Recorded interviews contained valuable information, but turning that material into usable documents required several manual steps and introduced quality risk along the way.
Teams had to manage transcription output, draft summaries, report formatting, and review separately. That slowed turnaround, reduced consistency, and made it harder to trust the final document set when multiple versions or reviewers were involved.
The business needed a workflow that could move from raw audio to finished documents while preserving traceability and giving administrators visibility into AI output quality.
We built a document workflow that starts with uploaded interview audio and moves through transcription, summarisation, and export using a shared document model.
The workflow compresses a multi-step documentation process into a connected pipeline that is easier to review, easier to export, and easier to govern.
Users get a faster path from interview recording to usable output, while administrators gain oversight of where summaries are compliant, where they need revision, and how quality changes over time.
Recorded interviews move through transcription, summarisation, and report generation in one connected workflow.
Primary document outputs generated from each interview recording.
Shared document model across preview and export, with quality oversight.