AI Integration for Cority Behavior Based Safety

AI connects directly to the observation record lifecycle within Cority. When a supervisor or peer submits a BBS observation—often via mobile app with free-text notes, photos, or checklist data—an AI agent can immediately analyze the submission. This analysis focuses on extracting key entities: the observed behavior (e.g., 'not wearing cut-resistant gloves'), the context (task, location, time), and the outcome (safe/at-risk). The AI then cross-references this against historical data to assign a predictive risk score and auto-categorize the observation into Cority's existing taxonomies, ensuring consistent data for downstream reporting.

The core workflow impact is in the coaching and closure loop. Instead of a generic task assignment, AI can draft a personalized coaching guide for the observer, suggesting talking points based on the specific at-risk behavior and the individual's past observation history. It can also recommend corrective actions from a library of proven controls and automatically log a follow-up task in Cority to verify effectiveness. For program managers, AI aggregates data across thousands of observations to surface leading indicators—identifying which critical behaviors are trending negatively or which departments have ineffective coaching closure rates—long before they manifest in lagging incident metrics.

Rollout is typically phased, starting with AI-assisted data enrichment to clean and categorize backlogged observation data, which improves the baseline for all existing Cority dashboards. The next phase adds real-time analysis for new submissions, with all AI-generated content (scores, categories, draft guides) written to dedicated custom fields in the Cority observation object for full auditability and optional human review. Governance is maintained through a prompt management layer that ensures coaching recommendations align with company safety principles and regulatory guidance, with all AI interactions logged in Cority's audit trail. This approach allows safety teams to scale proactive coaching without increasing administrative burden, directly linking behavioral data to preventative actions.

The integration connects at two primary points within Cority's data model: the BBS Observation record and the Corrective Action/Coaching workflow. An event-driven pipeline is established where new or updated observations trigger an API call to a dedicated AI service. The payload includes the observation's free-text description, observed behaviors, location, department, and any associated risk ratings. This service, built on Inference Systems' orchestration layer, uses a fine-tuned NLP model to analyze the narrative, classifying the criticality of the observed behavior, mapping it to known safe/unsafe behavior libraries, and extracting implicit context about task, environment, and potential consequences that may not be captured in structured fields.

Processed insights are written back to Cority via a secure API to populate custom fields on the observation record, such as AI_Behavior_Category, AI_Coaching_Priority_Score, and AI_Linked_Historical_Trend. High-priority observations can automatically generate draft Coaching Action records with AI-suggested discussion points and follow-up verification steps, routed to the appropriate supervisor via Cority's workflow engine. For trend analysis, a separate batch job runs nightly, aggregating processed observation data into a vector store. This enables a RAG (Retrieval-Augmented Generation) system that allows safety managers to query, in natural language, for patterns like "show me trends in line-of-fire violations in the packaging department over the last quarter" and receive a synthesized report with citations to specific observations.

Governance is embedded through a human-in-the-loop approval step before any AI-generated corrective action is finalized. All AI interactions are logged in a separate audit trail, capturing the original prompt, model response, and the user who approved the output. The architecture is deployed within the client's cloud environment (AWS/Azure/GCP), with the AI service accessing Cority data via a service account with strictly scoped API permissions, ensuring data never leaves the approved ecosystem. Rollout typically begins with a pilot on a single site's observation data, tuning the models on the organization's specific terminology and behavioral taxonomy before scaling enterprise-wide.

Phase 1: Pilot with a Controlled Data Slice Start with a single site or a specific high-risk department. Use a 3-6 month historical dataset of BBS observations, focusing on free-text observation notes, behavior categories, and linked corrective actions. This pilot phase validates the AI's ability to:

• Accurately categorize uncoded observations into Cority's standard behavior and sub-behavior taxonomies.
• Identify patterns between observed behaviors and subsequent coaching effectiveness scores.
• Surface correlations between behavioral trends and lagging indicators like recordable incident rates from the Incident Management module. The output is a validated, auditable analysis report, not live system writes.

Phase 2: Integrate with Guardrails & Human-in-the-Loop Upon validation, deploy a read-only AI agent that analyzes new BBS observations as they are entered. Implement critical guardrails:

• RBAC-Enforced Access: The AI agent's prompts and context are scoped by the user's existing Cority permissions (site, department).
• Approval Workflows: AI-generated insights—like a suggested critical behavior alert or a recommended coaching topic—are presented as drafts in a dedicated AI Suggestions queue within the BBS workflow for supervisor review and approval before becoming system records.
• Audit Trail: Every AI-generated suggestion is logged in Cority's audit module with a traceable session_id, linking it to the source observation data and the approving user.

Phase 3: Scale with Continuous Model Governance Expand the integration to additional sites and connect to broader datasets (e.g., training completion records, near-miss reports). This phase requires an ongoing LLMOps discipline:

• Prompt Management: Version-controlled prompts for different analysis tasks (trend detection, coaching recommendation) are managed externally, not hard-coded.
• Performance Monitoring: Establish a feedback loop where supervisors can flag inaccurate AI suggestions, triggering a review of the underlying model or prompt logic.
• Data Sovereignty: Ensure all processing for EU/other regulated entities occurs within approved geographic boundaries, with Cority data never persisted in external AI training sets. This structured rollout minimizes risk, builds organizational trust in AI-assisted insights, and ensures the integration enhances—rather than disrupts—the integrity of your safety management system.