Parliamentary transcript refinement
Government, Public affairs
An AI application that turns raw transcriptions of Canadian Parliament sessions into clean final transcripts with accurate speaker labels.
AI Automation & Agents· Mobile App
Doctor Notes
The AI pipeline behind a medical-visit recorder: one consultation recording becomes an accurate transcript, a plain-language visit summary, and clinician formats such as SOAP notes and referral letters.
- Becomes a transcript, summary, SOAP note and referral letter
- 1 recording
- Transcription models compared before one was chosen
- Evaluated
- Diagnoses, tests, medications and next steps patients can follow
- Readable
The starting point
The challenge
Patients forget much of what is said in a consultation, and clinicians lose hours to notes and letters. Both need accurate documentation of the same conversation, in very different formats.
The solution
What we built
Compared transcription models on consultation audio before building on AssemblyAI.
Built plain-language visit summaries covering diagnoses, tests, medications and follow-up steps.
Added clinician formats, SOAP notes and referral letters, generated from the same transcript.
Managed and versioned prompts in PromptLayer, with a Django backend and LangChain orchestration.
Architecture
How the system works
- 01ConsultationRecorded visit audio
- 02TranscribeAssemblyAI, chosen by evaluation
- 03GenerateSummary, SOAP note, referral
- 04Review & sharePatients, families, clinicians
Engineering
How it was engineered
Chose the transcription model through a side-by-side evaluation on consultation audio, rather than by default.
Versioned every prompt in PromptLayer, so changes to medical outputs can be compared and rolled back.
Scope
What was delivered
01
Transcription model evaluation
02
Transcription pipeline
03
Visit summary generation
04
SOAP note and referral letter generation
05
Prompt management in PromptLayer
Technology
Stack
AssemblyAIOpenAILangChainPromptLayerDjango
More work
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