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Machine learning and data operations teams of all sizes use Encord's collaborative applications, automation features, and APIs to annotate, manage, and evaluate their datasets for computer vision.

V7 Darwin is a data labeling platform used by AI developers who need to train specialized computer vision models. It supports diverse data types, including images, videos, and medical imaging formats like DICOM or WSI. The platform offers AI-assisted labeling, data management, and workflow orchestration tools to help companies, clinics, and research labs create high-quality training data for building sophisticated AI solutions. It is especially useful for managing complex review processes and real-time collaboration between multiple teams of annotators, engineers, and domain experts. V7 Darwin integrates with popular ML frameworks and infrastructure and maintains high security and compliance standards (SOC 2, HIPAA), which makes it suitable for industries such as healthcare, retail, security, and manufacturing.

Today's challenge to train machine learning models is not to get the data itself - but to get the clean labelled data - to avoid having a "garbage in garbage out" loop. While current digital transformation by AI is powered by machine learning models, this process of data annotation becomes critical. Kili Technology serves as the training data solution to facilitate data annotation for image, video and text for various Computer Vision and NLP tasks with a robust tool to manage data quality and simplify collaboration.

A complete training data platform for AI.

We specialize in annotating images and videos and creating consistent high-quality data for your machine learning models. We create superior quality data that is backed by excellent customer service. We work with you to find the best strategy for your project. By combining advanced tools with in-house professional annotators, we guarantee incredible results. We believe that any Artificial Intelligence can perform only as well as the training data that is used to create it, and that always starts with a human touch. Done properly, data annotation has limitless potential.