Kognic

Founded by Daniel Langkilde & Oscar Petersson in 2018

Annotate multi-sensor camera, LiDAR, and radar datasets for autonomous vehicles and robotics systems

Pricing model

Demo

You can use Kognic to annotate multi-sensor data like camera, LiDAR, and radar feeds for autonomous driving and robotics models. The system lets you combine raw sensor streams into unified 3D point cloud sequences and apply automated pre-labels to speed up manual labeling tasks. Annotators can track dynamic objects across frames, ground vision-language models with causal reasoning notes, and inspect complex edge cases. Built-in validation checks automatically review data consistency and automotive safety requirements before exporting clean training datasets directly into your machine learning pipelines.

What Kognic does

Annotate 3D LiDAR point clouds and camera video feedsFuse sensor data across multiple cameras and radar sensorsReview and correct automated pre-labelsValidate datasets against safety-critical driving checksTag driving scene events and object behaviorsExport validated training datasets to machine learning pipelinesMulti-sensor fusion combining camera, LiDAR, and radar dataNative 3D point cloud and bounding box annotation toolsOver 90 automated validation checkers built for driving scenariosLanguage Grounding workflows for vision-language-action modelsPre-label ingestion and automated review workflowsAggregated LiDAR view without manual slidersTISAX, ISO 27001, and SOC 2 Type II compliance