Precision Image Annotation for Computer Vision at Scale
From bounding boxes to pixel-perfect semantic segmentation, Centric Labs delivers the image annotation quality that production computer vision systems demand. Our teams annotate millions of images per month across medical imaging, autonomous driving, retail analytics, satellite imagery, and industrial inspection — with 98 percent or higher accuracy guaranteed. Every image is annotated by trained specialists using your taxonomy, reviewed through our multi-stage QA pipeline, and delivered in your preferred format.
Every Image Annotation Method Your Models Need
We support the complete range of image annotation techniques: bounding box annotation for object detection, polygon annotation for irregular shapes and precise boundaries, semantic segmentation for pixel-level classification, instance segmentation for distinguishing individual objects, keypoint and landmark annotation for pose estimation and facial recognition, and polyline annotation for lane detection and boundary mapping. Each technique is optimized for speed and accuracy using our AI-assisted pre-labeling pipeline.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
Image Annotation Expertise Across Critical Industries
Our image annotation teams are trained for domain-specific requirements across autonomous vehicles with sensor-calibrated bounding boxes and lane annotations, healthcare and medical imaging including radiology, pathology, and surgical planning, satellite and geospatial imagery for mapping, environmental monitoring, and defense, retail with product recognition, shelf analytics, and visual search, manufacturing with defect detection, quality inspection, and robotic guidance, and agriculture with crop health, pest detection, and yield estimation.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
Quality Architecture for Pixel-Level Precision
Our image annotation quality process includes consensus labeling where multiple annotators label the same image independently, inter-annotator agreement measurement with automatic flagging of disagreements, AI-assisted validation against annotation guidelines, senior reviewer audit on random and edge-case samples, and client-facing quality dashboards with real-time accuracy metrics. We deliver not just labeled images but annotated datasets with full quality documentation.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
See Our Annotation Quality on Your Own Data
Send us a sample of your images. We will annotate them using your guidelines and return them within 48 hours — completely free. Judge our quality before you commit.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
Ready to validate quality and security in a pilot?
We will scope a small, measurable dataset, define acceptance criteria, and stand up a managed team fast.