Buyer problem
Hebrew datasets often fail because the source rules are unclear, the language coverage is narrow, files are malformed, metadata is inconsistent, or quality checks occur too late. A production dataset needs explicit requirements before collection begins.
Suitable use cases
- LLM instruction-tuning and evaluation examples
- Hebrew NLP classification and intent datasets
- OCR and document AI source documents
- ASR and speech-recognition recordings
- Enterprise automation test data
Inputs required from the client
- Intended model or workflow use
- Required data modality and Hebrew variant
- Permitted and prohibited sources
- Volume, file formats, metadata schema, and timeline
- Privacy, retention, transfer, and exclusivity requirements
Method and workflow
The engagement begins with a written scope and source policy. Collection guidelines define what qualifies, what must be rejected, how files are named, which metadata fields are mandatory, and how sensitive information is handled. A pilot batch is reviewed before production.
Deliverables
- Organized source files
- Metadata sheet or JSON schema
- Source-status and rights-status fields
- Redaction log where applicable
- QA report and acceptance summary
Acceptance criteria and quality metrics
Metrics can include language compliance, duplicate rate, file validity, metadata completeness, redaction completeness, category balance, reviewer agreement, and batch acceptance rate. Thresholds are agreed during scoping.
Example output
Review the sample metadata schema and sample batch QA report.
Data-handling considerations
Projects should use client-provided, public, synthetic, consented, or otherwise rights-cleared sources. Do not send confidential production files through the first inquiry form. Secure exchange requirements are defined after scope review.
Request a Hebrew AI training-data pilot
A pilot can validate the source rules, schema, delivery format, and acceptance thresholds before the collection workflow expands.