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Data Annotation Use Case

Build annotation workflows with agent-powered QA

Use ContextOS to orchestrate multi-agent validation, connect your ML models, and maintain quality across annotation pipelines—all without migrating your existing tools.

Connect existing annotation platforms

Build agent workflows for validation & routing

Integrate your CV/ML models seamlessly

Full lineage and audit trails

How It Works

Quick overview. Learn more on the platform page →

1

Connect your data sources and annotation tools

2

Define ontology and quality rules

3

Build agent workflows for QA & validation

4

Deploy and monitor at scale

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Platform capabilities for annotation workflows

Build, connect, and orchestrate your data annotation infrastructure

Unified data ingestion

Connect to your existing annotation platforms, databases, and storage—no migration required. ContextOS orchestrates context across all sources.

Ontology-driven workflows

Define entity schemas and relationships once. Agents automatically understand structure, enforce consistency, and propagate changes across your pipeline.

Agent-based QA & routing

Build multi-agent workflows that validate annotations, flag issues, route edge cases to experts, and continuously improve quality thresholds.

Dynamic context for annotators

Surface only relevant examples, guidelines, and history for each task—reducing cognitive load and speeding up annotation without sacrificing quality.

ML model integration

Plug in your CV models, active learning loops, or pre-annotation services. ContextOS handles context routing, versioning, and feedback loops.

Real-time metrics & lineage

Track quality, throughput, and costs with full lineage. Every annotation decision is traceable, auditable, and replayable.

What you can build

Image & video annotation

Object detection, segmentation, classification—connect your CV models and orchestrate multi-stage validation workflows.

Structured data enrichment

Entity extraction, classification, linking across sources—use agents to enforce schema consistency and resolve conflicts.

Text & document processing

NER, sentiment, summarization—route ambiguous cases to experts while agents handle straightforward validation.

Ready to build better annotation workflows?

Start your free pilot and work with our team to build agent-powered QA workflows on your data

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