Case Study: Meeting Assistant

Problem

Professionals spend significant time taking notes during meetings rather than actively participating. Existing solutions often require inviting bots to calls, which can be intrusive or prohibited by company policies.

Constraints

The solution needed to run locally to capture system audio without requiring bot invitations. It also needed to be highly performant to capture audio seamlessly without impacting other system processes.

Architecture

Trade-offs & Engineering Decisions

• WPF over Electron: Chose WPF (.NET Core) for native Windows audio APIs (WASAPI) and lower memory footprint compared to Electron.
• Cloud APIs vs Local Models: Decided to use cloud APIs (Whisper/LLM) instead of running models locally to ensure the application could run on standard office laptops without discrete GPUs.

Challenges

Handling continuous audio streams without dropping frames while simultaneously preparing data payloads for the API was a significant challenge. Resolved by implementing asynchronous producer-consumer queues using C# Channels.

Outcome

A performant desktop tool that accurately transcribes and summarizes hour-long meetings into concise, structured notes in under 15 seconds after the meeting concludes.

Future Improvements

If this system needed to support enterprise-scale concurrent processing, I'd introduce an event-driven architecture using Kafka or SQS to queue transcription jobs, Redis for caching intermediate transcripts, and horizontal scaling of the backend workers.