Medical dictation workflow using local models and RAG.

A local AI workflow was created to reduce outsourced processing and cyber exposure by keeping transcription, drafting, and retrieval steps within a controlled local environment.

Problem

Dictation workflows can create operational drag and privacy risk when sensitive audio, transcripts, or draft notes are processed through external services. The goal was to create a smoother workflow that could reduce outsourced work while preserving control over sensitive content.

Approach

The workflow integrated multiple speech-to-text engines, local language models, and retrieval-augmented generation. Instead of relying on a single cloud tool, the pipeline allowed local transcription, structured drafting, reference retrieval, and review support.

The system design prioritised data boundaries, repeatable processing, and practical review steps rather than full automation without oversight.

Outcome

The proof of concept showed how local AI systems can reduce dependency on outsourced processing while supporting a more seamless internal workflow. The same pattern can apply to other sensitive document, audio, or knowledge workflows where privacy and operational control matter.