tl;dv

Captures audio and video from Zoom, Google Meet, and Teams calls directly through browser extension or native app integration without requiring a meeting bot to be invited. The capture mechanism operates client-side at the browser/app level, intercepting the media stream before it reaches the meeting platform's servers, then streams or buffers the raw audio/video for post-processing. This approach eliminates the need for explicit bot invitations and reduces meeting participant friction.

Converts captured meeting audio into timestamped text transcripts with speaker identification, enabling users to search and reference specific moments in calls. The transcription pipeline processes audio post-meeting (latency unknown) and generates word-level timestamps, allowing clips and summaries to reference exact moments. Speaker attribution mechanism is undisclosed but implied by action item extraction and CRM logging features that track who said what.

Offers a free tier that includes unlimited meeting recording, transcription, and basic summarization without time limits or meeting count restrictions. The free tier is designed to reduce friction for individual users and small teams to adopt tl;dv before upgrading to paid features. Specific limitations of the free tier (e.g., storage limits, feature restrictions, user seat limits) are not disclosed in documentation.

Generates post-meeting summaries using AI models with user-selectable frameworks (MEDDIC, Smart AI Topics, custom) that structure the summary output to match sales, product, or marketing workflows. The summarization engine processes the full transcript and produces abstractive summaries (not just extractive highlights) in 1-5 minutes (claimed 'instantly' but latency unknown). Users can define custom summary templates via prompts, enabling role-specific summaries (e.g., 'extract only objections and how they were handled' for sales, 'extract feature requests and prioritize by frequency' for product).

Identifies commitments, tasks, and next steps mentioned during meetings and extracts them as structured action items with speaker attribution, due date inference, and optional CRM task creation. The extraction uses NLP/LLM-based pattern matching to identify phrases like 'I'll send you', 'we need to', 'by next week', etc., and maps them to speakers and inferred deadlines. Extracted action items can be automatically logged to CRM systems or exported as task lists.

Automatically logs meeting summaries, transcripts, action items, and call outcomes to CRM systems (specific platforms unknown) without manual data entry. The integration maps tl;dv outputs (summary, action items, speaker attribution) to CRM fields (call notes, next steps, deal stage, etc.) and creates or updates CRM records based on meeting participants and detected deal context. Supports auto-drafting of follow-up emails and task creation within the CRM.

Enables full-text and semantic search across all recorded meetings to find specific topics, speakers, or moments, then generates shareable video/audio clips of matching segments. The search mechanism is undisclosed but likely combines transcript keyword matching with semantic embeddings to find conceptually similar moments across meetings. Clip generation extracts the relevant audio/video segment with context (speaker name, timestamp, summary) and produces a shareable link or downloadable file.

Analyzes patterns across multiple meetings to identify trends, recurring themes, and aggregate insights, then generates custom reports via email or dashboard. The analysis engine processes summaries and transcripts from multiple meetings, applies user-defined custom prompts (e.g., 'extract all customer pain points and rank by frequency'), and produces structured reports with visualizations (charts, tables — format unknown). Reports can be scheduled (daily, weekly, monthly) or generated on-demand.

Provides pre-configured dashboards tailored to sales, product, and marketing roles that surface relevant meeting insights without requiring custom configuration. Sales dashboards show objection tracking, deal progress, and commitment follow-ups; product dashboards show feature requests and bug reports; marketing dashboards show customer pain points and quotes. Dashboards aggregate data from multiple meetings and update as new meetings are recorded and processed.

Automatically identifies meeting participants from email addresses or video call metadata and matches them to CRM contacts, enabling automatic call logging to the correct account/contact record and enriching meeting context with CRM data (company, title, deal stage, etc.). The matching logic is undisclosed but likely uses email domain matching, exact name matching, and fuzzy matching for ambiguous cases. Matched contacts are used to populate CRM fields and determine deal/account context for reporting.

Supports meeting recording, transcription, and summarization in multiple languages, with explicit support for Japanese and Spanish mentioned and other languages unknown. The transcription and summarization pipelines process non-English audio and generate outputs in the source language, enabling global teams to use tl;dv without language barriers. Language detection is likely automatic based on audio characteristics.