As Zenseact ramped up ADAS development, their data preparation pipeline started to buckle. Manual annotation at scale was proving to be a major drag: slow, expensive, and hard to iterate on quickly. Quality consistency became a real issue when you're coordinating across multiple teams and external suppliers. One bad batch could ripple through to production and affect safety-critical perception systems.
On top of that, manual QC was eating up engineering time. Their team was spending hours reviewing annotations instead of developing models. They needed a way to keep quality high while dramatically speeding up throughput and cutting costs per annotated unit.
Kognic and Zenseact worked together to put two key workflows into production.
Pre-Label Workflow: Instead of annotating from scratch, the teams built a system where model-generated annotations are imported into Kognic and verified by annotators. Annotators just validate or adjust what the model produced, which saves time even when edits are needed. The pricing model is tied to how much work the annotator has to do—full creation, adjustment, or simple verification—so costs scale with model performance.
Automatic Quality Control (Auto-QC) Implementation: Kognic built Zenseact's Auto-QC rules directly into the workflow to catch errors early. These rules are customized for each task to flag the most expensive mistakes before they make it downstream, preventing costly rework later.
The partnership delivered real, measurable results. Zenseact cut time per sequence significantly and increased throughput without sacrificing quality. The "discount ladder", where pricing is tied to how much annotator effort is required, gave them better cost control and led to savings of up to about 47% on dynamic objects and 38% on static ones.
Auto-QC made a huge difference on the quality side. Engineering review time dropped dramatically, freeing up the team to focus on core development. That meant faster acceptance cycles and fewer late-stage iterations, speeding up the whole development process while maintaining the safety standards production vehicles demand.