Label at the speed of your model's appetite.
Bounding boxes, polygons, semantic labels — across images, video frames, and LiDAR point clouds. Ground truth data, at production scale.
Every sensor modality.
Every annotation type.
Image Classification & Object Detection
Bounding boxes, polygons, keypoints
Draw precise bounding boxes and polygons over objects in still images. Model-assisted pre-labeling suggests annotations before a human reviews — cutting annotation time by 74%.
Consensus voting · Inter-annotator agreement · Auto-reject below 94%
Video Object Tracking at 60fps
Frame-by-frame propagation, interpolation
Annotate the first and last keyframe — Annotate propagates labels across every frame in between. Works at 60fps with sub-pixel accuracy on fast-moving objects.
Temporal consistency check · Drift detection · Frame-gap validation
3D Point Cloud Segmentation
LiDAR, depth sensors, photogrammetry
Segment and classify millions of 3D points per second. Annotate pedestrians, vehicles, lane markings, and free-space in LiDAR scans from any autonomous vehicle sensor stack.
Point density validation · Z-axis consistency · Multi-sweep fusion check
Quality isn't a setting.
It's baked into every layer.
Verified across 847 enterprise annotation projects in 2025, covering 2.4M+ labeled assets across image, video, and LiDAR modalities.
Fits into your pipeline.
Not the other way around.
Export directly to 12+ annotation formats. Connect via REST API or Python SDK. Webhooks deliver ground truth the moment a batch clears QA — no polling, no waiting.
import annotate # Initialize clientclient = annotate.Client(api_key="ak_••••••••")# Create annotation jobjob = client.jobs.create(dataset="s3://my-bucket/lidar-scans/",modality="lidar_3d",annotation_types=["bbox_3d", "semantic_seg"],model_assist=True, # 91.4% pre-label rateqa_threshold=0.94, # auto-reject belowoutput_format="kitti",delivery="webhook") print(job.status) # → "queued" (avg: 4.2s/annotation)
"We cut our annotation cycle from 6 weeks to 9 days. The LiDAR pre-labeling alone saved us 3 engineers worth of work."
"The 98.6% inter-annotator agreement is real. We validated it against our own internal benchmark before migrating 800K images."
"We went from unlabeled dataset to first training run in 11 days. That's a record for us — and our model hit val mAP of 0.74 on the first shot."
Your annotation
environment awaits.
The full Annotate Workbench — desktop app with offline support, GPU acceleration, and direct pipeline integration. Free for datasets under 10K assets.
No credit card required · Free tier: 10,000 annotations/month