Buyer problem
Datasets can appear complete while containing hidden duplicates, contradictory labels, malformed records, non-Hebrew content, privacy exposure, missing metadata, or severe coverage imbalance.
Suitable use cases
- Pre-training dataset review
- Vendor-delivery acceptance testing
- Dataset migration or format conversion
- Remediation before resale or client delivery
- Regression checks after dataset updates
Inputs required from the client
- Dataset files and schema
- Intended model or evaluation use
- Existing guidelines and label definitions
- Known risk areas and required thresholds
- Privacy and access restrictions
Method and workflow
The audit combines automated structural checks with native Hebrew review and sampled manual inspection. Defects are grouped by type, severity, frequency, and remediation effort.
Deliverables
- Dataset inventory and validation summary
- Duplicate and malformed-file report
- Label-consistency findings
- Language and coverage analysis
- PII and sensitive-data findings
- Prioritized remediation plan
- Optional corrected dataset or repair scripts
Acceptance criteria and quality metrics
Metrics may include duplicate rate, invalid-record rate, missing-field rate, label-conflict rate, Hebrew-language compliance, PII finding rate, and coverage distribution by category.
Example output
Review the sample batch QA report and sample batch acceptance summary.
Data-handling considerations
Audits may involve sensitive or proprietary datasets. Access, storage, deletion, permitted tooling, and secure transfer requirements are defined before data is received.
Request a dataset quality audit pilot
A pilot audit can examine a representative sample, validate the defect taxonomy, and estimate the remediation scope before a full review.