AI Integration for Cority Hazard Identification

The integration typically connects at three key points within Cority's data model: the Hazard Register, Job Safety Analysis (JSA) modules, and Incident/Inspection Report objects. An AI service ingests free-text descriptions, observation notes, and corrective actions from these sources via Cority's REST API or a scheduled data export. The core AI task is entity resolution—recognizing that 'slip hazard near Bay 3 door' from a floor inspection and 'wet floor incident at loading dock entrance' from an incident report likely reference the same underlying risk. This creates a unified, deduplicated view that manual reviews often miss.

Implementation involves a middleware layer that queues incoming records, calls an LLM for classification and summarization, and posts enriched data back to a custom object or a dedicated 'AI-Hazard Correlation' field within the standard Hazard Register. A practical workflow: when a new JSA is submitted, the integration triggers, analyzes the described tasks and identified hazards, and checks for similar entries based on location, equipment ID, and hazard type. It can then suggest a link to an existing hazard record or recommend creating a new one with a pre-populated risk score based on historical frequency and severity data from linked incidents.

Rollout should be phased, starting with a single site or hazard type (e.g., ergonomic risks). Governance is critical: all AI-suggested consolidations and priority scores should be routed through an approval workflow, requiring a safety manager's review before updating the master register. This human-in-the-loop design maintains accountability and allows the AI to learn from corrections. The audit trail must log the original source records, the AI's reasoning, and the approving user, ensuring full traceability for compliance audits.

The integration architecture connects to three primary Cority data sources via its REST API and webhook system: Job Safety Analysis (JSA) records, inspection findings, and incident reports. A middleware layer ingests this unstructured text—hazard descriptions, corrective actions, witness statements—and normalizes it into a unified schema. This data is then vectorized and indexed in a dedicated vector database (like Pinecone or Weaviate) separate from Cority's operational store, enabling semantic search across all hazard entries regardless of source module. The AI layer, typically a hosted LLM such as GPT-4 or Claude, is invoked via secure API calls to perform core tasks: clustering similar hazards, de-duplicating entries, and assigning a composite risk score based on extracted frequency and severity indicators.

In practice, the workflow is event-driven. When a new JSA is submitted or an inspection is closed in Cority, a webhook triggers the pipeline. The AI processes the new entry, compares it against the existing vector index, and determines if it represents a new unique hazard or a recurrence of an existing one. For recurrences, it automatically updates the frequency count and severity assessment in the unified hazard register—a custom object or external data store linked back to Cority. High-priority hazards, or those with rising recurrence trends, can trigger automated alerts or create follow-up tasks in Cority's action tracking module. The system maintains a full audit trail, logging all AI-generated decisions and modifications for review by EHS personnel.

Rollout is typically phased, starting with a single data source (e.g., JSAs) and a pilot site. Governance is critical: a human-in-the-loop review step is configured for the first 90 days to validate AI clustering and scoring before automatic updates are made to the master register. The integration runs on a separate, secure infrastructure, ensuring no impact on Cority's performance while allowing for scalable processing of large text volumes. This architecture provides the analytical horsepower to transform scattered observations into a prioritized, actionable hazard register without replacing the trusted Cority system of record.

The integration architecture is designed to treat Cority as the system of record. AI agents operate on a read-copy-write pattern: they pull hazard data from the Cority API (e.g., HazardRegister, JSA, Inspection, Incident objects), process it in a secure inference environment, and write back structured updates—like de-duplicated entries, consolidated descriptions, or recalculated risk scores—as new records or updates via the Cority API. All AI-generated content is stored within Cority's native audit trail, maintaining a single source of truth. API calls are authenticated via OAuth 2.0 and respect Cority's role-based permissions, ensuring agents only access data permissible for the service account's assigned role.

A human-in-the-loop approval step is critical for high-stakes changes. For example, when the AI proposes merging two hazard records or significantly altering a risk priority, it can create a task in Cority's Action Tracking module for an EHS specialist to review and approve. This governance layer is configurable per hazard type or risk level. The AI's reasoning—such as the key phrases that triggered a match or the severity factors considered—is logged as a comment or attached document for full transparency. This balances automation with expert oversight, building trust in the system.

Rollout follows a phased, risk-based approach. Phase 1 targets a single, high-volume data stream, such as consolidating hazards from JSAs, in a single facility or business unit. This allows tuning of de-duplication logic and validation of business impact (e.g., reduction in redundant entries). Phase 2 expands to other data sources (inspections, incident reports) and introduces automated risk scoring. Phase 3 scales the integration across the enterprise, leveraging lessons learned. Each phase includes defined success metrics, like % reduction in duplicate hazard entries or time saved per JSA review, measured within Cority's reporting. This iterative approach de-risks the implementation and demonstrates incremental value, securing stakeholder buy-in for broader deployment.