tl;dv

Captures video, audio, and screen share streams directly from Zoom and Google Meet using platform-specific SDKs and browser extension APIs, maintaining synchronization across multiple participant feeds and screen content. Records at native resolution and frame rate without requiring separate recording software or manual setup per meeting.

Converts recorded audio to timestamped text using automatic speech recognition (ASR) with speaker identification, attributing each spoken segment to the correct participant. Uses deep learning models fine-tuned for meeting speech patterns (overlapping speakers, technical jargon, accents) and generates searchable, editable transcripts with millisecond-level accuracy.

Analyzes complete transcripts and video content using large language models to generate concise summaries highlighting decisions, action items, and key discussion points. Uses prompt engineering and structured extraction to identify commitments, owners, and deadlines, then formats output as actionable summary cards with links back to video timestamps.

Automatically or manually creates short video clips (10 seconds to 5 minutes) from recorded meetings, preserving audio and video with precise timestamp anchoring. Clips can be shared via shareable links with granular permission controls, enabling teams to distribute specific discussion moments without sharing entire recordings. Clips include transcript excerpts and metadata for context.

Indexes all transcripts and meeting metadata (participants, date, duration, summary) in a searchable database, supporting both keyword search and semantic search using embeddings. Queries like 'customer complained about pricing' return relevant meetings even if exact phrase wasn't used, by matching semantic intent. Search results include timestamp links to relevant moments in video.

Integrates with CRM systems (Salesforce, HubSpot) and productivity tools (Slack, Microsoft Teams) to automatically link recordings to customer records, sync action items to task managers, and post meeting summaries to team channels. Uses webhook-based event streaming and API polling to maintain sync between tl;dv and external systems without manual data entry.

Aggregates data across all recorded meetings to generate analytics on team communication patterns, including meeting frequency, duration trends, participant engagement, and discussion topics. Uses statistical analysis and topic modeling to identify patterns (e.g., 'sales calls average 45 minutes', 'pricing discussed in 60% of customer calls'). Dashboards display metrics with drill-down capability to underlying meetings.

Maintains immutable audit logs of all recording access, sharing, and modifications, including who viewed recordings, when, and for how long. Supports compliance requirements (GDPR, HIPAA, SOC 2) by enabling data retention policies, access controls, and deletion workflows. Generates compliance reports documenting data handling and access patterns.

Automatically detects the language spoken in meetings and generates transcripts in the original language plus translations to configured target languages. Uses multilingual ASR models and neural machine translation to maintain speaker attribution and timestamps across language boundaries. Supports 50+ languages with varying accuracy depending on language prevalence in training data.