Automated Quality Governance for AI Training Data

A voice data operation collecting Indic language recordings for AI training had no automated quality layer between contributor submission and dataset delivery. Every submission went to a human reviewer who had to assess audio quality, transcription accuracy, language authenticity, and prompt adherence manually — with no scoring system, no routing logic, and no audit record. Review throughput was capped at what human attention could sustain. Regulatory and lab compliance requirements increasingly demanded a traceable decision chain: which model evaluated which sample, what scores each dimension received, and what routing decision was made and why.

Built a 7-stage evaluation pipeline on top of the Autonmis governed infrastructure: audio quality gate, ASR transcription, language and code-switch detection, acoustic scoring across 7 dimensions, LLM semantic evaluation (authenticity, naturalness, prompt adherence), weighted score aggregation, and confidence-based routing to auto-accept, human review, expert review, or auto-reject. Every stage wrote structured outputs to the database. The human review interface showed the transcript with language segments highlighted, a 10-dimension radar chart, and a one-click accept/reject/flag decision — with every action logged to an immutable audit trail including model versions, score reasoning, and timestamps.

The governance layer tracked every state transition from submission through final dataset inclusion. A non-technical ops lead could query routing distributions, score trends, and per-contributor quality without writing a single line of SQL.