Deepen AI vs Kognic

Deepen AI labels the multi-sensor data behind autonomous vehicles and robots, and it calibrates the sensors those systems depend on. The calibration piece is what sets it apart from most annotation vendors.

Perception stacks fuse camera, LiDAR, and radar. Before any of that data can be labeled or trusted, the sensors have to be calibrated so their views line up in the same coordinate frame. Deepen Calibrate handles that step, from camera-to-LiDAR to radar alignment, and turns a slow manual process into something that runs in minutes. Get calibration right early and the labels downstream actually describe the same physical world across every sensor.

On the annotation side, the platform covers the formats AV and robotics teams work in:

• 3D bounding boxes and cuboids on LiDAR point clouds
• Semantic and instance segmentation in 3D and 2D
• Sensor-fusion labeling that ties camera, LiDAR, and radar together
• Object tracking across frames with time-synced sensors

Automation carries the first pass so annotators correct rather than start cold, which matters when a single driving log can run to millions of points. Review workflows sit on top, so quality control is part of the pipeline rather than a separate audit.

Teams use it in a few ways:

• AV companies labeling perception data for detection and tracking
• Robotics teams working with 3D and depth data
• Any team that needs sensors calibrated before a data campaign
• Groups that want tooling plus optional labeling services rather than one or the other

Because calibration and annotation live in the same place, Deepen fits teams that would otherwise stitch together a calibration tool and a separate labeler. That gap is a real source of drift and rework when the two live apart.

Pricing is enterprise, quoted by data volume, the sensor setup, and whether you want tooling, services, or both.

Best for autonomous-vehicle and robotics teams that need multi-sensor annotation and take calibration seriously. Less of a fit for a team labeling plain 2D images or text, where a general vision platform or workforce is a simpler match.

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.