AI Integration with VelocityEHS Ergonomics

AI connects directly to the core data objects and workflows within the VelocityEHS Ergonomics module. It acts on three primary surfaces:

• Assessment Data: AI analyzes free-text observations, posture codes (e.g., RULA, REBA scores), and task duration from ergonomic assessments to identify high-risk patterns that manual review might miss.
• Recommendation Engine: Based on historical control effectiveness within your VelocityEHS instance, AI suggests prioritized engineering controls (e.g., adjustable workstation specs), administrative controls (job rotation schedules), or PPE recommendations, auto-populating the corrective action fields.
• Risk Register Integration: AI continuously scores and updates ergonomic risk levels in the central risk register by correlating assessment data with incident reports of musculoskeletal disorders (MSDs) and absenteeism data, ensuring risks are dynamically prioritized.

The implementation typically uses a secure API layer between VelocityEHS and the AI service. When a new ergonomic assessment is submitted or updated, a webhook sends the assessment payload (including text, scores, and employee job code) to an inference queue. An AI agent processes this data to:

1. Extract and codify unstructured observations into standardized hazard categories.
2. Score severity and probability using a model trained on your industry's historical MSD data.
3. Generate a draft action plan with specific, measurable recommendations, referencing similar resolved cases from your VelocityEHS history. This enriched data is then posted back to the VelocityEHS Corrective Action object, creating a task for the ergonomics team to review, modify, and assign—turning a multi-day analysis process into a same-day workflow.

Rollout focuses on the assessment intake and review queue. Start by deploying AI as a "copilot" for ergonomists and site managers, where AI-generated analyses appear as draft suggestions within the assessment review screen, requiring human approval and edit. This builds trust and ensures governance. Over time, for low-complexity, repetitive assessments (e.g., standard office workstation setups), workflows can be configured for auto-approval and direct task creation, governed by pre-defined risk score thresholds. All AI actions are logged in the VelocityEHS audit trail, linking recommendations to the source assessment and model version for full traceability. The result is a scalable program where specialists focus on complex, high-risk cases while routine surveillance is automated, directly reducing the time from assessment to controlled risk.

The integration connects at two primary layers within the VelocityEHS platform: the Ergonomic Assessment data object and the Action Tracking system. AI agents ingest structured assessment data (e.g., REBA/RULA scores, task descriptions, body part discomfort surveys) and unstructured notes from field observations via the VelocityEHS API. This data is enriched with contextual metadata—such as job codes, departments, and historical incident rates—to create a comprehensive risk profile for analysis. The core workflow is event-driven: a completed assessment in VelocityEHS triggers a webhook to the inference layer, which processes the data, applies ergonomic risk models, and returns structured outputs.

Processing occurs in a dedicated inference pipeline where Large Language Models (LLMs) and specialized classifiers analyze the aggregated data. The system generates specific, evidence-based recommendations, classifying them as engineering controls (e.g., 'adjust workstation height to range X-Y'), administrative controls (e.g., 'implement job rotation every 2 hours'), or PPE suggestions. Each recommendation is linked to the source assessment data for traceability. The AI then scores and prioritizes these recommendations based on a composite risk score—factoring in the assessment severity, population size affected, and cost/feasibility estimates—before writing them back into VelocityEHS as pre-populated action items with assigned owners, target dates, and reference links.

Governance is maintained through a human-in-the-loop approval step before AI-generated actions are officially logged in the VelocityEHS Action Tracking module. This allows ergonomists or site managers to review, edit, or reject suggestions, ensuring professional oversight. All AI interactions are logged in an immutable audit trail, capturing the input data, model version, and rationale for each recommendation to support compliance and continuous improvement. The rollout typically starts with a pilot site, focusing on high-risk job assessments, before scaling to enterprise-wide deployment, ensuring the AI's recommendations align with company-specific ergonomic programs and control libraries.

A secure integration begins by treating the VelocityEHS platform as the system of record. AI agents should operate through a dedicated middleware layer that enforces role-based access control (RBAC) to the Ergonomic Assessment, Employee Health Record, and Task Library objects. All AI-generated recommendations—such as engineering controls or workstation adjustments—are written back to VelocityEHS as draft records, triggering the platform's native approval workflows and maintaining a full audit trail. This ensures all changes are traceable to a human-in-the-loop decision.

For governance, we implement a phased rollout starting with a pilot group of ergonomists and safety engineers. Phase 1 focuses on AI-assisted analysis of existing assessment data (e.g., Rapid Entire Body Assessment scores, employee symptom surveys) to generate draft narratives and control suggestions. This allows the team to validate outputs against expert judgment without disrupting live workflows. In Phase 2, the integration expands to automated risk prioritization, where the AI correlates assessment data with incident history to flag high-priority cases. Each phase includes defined performance metrics (e.g., reduction in manual data entry time, accuracy of control recommendations) and a clear rollback procedure.

Security is paramount when handling sensitive health data. The integration architecture ensures data never leaves your controlled environment unless explicitly configured for a hosted LLM, in which case all data is anonymized and transmitted over encrypted channels. We configure prompt guardrails to prevent the generation of medical diagnoses or treatment plans, strictly limiting output to ergonomic guidance and administrative controls. Regular model evaluations are scheduled to check for drift or degradation in recommendation quality, with results logged back to VelocityEHS as part of the quality management workflow.