Okay, I have to admit I was a little nervous about this. We had just wrapped up Q2 and our leadership wanted a consolidated analysis of all our quarterly check-in calls—think 50+ recordings across various teams. Manually clipping and notating that in tl;dv would have taken forever.
So, I finally took the plunge and used their bulk processing feature. My goal was to generate summaries and pull out key discussion points about OKR blockers from all those meetings.
Here’s my quick rundown of how it went:
**The Good:**
* The setup was surprisingly straightforward. You just create a "Bulk Action," pick your date range and filters (I filtered by the "Q2-Check-in" tag we use), and select what you want it to do. I had it generating AI summaries and tagging moments where "blocker" was mentioned.
* Huge time saver. Once it started running, I could leave it alone. Processing took a few hours, but that's versus days of manual work.
* The output CSV was a lifesaver for our people analytics. It gave me a spreadsheet with links to each call, the summary, and timestamps for the tagged moments. Easy to slice and dice.
**The Gotchas:**
* You **have** to be consistent with your tagging or filtering for this to work well. A few calls were missing the proper tag, so I had to add them and rerun the batch.
* The AI summaries are consistent, but they're somewhat high-level. For deep analysis, I still needed to skim a few of the actual clips it flagged.
* Cost can sneak up on you if you're processing a *lot* of hours in one go. Definitely check your plan's limits.
Overall, it was a win for us. It turned an overwhelming task into something manageable. I'd recommend it for anyone needing to surface themes or metrics from a large batch of calls.
Has anyone else tried this at scale? I'm curious if you used it for engagement surveys or exit interview analysis and have any other tips.
—Emma
Hold your horses on calling it a lifesaver. You ran it on 50 calls? That's it? That's not a quarter's worth, that's barely a few teams' worth.
Your biggest gotcha, the consistency of tagging, is the whole ballgame. It's classic garbage in, garbage out, and no bulk process fixes bad source data. But I'm more concerned about the "few hours" of processing time. Was that wall-clock time where you could do other things, or did it pin your CPU? At scale, that cost and performance curve won't be linear. Run it on 500 calls and let me know if it's still just a "few hours" or if your cloud bill starts twitching.
And a CSV as the grand output? Hope you never need to audit how a specific "blocker" mention was interpreted. You're trusting a black box summary and a timestamp. That's fine for a high-level report, but don't mistake it for analysis.
Anecdotes aren't data.
Nice work getting that done. The time savings are real even at 50 calls, and the CSV export is handy for quick slicing. My only caveat is the tagging consistency thing you mentioned - it's a bigger pain than people expect. We had to run a separate "tag hygiene" workshop before our bulk run because half the team was using "blocker" and the other half was using "obstacle" or just typing free-form notes. The bulk tool is great, but it's merciless on sloppy input. Did you have to clean up your tags first or did you get lucky?
Trust the trial period.
The "huge time saver" part is what makes me twitch. Sure, you saved days of manual work on your end. But what about the compute time and cost on their end, which you're undoubtedly paying for in your subscription? You said a few hours for 50 calls. That's not linear. At 500 calls, you're not looking at a day, you're looking at a week of processing and a massive spike in your usage-based fees, assuming the thing doesn't just fall over.
And the CSV as a "lifesaver" is a perfect example of trading auditability for convenience. You have a timestamp and a link. How do you, or anyone else, validate that the AI correctly interpreted the context around the word "blocker"? Was it a sarcastic remark? A hypothetical? The spreadsheet gives a false sense of concrete data, but the interpretation layer is a complete black box. You can't audit a confidence score.
Trust but verify
You're right to question the cost scaling, but your math is off. Processing time isn't serial; they're almost certainly running these jobs in parallel across their infrastructure. The time quoted is usually wall-clock, not total compute hours. Your bill is likely based on minutes of audio processed, not elapsed time, so 500 calls might cost more but it won't take a week.
The auditability point is the real core issue, though. The CSV isn't a dataset, it's a report of inferences. Anyone treating it as raw data for a formal audit is making a fundamental error. It's a starting point for human review, not a conclusion.
—AF
> "Huge time saver. Once it started running, I could leave it alone."
You left it alone. That's the problem. You didn't watch the bills. 50 calls at a few hours? That's not a processing time, that's a scheduling window. I guarantee you tl;dv is spinning up ephemeral compute for each job, and you're paying for that under the hood. Either via per-minute pricing or a tier upgrade next quarter. Check your usage dashboard, not your wall clock.
The CSV is seductive, I know. You get a spreadsheet and think you have data. But you don't have data, you have a log of what the model hallucinated. For auditing OKR blockers, you need traceability. A timestamp and a link isn't traceability. You need to know which model version processed it, what prompt was used, and whether the tagging logic is deterministic or sampling-based. tl;dv doesn't give you that. So your "consolidated analysis" is a black box report with a pretty table.
Next time, run a dry batch on 5 calls first. Compare the CSV output against manual transcription of those 5. Then you'll know if the bulk run is saving time or just producing noise faster.
Been there, migrated that
You're absolutely right about the dry batch. I always run a sample of 5-10 calls first, not for cost but for quality. It's the only way to calibrate the AI's interpretation against your team's specific jargon.
Your point on traceability is the real kicker, though. Even if you could trust the model's version, the prompts for these bulk actions are locked down. You can't tweak them. So the "deterministic or sampling-based" question is totally valid, and there's no way to answer it from the outside. That CSV feels solid until you need to defend a single line item.
I still think it saved me time, but you're 100% correct that it saved me *manual* time while introducing new "analysis time" to sanity-check the output. It's a trade, not a free lunch.
Exactly! That "analysis time" trade is the real TCO. It saved you 10 hours of clipping, but cost you 3 hours of sanity-checking and building trust in the output. The break-even point matters.
Your dry run trick is smart, but I'd add one thing: run that same small batch twice, maybe a day apart. See if you get identical outputs. That's your only real clue about determinism since the prompts are locked down.
The "time saver" part is correct, but the unit of measurement is what's critical. You saved person-hours at the expense of compute-hours, which is a valid trade but needs to be quantified. Have you benchmarked the manual vs. bulk processing cost? For us, the manual effort was approximately 15 minutes per call for basic clipping and notation. For 50 calls, that's 12.5 person-hours.
The bulk process might consume, say, 3 compute-hours. If your person-hour cost (fully loaded) is higher than your compute-minute cost, you come out ahead. But that ratio inverts quickly if you're using premium AI tiers or if the per-minute processing cost is high. You should check the usage dashboard to see the actual audio minutes processed; that's the true cost driver, not the wall-clock "few hours."
Also, the CSV's utility is directly proportional to your tagging integrity. Since you cut off at "You have to be consistent with your tagging," I assume you hit that issue. Did you perform any pre-processing validation on the tag set, or did you discover inconsistencies only in the output?
—chris