Triplet OZO
Safety & Risk AI
Detect early warning signs before they escalate into accidents. Deliver immediate alerts on-site, and actionable notifications to managers for informed decision-making. The moment a risk is identified,
Triplet OZO enables both on-site teams and administrators to respond without delay.
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Recurring Problems
Accidents seem sudden,
but small deviations always precede them.
- Restricted-zone breaches must be stopped instantly, but constant monitoring isn’t feasible.
- PPE compliance policies crumble the moment workers cut corners for convenience.
- By the time you review the record, it’s already too late — you’re looking at an incident, not a warning.
How Triplet AI
detects risk
Triplet OZO records on-site movements and organizes them into a format managers can act on immediately.
Not complex numbers — an intuitive view of what’s happening in the space right now.
- Real-time risk signal detection
- Cumulative risk event logging
- Analyze recurring patterns to prevent recurrence
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From real-time monitoring to recurrence prevention
Spot abnormal behavior and early warning signs before incidents occur.
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Detection Categories
Detects every situation that can pose a risk to the worksite. Fire / Smoke / PPE non-compliance / Falls / Smoking / Phone use / Oil spills
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Fire
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Smoke
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Missing safety gear
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Falls
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Smoking
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Phone use
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Oil spills
+ More
Building benchmarks for decision
We help operators understand and act on risk signals occurring in the space.
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1. We observe warning signs.
Detect small deviations—smoke, intrusion, missing gear, falls—before they become incidents.
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2. We remember the patterns.
Accidents seem random, but risks repeat structurally. We remember signals just before risks occur, not after.
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3. We interpret why things change — as patterns.
Where warning signs start, where intervention lags, what conditions amplify risk—we organize on-site concerns into explainable risk patterns.
Real-World Applications
One miss can lead to major incidents, but constant monitoring isn't feasible.
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AI auto-detects pre-incident signals: fire, smoke, abnormal behavior.
Speaker alerts + manager notifications reduce on-site response lead time.
After Implementation
- Frame-level real-time risk signal detection
- 24/7 non-stop AI monitoring
After incidents, you have to rewind footage, and root cause analysis takes time.
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Event timestamps and zones are auto-logged.
Accumulate recurring risk patterns to establish prevention standards.
After Implementation
- Event-based auto-logging & search
- Risk pattern reports by zone and time
- Not post-incident tracking—proactive prevention
USE CASE
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GS Caltex
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Hansol Paper
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Starfield
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Suan Coffee
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