Encord provides a multimodal data layer infrastructure for training and deploying physical AI systems across various modalities like video, LiDAR, and sensor fusion. The platform supports the entire AI lifecycle, from data collection and automated labeling to dataset curation and post-training model alignment. This unified solution enables AI teams to manage and scale complex data workflows for robotics, autonomous vehicles, and generative AI applications.
Funding
$50M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI and computer vision teams face challenges in managing, curating, and annotating diverse data types like images, videos, and documents, which are essential for training high-performing AI models. Handling petabytes of unstructured data and transforming it into quality training datasets can be slow and complex, hindering AI development.
Solution
Encord offers an AI data development platform that consolidates data management, curation, annotation, and model evaluation into a unified environment. The platform enables users to manage multimodal data, leverage smart collections, and use advanced filtering options to organize data. Encord provides best-in-class labeling tools, customizable workflows, and quality assurance measures to accelerate labeling projects and ensure training data quality. The platform also facilitates AI model evaluation, error analysis, and performance comparison to refine model performance and reduce deployment timelines.
Target Audience
Encord is designed for AI and computer vision teams deploying production-ready AI applications, including ML practitioners, data scientists, and AI engineers.
Features
- Unified platform for managing, curating, and annotating image, video, audio, document, text, and DICOM files
- Smart collections, bulk classification, and role-based access control for efficient data management
- Natural language search and filtering by 40+ quality metrics and metadata
- Best-in-class labeling tools for creating pixel-perfect masks and reducing labeling hours
- Customizable workflows and expert review for reliable quality assurance
- Actionable dashboards for monitoring team and annotator performance
- AI model evaluation with error analysis, performance comparison, and reporting on metrics like mAP, mAR, and F1 Score
- Integrations with AWS, GCP, Azure, OTC cloud storage, and MLOps tools
- API/SDK for programmatic access to projects, datasets, and labels