Documentation Team
Streamlines Interview-to-Report Workflows with AI-Assisted Transcript and Summary Generation

Recorded interviews contained valuable information, but turning that material into usable documents required several manual steps and introduced quality risk along the way.

A single interview recording might produce two to three hours of audio. Transcribing that manually consumed most of a working day, and the resulting document still required a documenter to read through it entirely before they could draft a summary. That summary then needed to be reformatted into a branded report template, checked against the original recording for accuracy, and submitted for supervisor review — often days after the interview took place.

Version control was a persistent problem. When multiple reviewers had access to the same document, edits accumulated without clear attribution. A summary that had been corrected by one reviewer might be overwritten by another working from an older copy. There was no reliable way to know which version of a document was current or what had changed between drafts. Different documenters also produced outputs of noticeably different quality — some followed the required structure precisely while others summarised at a higher level, producing reports that were harder for supervisors to evaluate consistently.

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 shared document model is the architectural decision that holds the workflow together. Rather than generating a transcript in one place, a summary in another, and a report in a third, all three outputs draw from and write back to a single underlying document record. When a reviewer makes a correction to the summary, that change is reflected in the report preview immediately. There is no re-import step, no copy-paste between documents, and no risk of a reviewer working from a transcript that has already been superseded. One change propagates correctly to every output that depends on it.

The quality review dashboard gives supervisors oversight without creating a bottleneck. Rather than having to read every summary in full to assess quality, supervisors can see at a glance which submissions are flagged for review, which have been approved, and where patterns of non-compliance or recurring issues are emerging across the full document set. That overview allows targeted intervention — a supervisor can identify that a particular documenter consistently omits a required section and address it directly, rather than discovering the problem one submission at a time.

Technology Stack

• Transcription AI: Recorded interviews are transcribed with speaker-aware processing so transcript output is easier to review and reuse.
• Document AI: Summaries are generated from completed transcripts through a structured summarisation pipeline rather than ad hoc drafting.
• Document builder: Transcript and summary outputs are assembled into consistent report formats ready for export and download.
• Version management: Historical summary versions can be previewed and exported so teams are not locked into a single output state.
• Quality dashboard: Administrators can monitor audit outcomes, issue categories, and recent problem summaries through one analytics surface.

Across the team, output consistency improved in a way that speed metrics alone would not capture. Every documenter now works from the same structured pipeline: the same transcript format, the same summary fields, the same report template. The variation that previously existed between a highly experienced documenter and a recently trained one narrowed substantially, because the workflow enforces structure rather than relying on individual discipline to maintain it.

The version control problem was resolved by design. Because there is only one document record per investigation, there is no scenario in which two reviewers can produce conflicting edits to different versions of the same document. Changes are sequential and attributed, and previous versions remain accessible if an earlier state needs to be reviewed or recovered.

Supervisors report that the quality dashboard changed how they manage the team. Rather than sampling submissions randomly and hoping to catch problems, they can prioritise review time toward the submissions the system has flagged, and track whether specific issue types are recurring or resolving over time. That combination of speed and governance — faster output with better oversight — is what the team needed from day one.