence base.
Timeline development
Reconstruct the event sequence and flag where the timeline is incomplete or contradicted.
Contradiction detection
Surface conflicts between sources so investigators can resolve them before they harden into conclusions.
Multi-threaded causal reasoning
Explore more than one causal pathway rather than stopping at the first plausible explanation.
Control-focused analysis
Examine which controls failed, degraded, or were missing, not just what the immediate cause was.
Corrective action development
Connect proposed actions directly to the causes and failed controls identified.
From first evidence to a structured report.
Evidence is captured
Witness statements, documents, photographs, and records are brought into one workspace.
Haven organizes and cross-references
Evidence is structured into a timeline, and contradictions or gaps are flagged for the investigator.
Causal pathways are explored
Haven supports multi-threaded reasoning rather than a single linear cause chain.
Controls are examined
Failed, missing, or degraded controls are analyzed alongside the causal findings.
Corrective actions are drafted
Actions are tied to specific causes and control failures, with an owner and verification plan.
The report is assembled
Evidence, reasoning, and actions are compiled into a structured, defensible investigation record.
Support the methods your organization already trusts.
Haven does not force every organization into a single method. It strengthens the depth and consistency of whichever reasoning approach your standard requires, grounded in the Haven Industry Knowledge Graph.
AI-inferred causal pathways for complex events.
Serious incidents rarely trace back to a single cause. Haven identifies and traces multiple causal threads that unfold in parallel, reinforced against historical incident patterns rather than treated as isolated failures.
- Parallel, AI-inferred causal threads
- Mapping interactions across conditions, actions, and failures
- Automated detection of missing contributing factors
- Timeline alignment using collected evidence
- Full human-in-the-loop control of the analysis
Five Whys with AI guardrails and knowledge graph context.
Each Why is grounded in evidence and anchored to the Industry Knowledge Graph. Haven flags circular reasoning, vague causal statements, and breaks in logic as the analysis progresses.
- 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 recommendations
AI-assisted cause classification across standard domains.
Haven categorizes causes under people, process, equipment, environment, materials, and management, then highlights missing evidence and factors that appear in similar incidents.
- AI-recommended contributing factors by category
- Automated identification of category gaps
- Evidence-anchored classification
- Full human-in-the-loop control over the analysis
Recommendations grounded in causal reasoning, not generic checklists.
Haven generates corrective action recommendations using causal reasoning, the Industry Knowledge Graph, and patterns from prior incidents. Each recommendation is scored for effort and impact, mapped to the Hierarchy of Controls, and reflects interventions proven effective in similar events.
- AI-generated corrective actions tied directly to root causes
- Scoring based on effort and estimated impact
- Knowledge-graph-aligned control mapping
- Learning from historical incident outcomes
- Traceability back to the RCA pathway that produced them
Safety Initiative Matrix
EFFORT VS. IMPACT ANALYSISPowered by the Haven Industry Knowledge Graph.
Haven's reasoning is grounded in a knowledge graph built from high-signal industry sources, including regulatory frameworks, incident investigation findings, and recognized safety management standards. This gives Investigate a working understanding of hazards, controls, exposures, and failure modes, rather than relying on generic language patterns alone.
Investigation records built to withstand scrutiny.
Haven produces comprehensive, consistent outputs that safety teams can use internally or share with regulators, auditors, and insurers. Every output is linked back to source evidence and causal logic.
Multi-threaded causal diagrams
Visual maps of parallel causal pathways, tied to the evidence that supports them.
Five Whys chains
Fully documented Why sequences with evidence citations at each step.
Fishbone diagrams
Category-organized causal factors, generated directly from the investigation.
Corrective action plans
Prioritized actions with effort and impact scoring, ready for assignment.
Executive investigation summary
A concise, defensible account of the incident, its causes, and the response.
Give every investigation the same depth of reasoning.
See how Haven can help your organization investigate more consistently while keeping investigators accountable for the conclusions.