AI Integration for Cority Safety Reporting

The integration connects at three key surfaces within Cority's data model and user workflows:

• Frontline Reporting Interface: AI acts as a conversational layer, allowing workers to report incidents, hazards, or near-misses via voice notes, text messages, or simple descriptions. This interface, often a webhook-integrated chatbot or mobile app, captures unstructured input before the user ever sees a Cority form.
• Data Structuring Engine: Using natural language processing (NLP), the AI parses the worker's description to auto-populate critical fields in the underlying Cority Incident, Observation, or Hazard object. This includes extracting entities for location, equipment involved, people affected, injury type (mapping to OSHA recordability logic), and immediate actions taken.
• Report Generation & Workflow Trigger: Once structured, the AI can draft the formal narrative for the report, suggest an initial severity classification based on historical data, and automatically trigger the appropriate Cority workflow—whether that's routing for supervisor review, creating a linked Investigation record, or assigning corrective actions.

In practice, this means a field operator can describe a near-miss ('slipped on an oil patch near compressor C-101, caught myself on the railing, no injury') and within seconds have a fully drafted Safety Observation in Cority with the hazard categorized, location tagged to the asset hierarchy, and a preliminary risk score assigned. The AI ensures data quality and consistency at the point of entry, which is critical for reliable analytics and compliance reporting downstream. The technical implementation typically involves a middleware layer (like an AI orchestration platform) that handles the NLP, calls Cority's REST APIs to create and update records, and manages the approval queues and audit trails.

Rollout is phased, starting with a pilot for non-injury incidents to build trust and refine prompts. Governance is essential: all AI-generated content is flagged in the Cority audit trail, and critical classifications (like recordability) always route for human supervisor confirmation. The value isn't just in saving minutes per report; it's in increasing report volume and quality by lowering the barrier to entry, which provides a richer, more proactive dataset for your safety program and helps move from lagging to leading indicators.

The integration architecture is event-driven, anchored on Cority's Incident and Observation APIs. The flow begins when a frontline worker submits a report via a mobile app, voice note, or simple web form. This unstructured input (text or audio) is sent to a secure queue. An AI agent listens to this queue, transcribes audio if needed, and uses a specialized LLM prompt to extract structured data: incident type, location, date/time, involved personnel, equipment, and a clear narrative. The agent then maps this data to the required fields for a Cority Incident or Safety Observation object, including custom fields for site-specific data.

Before creating the record, the system performs a confidence check and can route low-confidence extractions for human-in-the-loop review via a simple dashboard. Once validated, the agent calls the Cority REST API to create the draft record, attaching the original audio/text as a document. Key workflows are then triggered automatically: assigning the case to the correct EHS coordinator based on location, setting priority based on extracted severity keywords, and populating related objects like Actions or Investigations. This reduces report creation from a 15-30 minute manual data entry task to a sub-60-second automated flow, ensuring critical details are captured immediately and consistently.

Governance is built into the data layer. All AI interactions are logged with the original prompt, extracted data, and model version for auditability. The system respects Cority's existing role-based access controls (RBAC), so agents only create and update records for sites and modules the submitting user can access. A weekly feedback loop analyzes corrections made by EHS professionals to the AI-generated drafts, which are used to fine-tune the extraction prompts, creating a continuous improvement cycle that increases accuracy and reduces review workload over time.

Integrating AI into Cority's safety reporting workflows requires a governance-first architecture. This typically involves a dedicated middleware layer that sits between frontline input channels (mobile apps, voice, simple text) and the Cority API. This layer handles the initial AI processing—transcribing voice, extracting entities from free text, and structuring data into Cority's required objects like Incident, Observation, or NearMiss—before submitting a draft record. All AI-generated content is tagged with a source (ai_structured) and stored in an immutable audit log alongside the original user submission. Access to modify AI-drafted reports is controlled via Cority's existing Role-Based Access Control (RBAC), ensuring only authorized investigators or supervisors can finalize entries.

A phased rollout is critical for adoption and risk management. Start with a pilot program targeting a single, low-risk report type, such as safety observations or near-misses, within one facility. Configure the AI to operate in a human-in-the-loop (HITL) mode where every AI-drafted report is presented as a suggestion for a supervisor to review, edit, and approve before creation in Cority. This builds user confidence and generates training data to refine prompts. Success metrics for this phase focus on time-to-report (reducing hours to minutes) and data completeness (increasing structured field population). Subsequent phases can expand to more complex incident reports, introduce automated severity triage, and eventually enable direct submission for low-severity events based on confidence scores.

Security is non-negotiable. All audio and text data processed by AI models should be encrypted in transit and at rest. For on-premise or VPC deployments, ensure AI inference occurs within your own cloud tenancy, with no data persisted by external LLM providers. Implement prompt shielding to prevent injection attacks and output validation against Cority's data schemas to block malformed records. Finally, establish a clear rollback protocol. This includes the ability to disable AI features per user group or report type via the middleware configuration, ensuring operations can continue on the native Cority platform if needed, with all historical data remaining intact and traceable.