Encord
Officially listedEnterprise multimodal data annotation and curation platform powering physical AI such as autonomous driving and robotics.
Encord
Encord is an enterprise-grade multimodal data infrastructure platform designed specifically to power Physical AI. Beyond powerful automated annotation tools, it uses a closed-loop active learning mechanism to help algorithm teams train, evaluate, and deploy models on high-quality complex data.
Core Capabilities
- Multimodal data support: Natively handles highly complex data formats including video, audio, LiDAR, RGB-D, and DICOM/NIfTI medical imaging, meeting the data needs of the physical world.
- AI-automated annotation: Built-in foundation models such as SAM 2, plus video-native annotation and LiDAR pre-annotation tools, use AI to drive AI and cut labeling time dramatically.
- Advanced data curation: Active learning modules based on vector embeddings precisely retrieve edge cases and fix label errors, greatly reducing redundant dataset size.
- Model evaluation and alignment: Supports RLHF workflows and rule-based evaluation, directly surfacing failed model predictions and feeding them back into the dataset.
- Enterprise automation SDK: A powerful API-first Python SDK integrates seamlessly into enterprise CI/CD processes and existing data pipelines.
- Highest-standard security and compliance: Fully compliant with HIPAA, SOC 2, and GDPR, with VPC and on-premises deployment options ensuring zero-risk leakage of core enterprise data assets.
Use Cases Best suited to mid-size and large Physical AI algorithm teams in autonomous driving, robotics, drones, and medical image analysis. If you need to process extremely complex long video sequences, 3D spatial data, or medical imaging, with strict compliance and data governance requirements, this is the ideal tool.
Unique Advantages Unlike general annotation platforms, Encord abandons simple human outsourcing logic in favor of a true active learning data loop engine. It leads by a wide margin on long-video inter-frame tracking and high-dynamic-range medical image processing, and specializes in solving failures caused by model "edge cases."
Editor's Review Encord is without question a top choice among enterprise computer vision and multimodal data platforms. Although its enterprise pricing and complex configuration create a learning curve for small teams, when facing tens of millions of complex data sources and demanding compliance requirements, the AI data layer it builds genuinely helps models reach production safely and quickly.
Pricing
### 💰 定价模式：付费企业订阅 **起步价**：联系销售获取报价 #### 主要方案 - **Starter**：面向个人与小型团队，提供图像/视频标注、自定义工作流及复杂本体定义。 - **Team**：面向扩展多个AI应用的团队，包含 Starter 且增加数据代理、模型评估与分析功能。 - **Enterprise**：面向大规模部署组织，包含 Team 且增加多工作区管理、SSO、自定义 SLA及本地部署支持。 #### 试用/其他信息 医疗 DICOM、地理空间、3D/LiDAR、LLM 评估等特殊数据格式作为增值组件提供。 — Visit website
FAQ
Is Encord free?
Encord's core features use paid enterprise subscriptions scaled to team size, with Starter, Team, and Enterprise editions available; contact sales for specific pricing.
What can Encord be used for?
It is a multimodal data platform powering Physical AI, mainly used for automated annotation of video, LiDAR, and medical imaging, data curation to discover edge cases, and model evaluation.
Which special data formats does Encord support?
The platform natively handles highly complex multimodal data, including long video sequences, audio, LiDAR, RGB-D sensor fusion, and DICOM/NIfTI medical imaging.
How does Encord protect enterprise data privacy?
The platform enforces a zero data migration policy, meets HIPAA, SOC 2, and GDPR at the highest standard, and supports VPC and on-premises deployment, keeping sensitive data assets secure and compliant.
How does Encord differ from other annotation platforms?
Unlike ordinary data outsourcing annotation tools, Encord's core is an active learning closed loop: it feeds model predictions back into the dataset to precisely find weak spots and improve the model.
What kind of teams is Encord for?
It is best for mid-size and large algorithm teams working in Physical AI fields such as autonomous driving, robotics, and healthcare, especially R&D organizations with hard requirements around data compliance and complex workflows.