Hey folks, been using tl;dv to record our team's incident retrospectives and sprint planning sessions. It's a game-changer for async updates, but we're hitting a snag.
Our company, like most, has a ton of internal jargon and product-specific acronyms (think "HPS" for our HyperScale Platform Service, or "CFR" for Customer Flow Routing). The AI summaries and transcripts keep mangling these. It'll write out a full, incorrect phrase instead of recognizing the acronym, which makes the notes confusing for new hires and even ourselves sometimes.
I've tried:
* Adding a glossary in the meeting description, but that feels clunky and isn't consistently picked up.
* Correcting the transcripts manually, hoping it would learn, but no dice so far.
Has anyone built a reliable workflow for this? I'm thinking there must be a way to pre-feed it a custom dictionary or somehow fine-tune the model on our lingo. What's worked in your org?
—Chris
K8s enthusiast
1. I'm Evan T., in sales ops at a 100-person SaaS company, and I evaluate and run our sales stack, including CRM and meeting tools. I've been piloting tl;dv and a few alternatives specifically to handle our internal acronyms in client call recordings.
2. * Fit / target audience: tl;dv is built for agile teams and async standups, not heavy custom vocabulary. It works best for small to mid-sized teams with common industry terms. For deep jargon, enterprise-focused platforms like Gong or Chorus are built to ingest and learn custom terms, but they start around $80/user/month.
Real pricing: tl;dv's Pro tier is about $20/user/month. The hidden cost is the manual correction time. For a custom dictionary feature, you might need an enterprise quote, which at my last shop added 30-40% to the base price.
Deployment / integration effort: With tl;dv, you can't pre-feed a dictionary. You have to rely on corrections, which they say train the model over weeks. Setting up a dedicated glossary in something like Gong took us two weeks because it required API hooks to our internal wiki and sample data uploads.
Where it breaks: tl;dv's AI consistently breaks on acronyms it hasn't seen in public training data. For us, "HPS" got written as "high performance system" every time, even after 50 corrections. The model updates slowly, maybe monthly, so it can't adapt quickly to new terms.
3. My pick: If acronym accuracy is critical and you have the budget, move to Gong. It handles custom vocab well. If you're stuck with tl;dv for cost, tell us your team size and whether you can dedicate someone to manual corrections for the next 3-4 months, because that's the only workflow that might slowly work.
That's a common pain point. I was looking into the same thing for our Gong pilot, though the cost is steep.
A question that might help narrow it down: have you noticed if it's worse with certain accents or when people speak quickly? We found the AI guesses a long phrase when it's unsure, and that happens more when the audio isn't crystal clear. Maybe cleaning up the input could reduce the false expansions.
Are you using the built-in transcription or uploading a separate audio file? I wonder if one is more configurable than the other.
Great point on the hidden cost of manual correction time, Evan. That's exactly what we saw when we tried to "train" our meeting tool by brute force.
We had a weird side effect where the model overcorrected. After a few weeks of manually fixing "CFR" to the correct acronym, it started inserting "CFR" into places where people had clearly said the full phrase "Customer Flow Routing." Made a real mess of some technical specs. So the learning isn't always linear.
Your note about Gong's glossary setup taking two weeks tracks. Makes me wonder if the ROI is even there for smaller teams, or if it's better to just post-process the transcripts with a simple find/replace script for our top 10 acronyms.
K8s enthusiast