Encord
The AI-native data platform for data-centric computer vision development.

Open-source data labeling for machine learning practitioners focused on privacy and speed.
Doccano is a specialized open-source data labeling tool designed for text-based machine learning tasks. Architecturally, it is built on a Django backend and a Vue.js frontend, offering a robust and extensible environment for teams to collaborate on supervised learning datasets. By 2026, Doccano has cemented its position as the leading choice for privacy-first enterprises that require air-gapped or self-hosted annotation environments to satisfy GDPR and HIPAA requirements without the costs associated with SaaS platforms. Its technical strengths lie in its support for Named Entity Recognition (NER), Sentiment Analysis, and Sequence-to-Sequence tasks. Unlike proprietary alternatives, Doccano allows for high-granularity control over the labeling UI and seamless integration into CI/CD pipelines via its REST API. It is particularly effective for small-to-medium research teams and data scientists who need to rapidly prototype and iterate on gold-standard datasets. The platform's commitment to community-driven development ensures broad support for international languages and various text encoding standards, making it a foundational tool in the modern NLP stack.
Integration with external REST endpoints to provide model-assisted labeling and pre-annotation.
The AI-native data platform for data-centric computer vision development.
Open-source, browser-based image labeling for high-velocity computer vision pipelines.
Enterprise-grade human-in-the-loop data labeling for high-precision computer vision and NLP models.
Powering the AI lifecycle with high-quality, human-centric data and RLHF at scale.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Real-time multi-user synchronization and conflict resolution for large-scale datasets.
Granular permissions for Project Managers and Annotators within the Django authentication framework.
Configurable keyboard mappings for labeling categories to minimize mouse movement.
Support for CoNLL, JSONL, and CSV export logic natively in the UI.
Standardized deployment via Docker Compose for easy environment replication.
Built-in dashboarding to monitor completion status and label distribution.
Secure extraction of patient entities without uploading PII to third-party clouds.
Registry Updated:2/7/2026
Export as JSONL for SpaCy training.
Categorizing reviews across 15+ languages using native speakers.
Creating ground-truth summaries for complex contract clauses.