ClinNote AI

01

The Challenge

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.

02

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.

03

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.

04

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.

05

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.