AI Integration for Cority Safety Performance

The integration connects to Cority's core data objects—Incident records, Observation reports, Audit findings, and Training completion logs—via its REST API or direct database connectors. An AI layer ingests this structured data alongside unstructured text from incident narratives and observation notes. Using models fine-tuned on EHS taxonomy, the system calculates not just traditional lagging indicators like TRIR and DART, but synthesizes predictive leading indicators. For example, it can correlate a spike in near-miss reports about a specific work area with a recent change in contractor crews and flag it as a high-probability incident scenario for the next week.

Implementation typically involves a sidecar service architecture where the AI service runs parallel to Cority, polling for new data or responding to webhooks. This keeps the core platform stable while enabling advanced analytics. Key workflows include:

• Automated Benchmarking: AI compares site-level safety metrics against internal historical trends, corporate goals, and anonymized industry indices, highlighting outliers.
• Driver Analysis: For a site with a rising incident rate, the AI analyzes dozens of potential drivers (training gaps, audit scores, maintenance backlog, weather data) to rank the most likely contributing factors.
• Dynamic Risk Scoring: Instead of a static quarterly risk register, AI updates location or task risk scores daily based on the latest observations, incidents, and even external data like local weather warnings.

Rollout is phased, starting with a pilot site where the AI generates insights in a read-only dashboard separate from Cority. This allows safety leaders to validate predictions and build trust. Governance is critical: all AI-generated risk scores or recommendations are logged with explainability trails (e.g., "This high-risk flag was triggered due to 3 recent LOTO procedure deviations and 1 minor injury in the same department"). The final phase embeds these insights directly into Cority user workflows—for instance, automatically elevating the priority of a scheduled audit for a location whose predictive risk score exceeds a threshold, or suggesting specific refresher training modules in the Cority Training Management system for a crew showing behavioral trends linked to past incidents.

The integration architecture is built on Cority's primary safety performance objects and their related APIs. Key data sources include:

• Incident Records (including severity, body part, nature of injury, and direct/root causes) for trend analysis.
• Observation & Inspection Data for leading indicator correlation.
• Employee & Organizational Data (job titles, departments, sites, tenure) for demographic benchmarking.
• Historical Metric Tables storing calculated TRIR, DART, LTIR, and severity rates over time.
• Corrective Action (CAPA) Records to measure the effectiveness of past interventions.

The AI layer ingests this data via scheduled batch pulls from Cority's REST API or listens for webhook events on record creation/update. For predictive analytics, a time-series dataset is constructed, aligning incident metrics with operational data (e.g., production volume, training hours, observation counts) to identify hidden drivers.

Integration points are designed for minimal disruption, acting as a read-and-enrich layer. A typical workflow:

1. Data Extraction & Vectorization: A secure middleware service (e.g., an Inference Systems agent) authenticates via OAuth 2.0, queries the Cority API for relevant records from the last 24-36 months, and creates vector embeddings of incident narratives and observation text.
2. Analytical Processing: The AI model runs against this prepared dataset, performing:
   • Cluster Analysis to group similar incidents beyond standard classification codes.
   • Driver Correlation to statistically link metric fluctuations with operational variables.
   • Benchmarking against internal goals and, where permissible, anonymized industry indices.
   • Predictive Scoring to flag sites, departments, or job roles at elevated risk in the upcoming quarter.
3. Insight Injection: Results are written back to Cority as:
   • Custom Objects storing AI-generated risk scores and driver summaries, linked to sites or departments.
   • Dashboard Widgets via Cority's BI framework, showing predictive metrics alongside traditional ones.
   • Automated Tasks or Alerts in the Action Tracking module, prompting proactive reviews for high-risk areas.

Governance and rollout are critical. The integration should be deployed in phases:

• Phase 1 (Read-Only Pilot): Connect to a single site or business unit's data. Generate insights in a separate dashboard for validation by EHS analysts. No writes back to Cority.
• Phase 2 (Controlled Enrichment): After validating AI accuracy and business relevance, begin writing custom risk scores and narrative summaries to dedicated fields in Cority, with clear data lineage tags.
• Phase 3 (Workflow Integration): Embed AI-generated recommendations into existing Cority workflows, such as automatically suggesting focus areas for the next site inspection based on predictive scores.

All data flows are logged, and AI-generated content is flagged for traceability. This architecture ensures the AI augments—rather than replaces—the safety professional's judgment, providing a data-driven layer on top of Cority's robust record-keeping.

Implementation begins by connecting to Cority's core data objects via its REST API or direct database connection, focusing on the Incident Management, Risk Assessment, and Observation modules. The AI layer ingests structured fields (e.g., incident type, severity, department) and unstructured narratives to build a unified safety data lake. A critical governance step is establishing a read-only service account with scoped permissions, ensuring the AI system cannot modify source records. All data flows are logged for a full audit trail, and Personally Identifiable Information (PII) is masked or excluded before processing to maintain privacy compliance.

A phased rollout minimizes risk and builds trust. Phase 1 focuses on a single, high-value workflow, such as automated weekly executive summaries that explain TRIR fluctuations and highlight leading indicator trends. This provides immediate value without altering user workflows. Phase 2 introduces interactive, role-based copilots—for example, a Site Safety Manager Copilot that answers natural language questions like, 'What were the top three root causes for recordables in Q3, and which controls are failing?' This agent would be surfaced via a secure, embedded web component in Cority or a companion dashboard, with access strictly governed by Cority's existing user roles and permissions.

For predictive analytics and driver analysis, the system employs a human-in-the-loop approval step before any high-stakes recommendations—like predicting a site is trending toward a severe incident—are shared. These insights are presented as 'flagged for review' within a dedicated AI Insights module in Cority, requiring a safety leader to acknowledge and act. This controlled approach ensures AI augments rather than automates critical judgment. Finally, a continuous evaluation framework monitors model performance for drift against actual safety outcomes, with regular retraining cycles scheduled during low-activity periods to avoid impacting system performance.