0%Transcription errors

Taking the rework out of transcription

Transcription accuracy is not really a transcription problem. It is a review problem: the cost sits in catching and correcting what went wrong, and in the downstream consequences of what nobody caught.

A copyist writing with a quill at a pale wooden desk beside a tall open window overlooking a sunlit sea, with completed pages stacked beside him and a laurel sprig across the desk

// engagement

Sector: Document-intensive operation
Services: Speech-to-textDocument processingHuman-in-the-loop review
Headline result: 0% transcription errors achieved

// the challenge

What was in the way

Transcription was manual and slow, and turnaround stretched as volume grew.

Errors were found downstream rather than at the point of entry, so corrections rippled through records that had already been used.

There was no measurement of the error rate, which made it impossible to say whether any change was an improvement.

// the approach

How we worked it

Measure the baseline first

An accuracy claim means nothing without a defined sample and a defined method. We establish how errors are counted before changing anything, or there is no way to prove the result.

Automate the transcription, keep the verification

Speech-to-text handles the volume. The accuracy comes from the checking layer around it — validation rules, confidence thresholds and review of anything the system is unsure about.

Put review where errors are cheap

Catching an error at entry costs a moment. Catching it three systems downstream costs a great deal more. The workflow is built so verification happens at the earliest point.

Keep measuring after launch

Accuracy drifts as inputs change. Ongoing sampling means a decline surfaces as a metric rather than as a complaint.

// results