Medical Transcription & Clinical Reporting Software
ClinNote AI
A cross-language desktop product for capturing clinical audio, transcribing it locally, and producing structured medical reports.
Client-Used Software
- Tauri
- TypeScript
- Rust
- Python
- WASAPI
- Whisper
- Firestore

Ownership
Engineered the product end to end across its desktop interface, native audio layer, transcription pipeline, and supporting services.
Domain
Medical Transcription & Clinical Reporting Software
Store Status
Client-used product
01Challenge
Product Constraints & Engineering Scope
Build a high-reliability desktop clinical recording and transcription solution that captures clean multi-channel audio, executes local AI speech-to-text without cloud latency, and generates structured medical documentation.
02Architecture
Mobile Architecture & State Patterns
- Architected using Tauri desktop runtime with a TypeScript frontend and high-performance native Rust core.
- Implemented cross-language inter-process communication (IPC) bridging the UI with native audio pipelines.
- Engineered patient records, clinic management, and customizable clinical report templates.
03Integration
Backend, APIs & Platform Boundary
- Integrated Windows WASAPI in native code for dual microphone and system-audio loopback capture.
- Integrated local Whisper transcription pipeline with fallback to OpenAI cloud endpoints.
- Synchronized structured patient clinical notes and templates with Google Cloud Firestore.
04Performance
Performance & Data Optimizations
- Utilized Rust for low-overhead audio buffering and WASAPI hardware interaction, minimizing CPU footprint during active consultations.
- Local Whisper speech processing ensures confidential patient audio remains on-device whenever required.
05Outcomes
Verified Outcomes & Production Delivery
- Delivered end-to-end desktop software currently deployed and used by the healthcare client.
- Eliminated transcription delays and streamlined clinical SOAP note documentation.
- Showcases systems-level engineering breadth across Rust, Python, Tauri, and native audio APIs.