Automated Quality Governance for AI Training Data
A voice data operation replaced manual QC review with a 7-stage automated evaluation pipeline — routing 11 submissions per evaluator-hour, with full audit provenance for every decision.
Multi-language pipeline, 60–70% auto-routed, 100% audit provenance.
At a Glance
Key Metrics
Human Review Reduction
60-70%
Submissions resolved without a human reviewer touching them.
Time to POC
2 weeks
Read-only connection to a live proof-of-concept pipeline.
Audit Provenance
100%
Decisions with a traceable model, score, and timestamp record.
The Situation
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.
Data sources
Submission Database
Contributor audio submissions
Audio Storage
Raw recording files
LLM Evaluation API
Semantic quality scoring
Failure events
What Was Breaking
Why manual operations couldn't scale
No triage before review.
Every submission — obvious pass or genuine edge case alike — went to the same human review queue, with no scoring system to separate them.
Throughput was capped at human attention.
Review speed could only ever match reviewer capacity, with no way to absorb a submission spike.
There was no decision record.
Nothing captured why a submission was accepted or rejected — a growing liability as DPDP and EU AI Act provenance requirements started asking not just 'was this reviewed' but 'what specifically was evaluated, by what standard, and by whom.'
The Approach
Connect your pipeline sources
Submission DB, audio storage, and LLM API connected read-only — 2 weeks to POC.
Configure quality thresholds
Scoring dimensions, routing rules, and compliance requirements set in plain language — no ML engineering.
Autonmis routes every submission
7-stage evaluation runs automatically. Every decision is logged with full model and score provenance.
After
Submission Database
Contributor audio submissions
Audio Storage
Raw recording files
LLM Evaluation API
Semantic quality scoring
Autonmis
Governed Intelligence Layer
Knowledge Base
rules · thresholds · logic
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.
The Workflow
Results
60-70% reduction
Human review load through automated routing
Submissions scoring >0.85 composite auto-accepted
Audited against reviewer throughput in the period immediately before and after pipeline go-live.
>0.85 composite
Auto-accept threshold
No human review required above this score
100%
Audit trail completeness
Every decision traceable — model, score, timestamp
Every routed decision checked for a complete model-version, score, and timestamp record.
Under 5 minutes
Raw audio to routed and logged decision
End-to-end through 7-stage pipeline
Day one
Regulatory readiness
DPDP / EU AI Act provenance from first submission
Governance Note
Every routing decision resolves to a score computed against the same 7-dimension rubric the ops and compliance team defined up front — not a black-box model call with no record of what it evaluated.
Implementation
Time to live
2 weeks to live pipeline (POC); 6–8 weeks to production hardening
Sources connected
3 (submission database, audio storage, LLM evaluation API)
Engineering dependency
Zero for ops team queries; engineering only for model version updates
Ready to see it in your stack?
We can scope your use case to a live workflow
in the first session.
Three sources. No engineering dependency. First automation in under three weeks.
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