Specializations
Quality Assessment for Responsible AI
Independent review workflows for annotation quality, bias signals, model outputs, and compliance readiness before AI systems move forward.
Annotation quality checks
Measure consistency, reviewer agreement, edge-case handling, and readiness against the labeling guideline.
Bias and risk signals
Flag skewed samples, sensitive attributes, harmful content patterns, and fairness concerns for review.
Output evaluation
Review model responses for accuracy, instruction-following, safety, and domain-specific quality expectations.
Review reporting
Deliver clear findings, issue patterns, and remediation notes your team can act on before deployment.
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