Kognic

Kognic annotates the camera, LiDAR, and radar data that ADAS and autonomous-vehicle teams train on, and it brings a specialist workforce along with the tooling.

The company came out of Gothenburg in 2018, originally as Annotell, and built for the demands of automotive perception, where a mislabeled object is a safety problem rather than a metrics footnote. The platform handles sensor fusion natively: multiple LiDARs, cameras, and radar in one environment, with calibration-aware projections so a box drawn in one view lands correctly in the others.

What you get:

• 3D annotation on LiDAR point clouds, including multi-LiDAR setups
• Fused camera, LiDAR, and radar labeling in a single workflow
• Support across perception, planning, and end-to-end model data
• A dataset and quality layer built for large, long-running programs

There is a workforce behind it too. Rather than handing you empty software, Kognic can supply trained annotators who know automotive data, which suits OEMs and Tier 1 suppliers that need volume without building an annotation org from scratch. The company has delivered at production scale, with tens of millions of annotations across a large number of programs.

Common uses:

• OEMs and Tier 1 suppliers labeling perception data for driver assistance
• Teams validating models against carefully reviewed ground truth
• Programs that need consistent quality across months of data, not a one-off batch

The focus is narrow on purpose. Kognic is built for automotive-grade sensor data and the safety and validation bar that comes with it, which is a different problem than labeling web images.

Pricing is enterprise, quoted per program by data volume, sensor configuration, and how much of the labeling you want Kognic's workforce to handle.

Best for OEMs, Tier 1 suppliers, and AV teams that want automotive-grade sensor-fusion annotation with a workforce attached. Less of a fit for a team that mainly labels 2D images or text, where a general platform or crowd is cheaper to run.