SOLUTIONS · AI Risk Prediction
AI algorithm-based
risk prediction system.
Using accumulated data and AI models, it analyses per-worker risk scores and per-zone/process risk levels to predict accident patterns before they happen.
Data alone is not enough to identify risk
AI-based risk prediction solves the limitations of conventional approaches.
Without numerical measures of which workers are at risk, managers are forced to rely solely on experience.
It is difficult to quantify which zones or processes are dangerous, making proactive prevention impossible.
Post-accident recurrence analysis is unsystematic, so the same type of accident keeps happening.
How does it work?
A 3-stage AI pipeline from data collection through to corrective action.
Data collection
Behavioural patterns, environmental data, and biometric signals are collected from wearables, BLE beacons, and sensors over a 60-minute rolling window.
AI risk analysis
The 8-axis weighted confidence engine calculates a risk score (0–100) and automatically classifies the risk level.
Alerts & action
Immediate action messages are delivered to managers based on P0–P4 priority, and reports are generated automatically.
8-axis confidence analysis engine
Not a simple number — a comprehensive weighted analysis across 8 axes that produces an accurate risk score.
Key features
AI analyses every risk factor across worksite safety.
Per-worker risk score
Comprehensively analyses each individual's biometric data, behavioural patterns, and PPE compliance history to calculate a real-time risk score of 0–100.
Per-zone and per-process risk analysis
Analyses accident history by zone, hazard-zone entry frequency, and environmental data to generate a process-level risk map.
Risk pattern report
Automatically analyses risk patterns by time of day, zone, and worker group, and delivers trend changes as a report.
Recurrence prevention analysis
AI analyses the root cause of each accident type and provides early warnings of similar pattern recurrence to support proactive response.
Immediate-action priority system
Automatically classifies incidents into 5 priority levels based on risk severity to support efficient response.
SOS · fall detection
Abnormal heart rate · stress
Repeated hazard-zone entry
Repeated PPE non-compliance
Minor anomaly trend
Outcomes you can expect after deployment
Quantitative results expected from deploying the AI risk prediction system.
Accident prevention through early discovery of risk patterns
Improvement in proactive accident prevention response rate
Safety decisions based on data, not experience alone
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