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AI quality assurance: from recordings to service evaluation

Recorded conversations provide useful context for reviewing service quality. Our development experience includes a system that turns audio into transcripts and structured evaluations, while allowing reviewers to check and correct results. This summary does not disclose client identities or conversation data.

From audio to reviewable transcripts

The workflow starts with recording uploads, speech-to-text transcription, and speaker role identification. Audio feature extraction adds information for evaluation. Processing runs through a queue with job status and failure handling, so the team can follow each stage without carrying out every step manually.

Evaluation grounded in rubrics and reference knowledge

The AI evaluator uses transcripts, assessment rubrics, and product and procedure knowledge as references. Outputs include structured assessments, evidence from the conversation, and coaching notes. The system recalculates final scores using rubric weights. This separates AI interpretation from score calculation and makes the reasoning behind an assessment easier to inspect.

Reviewers retain the final decision

Reviewers can inspect and correct assessments with notes, after which the system recalculates the results. Corrections can inform subsequent evaluations as examples and context; this does not imply model retraining. Processing history supports traceability, while report exports help the team summarize and discuss results.

Lessons for your business

A workflow like this needs clear quality definitions, human assessment examples, and testing against representative recordings. Access to recordings, processing permission, storage, and the AI services involved should be discussed early. Each business has different requirements. This experience informs scope and evaluation discussions, rather than guaranteeing a particular accuracy or savings figure.

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