Buyer problem
Annotation quality declines when labels are ambiguous, edge cases are undocumented, reviewers apply different interpretations, or Hebrew language details are treated as generic multilingual content.
Suitable use cases
- Intent, topic, domain, safety, and quality labels
- Email and business-text classification
- Document fields, layout zones, tables, and key-value pairs
- Speech transcription and audio-quality labels
- LLM output grading and error taxonomy assignment
Inputs required from the client
- Task definition and intended model use
- Draft label taxonomy or target outcomes
- Examples of clear positives, negatives, and edge cases
- Required annotation platform or output format
- Agreement and acceptance thresholds
Method and workflow
Guidelines define each label, exclusion criteria, ambiguity rules, and review path. A calibration set is annotated first, disagreements are reviewed, and instructions are revised before production.
Deliverables
- Annotated files or records
- Annotation guideline and decision log
- Reviewer notes and disagreement records
- Redaction or anonymization log
- QA summary and acceptance counts
Acceptance criteria and quality metrics
Relevant metrics include annotation completeness, inter-reviewer agreement, adjudication rate, redaction recall, schema validity, duplicate rate, and error rate by category.
Example output
Review the sample annotation guideline and sample redaction log.
Data-handling considerations
Annotation projects should define whether data contains personal information, confidential client material, regulated content, or restricted fields. Access and retention rules are set before production.
Request a Hebrew annotation calibration pilot
Start with a small calibration set to test the label taxonomy, reviewer instructions, output schema, and acceptance method.