tl;dv

tl;dv (short for “too long; didn’t view”) started life as a meeting recorder built around shareable clips. That is no longer the whole story. Over 2025 and into 2026 the Aachen, Germany company repositioned itself as a company-wide conversation-intelligence platform, adding autonomous AI agents, multi-meeting analysis, and sales coaching. The clip-sharing roots are still there, but tl;dv now competes more directly with Gong and Avoma than with a simple note-taker.

The platform records meetings on Zoom, Google Meet, and Microsoft Teams. A notetaker bot can join the call, and there is now a botless mode that records without a visible participant joining, though some users report the seamless path still leans on Zapier-style plumbing. During a meeting you or the AI can tag important moments, which become timestamped highlights in the transcript. Afterward you get a full transcript, an AI summary, and the ability to spin up short clips around any moment to share via link, embed in docs, or push to Slack, Notion, and CRM tools.

tl;dv built its early following in product and customer-research teams, where pulling a customer’s exact quote out of an hour-long call is genuinely valuable. That use case still holds, but the heavier investment now sits in revenue workflows. Transcription covers 40+ languages through OpenAI’s Whisper, which keeps it credible for international teams.

Key Features

The clip and highlight system remains the recognizable surface: create a shareable snippet from any moment, and let the AI auto-tag decisions and action items. Underneath, the platform has grown considerably.

AI Agents run after a meeting and act without a human in the loop. They draft follow-up emails, update deals in HubSpot or Salesforce, and create tasks in Trello, Asana, or Slack. Multi-Meeting Intelligence aggregates analysis across hundreds of calls to surface topic trends, recurring objections, and feature requests as company-wide reports rather than per-meeting summaries. For sales teams, AI Sales Coaching scores each rep against frameworks like MEDDIC, BANT, and SPIN, tracking objection handling, filler words, and playbook adherence.

The integration footprint has expanded sharply, from roughly 30 native connections to 5,000+ once Zapier and n8n are counted. A searchable meeting library organizes every recording. The free tier still includes unlimited recordings and transcripts, but AI summaries are the catch: you get 10 for the lifetime of the account, not 10 per month, and after that only the first ~10 minutes of each meeting are summarized. Free recordings are auto-deleted after 90 days.

Pricing

tl;dv now runs four tiers, not three. The free plan covers unlimited recordings and transcripts in 40+ languages but limits AI summaries to 10 total over the life of the account, with recordings deleted after 90 days. That lifetime cap is the main reason most users end up upgrading.

Pro is $18 per user per month on annual billing ($29 if paid monthly) and adds unlimited AI summaries, CRM integrations, unlimited clip downloads, and custom vocabulary. Business is $35 per user per month annual ($59 monthly) and houses the sales-intelligence stack: AI sales coaching, customizable MEDDIC/BANT/SPIN playbooks, objection-handling analysis, native Salesforce and HubSpot, and multi-meeting intelligence. Enterprise is custom-priced and adds privately hosted AI, SSO/SCIM, and advanced admin controls. The wide gap between annual and monthly rates comes from a standing 40%-off-annual promotion. Per-user pricing still adds up quickly for larger teams, and the features most revenue orgs actually want sit on Business rather than Pro.

Privacy & Security

tl;dv processes all recordings through its cloud infrastructure. The company is GDPR-compliant and operates under European data-handling standards, a genuine plus for EU-based teams. Meeting data is encrypted and users can set retention policies. Enterprise customers can opt for privately hosted AI plus SSO/SCIM. Even so, the cloud-based architecture means every meeting passes through tl;dv’s servers, and the AI analysis happens off-device. Recording can be done with a visible bot or in botless mode; either way, consent and participant awareness are worth establishing up front.

Best For

tl;dv fits revenue and customer-facing teams that want conversation intelligence without standing up a heavier platform like Gong. If you run a sales org that cares about playbook adherence, objection trends across deals, and an agent that updates the CRM on its own, the Business tier is built for exactly that. Product and UX researchers still get real value from the clip-and-share workflow. It is a weaker fit for teams with strict privacy requirements that rule out cloud processing, or anyone who needs full offline, on-device capability.

How tl;dv Compares to Hedy

tl;dv has grown well past its clip-sharing origins, and it is fair to say so: AI agents that act on the CRM, multi-meeting intelligence, sales-coaching scorecards, a botless recording mode, and native mobile apps are all real capabilities now. For a revenue team that lives in HubSpot or Salesforce, that stack is compelling.

Where Hedy holds a durable edge is architecture. Hedy runs its AI end-to-end on-device, transcription, summaries, and real-time coaching all happen locally, so meeting content never leaves the machine. tl;dv processes everything in the cloud. Hedy also captures audio directly without putting a bot in the call; tl;dv offers a botless mode but its core flow still centers on the notetaker participant. Both run on mobile, but Hedy’s mobile experience does on-device transcription and AI, while tl;dv’s app is a record-and-sync ‘Lite’ tool that ships audio back to the cloud for processing. And both provide live, in-meeting assistance, though Hedy’s real-time coaching runs offline by design.

The honest summary: if your goal is cloud-based conversation intelligence wired into a CRM, tl;dv is a strong, mature option. If you need a private, on-device assistant that works without a bot and without an internet connection, Hedy is the better architectural match.