How to build a meeting assistant with async transcription and LLM: Complete architecture guide

The comprehensive guide outlines the architecture for building a meeting assistant that utilizes asynchronous transcription and language models (LLMs) to enhance meeting intelligence. It highlights that asynchronous transcription is favored over real-time processing for its accuracy, cost-effectiveness, and infrastructure simplicity, offering advantages like full-context processing which aids in accurate punctuation, word disambiguation, and speaker diarization across multiple languages. The guide details a pipeline comprising steps from audio ingestion to LLM-based summarization, emphasizing the importance of choosing the right speech-to-text (STT) infrastructure to avoid unexpected costs and accuracy issues. It presents a comparison between self-hosted solutions and managed APIs, illustrating how bundled features at a fixed rate can be more economical than feature-metered pricing, especially at scale. Additionally, it discusses the importance of compliance and data privacy, outlining certifications and encryption measures. The document also addresses integration challenges with diverse audio inputs, such as code-switching, and provides insights into deploying a production-ready system that balances error handling, rate limits, and scalability.

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