Upcoming Event 📣 AI Evaluation: Ensuring Mission-Critical Trust & Safety
Contact Sales
Data Labeling Platform

Label Studio Enterprise

Unlock the power of data-centric AI with the data labeling platform trusted by more than 250,000 data scientists and annotators.

Discover

Identify, catalog, and operationalize all your unstructured datasets in a single view.

Surface high-impact data to label

Source and select the best data for your training or fine-tuning task.

  • Securely connect and index data from any cloud source.
  • Explore data in a visual grid or list view.
  • Find relevant data using semantic search or reference data.
  • Filter data based on similarity to curate the perfect data subset.
  • Send data to new or existing projects for review and annotation.

Learn more

Signal

Enable human feedback to give your models a competitive edge.

Accelerate labeling with automation

Securely and easily connect large foundation models or custom ML models to:

  • Pre-label samples based on model predictions to expedite and simplify manual labeling
  • Implement AI-assisted labeling using models like Segment Anything to speed up tedious labeling tasks
  • Generate predictions for model evaluation and fine-tuning, and more

Manage all of your projects in one place

Organize your human workforce with projects, roles, customizable workflows, and collaboration tools.

  • Create workspaces and projects to manage team collaboration.
  • Define roles and access at the project level for team members and outside contractors.
  • Define rules and criteria to automatically assign or manually assign tasks to annotators.
  • Configure annotator workflows and permissions at a granular level to follow your labeling process and guidelines.


Customize your labeling UI

Easily customize the labeling interface to fit your use case and data type using a variety of pre-made templates or simple HTML.

Supervise

Better data means better models. Label Studio provides your team powerful workflows and advanced automation to ensure accurate, high-quality annotations—even on tight deadlines.

Improve data quality with automated reviewer workflows

Ensure your training and fine-tuning data delivers optimal model results with highly customizable reviewer workflows that put the right data in front of the right annotators—at just the right time.

  • Identify and resolve problematic items quickly using the smart annotator agreement matrix.
  • Configure reviewer workflows by model confidence score to enable human-in-the-loop review of annotations that a machine learning model was less certain about.
  • NEW! Automatically bring in additional annotators to look over any tasks with low agreement

Collaborate on individual labeling tasks

Speed up the labeling process, increase annotation quality, and build more solid labeling and review processes with comments and notifications.

  • Comment on tasks to raise issues with subject matter experts and reviewers; see notifications in your workspace to respond.
  • Submit annotation drafts with comments before submitting final annotations that affect project statistics.
  • Sort tasks in the data manager by unresolved comments, the total number of comments, or specific comment authors.

Report on quality and performance

Monitor progress, throughput, and labeling effectiveness with performance dashboards.

  • Make informed decisions quickly to improve efficiency and effectiveness of data labeling projects.
  • View project-level highlights for lead times and other KPIs by user-defined time periods.
  • Drill into time series charts for specific work being performed for tasks, annotations, and reviews.
  • View label distribution for the top 30 labels in a project.
  • NEW! Use the Annotator Dashboard to ensure labeling contractors are performing well and paid accurately, and explore how annotators have allotted time across tasks.

Keep your data private and secure.

We never touch your data, and our platform is SOC2 certified.

Learn More About Security

See how Label Studio Enterprise can work at your organization.