TensorAct is a cutting-edge data annotation platform built for AI teams working across image, video, text, audio, PDF, and robotics workflows.

TensorAct supports annotation workflows for image, video, text, audio, PDF, and specialized formats like DICOM imaging.

Yes. TensorAct supports custom templates, structured review systems, AI-assisted workflows, and configurable annotation pipelines.

Yes. TensorAct supports data annotation for robotics involving egocentric video, teleoperation data, temporal annotation, object tracking, and multimodal AI datasets.

Plugins in TensorAct make it possible for teams to connect AI models, automation tools, and AI directly into annotation workflows. They can be used for tasks like pre-labeling data, automating review steps, triggering AI agents, and building more advanced workflow pipelines across different annotation projects. The plugin system is designed to be flexible and easy to integrate, giving teams a more streamlined way to customize and scale annotation workflows.

Custom templates in TensorAct define how annotation tasks are displayed and how annotators interact with data inside the platform. They control the annotation interface, required inputs, workflow structure, and how annotations are captured across different projects. Teams can use workflows that feel familiar and easier to use instead of forcing annotators to adapt to rigid interfaces.

For example, teams already using tools like Amazon SageMaker Ground Truth can structure similar annotation experiences and workflows within TensorAct through custom templates.