Encord

Encord | Label & Curate Multimodal Data for AI

Encord is a platform designed to manage, curate, annotate, evaluate, and monitor multimodal AI data (image, video, audio, document, text, and DICOM files) all in one place. It helps transform petabytes of unstructured data into high-quality training data for model training, fine-tuning, and alignment, enabling fast, scalable data workflows for production-ready AI applications.

Key capabilities include end-to-end data management, advanced labeling tools, human-in-the-loop workflows, quality assurance, model evaluation, and seamless integrations with cloud storage and MLOps toolchains. The platform emphasizes speed, data quality, and governance, supporting teams to identify outliers, fill data gaps, create balanced datasets, and monitor labeling performance and model metrics (e.g., mAP, mAR, F1).

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How Encord Helps

• Manage, curate, annotate, review, and monitor multimodal data (image, video, audio, document/text, DICOM) from a single platform.
• Connect to AWS, GCP, Azure, or OTC cloud storage; reflect data changes in real-time within Encord Index.
• Accelerate labeling at scale using custom or foundational models to generate pixel-perfect masks and reduce manual effort.
• Implement customizable human-in-the-loop workflows to tailor reviews and approvals for your project.
• Label data across modalities with organized ontologies and relationships to support robust model training.
• Evaluate model performance with automated reporting on metrics such as mAP, mAR, and F1 Score; compare models and iterate with active learning workflows.
• Monitor team performance with dashboards; manage user roles, permissions, and task assignments to scale MLOps workflows.
• Ensure security and compliance (SOC2, HIPAA, GDPR) with strong encryption and governance.

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How It Works

1. Connect your data stores (cloud storage) and import data into Encord.
2. Use Annotate to label data at scale, leveraging human-in-the-loop and model-assisted labeling to speed up production-grade datasets.
3. Apply advanced filtering to find outliers, underrepresented cases, and data gaps; create balanced datasets.
4. Review and QA labeling with customizable workflows; monitor progress via dashboards.
5. Evaluate models with automated performance reporting and iterative refinements using active learning.

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Core Modules

• Manage & Curate
• Annotate & Review
• Evaluate & Monitor
• Integrations
• Multimodal Annotation
• Data Security & Compliance

How to Use Encord

• Connect cloud storage and import data.
• Configure labeling workflows and ontologies for your modalities.
• Label data with the assistance of AI models and human reviewers.
• Run quality checks and evaluate model performance.
• Iterate with active learning to improve data quality and model outcomes.

Safety and Legal Considerations

• Ensure appropriate data handling, consent, and compliance for sensitive data (e.g., medical images, personal data).
• Follow organization policies and applicable laws when sharing or deploying labeled data.

Core Features

• Multimodal data support: images, videos, audio, documents/text, DICOM/medical imagery
• End-to-end data management: ingest, organize, label, review, and evaluate within one platform
• Advanced labeling tools with pixel-perfect masks and model-assisted labeling
• Human-in-the-loop workflows: customizable review and approval processes
• Automatic and manual quality assurance with configurable dashboards
• Robust model evaluation: automatic reporting of mAP, mAR, F1, and other metrics
• Data governance and security: SOC2, HIPAA, GDPR compliant, encryption
• Cloud integrations: connect to AWS, GCP, Azure, or OTC storage with real-time sync
• Collaboration and permissions: role-based access and scalable MLOps workflows
• Active learning and iterative model improvement capabilities