The AI Sees It 11 Days Before It Happens.
The OSHA recordable happened on a Tuesday. The data that predicted it had been accumulating for 11 days: 3 near-misses in Zone 3, a new worker on day 19, overtime hours climbing. POD's AI safety watchdog had a prediction score of 91% on the prior Friday. Nobody saw it — because their tools only counted incidents after they occurred.
The Problem With Counting After the Fact
Reactive safety management documents what went wrong. Predictive safety management prevents it. These are not the same discipline.
Lagging metrics only count what already happened
TRIR, LTIR, and recordable counts document injuries after they occur. They measure the past. They cannot prevent the future. Your safety culture deserves more than a body count.
New workers carry invisible elevated risk for 30 days
Workers in their first month on a new site are statistically overrepresented in incident reports regardless of prior experience. Most systems track their orientation date. None track their risk window.
Near-miss velocity accelerates before every serious incident
Near-misses cluster in the weeks before recordables — not randomly, but with measurable acceleration. Without velocity tracking, each near-miss looks isolated. Together, they tell a story.
How the AI Safety Watchdog Works
Signals feed the AI from every corner of the site
Near-miss reports, new worker orientation records, overtime hours, inspection findings, and schedule pressure signals all stream into the AI safety watchdog continuously.
The AI synthesizes signals into a prediction score
When signals cluster above individual thresholds, the AI calculates a zone-specific prediction score with confidence percentage — based on pattern analysis across the full project dataset.
Actionable alerts arrive before the incident does
Safety managers receive a prediction with location, confidence, contributing factors, and recommended interventions — days before the statistical window for an incident peaks.
Every Signal Feeds the Prediction
Near-miss velocity, new worker exposure, fatigue accumulation, and overtime signals converge into one AI brain — and one prediction arrives before the incident does.
Safety Intelligence That Rewrites the Standard
NearMissTracker and NewWorkerRiskWindow — the leading metrics that turn incident prevention from intention into intelligence.
New Worker Risk Window
PODWhat Predictive Safety Actually Delivers
AI synthesizes what no person could track manually
Monitoring near-miss velocity, new worker exposure hours, fatigue accumulation, and schedule pressure simultaneously across a 200-person crew is not humanly possible. The AI does it continuously.
Always-on monitoringPredictions arrive days before peak risk window
The 11-day pattern that preceded the last recordable? POD would have surfaced a prediction on day 7 with 87% confidence. That is the window that changes outcomes.
87% prediction confidenceNew worker risk windows tracked automatically
Every new worker entering the site starts a risk-window clock. Orientation completeness, task complexity, and exposure hours are tracked for 30 days — with supervisor alerts when any threshold is crossed.
30-day risk trackingFatigue and schedule pressure correlations revealed
The months with the most overtime are the months with the most near-misses — but the lag is never obvious in real time. POD tracks the correlation continuously and alerts when the risk window opens.
Fatigue-safety correlationThe Safety Intelligence Platform
AI Safety Watchdog
Specialized AI agent that synthesizes every leading safety signal into a single prediction score with zone and confidence data.
Near-Miss Velocity Tracking
Measures the rate and acceleration of near-miss events — not just the count. Acceleration is the warning sign.
New Worker Risk Window
Tracks every new worker through their first 30 days with orientation completeness, task exposure, and supervisor alert triggers.
Schedule Pressure Safety Index
Quantifies the inverse relationship between schedule pressure and safety performance — surfacing the safety cost of rushing.
Fatigue-Incident Correlation
Measures accumulated fatigue across shifts and correlates it against near-miss frequency to predict elevated risk windows.
Real-Time Alerts
Predictions delivered before the risk window peaks — with specific zone, confidence score, and recommended interventions.
“We had a safety director who knew something was off in Zone 3 — but couldn't prove it. POD's near-miss velocity gave him the number. We made the intervention. The recordable that was coming never arrived.”
— Project Executive, Top 50 ENR General Contractor
Frequently Asked Questions
See the Incident Coming. Before It Does.
NearMissTracker, NewWorkerRiskWindow, and AI-powered safety prediction — live with your project data.