Annotation operations come in many shapes. Some have a small team of domain experts reviewing every label. Others have hundreds of annotators across time zones feeding into a multi-stage review pipeline. Kili's workflow engine supports both — and everything in between. Define multi-step review chains with configurable sampling rates between steps. Enforce step separation so no one reviews their own work. Set up consensus workflows where multiple annotators label the same asset and agreement is measured automatically. Assign roles — Labeler, Reviewer, Team Manager, Project Admin — and let the platform handle task distribution, conflict prevention, and quality gating. The result: consistent output quality whether you're running 5 projects or 50.

When you're running annotation at scale, you don't create projects one by one in a UI. You create them programmatically. Kili's Python SDK and GraphQL API give you full control over the annotation lifecycle: create projects, upload assets, configure ontologies, assign teams, trigger quality workflows, and export labeled data — all from code. Connect your ML models for pre-annotation that makes human labeling 2–10x faster. Use webhooks to trigger downstream pipelines when labeling reaches your quality threshold. Build custom QA plugins that flag annotations automatically. Import model predictions for human review. Export in Kili, YOLO, Pascal VOC, or COCO format directly into your training pipeline. For operations that run continuously, this is how you keep the work flowing without manual bottlenecks.