At Enrovix AI, our engineering focus is grounded in practical urban mechanics: using lightweight, zero-effort passive spatial telemetry to solve everyday friction points for drivers across India.
Rather than relying on intrusive hardware installations or asking drivers to manually type queue reports while driving, our QuickNG platform utilizes anonymized, spatial dwell-time analytics to estimate CNG pump waiting times.
1. Why Crowdsourced Manual Reporting Fails
In the early days of transit apps, platforms relied on manual user submissions ("How long is the line right now?"). In real-world driving environments, manual input fails for three basic reasons:
- Safety Hazards: Typing numbers on a smartphone while navigating a busy fuel station lane is dangerous.
- Reporting Friction: Drivers forget to submit updates after completing their fuel fill.
- Data Staleness: A manual report submitted 20 minutes ago is useless when 10 new vehicles pull up in the interim.
2. Passive Spatial Telemetry Architecture
QuickNG addresses this by analyzing passive spatial movement data. When vehicles approach a station geofence, anonymized velocity vectors indicate deceleration into the station approach zone, dwell duration inside the queue boundary, and acceleration back onto the roadway.
By clustering spatial data points across multiple active devices, the platform calculates probabilistic wait time windows (e.g., "8–12 mins wait") without requiring any action from the driver.
🌐 Practical & Grounded Engineering
We do not promise magical instantaneous predictions. Instead, we provide reliable, statistically verified dwell-time confidence intervals that help drivers make informed decisions on the road.