The Two-Layer Problem
AI meeting tools solve two distinct problems: accurate speech-to-text transcription and intelligent meeting analysis (summaries, action items, topic detection). The quality of the second layer depends entirely on the quality of the first — garbled transcription produces useless summaries.
Speech Recognition Architecture
Both Otter.ai and Fireflies use large neural ASR (Automatic Speech Recognition) models based on the Wav2Vec or Whisper architecture — transformers trained on massive audio datasets. Speaker diarization (identifying who is speaking) uses a separate model that clusters audio embeddings by speaker characteristics. Accuracy on clear audio with native English speakers approaches 95%+; accuracy degrades with accents, background noise, and technical vocabulary.
Meeting Intelligence: Where They Differ
Otter's summary and action item extraction uses a fine-tuned LLM with meeting-specific training — optimized to identify commitments, questions, and decisions in conversational contexts. Fireflies adds CRM integration, searching across meeting history, and topic-based meeting analytics. The underlying NLP quality is comparable; the differentiation is in workflow integration.
The Real Limitation
Neither tool handles technical vocabulary well without customization — medical terms, legal jargon, product names, and acronyms are frequently misrecognized. Domain-specific vocabulary lists improve accuracy significantly but require manual maintenance. For highly technical meetings, human review of AI transcripts remains necessary. See Otter.ai in our catalog →