Building a meeting summarization pipeline: async STT + LLM in 5 steps

Building a meeting summarization pipeline involves integrating asynchronous speech-to-text (STT) technology with large language models (LLMs) to accurately transcribe and summarize audio from meetings. The process includes five key steps: configuring audio ingestion, integrating an async STT API, validating diarized speech data, engineering LLM prompts, and formatting output. The async STT approach, exemplified by Solaria-1, provides advantages in accuracy and diarization quality by processing the entire audio context before generating transcriptions, which is crucial for reliable downstream summaries. This method is cost-effective, supports over 100 languages, and handles code-switching, making it suitable for multilingual and complex meeting scenarios. The pipeline reduces infrastructure overhead by using webhook-driven architectures and offers predictable pricing, while ensuring high transcription accuracy that prevents errors from propagating into summaries and CRM entries. The ultimate goal is to deliver actionable meeting insights efficiently, with the pipeline designed to handle large volumes of audio data while maintaining low latency and high reliability.

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