Who this is for
Applied AI teams that need to:- Build reliable, repeatable annotation workflows for supervised learning
- Evaluate model performance and trace failures back to training data
- Coordinate ML engineers, data teams, and annotation workforces
- Continuously improve models as production data evolves
Key capabilities
Annotation for production AI
Label any combination of image, video, audio, text, and DICOM data with tools optimized for accuracy and throughput.- Images — bounding boxes, polygons, polylines, keypoints, bitmasks, and object primitives; use SAM 2 natively to segment and classify objects up to 10x faster
- Video — video-native annotation with temporal context, automated object tracking, and single-shot labeling across scenes; label up to 6x faster
- Audio — transcription, classification, and sequence labeling
- Text and documents — entity recognition, classification, and structured extraction
- DICOM / medical imaging — specialized tooling for clinical and research annotation
Human-in-the-loop (HITL) workflows
Build annotation and review pipelines that combine human judgment with model automation.- Route tasks through multi-stage Workflows: annotate → review → QA
- Assign tasks by role, skill, or dataset
- Use consensus labeling to measure inter-annotator agreement and surface disagreements
- Escalate edge cases for expert review
Model evaluation and debugging
Understand where your models fail and what data to collect or re-label next.- Import model predictions and compare against ground truth labels
- Automatic reporting on mAP, mAR, F1 Score, and other metrics
- Identify underperforming clusters, edge cases, and underrepresented classes
- Surface the exact data that led to unexpected model behavior
Active learning and continuous improvement
Turn production deployment into a training signal.- Route low-confidence predictions back into annotation queues
- Track failure modes and underrepresented patterns across production data
- Continuously tighten your training distribution as requirements evolve
- Version datasets and track ground truth against each model iteration
Multimodal and cross-team collaboration
Encord supports the full range of applied AI data types and coordinates work across distributed teams.- Unified platform for ML engineers, data managers, and annotators
- Fine-grained role-based access control
- Shared Ontologies and reusable Workflow templates across Projects
- Dataset versioning and label export in standard formats
API and SDK integration
Encord integrates into your existing MLOps stack.- Python SDK for programmatic access to Projects, Datasets, and labels
- Automate data pipelines and trigger workflows from external systems
- Export labels in JSON, COCO, and other formats
- Webhook notifications for task-level events
Common use cases
| Industry | Use case |
|---|---|
| Healthcare | Medical image annotation (DICOM, radiology, pathology) |
| Manufacturing | Defect detection and quality control |
| Smart cities | Object detection and tracking in video |
| Sports analytics | Pose estimation and player tracking |
| Autonomous systems | Sensor fusion annotation for camera and LiDAR data |
| Retail | Product recognition and shelf compliance |
Where to go next
- Data Lifecycle — how data moves through ingestion, curation, annotation, and export
- End-to-End Walkthrough — a complete example from raw data to exported labels
- Platform documentation — feature-level documentation for Annotate, Index, and Active
- SDK documentation — automate and integrate with the Encord API