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Karmyug

Perception and cabin data from the conditions that actually break driving stacks.

Perception, driver monitoring and in-car speech for ADAS and autonomy.

The frames that matter are the ones nobody wants to collect: night rain, low sun, a pedestrian half-occluded by a parked van, a road sign in a script your OCR has never seen.

We annotate those deliberately and tell you what fraction of the delivered set is hard, because a benchmark built only from clear daylight gives a number you cannot trust.

Where AI is being applied in automotive

The applications teams actually come to us for, rather than the full theoretical list.

  • Object detection

    Vehicles, pedestrians, cyclists and road furniture across conditions.

  • Lane and drivable area

    Pixel-level segmentation including worn and missing markings.

  • LiDAR and sensor fusion

    3D cuboids with camera and point cloud synchronised.

  • Driver monitoring

    Drowsiness, distraction and eyes-off-road labelled at frame level.

  • In-car speech

    Commands and conversation recorded at speed with road noise.

  • Signboard OCR

    Traffic and road signage across scripts and weather.

Data security and compliance

The controls your risk team will ask about, before they ask.

  • GDPR

    EU data subject rights and DPAs

  • CCPA

    California consumer privacy

  • DPDP Act 2023

    India data protection, in-region storage

Automotive questions

Do you collect real road data?

We run instrumented collection with contracted drivers where permitted, and simulated cabin capture for driver-state work.

Can you anonymise faces and plates?

Yes, with a verification pass that confirms the redaction actually happened before delivery.

Power your automotive AI with data you can audit.

Start with a pilot batch against your real guidelines. You accept it before anything scales.