Encord is the data labeling platform that scales with frontier model development. Orchestrate AI-assisted Human-in-the-Loop workflows to accurately label and review multi-modal data, such as videos, images, text, audio, DiCOM, & 3D point cloud data.
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Evaluating data labeling platforms? Leading AI teams focus on: native multimodal support with best-in-class computer vision annotation & curation, consolidated data pipelines, and enterprise-grade scalability that delivers measurable ROI.
Complete data labeling suite with native multimodal support
Label multimodal data such as video, image, DICOM, document, text, and audio files with AI-assisted labeling tools and RLHF workflows. Key differentiating features include native video annotation, customizable workflows, complex data structuring with nested ontologies, multimodal annotation interface with synchronized multi-file analysis, and more.
Consolidated platform for your entire data pipeline
Not just an annotation solution, Encord enables teams to consolidate data curation and annotation workflows into one platform. Eliminate the need to build fragmented data management infrastructure. Explore and visualize data with advanced natural language search. Evaluate and validate models to prioritize the most valuable data for model training and fine-tuning.
Enterprise platform with future-proof scalability
Transform petabytes of multimodal data with Encord's highly scalable architecture. Upload data once and leverage it across unlimited projects, significantly reducing storage overhead and management complexity. Comprehensive Python SDK enables teams to build sophisticated automated pipelines that seamlessly integrate with existing MLOps stacks.
Customized workflows
Orchestrate and accelerate your labeling data pipeline with advanced no-code workflow builders. Integrate SOTA models such as GPT-4o and Gemini or your own models directly into data workflows to automate data reviews, pre-labeling, data classification, filtering and more.
API/SDK for programmatic access to Encord
Leverage our customizable SDK to interact with the Encord platform programmatically. Automate data pipelines that integrate with existing MLOps stacks. Upload data or integrate with cloud storage, access and manage projects, datasets, ontologies and labels within the platform via API.
Strict adherence to compliance standards
Rigorous compliance with industry standards to protect your most sensitive data. Safeguard personal information, protected health data, and valuable intellectual property through comprehensive security protocols and data handling practices that meet the highest regulatory requirements.
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We now have an integrated, one-stop solution where we can manage our data and also understand our model performance to create feedback mechanisms to improve data and models.
Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also has this great interpolation tool which was especially useful.
It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.
Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted.
We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have.
Getting started with Encord and integrating it into our workflow was really fast. The thing that I find the most valuable is the flexibility of how we can integrate the Encord pipeline into our own pipeline.
Encord Index is a high-performance system for our AI data, enabling us to sort and search at any level of complexity.
Before using Encord, it was challenging to see all the data, projects, and annotations in one place. Now, with Encord I feel like we have a much clearer understanding of everything that's happening.
We now have an integrated, one-stop solution where we can manage our data and also understand our model performance to create feedback mechanisms to improve data and models.
Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also has this great interpolation tool which was especially useful.
It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.
Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted.
We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have.
Getting started with Encord and integrating it into our workflow was really fast. The thing that I find the most valuable is the flexibility of how we can integrate the Encord pipeline into our own pipeline.
Encord Index is a high-performance system for our AI data, enabling us to sort and search at any level of complexity.
Before using Encord, it was challenging to see all the data, projects, and annotations in one place. Now, with Encord I feel like we have a much clearer understanding of everything that's happening.
Encord combines advanced annotation tools, AI-assisted labeling, and collaborative workflows to make labeling faster, more accurate, and scalable. Teams using Encord reduce errors, improve dataset quality, and accelerate AI model development.
Encord provides robust API/SDK options, featuring a comprehensive Python API and SDK that allows developers to programmatically access all platform functionalities. This enables seamless integration with existing MLOps toolstacks and custom workflows.
Yes. By combining AI-assisted annotation, workflow automation, and quality controls, Encord reduces manual labor, speeds up projects, and improves labeling accuracy, lowering the total cost of creating training datasets.
AI-assisted labeling: Reduce manual work while maintaining high accuracy
Collaboration: Assign tasks, track progress, and manage review workflows for teams of any size
Integration-ready: Seamlessly connects with ML pipelines and cloud storage
Security and version control: Enterprise-grade security and dataset versioning
Efficiency: Optimized for speed and consistency, even with large datasets
Just Released: The World's Largest Open-Source Multimodal Dataset
