A technical publication on how AI meeting intelligence actually works — models, pipelines, tradeoffs, and the architecture behind accurate t

Signal to Summary — Page 2

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Signal to Summary

A technical publication on how AI meeting intelligence actually works — models, pipelines, tradeoffs, and the architecture behind accurate t

Cover illustration for “Bot-Based vs. Bot-Free Meeting Capture Architecture”
Recording Architecture

Bot-Based vs. Bot-Free Meeting Capture Architecture

 Cover illustration for “Topic Segmentation and Meeting Structure Inference From Transcripts”
Summarization and NLP

Topic Segmentation and Meeting Structure Inference From Transcripts

 Cover illustration for “Noise Robustness in Real-World Meeting Environments”
Speech Recognition

Noise Robustness in Real-World Meeting Environments

 Cover illustration for “Structured Output Formats for Meeting Note Portability”
Summarization and NLP

Structured Output Formats for Meeting Note Portability

 Cover illustration for “Streaming vs. Batch Transcription for Live Meeting Use Cases”
Speech Recognition

Streaming vs. Batch Transcription for Live Meeting Use Cases

 Cover illustration for “Sentiment and Tone Analysis in Sales Call Intelligence”
Summarization and NLP

Sentiment and Tone Analysis in Sales Call Intelligence

 Cover illustration for “Word Error Rate as a Meeting Transcription Quality Metric”
Speech Recognition

Word Error Rate as a Meeting Transcription Quality Metric

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Summarization and NLP

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Cover illustration for “Decision Detection in Meeting Transcripts”
Summarization and NLP

Decision Detection in Meeting Transcripts

Separating real commitments from sounding-like-agreements is where meeting AI still struggles most.

Marcus Llewellyn · September 30, 2026 · 10 min read
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Speech Recognition

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