Otter.ai

Captures live meeting audio streams and converts speech to text in real-time using automatic speech recognition (ASR) models, with speaker identification that labels which participant spoke each segment. The system likely uses streaming ASR APIs (possibly cloud-based like Google Cloud Speech-to-Text or proprietary models) combined with speaker embedding models to distinguish between multiple voices without requiring manual speaker identification.

Processes the full transcript and audio metadata to automatically generate structured meeting notes by identifying and extracting key discussion points, decisions, and action items using NLP-based summarization and entity extraction. The system likely uses transformer-based models (BERT, T5, or similar) to identify important segments, cluster related topics, and rank them by relevance, then formats them into a structured note document.

Detects when slides are shared during a meeting (via screen sharing detection or direct slide input) and automatically captures slide images, then applies optical character recognition (OCR) to extract text content from slides. The system likely monitors video frames during screen sharing, detects slide transitions using image hashing or scene detection, and runs OCR (possibly Tesseract or cloud-based vision APIs) to index slide content alongside the transcript.

Provides full-text search and semantic search capabilities across all captured meeting data (transcripts, generated notes, and OCR'd slide text) using indexed search databases and embedding-based retrieval. The system likely maintains a searchable index of all meeting content, supports keyword search with filters (by date, speaker, meeting type), and may use semantic embeddings to find conceptually related content even with different wording.

Generates concise summaries of meetings at different abstraction levels (executive summary, detailed summary, key points only) using abstractive summarization techniques. The system likely uses transformer-based summarization models (T5, BART, or similar) trained on meeting data, with configurable length constraints and focus areas (decisions, action items, discussion topics) to produce summaries tailored to different audiences.

Stores audio and video recordings of meetings in cloud infrastructure with indexed playback capabilities, allowing users to jump to specific timestamps, search for content, and replay segments. The system likely uses cloud object storage (S3-like) for recordings, maintains a searchable index of timestamps linked to transcript segments, and provides a web/app player with seek-to-timestamp functionality.

Automatically detects and captures meetings from calendar systems (Google Calendar, Outlook) and links meeting recordings/notes to CRM records (Salesforce, HubSpot) or project management tools. The system likely uses OAuth-based calendar API integrations to detect meeting invites, automatically joins or records meetings, and provides webhook/API endpoints to push meeting data to downstream systems.

Allows multiple team members to view, edit, and comment on meeting transcripts and notes in real-time or asynchronously, with version history and change tracking. The system likely uses operational transformation or CRDT-based conflict resolution for concurrent edits, maintains a change log with timestamps and user attribution, and provides commenting threads linked to specific transcript segments.

Aggregates data across multiple meetings to provide analytics and insights such as meeting frequency, attendee patterns, discussion topics over time, and meeting effectiveness metrics. The system likely processes transcript metadata (duration, attendees, topics) and generates visualizations using business intelligence patterns, with configurable dashboards showing trends and anomalies.