AI Integration for Cority Incident Management

AI integration for Cority incident management connects at three primary surfaces: the initial report intake, the investigation and classification workflow, and the analytics and reporting layer. For intake, AI can act as a first responder, processing free-text descriptions, voice notes, or form data to auto-populate critical fields in the Incident object—like severity, body part, event type, and potential OSHA recordability. This reduces data entry errors and ensures immediate triage. During investigation, AI agents can be triggered from the Investigation record to analyze attached documents, witness statements, and historical similar incidents to suggest root cause codes (e.g., 5 Whys, Fishbone categories) and draft narrative summaries for the investigator's review.

The implementation typically involves a middleware layer that subscribes to Cority webhooks (e.g., Incident.Created, Investigation.StatusChanged) and uses the Cority REST API to read and write back enriched data. A common pattern is to deploy a queue for incoming incidents where an AI agent performs initial classification, then posts results back to a custom field like AI_Initial_Assessment. For investigations, the system can call an AI service when the status moves to 'Under Investigation', passing the incident ID to retrieve all related data, then returning structured suggestions to a AI_Recommendations related list. This keeps the AI workflow auditable and allows for human-in-the-loop approval before any automatic updates to core fields.

Rollout should be phased, starting with a pilot on high-volume, lower-severity incidents (e.g., first aid cases) to tune classification models and build user trust. Governance is critical: all AI-generated content should be logged in a dedicated AI_Audit_Log object within Cority, tagging the model version, prompt used, and confidence scores. This creates a transparent chain of custody for compliance audits. Finally, the integration should be designed to fail gracefully—if the AI service is unavailable, the incident workflow continues manually, preventing operational blockage. The goal is not to replace the EHS professional but to augment them, turning hours of manual data wrangling into minutes of focused review and action.

The integration connects at two primary layers: the Cority API for structured data and the Cority Webhook framework for real-time event triggers. When an incident is created or updated, a webhook payload containing the incident ID and key fields (e.g., IncidentType, Severity, Description) is sent to a secure ingestion endpoint. This triggers the AI pipeline, which first enriches the data by fetching the full incident record, any attached documents (witness statements, photos), and linked objects like the Location or Person involved via the Cority REST API.

The core AI processing involves a multi-step agent workflow: a Triage Agent first classifies the incident against OSHA recordability guidelines and internal categories using the narrative and form data. A Narrative Structuring Agent then extracts entities (equipment, chemicals, PPE), timelines, and contributing factors from free-text fields, populating corresponding Cority custom objects or data points. For investigations, a Root Cause Analysis Copilot can be invoked, which analyzes the structured data to suggest applicable RCA methodologies (5 Whys, Fishbone) and auto-generates a draft investigation report with proposed corrective actions, ready for reviewer approval within Cority.

Governance is maintained through a human-in-the-loop pattern. All AI-generated classifications and narratives are written to dedicated AI_Suggestion custom fields, not directly to primary fields, requiring a reviewer with appropriate Cority permissions to approve and promote them. An audit trail is maintained by logging all AI interactions, prompts used, and model versions to a separate AI_Audit_Log object within Cority, ensuring full traceability for compliance audits. Rollout typically starts with a pilot on high-volume, low-severity incidents to refine prompts and workflows before scaling to critical investigations.

A production-ready AI integration for Cority Incident Management is built as a secure, governed layer that sits alongside your existing Cority instance. It connects via Cority's REST API and webhooks to listen for new incident records, status changes, and investigation updates. The core AI logic—handling initial triage, classification, and narrative generation—runs in a dedicated Inference Systems environment, ensuring no sensitive PII or incident details are sent to external LLM providers without proper anonymization or customer-managed key encryption. All AI-generated outputs, such as suggested incident types, severity scores, or draft investigation narratives, are written back to designated custom fields in the Cority incident object, maintaining a complete audit trail within the system of record.

Rollout follows a phased, risk-aware model. Phase 1 typically starts in a single business unit or site, with AI acting in an 'assistive' mode. The system suggests classifications and auto-populates fields like Incident Category or Initial Severity, but all outputs require a human reviewer's approval within the Cority workflow before being finalized. This builds trust and gathers feedback. Phase 2 expands to automated narrative generation for the Incident Description field, pulling from initial reporter notes and witness statements to create a coherent first draft, saving investigators hours of manual compilation. Phase 3 introduces predictive elements, such as flagging incidents with similarities to past high-severity events for expedited review, directly within the Cority dashboard.

Governance is embedded through role-based access controls (RBAC) aligned with Cority's security model, ensuring only authorized personnel (e.g., EHS Managers, Site Investigators) can approve or override AI suggestions. Every AI interaction is logged with a trace ID, linking the generated content to the source data and model version used. This is critical for compliance audits and continuous improvement. A key success factor is establishing a feedback loop where investigator overrides and corrections are used to retune the AI's classification models, ensuring the system adapts to your organization's specific terminology and risk profiles over time.