AI Integration for Cority Lockout Tagout

AI integration connects directly to Cority's Equipment Master and LOTO Procedure data objects. The primary workflow begins when a new or updated LOTO procedure is submitted for approval. An AI agent, triggered via a webhook or scheduled job, analyzes the procedure's text and associated equipment tags. It cross-references the listed isolation points (e.g., valves, electrical disconnects) against the official plant equipment hierarchy and P&ID data stored in Cority to identify discrepancies, missing energy sources, or points that have been decommissioned. This automated validation happens before human review, flagging potential issues for the procedure author or EHS coordinator.

The second major integration point is within the Management of Change (MOC) workflow. When an MOC for equipment modification is approved and closed in Cority, an AI process automatically scans all active LOTO procedures linked to that equipment or area. It assesses the impact of the change—such as a new valve, relocated panel, or changed energy source—and generates a task for the responsible party to review and update the affected procedures. This creates a closed-loop system where procedural documents are dynamically kept in sync with the as-built environment, a critical factor for audit readiness and frontline safety.

For rollout, this AI layer is deployed as a middleware service that calls Cority's REST APIs to read procedure and equipment data and write back validation results or tasks. Governance is managed through Cority's existing Role-Based Access Control (RBAC); the AI's outputs and suggested changes appear as system-generated comments or tasks within the standard LOTO approval workflow, requiring final human sign-off. This ensures the EHS team maintains oversight while eliminating hours of manual cross-checking and reducing the risk of procedures being outdated the moment they are printed.

The integration architecture is built around Cority's core LOTO data objects: Equipment Records, Energy Isolation Points, LOTO Procedures, and Work Permits. An AI agent, deployed as a secure microservice, listens for events via Cority's API or webhooks—such as a new procedure draft, a work permit creation, or an equipment modification. The agent retrieves the relevant procedure text, equipment hierarchy, and historical lockout data, then uses a configured LLM to perform validation tasks like cross-referencing isolation points against the equipment's documented energy sources or flagging procedures that reference decommissioned assets.

For ongoing governance, all AI-generated validations and suggestions are logged as System Notes within the corresponding Cority record, creating a full audit trail. Critical recommendations, such as identifying a missing isolation point for a high-energy piece of equipment, can be configured to trigger a Mandatory Review Task assigned to a qualified safety engineer before a procedure is approved. The AI service itself operates under strict role-based access controls (RBAC), mirroring Cority's permissions, and only processes data necessary for the specific validation task, never retaining it post-analysis. This setup ensures the AI acts as a copilot, enhancing human review without bypassing established approval workflows.

Rollout typically follows a phased approach: starting with a pilot on non-critical equipment procedures to tune prompts and build trust, then expanding to cover high-risk assets and integrating with the change management (Management of Change) module. The final architecture enables proactive compliance, where AI automatically reviews new plant modification requests in Cority, identifies affected LOTO procedures, and flags them for update—turning a manual, after-the-fact checklist into an integrated, preventive control loop.

AI integration for Cority LOTO is implemented as a recommendation and validation layer, not an autonomous control system. The core architecture involves:

• API-first integration: AI services connect to Cority's Equipment, Procedures, and Energy Isolation Point objects via REST APIs to read existing data and write suggestions back as draft updates or review tasks.
• Human-in-the-loop workflows: Every AI-generated suggestion—such as a new isolation point or a procedure update—creates a task in a designated review queue (e.g., LOTO_AI_Review) for a qualified safety engineer or maintenance planner to approve, modify, or reject.
• Audit trail enforcement: All AI interactions are logged as system notes on the relevant Cority records, capturing the prompt, model response, reviewer identity, and final action, creating a defensible audit trail for compliance and internal audits.

A phased rollout minimizes operational risk and builds organizational trust:

1. Phase 1: Read-Only Analysis (Weeks 1-4): The AI system runs in a parallel, non-production environment, analyzing historical LOTO procedures against equipment hierarchies and change logs. It generates "what-if" reports highlighting potential gaps or outdated steps without making any changes in Cority, allowing the team to calibrate its accuracy.
2. Phase 2: Assisted Drafting in Sandbox (Weeks 5-8): For new procedures or major revisions, engineers use a dedicated sandbox interface where the AI suggests a complete draft procedure based on equipment tags, P&IDs, and similar historical LOTOs. All outputs remain in the sandbox until manually vetted and copied into the live Cority environment by the responsible party.
3. Phase 3: Proactive Alerts & Change Tracking (Ongoing): The AI monitors Cority's Management of Change (MOC) and Work Order modules for plant modifications. When a change potentially impacts an existing LOTO, it automatically generates a review task for the procedure owner, linking the MOC record and suggesting specific updates, ensuring procedures stay current with plant state.

Governance is anchored in role-based access control (RBAC) and clear accountability. Access to approve AI suggestions is restricted to roles like LOTO Coordinator or Area Safety Lead. A quarterly review of the AI's suggestion log—tracking approval rates, common rejection reasons, and any post-approval incidents—is conducted to continuously refine prompts and ensure the tool aligns with site-specific safety rules. This controlled, audit-friendly approach ensures AI augments the stringent safety culture around LOTO without introducing ungoverned risk.