Our AI-Engine is an end‑to‑end safety engine combining advanced monitoring, AI video analytics, RTLS, and automated JSA to reduce incidents and raise compliance at enterprise scale.
Value Propositions
- Unified tech stack: Fuses advanced monitoring, AI video analytics, and RTLS into a single pane for HSE and operations.
- Deep‑learning core: Models learn site‑specific risk patterns—from close calls to near‑miss hotspots—so detection gets smarter over time.
- Proactive by design: Anticipates hazards and unsafe behaviors before they escalate; not just retrospective reporting.
- Real‑time intervention: Instant, role‑based alerts with guided actions to supervisors, safety officers, and frontline teams.
- Instant compliance reporting: Automated evidence trails for audits, toolbox talks, JSAs, and HSE reporting.
- Built for high‑HSE sectors: Construction, energy, manufacturing, logistics—where safety is non‑negotiable.
- Scales across sites: Standardize safety across projects, regions, and contractors with enterprise controls.
- Tangible outcomes: Fewer accidents, saved lives, lower costs, higher productivity and community uplift.
- End‑to‑end platform: Integrated safety, security, and monitoring—more than a single tool.
Fetures of Leap AI-Engine ?
- AI Video Analytics: PPE detection, line‑of‑fire, fall risks, zone breaches, equipment‑human proximity, unsafe posture, vehicle‑pedestrian conflicts, and custom events.
- RTLS (Real‑Time Location Services): Sub‑meter tracking of people, vehicles, and assets; geofenced zones; dwell‑time and mustering.
- JSA Automation: Digital Job Safety Analysis with dynamic hazard libraries, AI‑assisted controls, and auto‑generated permits and checklists.
- Compliance Engine: Evidence capture (video snapshots, timestamps, locations), audit logs, and report packs mapped to HSE frameworks.
- Playbooks & Workflows: Role‑aware SOPs, escalation paths, and post‑incident debriefs with root‑cause insights.
How does it work
- Ingest: Cameras stream via RTSP into the video pipeline.
- Infer: An AI model runs on edge/cloud GPU, performing object detection to spot policy violations (e.g., PPE, zone breach, proximity).
- Decide: Detections are matched against a site rule library with confidence thresholds, and false‑positive suppression.
- Alert: A violation event is created and pushed to the real‑time dashboard and emailed to the responsible officer with a snapshot and short clip link.
