Automated Near-Miss and Incident Report Triage Workflow

Manual report triage creates dangerous delays. This workflow uses NLP agents to parse mobile app submissions, photos, and voice notes in real-time, immediately classifying events by severity (e.g., 'lost-time incident' vs. 'near-miss') and routing critical alerts via SMS or dashboard to the on-call safety officer. The architecture connects ingestion endpoints (Twilio, mobile APIs) to a LangGraph-based classification engine, ensuring life-critical signals bypass administrative queues.

Safety managers spend hours daily logging, categorizing, and distributing reports. This workflow automates the entire intake-to-routing pipeline: extracting entities (location, personnel, equipment), populating standardized forms in Procore or SiteDocs, and assigning to the correct reviewer based on incident type and subcontractor. The savings come from eliminating manual data entry and the email/Spreadsheet coordination toil that currently consumes 15-20 hours per week per project.

Without automated aggregation, safety data sits in silos. This workflow feeds all classified reports into a vector database, enabling semantic search and RAG-powered analysis. Safety leads can query for patterns (e.g., 'slip hazards near Crane 3 in wet conditions') to identify root causes. By connecting the triage output to analytics dashboards and weekly safety meeting agendas, the system shifts focus from reactive record-keeping to proactive prevention, directly impacting EMR and insurance costs.

Manual processes create gaps in the chain of custody for incident reports. This workflow enforces a complete, timestamped audit trail: from initial submission, through AI classification confidence scores, to human review actions and closure. Every step is logged in a system like Salesforce or a dedicated compliance database. This defensible documentation is critical for OSHA inspections, insurance claims, and legal discovery, reducing regulatory risk and supporting faster claim resolution.

Full automation is neither safe nor compliant. The workflow's core architecture uses a state machine (e.g., LangGraph) to route cases based on confidence thresholds. High-severity, low-confidence, or litigation-prone incidents are automatically escalated to a human review queue in the safety team's existing dashboard. This ensures experts handle complex judgments while the system manages the routine, creating a scalable operating model that balances speed with necessary oversight.

The business impact depends on connection, not just classification. The workflow acts as an orchestration layer, using browser automation (Playwright) and API connectors to push finalized reports into legacy systems with poor APIs, such as older insurer portals or corporate ERP safety modules. It also triggers downstream actions—like generating First Report of Injury forms for workers' comp or notifying the insurance liaison—closing the loop between field reporting and enterprise risk management.

This initial agent orchestrates data collection from fragmented, often API-less sources: mobile safety apps (e.g., Procore Safety), emailed PDF reports, scanned paper forms, and even SMS alerts. It uses browser automation and OCR to normalize unstructured text, images, and geolocation data into a unified event stream, solving the primary bottleneck of manual data entry and consolidation.

A core LLM-based agent analyzes the normalized report text. It classifies incident severity (e.g., near-miss, first-aid, recordable) using regulatory frameworks (OSHA) and identifies potential root causes (e.g., 'fall hazard,' 'equipment malfunction,' 'PPE violation'). This layer transforms subjective narratives into structured, actionable data, enabling priority-based routing. Confidence scores are attached to each classification for human review gates.

This is the workflow's decision core, often built with a framework like LangGraph. It uses classification outputs to route events through predefined paths: high-severity incidents go immediately to the site safety officer and insurance liaison via SMS/email; potential trends are queued for the corporate safety manager; incomplete reports are routed back to the originator. It integrates with communication systems (Twilio, Microsoft Teams) and safety platforms to trigger actions.

Not all decisions are fully automated. A centralized dashboard presents triaged events requiring human judgment—low-confidence classifications, severe incidents for official logging, or potential legal exposure. Reviewers can override agent decisions, add context, and close the loop. This control point ensures regulatory compliance and accountability, feeding corrections back to improve the classification models.

The workflow's value is realized by writing structured data back to enterprise systems. This component uses a mix of APIs (where available) and browser agents to update records in the company's safety management software (e.g., Intelex, Cority), log incidents in the ERP/HR system (e.g., Viewpoint, SAP) for recordkeeping, and initiate claims in the insurance carrier's portal. It acts as the connective tissue between the AI layer and systems of record.

Every action—ingestion, classification, routing, override—is logged with a full audit trail, essential for regulatory compliance and potential litigation. This backend also aggregates anonymized data to power dashboards that visualize trend lines (e.g., 'PPE violations rising in Sector B'), measuring the workflow's ROI in risk reduction and providing data for proactive safety investments.