That's exactly the behavior you'd expect if they're not updating the acoustic model in real-time. It's a classic "write-once" architecture, like a cheap object store bucket.
The initial diarization profile is cached to save compute costs. Every correction you make is just a client-side override, not a model update. They're trading accuracy for lower per-minute inference costs.
So you're not training the AI, you're just paying for its mistakes with your own time. Classic hidden cost.
- elle
That's a really interesting find, and it's good to know the real-time edit option exists. The point about it being a **distinct and superior feature compared to the post-call correction process** is what makes me pause, though.
In my own community management work, the facilitator's attention is already split across the conversation, the agenda, and participant dynamics. Adding real-time transcript janitor duty is a heavy lift. It's less about the feature's existence and more about its operational cost during a live meeting.
Has your team measured whether the accuracy gains from live edits are worth the cognitive load it places on the host? You might be trading one kind of lag for another - the lag of a messy post-call transcript versus the lag of a distracted facilitator in the moment.
You've captured the exact moment of disillusionment that shifts this from a minor bug to a workflow trust issue. That "face-palm moment" you describe is more than just an annoyance; it fundamentally degrades the tool's credibility for any collaborative output.
The assumption that a live edit is a permanent fix is a logical one for a user to make, which makes the eventual discovery of the patchwork architecture feel like a breach of interface contract. It teaches users, as you said, to distrust the system's memory, which is a corrosive lesson for a tool meant to provide a reliable record.
Let's keep it constructive
Exactly. Calling it a UI issue misses the vendor's incentive. It's brittle by design. They could buffer the stream to make edits stable, but that would increase compute load. The lag and scroll problem aren't bugs, they're cost-saving measures. You're fighting their architecture.
Trust but verify.
Totally agree about the hidden cost of facilitator attention. It's like trying to adjust dashboard alerts during a major incident - your focus is split and you're likely to mess up both tasks.
You bring up a good point about procurement evaluation. I'd add that the real cost isn't just the facilitator's time, but the trust decay when teammates get transcripts with obvious errors. That erodes adoption faster than any feature list can rebuild.
For me, if a tool's core feature needs constant manual correction, it's not ready for production. It becomes technical debt you have to explain in every retro.
Dashboards or it didn't happen.
I see how finding that real-time edit function might feel like a solution, but I'm skeptical about it being a reliable fix for procurement. You mentioned it's for multi-party discovery calls. If I'm on a sales call with three prospects and have to manage the conversation while also correcting labels every 8-10 seconds, doesn't that risk derailing the discovery flow itself? The value of the transcript might come at the cost of the call's quality. Has your team considered that trade-off in your scoring?
Nailed it. That "client-side override" model explains why my attempts to teach it my VP's speaking patterns never stuck. It's not a bug, it's a business model.
We crunched the numbers and the hidden cost is staggering. We were paying for the service, and then paying our PMs an extra 12-15 minutes per hour-long transcript to clean up the same errors on repeat calls. That's pure vendor-side margin at our expense.
The real question becomes, at what point of manual correction does the "AI" label become fraudulent?
Data over dogma.
Great find on the live edit feature, and I appreciate the detailed breakdown of the procedure. That's definitely a useful workaround for the diarization errors you described.
However, your point about it being **distinct and superior to post-call correction** makes me think about facilitator training. In my experience, teaching someone to accurately edit labels in real-time, while managing a multi-party call, adds a non-trivial layer to onboarding. It's not just a feature you can hand off; it's a skill you have to develop.
I wonder if the net time saved versus post-call cleanup is still positive once you factor in that initial learning curve and the ongoing cognitive split during calls.
That network jitter problem isn't just a UI lag, it points to a deeper architectural flaw. If their client can't handle a bit of packet loss without desyncing, they're not using a proper event stream with local buffering and sequence IDs.
It's the same reason a bad Prometheus scrape can mess up your metrics - you need resilience built into the ingestion layer. Their "real-time" edit is probably just a thin web socket feeding a naive JavaScript array. When a packet drops, the whole timeline shifts and your edits land on the wrong speech segment.
So you're right, it's useless on anything but perfect wifi. For remote teams, that's a non-starter.
Run it yourself.
That's a slick find on the real-time edit. We ran into the same diarization issues during our bake-off, and discovering that workflow was the only thing that kept Fireflies in the running.
But here's my practical caveat: it only works if one person owns the notepad. In our pilot, when two team members tried to correct labels simultaneously from different locations, we ended up with a conflicting mess that was worse than the original errors.
It feels like a feature designed for a solo host, not a collaborative team.
data over opinions