havenSIGHT is an AI-guided evidence capture and witness statement system designed for serious investigations. It improves the accuracy, completeness, and consistency of frontline inputs so every investigation starts with better facts.
Built for real-world conditions, variable and multi-lingual witnesses, and high-consequence incidents.
Incident investigations are only as strong as the evidence they start with. In practice, witness statements are inconsistent, incomplete, and shaped by investigator skill, time pressure, and cognitive bias. Critical details are missed, timelines are unclear, and downstream RCA is forced to compensate for weak inputs.
havenSIGHT addresses this problem at the source. It guides evidence capture with AI-driven structure, adapts questions to context, and transforms raw inputs into investigation-ready narratives aligned to Haven's reasoning engine.
Better inputs produce better analysis. havenSIGHT ensures investigations begin with defensible evidence.
havenSIGHT combines adaptive questioning, multi-format ingestion, and structured synthesis to capture what actually happened. It works across witnesses, supervisors, contractors, and supporting documentation, producing clean, consistent evidence that feeds directly into Haven's timeline and RCA workflows.
Witnesses recall events differently. Language varies. Details emerge unevenly. havenSIGHT is designed to handle this variability without forcing rigid forms or generic questionnaires. It adapts in real time while maintaining structure and consistency across sites and investigators.
Outcome: More complete statements with less investigator effort and less variability across investigations.
havenSIGHT is tightly integrated with Haven's intelligence engine. Extracted facts and timelines flow directly into analysis workflows, enabling deeper RCA and more accurate corrective actions downstream.
Evidence feeds Haven's timeline reconstruction
Facts support multi-threaded RCA and Five Whys analysis
Conditions and actions map directly to hazard and control reasoning
Narratives link to RCA outputs and corrective actions
havenSIGHT ensures analysis is grounded in evidence, not assumptions.
Outcomes typically observed:
Higher completeness and clarity of witness statements
Reduced follow-up interviews and rework
Greater consistency across investigators and sites
Stronger alignment between evidence and RCA conclusions
Improved audit and regulatory confidence
Teams see gains from the first investigation onward.
Zero customer data used for AI model training
Encryption in transit and at rest
Role-based access control
Customer-specific data isolation
PII detection and redaction support
Audit-ready evidence handling
