Quality Control
The ongoing verification of AI outputs and processes against benchmarks and test cases to catch defects, bias incidents, or policy violations.
A continuous practice where sample outputs are tested against known ground-truth cases, policy-violation detectors, and fairness checks. QC pipelines run in parallel to production, flag anomalies (e.g., unexpected error patterns), and feed issues into defect-tracking systems. Governance defines QC sampling rates, validation criteria, and remediation workflows to keep system integrity high.
A content-moderation AI’s Quality Control system samples 1% of flagged posts daily and compares them to human-moderator judgments. Discrepancies trigger immediate retraining if error rates exceed 5%, ensuring ongoing alignment between automated and human standards.
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
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Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.