SASA Pratibimbh OS
AI-Native Digital Twin Operating System for Predictive Prevention
Transform reactive infrastructure management into real-time predictive prevention. SASA Pratibimbh fuses physical telemetry from ultra-low-power Kavach edge hardware with six cloud AI modules to automate structural safety, sustainability, and lifecycle decision-making.
The Industry Challenge
Why traditional engineering processes fail to meet modern structural schedules.
Physical infrastructure monitoring is traditionally fragmented, reactive, and reliant on manual visual inspections. Critical stress, tilt, seismic shifts, and concrete degradation are often detected only after structural damage or catastrophic failure has occurred.
Operational Pipeline
Edge Telemetry Mesh (Kavach)
Ultra-low-power Kavach edge hardware captures live strain, tilt, vibration, and environmental telemetry from physical structures.
Cloud Ingestion & Processing
Physical telemetry streams into Pratibimbh cloud infrastructure in real time with end-to-end encryption.
6 Specialized AI Modules
Dhruv (Sensor Mesh), Raksha (Safety & Code), Prakriti (Sustainability), Hriday (Health Analytics), Sankalp (Lifecycle Planner), and Manas (Predictive PINN Engine) analyze data concurrently.
Predictive Prevention
Automated alerts, real-time 3D twin visualization, and structural degradation forecasts empower proactive maintenance.
Core Module Capabilities
Dhruv — Sensor Mesh & Telemetry
Ingests physical telemetry streams from Kavach edge hardware across tilt, strain, accelerometers, and ambient sensors.
Raksha — Safety & Compliance
Real-time structural code compliance monitoring, IS-Code threshold validation, and instant hazard alerts.
Prakriti — Sustainability Analytics
Monitors carbonation, ambient thermal stress, environmental impact, and embodied carbon metrics.
Hriday — Structural Health Index
Computes continuous structural integrity scores, fatigue limits, and concrete degradation trajectories.
Sankalp — Lifecycle Maintenance Planner
Generates predictive maintenance schedules, automated repair cost projections, and asset longevity strategies.
Manas — Autonomous PINN Engine
Physics-informed neural network engine executing real-time stress-strain field approximations and failure prediction.
Infrastructure Bridge Telemetry Pilot, India
SASA Pratibimbh parsed 2.4 million telemetry data points per day from Kavach edge sensors installed on a key transport bridge, detecting micro-strain anomalies 14 days before visible surface cracking appeared.