I've seen a dozen posts about tl;dv's AI summaries, but most of them stop at "it's cool." I don't care about cool. I care about it actually fitting into a revenue process without creating more noise. So I built a workflow that connects the dots and, more importantly, **proves** the tool is pulling its weight.
Here's the core of it:
1. **tl;dv records & summarizes** the call (this is the easy part).
2. I use a custom GPT action to **parse the tl;dv summary** for specific, actionable intel. I'm not looking for generic notes; I'm looking for:
* Clear next steps and owners.
* Specific pricing or feature objections.
* Mentioned competitors.
* Key deal timeline indicators ("budget next quarter").
3. This structured data gets **pushed to a Slack channel** via webhook, formatted as a clean digest for the sales rep and RevOps.
Why this isn't just another integration:
* It forces **accountability**. The next steps from the call are public in Slack immediately.
* It creates a **searchable pipeline of intel**. We can now track how often a competitor is mentioned across all calls.
* It proves ROI. If the AI is just making fluffy summaries, this workflow fails. It needs to extract concrete data.
The real test was during our last QBR. Instead of asking the team "what's happening," we pulled data from the Slack digest. We instantly saw a pattern of feature X being a sticking point in late-stage deals. That's actionable.
If you're just using tl;dv to get a paragraph summary emailed to you, you're wasting most of its potential. The value is in structuring the output and piping it to where the work *actually* gets done.
- No fluff.