AI engineering and the road-safety industry in one team — serving India, with delivery capability across the Gulf and ASEAN.
YNMDrishti was started by two people whose experience meets in the middle of the problem: enterprise analytics and AI delivery on one side, a Hyderabad road-safety products business on the other. One built the detection pipeline; the other knows the roads it inspects.
Has led analytics and AI delivery at HSBC, ITC Infotech, Infosys and Deloitte. Brings the AI engineering depth behind YNMDrishti's detection and audit pipeline.
Managing Director of YNM Pan Global Trade, a Hyderabad road-safety products company. Brings on-the-ground safety industry experience and the distribution network behind YNMDrishti's field operations.
We hold our own AI to the standard road authorities are held to: every result must be explainable, repeatable and traceable to its source. In practice that means versioned datasets, automated validation, drift monitoring and a model registry — so a detection made this month can still be reproduced and defended next year.
Training datasets are version-hashed, so we can always say exactly which images taught a model what it knows.
Images and labels pass automated validation checks before any training run is allowed to start.
Each model version lives in a registry with its metrics and lineage — promotion is a recorded decision, not a file copy.
Health, latency, and accuracy are monitored continuously, with alerts that reach us before they reach you.
The questions prospects ask us first, with the numbers behind the answers — who is behind YNMDrishti, where it is built, how the detection was trained and validated, and what happens to survey data once it leaves your vehicle.
Something we haven't covered? Ask us directly →An AI road-audit system built in Hyderabad: point an ordinary vehicle camera at the road, and it returns a classified, GPS-tagged record of defects such as cracks and potholes, with severity and affected area measured for each one.
The platform splits the job across three modules. Pavement handles the surface: potholes, cracks and kerb condition, each with severity and measured dimensions. Infrastructure outlines what stands beside the road: kerb paint, lane and edge markings, speed breakers, barriers, rumble strips and reflective road studs. Signage goes deeper on boards specifically — category, reflectivity, and one GPS-located record per sign.
The current ensemble — schooled on 34,540+ augmented images — measures 85% on pothole detection, 97% on road-surface classification, and 71% on kerb-condition assessment.
Footage runs through the pipeline at 45–60 frames per second, and the outputs — interactive maps, dashboards, and automated reports — are typically ready the same day.
Anyone accountable for road condition — city corporations and national highway bodies first of all, plus construction firms proving completed works and fleet operators watching the routes they run.
We're the engineers who answer our own email. Reach out and we'll show you what our AI sees on your roads.
No specialist hardware, no lane closures — and a complete sample audit you can open right now without talking to anyone.