All use cases
- manufacturing
- Safety & Security
- representative
Autonomous Worker Safety & PPE Compliance Monitoring in Heavy Plants
A primary steel manufacturing mill operating high-risk melt shops, rolling mills, and heavy material handling yards equipped with 250 existing CCTV security cameras.
Runs onArjuna- PPE compliance rates increased
- 68%PPE compliance rates increased
- reduction in lost-time workplace injury incidents
- 72%reduction in lost-time workplace injury incidents
How the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Worker safety non-compliance (missing hard hats, high-visibility vests, safety goggles, or thermal suits) caused high workplace injury rates.
- Safety officers could not monitor 250 video feeds manually in real time, catching safety violations only after accidents occurred.
- Installing specialized AI safety cameras across the 50-acre plant was cost-prohibitive.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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1
Retrofit RTSP CCTV Stream Ingestion
Arjuna connects to all 250 existing CCTV camera RTSP video streams without requiring hardware replacements.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- PPE compliance rates increased from 68% to 99.4% within 30 days of deployment.
- 72% reduction in lost-time workplace injury incidents.
- Zero capital expenditure spent on new cameras by retrofitting 100% of existing CCTV infrastructure.
Capabilities this relies on
- vision perception
- anomaly detection
- alert routing
- policy compliance
- in boundary deployment
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