AI meeting clips and highlights tool that auto-generates shareable video clips from recorded calls. Enables async video updates and meeting summaries for remote teams.
tl;dv is an AI-powered meeting recording and clipping platform designed for remote teams who need to share meeting context asynchronously. The tool records video calls, automatically identifies key moments, and generates short clips that team members can watch on their own time—eliminating the need to replay full recordings or attend meetings synchronously. For remote operators, the core workflow is: record meeting → AI identifies highlights → auto-generate clips → share async. This reduces meeting fatigue and improves information distribution across distributed teams. The platform integrates with Zoom, Google Meet, and Microsoft Teams, capturing both video and transcripts. Key operator use cases include: (1) sharing critical decisions with stakeholders who missed the meeting, (2) creating onboarding clips from team calls, (3) building an async-first knowledge base of meeting moments, and (4) reducing time spent in status-update meetings by distributing highlights instead. tl;dv's AI identifies speakers, topics, and decision points automatically. Users can manually create clips or let the system suggest highlights based on engagement patterns and speaker changes. Clips are timestamped, searchable, and embeddable—making them useful for documentation and knowledge management. Pricing model is freemium: free tier includes basic recording and limited clip generation; paid plans unlock unlimited clips, advanced search, and team collaboration features (verify on vendor site for current pricing). The platform stores recordings in the cloud and provides transcript search, making it valuable for teams that need to reference past conversations. Limitations include: AI clip suggestions can miss context-specific importance (operator must review and curate), storage limits on free tier, and dependency on meeting platform integrations. Some teams report that clip generation works best when meetings have clear structure and speaker changes; less effective in highly collaborative or overlapping-speech scenarios.