Data Validation

Data your model can actually trust.

Independent validation and quality assurance of datasets and annotations — schema checks, gold-set audits and human review that catch the errors before your model does.

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98%accuracy after QASchema checksConsistencyGold-set auditHuman review
98%
Post-QA accuracy
4-stage
QA workflow
700+
Reviewers & SMEs
25+
Languages
What we check

Quality you can prove

Whether the data is ours or yours, we validate it against a written standard and give you the metrics to trust it — or the findings to fix it.

Schema & Structure

  • Format & schema conformance
  • Completeness & coverage
  • Duplicate & leakage checks
  • Label taxonomy compliance

Accuracy & Gold Sets

  • Gold-standard benchmarking
  • Sampling & error rates
  • Inter-annotator agreement
  • Root-cause analysis

Human Review

  • Expert spot-checks
  • SME domain validation
  • Edge-case adjudication
  • Rework recommendations

Bias & Drift

  • Distribution & balance checks
  • Bias & fairness review
  • Drift monitoring over time
  • Representativeness analysis

Multilingual QA

  • Native-speaker validation
  • Locale-specific checks
  • Terminology consistency
  • Cross-language parity

Reporting

  • Audit-ready QA reports
  • Metric dashboards
  • Acceptance sign-off
  • Continuous monitoring
Why Graveiens

Why teams choose Graveiens

Compliance-first delivery and a pay-on-approval model that de-risks every engagement.

Independent view

A second set of expert eyes on data quality — yours or a third party.

Measurable

Gold-set metrics and agreement scores you can defend.

SME depth

Domain experts for judgments automated checks cannot make.

Pay on approval

Invoiced only for approved deliverables.

FAQ

Questions, answered

Can you validate data we produced elsewhere?
Yes. We run independent QA on datasets and annotations from any source, benchmarking against a gold set and reporting accuracy, agreement and error types.
What metrics do you report?
Accuracy vs. gold set, inter-annotator agreement, error taxonomy, coverage and distribution checks — packaged into an audit-ready report.
Do you check for bias and drift?
Yes — distribution balance, fairness review and drift monitoring over time.
How do we start?
A sample validation batch so you can see the depth of review before scaling.

Related services

Data Annotation LLM Evaluation Data Collection Specialized Workforce

Validate your dataset with experts

Send us a sample task. You only pay for deliverables you approve.

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