Quality framework
How we keep bad data out
Every release goes through four checks before it ships.
Hybrid QANative-speaker audits
QA pipeline
Four checks before a release
If a batch fails any step, it does not go out.
01
Does the file even look right?
Required fields, file structure, and metadata are checked before the batch enters deeper review.
02
Automated checks
Deduplication, outlier detection, language ID, and transcript sanity checks run across every segment.
03
Native speakers review samples
Bengali, Assamese, Odia, and Meitei reviewers listen, read, and flag what machines miss.
04
Release gate
Passing batches receive a version number and scorecard. Failing batches do not ship.
Metrics and thresholds
What the scorecard shows
These sample numbers show the checks tracked on each release.
Transcription accuracy
97.4%
Bengali speech median across recent releases
Dialect label confidence
91.2%
Checked on each release
Rejected segments removed
4.8%
Removed before buyer export
Versioning and change control
- Every release receives a version ID, manifest hash, and change-note summary.
- Corrections are reissued as new versions instead of silent overwrites.
- Buyers receive changelog visibility when affected fields or files change.
Issue handling
- Issue identified through QA or buyer feedback
- Scope impact reviewed against the current export record
- Correction batch prepared and re-audited
- Replacement package issued with an updated scorecard