Loopin AI

Captures audio/video streams from calendar-integrated meetings (Zoom, Google Meet, Microsoft Teams) and applies automatic speech-to-text conversion with speaker identification. Uses audio processing pipelines to segment speakers and timestamp utterances, enabling accurate multi-participant transcripts without manual speaker labeling. Integrates directly with calendar systems to auto-detect meeting start/end times and participant lists.

Processes transcripts through NLP models to generate concise meeting summaries using both extractive (key sentences from original transcript) and abstractive (LLM-generated synthesis) approaches. Applies topic modeling to identify discussion themes, action items, and decisions. Summaries are generated asynchronously post-meeting and can be customized by summary length, focus area (decisions vs. action items vs. full recap), and audience (executive summary vs. detailed notes).

Automatically structures meeting transcripts, summaries, and action items into formatted notes and syncs them back to calendar events as descriptions, attachments, or linked documents. Uses calendar API integrations (Google Calendar, Outlook) to update event metadata, create follow-up tasks, and link related meetings. Supports multiple output formats (Markdown, HTML, PDF) and can push notes to external tools (Notion, Confluence, OneNote) via API webhooks or native integrations.

Provides a shared workspace where meeting participants can view transcripts, summaries, and notes in real-time or asynchronously, with inline commenting, highlighting, and annotation capabilities. Uses operational transformation or CRDT-based conflict resolution to handle concurrent edits from multiple users. Supports threaded discussions on specific transcript segments, allowing teams to debate interpretations or clarify action items without disrupting the original record.

Integrates with calendar systems to display real-time availability across meeting participants, detect scheduling conflicts, and suggest optimal meeting times. Uses calendar data (busy/free blocks, time zone information, existing commitments) to rank time slot suggestions by participant availability. Can auto-schedule follow-up meetings based on action items or decisions from previous meetings, with automatic invitations sent to relevant participants.

Aggregates data across multiple meetings to surface patterns: meeting frequency trends, average duration, participant overlap, decision velocity, action item completion rates, and topic clustering. Uses time-series analysis to detect anomalies (e.g., meetings becoming longer over time) and provides visualizations (charts, heatmaps) of meeting patterns. Can segment insights by team, project, or participant to identify bottlenecks or inefficiencies in meeting culture.

Transcribes meetings in 50+ languages and automatically detects language switches mid-meeting. Uses language-specific acoustic models and can handle regional dialects (e.g., Indian English, Brazilian Portuguese). Provides real-time or post-meeting translation to English or other target languages, with speaker-aware translation that preserves speaker identity in translated transcripts. Supports code-switching (mixing multiple languages in single utterance) common in multilingual teams.

Indexes all meeting transcripts, summaries, and metadata to enable full-text and semantic search across meeting history. Uses vector embeddings to find semantically similar meetings (e.g., 'meetings about pricing strategy') even if exact keywords don't match. Supports filtering by date range, participant, topic, or meeting outcome (decisions made, action items created). Returns ranked results with highlighted relevant transcript segments and context snippets.