A data annotation platform is software used to label and organize data for AI and machine learning systems. Teams use data annotation platforms to annotate images, video, text, audio, documents, and other multimodal datasets used for training AI models. TensorAct is a cutting-edge data annotation platform designed to support structured workflows, custom templates, AI-assisted automation, and built-in review systems within a single platform

On TensorAct, AI data annotation costs are designed to scale with your team, workflows, and project requirements. TensorAct offers flexible Basic, Professional, and Enterprise plans to support everything from early-stage AI projects to large-scale production workflows.

TensorAct supports annotation workflows for image, video, text, audio, and PDF data, including specialized image formats like DICOM used in medical imaging. This means teams can work across different types of AI training data within a single platform and structured workflow environment.

Yes. TensorAct is designed to support robotics AI workflows involving egocentric video, teleoperation data, temporal annotation, object tracking, and multimodal datasets used for robotics and physical AI systems.

Yes. TensorAct supports custom templates, structured review systems, and configurable workflows that can be adapted to different industries and AI use cases.

Custom templates in TensorAct define the annotation interface your team works in and how annotation tasks are structured. They control how data is displayed, what inputs are required, and how annotations are captured across different workflows. This makes it easier for teams to work in interfaces they are already familiar with instead of adapting to entirely new annotation experiences. For example, teams using tools like Amazon SageMaker Ground Truth can bring existing annotation interfaces and workflows into TensorAct through custom templates.