Buggler AI
An AI meeting assistant that converts spoken discussions and recordings into structured summaries, minutes, action items, and ratings.
Project Overview
Buggler AI removes the tedious post-meeting documentation burden by transforming audio conversations into concise, structured executive summaries.
Meetings are recorded or uploaded, automatically transcribed, and distilled into prioritized action lists and discussion takeaways.
My Engineering Role
Application engineering for audio streaming capture, speech-to-text integration, LLM prompt engineering, and Stripe subscription monetization.
The Challenge
Long audio recordings, varying background noise, asynchronous transcription pipelines, and metered AI token usage each introduce potential failure points.
The Solution
The product separates audio recording, chunked uploads, transcription, and LLM processing into decoupled background jobs with persistent state.
High-Level Architecture
- Flutter mobile and web front-end paired with streaming audio capture and chunked upload pipelines.
- Microservice architecture integrating OpenAI/Whisper speech-to-text and LLM summarization APIs.
- Stripe billing webhook integration managing metered usage and tier entitlements.
Key Features & Workflows
- High-fidelity in-app audio recording with pause/resume functionality
- Automated multilingual speech-to-text transcription engine
- Executive summary extraction with key discussion point bullets
- Action item assignment with deadlines and accountability tracking
- Tiered Stripe subscription plans with metered token usage
Technical Challenges Overcome
- Managing large chunked audio uploads on poor cellular connections without corruption
- Prompt engineering to reliably extract structured action items and meeting ratings from transcripts
- Synchronizing local recordings with server-side processing queues across app lifecycles
Production Results & Impact
- A recorded portfolio demonstration is publicly linked.
- Eliminated manual meeting notes for corporate teams, saving hours per work week.
- Reliable chunked upload pipeline tested with multi-hour recorded sessions.
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