Your annotation quality partner
High-quality, worry-free & production-ready
Industries we serve
Built for teams dealing with messy, real-world data
Agriculture
Food processing
Industrial automation
Trusted by AI teams including
Used by teams building and improving computer vision models.
Built for computer vision teams
Clean, structured datasets ready for training
- Classification, (rotated) bounding boxes, segmentation, and keypoints.
- COCO, YOLO, PASCAL VOC, or CVAT formats
- Seamless Python integration
Consistent annotation standards across datasets
- Clear instruction design and continuous refinement
- Structured multi-labeller workflows
- Strict quality control before delivery
- Custom QA setups tailored to your project
Repeatable workflows for continuous improvement
- 100.000+ Annotations per week build for production-scale pipelines
- Cost-effective without sacrificing quality
Process
From pilot projects to production-scale annotation workflows.
Intake
Let’s meet! We discuss your project goals, desired outcomes, and data volume. You share sample images that illustrate your annotation requirements. Clear expectations upfront ensure a smooth and efficient process.
Step 01
Pilot
Together we formulate or refine instructions and annotate a trial batch, showcasing our quality. Only if the pilot meets your expectations, we agree on definitive requirements, ensuring consistent and high-quality annotations.
Step 02
Scale
We deliver high-quality annotations at volume, with fast turnaround times. From early experiments to production-level datasets, we support ongoing batches so your computer vision models maintain strong and reliable performance over time.
Step 03
"We’ve seen how unclear instructions ruin datasets. That’s why we lock quality before scaling."
"We understand the importance of qualitative data to improve your model performance at experimentation and in production"
