Safety Pattern Detection

AI-powered clustering detects systemic safety risks before accidents happen. Identify patterns across observations that might appear isolated.

How It Works

Observation Extraction: Safety incidents are automatically extracted from daily logs and meeting minutes
Vector Clustering: Similar observations are grouped using AI embeddings and semantic similarity
Pattern Recognition: Clusters are analyzed for systemic issues that require attention
Automatic Alerts: Detected patterns are surfaced in the Intelligence Feed with severity ratings

Example Patterns

🪜 Equipment Failures

"Ladder slipped in Zone A" + "Ladder broken in Zone B" = Equipment maintenance issue

Pattern detected across 3 observations over 2 weeks. Linked to supplier: SafetyFirst Equipment.

🦺 PPE Violations

Multiple hard hat violations across different crews = Training gap

Pattern detected across 5 observations over 1 week. Suggests need for safety briefing.

⚠️ Near Misses

Repeated close calls in specific areas = Hazardous conditions

Pattern detected in Zone C. Suggests environmental hazard requiring immediate attention.

Benefits

Proactive Prevention: Fix systemic issues before injuries occur, not after
Liability Reduction: Demonstrate proactive safety management to insurers and regulators
Supplier Accountability: Link patterns to specific subcontractors for performance reviews
Compliance: Maintain comprehensive safety records with automatic pattern detection

Real-World Example

Over a 3-week period, Forge detected 4 separate ladder-related incidents across 2 different zones. While each incident appeared minor in isolation, the pattern revealed a systemic equipment maintenance issue with the ladder supplier. The PM was alerted, inspected all ladders, and discovered widespread wear on safety locks—preventing a potential serious injury.