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Structured frontline insights for higher-quality investigations

HavenSIGHT is an AI-guided evidence and witness statement capture module designed for complex investigations. It improves the accuracy, completeness, and consistency of frontline inputs so every investigation starts with better facts. SIGHT is built for real-world conditions, diverse 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 not always 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. It reconstructs the timeline based on inputs and flags any inconsistencies or missing data.

Core Capabilities

AI-Guided Witness Interviews

HavenSIGHT dynamically adapts questions based on role, exposure, task, and responses. It probes for conditions, actions, deviations, and contextual factors without leading the witness.

Multi-Format Evidence Ingestion

Capture and process written statements, voice recordings, images, and other relevant documents. HavenSIGHT extracts facts and conditions regardless of input format.

Automated Data Collection

Automatically collects relevant evidence and potentially impactful factors including environmental, weather, key contractor data, hazardous material safety data sheets, and previous relevant incidents.

Fact and Condition Extraction

Automatically identifies actions, hazards, environmental conditions, equipment states, timing cues, and deviations embedded in unstructured inputs.

Timeline Reconstruction

Maps extracted facts to time markers and sequences them into a coherent event timeline, ready for investigation review.

Inconsistencies Flagging

Detects and flags any inconsistencies across provided evidence, witness statements, and automatically collected facts.

Narrative Synthesis

Transforms fragmented inputs into a clean, neutral, investigation-ready narrative suitable for audits, regulators, and internal review.

Quality and Completeness Checks

Flags missing information, internal inconsistencies, and areas requiring follow-up before analysis begins.

Built for frontline variability and operational reality

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.

The foundation of structured investigation intelligence

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.

Integration Highlights

Evidence feeds Haven's timeline reconstruction

Facts support the various root cause analysis methods

Conditions and actions map directly to hazard and control reasoning

Narratives link to AI recommended root causes and corrective actions

HavenSIGHT ensures analysis is grounded in evidence, not assumptions.

Immediate improvements in investigation quality

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