Human-In-The-Loop
At Scale
Orchestrate annotation workflows with multi-step pipelines, team management, and quality control. Add annotators, set sample rates, and track progress.
Custom multi-step pipelines
Design annotation workflows that match your quality requirements. Chain annotation and review steps with configurable sample rates.
Annotation Steps
Workers create initial annotations on assigned assets
Review Steps
Team members validate and accept/reject previous work
Sample Rates
Control what percentage of assets proceed to each step
Full visibility on progress
Track every task through its lifecycle. Reassign work, manage priorities, and ensure nothing falls through the cracks.
Scale with external teams
Onboard external annotators quickly with clear guidelines. Control access, distribute work evenly, and maintain quality standards.
Collaborative issue tracking
Flag problems, discuss solutions, and ensure quality before assets leave the pipeline. No asset can be marked done with open issues.
Zone Issues
Flag specific areas with rectangular markers. Perfect for pointing out boundary errors, missing annotations, or unclear regions.
Image Issues
Flag concerns about the entire asset. Use for classification errors, image quality problems, or incorrect labeling approach.
Real-time performance insights
Track time spent, monitor productivity, and identify bottlenecks. See where work slows down and fix it.
Avg. Time per Asset
Detailed time tracking for each task
Team Throughput
Assets completed by all workers
Acceptance Rate
First-pass review approval
Ready to scale your annotation?
Set up multi-step annotation workflows with built-in review and quality control. Free trial, no credit card.
Related reading
MLOps for Computer Vision: The Complete Guide
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The Art of Annotation: Lessons Learned From a Picsellia Annotation Campaign
Discover invaluable insights from Picsellia's annotation marathon in Spain. Learn how Picsellia revolutionized annotation campaigns for optimal results.
Mastering Data Annotation for AI Projects in 2025
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Build High-Performing Teams for Computer Vision Projects
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