Predictive Safety

AI Predicted This Incident
3 Weeks Before It Happened.

0
Day Avg Prediction Lead
24/7
AI Agents Analyzing
0%
% Incidents Preventable
0
Crystal Balls Needed

Reactive Safety Is Failing

You investigate incidents. You should predict them.

Safety programs react to injuries, not prevent them

Your safety program responds to incidents after they happen. Root cause analysis, corrective actions, stand-downs — all reactive. The injury has already occurred.

Leading indicators are not being tracked

TRIR, DART, and EMR are lagging indicators — they measure what already happened. Leading indicators like near-miss velocity, observation rates, and fatigue factors predict what will happen.

Pattern recognition requires data nobody has

Predicting incidents requires correlating safety data with schedule pressure, overtime, weather, crew changes, and dozens of other factors. Manual analysis of this data is impossible.

Seasonal and cyclical risks are untracked

Heat-related incidents spike in August. Fall incidents increase during facade work in wind season. These patterns exist in your data but nobody is analyzing them.

How POD Works

1

Speak Your Report

60-second voice input captures crew counts, activities, safety observations, and weather.

2

AI Structures Data

POD automatically maps your voice input to hundreds of KPIs and populates all required forms.

3

Dashboard Updates

Real-time metrics, trend detection, and AI-powered insights appear instantly.

The POD Advantage

AI safety prediction from daily report data

AI Safety Analysis Agent

Dedicated AI agent continuously monitors safety-related data: near-miss rates, observation trends, overtime hours, weather forecasts, and schedule pressure. Flags emerging risk patterns.

AI safety agent

Predictive Risk Scoring

Each project receives a daily predictive risk score combining 12+ factors. Rising scores trigger automatic alerts 21 days before the risk materializes into an incident.

21-day prediction

Leading Indicator Dashboard

Real-time visualization of leading safety indicators: observation rates, near-miss velocity, crew fatigue index, schedule-risk correlation. The data that predicts incidents.

Leading indicators

Intervention Recommendations

When risk scores rise, POD recommends specific interventions: "Add safety observer to Level 3 concrete pour. Reduce overtime for Crew B. Schedule stand-down after weather event."

Action recommendations

Predictive Safety Features

AI Prediction

AI safety monitoring AI predicts incidents 21 days before they occur.

Risk Scoring

Daily predictive risk scores from 12+ correlated safety factors.

Interventions

AI-recommended actions to prevent predicted incidents.

“POD AI safety monitoring flagged a "high risk convergence" on one of our projects — overtime increasing, safety observations declining, and a critical concrete pour scheduled during a heat advisory. We added safety staff. Nothing happened. That was the point.”

— Chief Safety Officer, National GC

Live Demo

Every KPI From a 5-Minute Voice Report

POD tracks hundreds of KPIs from a 5-minute voice report. Here are just 2 of them.

Schedule vs Budget

Schedule & Budget Performance

SPI 0.00CPI 0.00
25%50%75%100%$0$755K$1.5M$2.3M$3.0MJanFebMarAprMayJun
Planned Progress
Actual Progress
Planned Spend
Actual Spend
SPI0.00
CPI0.00
Sched Var+0.0%
Cost Var+0.0%
Schedule 3.0% behind planbudget 1.8% under target
PODMomentum Score

Momentum Score

POD
0
Accelerating — Velocity: 0.0
Dimensions
Schedule+0.0
Budget+0.0
Quality0.0
Safety+0.0
Momentum0
Velocity0.0
Accel+0.0
Project accelerating — momentum 78, velocity increasing by 3.0/period

These update in real time from a 5-minute voice report. No spreadsheets. No data entry.

Frequently Asked Questions

What If You Could Prevent the Next Incident Before It Happens?

Your data already contains the prediction. POD AI reads it for you.

Last updated: February 2026