Why Us
Why TensorAct Studio?
Relaxed Experienced
Workflow Engine
Plugin-Based Automation
Multi-Step Review and QA
Flexible yet Relaxed Experience
Typically, adapting to a new data annotation platform means rebuilding your templates and workflows from scratch. TensorAct changes that.
It supports custom labeling templates, including those used in Amazon SageMaker Ground Truth, so you can run existing setups in a more flexible environment.
Templates define the interface your annotators see and how each task works. They control how data is displayed, what inputs are required, and how annotations are captured. This makes it easy to handle everything from simple labeling tasks to more complex, multi-step annotation workflows across image, video, text, audio, PDF, and specialized formats like DICOM.
By supporting reusable templates, TensorAct makes it possible for teams to stay consistent across projects while reducing setup time. You can reuse what you have, adapt workflows as needed, and scale annotation more efficiently.
Design and Run Annotation Workflows Your Way
A traditional data annotation platform that works in silos can't support the complexity of real-world AI workflows. Annotation isn't just a single step. It involves multiple stages, reviews, and decisions along the way.
TensorAct is built with that in mind. With its workflow engine, you can design how your annotation process runs from start to finish. Define steps for annotation, review, and validation, and control how tasks move between them.
Whether you need a simple setup or a multi-stage pipeline with multiple reviewers, TensorAct gives you the flexibility to structure workflows around your needs. You can route tasks, add review layers, and manage quality checks, all within a single system.
Bring AI Agents Into Your Annotation Workflow Through Plugins
AI-assisted annotation can dramatically improve how teams work, with productivity gains of up to 80% by reducing repetitive tasks and speeding up labeling.
TensorAct makes it easy to bring that into your workflow. With plugin-based automation, you can connect AI agents, models, and external systems directly into your annotation pipeline. Tasks can be pre-labeled, enriched with predictions, or processed before they reach annotators.
This means your team can focus on what matters most. Automation handles repetitive work, while humans focus on review, validation, and edge cases.
You stay in control of when automation is applied and how tasks move through each stage. The result is a faster, more efficient annotation process that scales with your needs without sacrificing quality.
Build Quality Into Every Step of Your Annotation Workflow
Human-in-the-loop data annotation can make a huge impact on the quality of your data, especially when review and validation are built into the process.
TensorAct helps you turn this into a structured, repeatable process. With multi-step review and QA, you can design workflows that include multiple layers of validation. Tasks can be reviewed by one or more reviewers, escalated when needed, and routed based on decisions at each stage.
This gives you full control over how quality is handled. You can define review steps, assign roles, and ensure that every annotation meets your standards before it is finalized.
Annotate Different Types of Data 
