Encord
The data infrastructure layer for Physical AI and Enterprise teams — multimodal by design
Paid·Technical·API available
Key strengths
Multimodal data annotation across video, image, LiDAR, audio, text, and geospatialEnd-to-end data pipeline from collection and curation to post-training alignmentEmbedding-based search and model-in-the-loop curation for edge case discoveryNative support for Physical AI use cases including robotics, AVs, and dronesAPI/SDK-first architecture with zero data migration — data stays in your cloud
Paid only
San Francisco, USA
Founded 2021
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Developer Documentation & Integration
Encord is built API/SDK-first, making it straightforward to integrate into existing MLOps pipelines:
- Python SDK — The primary integration path. Install via
pip install encordand authenticate with your API key to programmatically manage datasets, labels, and workflows. - REST API — Full REST API coverage for dataset management, annotation task creation, label retrieval, and model evaluation orchestration.
- Cloud integrations — Native connectors for AWS S3, GCP Cloud Storage, and Azure Blob Storage. Data remains in your cloud; Encord accesses it via secure, credentialed connections with zero data migration required.
- Data agents — Encord supports native agent integrations within annotation and curation workflows, enabling model-in-the-loop automation at scale.
- Formats supported — Video, image (JPEG, PNG, TIFF), audio, LiDAR (point clouds), 3D sensor fusion, DICOM, NIfTI, text, HTML, and geospatial data.
- Webhooks & Exports — Export labels in COCO, YOLO, and custom JSON formats. Webhooks can trigger downstream pipeline steps on label completion or QA events.
- Full docs available at
docs.encord.com