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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- Sensor fusion annotation — Synchronize and annotate LiDAR, camera, and radar streams simultaneously for autonomous vehicle perception system training.
- Embedding-based dataset curation — Use vector embeddings to search across large multimodal datasets, detect distribution gaps, and identify underrepresented edge cases.
- RLHF & post-training alignment — Orchestrate human feedback collection via pairwise comparisons and rubric-based scoring to fine-tune production LLMs and VLMs.
- Model-in-the-loop labeling — Integrate custom or third-party models as pre-labeling assistants within annotation workflows to reduce manual effort and increase throughput.
- Programmatic dataset management via SDK — Automate dataset versioning, label exports, QA workflows, and annotator task assignment through the Encord Python SDK or REST API.
- Production feedback loops — Connect model monitoring signals back to Encord to automatically queue failing predictions for re-annotation and retraining.