Automate & Scale Your Annotation Workflows

Stop juggling manual processes and repetitive tasks. Orchestrate transforms your annotation workflows into efficient, automated pipelines—freeing your team to focus on delivering high-quality data at scale.

What You Can Do With Orchestrate

Automate Quality Control

Automatically run QA checks before sending annotations to reviewers, catching issues early and ensuring consistent standards.

Leverage LLM Judges

Insert custom LLM-based evaluation steps for nuanced quality checks, improving accuracy without increasing manual overhead.

Calculate Consensus

Build custom consensus mechanisms to refine annotation quality, reduce errors, and increase confidence in your dataset.

Track Projects Automatically

Sync with tools like Jira or Slack so teams stay updated without constant manual reporting.

And Everything Else

Use Orchestra’s workflow editor and Python environment to build any automation your team needs

How Orchestrate Works

Write your Code

Create Python scripts directly within SuperAnnotate, and specify the environment that you need.

Test & Dockerize

Validate scripts in our built-in testing environment. Once ready, dockerize them with a click to run them reliably at scale.

Set Triggers

Define when and how scripts run: on project updates, folder completions, status changes, and many other events .

Deploy & Monitor

Monitor performance in real-time and adjust your workflows as projects evolve, all within one platform.

Complete Control Through the SuperAnnotate SDK

Go beyond basic automation. Orchestra taps into the full power of the SuperAnnotate SDK and Python, letting you:

• Communicate faster with built in commenting
• Reassign annotators as workloads shift
• Spin up new projects on the fly
• Integrate seamlessly with your entire ML ecosystem