Annotation & Workflow Management :
Build end-to-end annotation workflows
Standardize annotation processes and guidelines
Develop team instructions and training modules
Create evaluation criteria and scoring frameworks
Manage multi-layer quality-control loops
Ensure labeling consistency across teams and datasets
Dataset Organization & Structure :
Organize large datasets for efficient AI model training
Maintain clean, scalable data structures
Structure files, metadata, and export formats
Perform ongoing dataset updates and quality audits
AI Output Review & Human-in-the-Loop :
Review, correct, and validate AI-generated outputs
Provide human-in-the-loop feedback to improve model performance
Deliver targeted corrections as AI Data Trainers
Data Preparation & Collection :
Collect raw text, images, audio, video, and document datasets
Clean, validate, and filter data (remove noise, errors, duplicates)
Prepare training-ready datasets in standard formats
Safety & Moderation Support :
Identify and flag unsafe or harmful content
Provide moderation datasets used for AI safety training
