Visual AI training data with precision
Computer Vision
Image and video annotation for object detection, segmentation, tracking, 3D scene understanding, and autonomous systems.
Overview
What is Computer Vision?
Computer vision models require massive amounts of precisely annotated visual data. Every pixel counts — from autonomous vehicles to medical imaging to retail analytics.
Why it matters: Vision AI systems have zero tolerance for annotation errors. A mislabeled pedestrian or incorrect bounding box in training data can cascade into dangerous model failures in production.
2D/3D Bounding Boxes
Precise object localization for detection and autonomous systems
Segmentation
Semantic and instance segmentation for scene understanding
Keypoints
Pose estimation and anatomical landmark annotation
LiDAR/Sensor Fusion
3D point cloud annotation with multi-sensor alignment
Workflow
How We Do It
01
Taxonomy Definition
Define annotation classes, hierarchies, and edge case rules for your CV model.
02
Tool Setup
Configure annotation tooling optimized for your task — CVAT, Label Studio, or our platform.
03
Annotation Sprint
Trained specialists annotate bounding boxes, polygons, keypoints, semantic maps, or 3D cuboids.
04
Cross-Review QA
Peer review with IoU scoring to maintain spatial accuracy thresholds.
05
Dataset Export
Export in COCO JSON, YOLO, Pascal VOC, or custom format with train/val/test splits.
Case Study
AV Startup
AV Startup
Train computer vision model on real-world driving scenarios
Solution
Custom 3D bounding box pipeline with sensor fusion validation across 50+ object classes
Results
3M
Images annotated
94%
Model accuracy
6 wks
Faster deployment
Ready to Get Started with Computer Vision?
Tell us about your project and we'll scope a pilot within 48 hours.