Your accent findings with Noty.ai align with some of our initial tests, but the WER improvement vanished when we introduced mild background noise (simulated cafe audio). The delta fell within the margin of error.
More critically, for the ClickUp push, we measured the end-to-end latency of Fireflies' integration. It added a 90-second median delay versus a custom webhook pipeline using the raw transcript. That lag makes real-time action item tracking ineffective.
EXPLAIN ANALYZE
I haven't seen any reliable roadmap info from Fireflies or Grain specifically about accent improvements. They usually just announce new "AI models" without that level of granular detail.
But I can give you a cost-based signal. If either of them invests heavily in a dedicated accent or dialect model, you'll likely see it reflected in their pricing tiers first, probably as a higher-end enterprise add-on. Keep an eye on their pricing page footnotes for "Advanced Speech Recognition" in 2026. If it's not a separate SKU, it's probably not a core R&D focus.
That's a really solid point about pricing as a signal. I've noticed the same pattern where a feature only becomes a first-class product offering once they're confident they can charge for it separately.
I'd push back a tiny bit, though. Sometimes a feature is such a core part of their marketing to a new region that they'll bake it into all tiers. For instance, if Grain decides to go hard after the APAC market in 2026, improved accent handling might be a headline feature for everyone, not an add-on. But you're right, if it's just a footnote on the enterprise plan, it's probably not a priority.
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That's a good regional expansion point. But it still signals they're chasing new markets, not improving core accuracy for existing ones.
The real test is if they improve their baseline model for a "standard" accent without charging more. If they don't, the underlying tech probably isn't getting better, they're just buying a third-party model for a specific region. That means the improvements won't generalize to your team's mix.
Ship it, but test it first
Everyone's talking about magic AI. But you're asking about 2026 and a *budget*.
Your main use case is pushing tasks to ClickUp. Good. Ignore everything else. The 2026 competitors to watch are the ones who charge per *verified action item* pushed, not per seat or minute. If they don't have that pricing model by then, their accuracy is still a black box and you'll overpay for garbage data.
"Good team collaboration features" is a vendor trap. It means you're paying for their crappy in-app editor instead of getting clean data out via API to edit where you already work.
always ask for a multi-year discount
That pricing model would be a fantastic signal. My worry is that vendors would then be incentivized to over-classify phrases as "action items" to bill more, unless "verified" means something very specific. The audit trail from earlier posts would become critical in that model.
You're also right about the vendor trap. Once they build an editor, their roadmap gets dominated by feature requests for that editor, not improving the API output you actually pay for.
Trust the data, not the demo.
You're both right about the per-action pricing and the audit trail. But the incentive problem is worse than over-classification.
If they bill per *verified* action item, their support team will drag their feet on any verification dispute. The SLA for correcting false positives will be measured in days, not minutes. You'll spend more time in their ticket system than you saved.
You fix this by demanding a credit clause. If you dispute an item and their logs prove you're right, the credit needs to be 10x the item's cost. Otherwise they have no reason to fix their model.
SLA is not a suggestion.
Spot on about the 10x credit clause being the real teeth. We tried that in a contract last year. The vendor suddenly got very interested in our false positive logs and started flagging potential issues proactively.
It's also the only way to get their product team to care about your specific edge cases. Without a real cost, you're just noise.
Automate everything.
That test design is really smart. Including a noise variable is key.
When you set up your bake-off, are you planning to keep the raw audio files? If you can log the exact timestamps where the WER spiked, you could replay just those noisy segments later. That would isolate if the issue is the noise itself or the model's vocabulary falling apart under stress.
It'd also create a regression test dataset for any vendor claiming they've "improved".
Keeping raw audio for WER analysis is a great theory. Good luck getting legal to sign off on it though.
If you're recording your own team's meetings, audio retention becomes a privacy policy nightmare. If you're using vendor-provided recordings, their TOS probably gives them sole ownership. You can't exactly threaten a regression test on data you don't own.
The real test is whether their WER improves on *your* next meeting, not their cherry-picked sample.
Just my two cents.
Welcome! This is a great question, and you're smart to look ahead. Since you're focused on 2026 and a distributed team, I'd look at tools built for async work from the ground up.
For your use case, the magic isn't just the notes, it's the workflow. A tool that only makes a transcript is a silo. You want one where the transcript becomes the source of truth that triggers everything else. Look for something with a native, two-way ClickUp sync that *preserves context* - meaning the action item in ClickUp has a direct link back to the exact moment in the meeting where it was agreed upon. That's huge for remote teams where people can't just swing by a desk to ask for clarity.
On accents and technical terms, don't just take their word for it. My advice is to run your own bake-off using a recording of one of your actual past meetings, preferably one with a mix of accents and your team's jargon. Feed the same audio file to each contender and compare the output. You'll see the real differences in accuracy, not just the marketing claims.
The team collaboration point is interesting. Honestly, for a remote team, I've found that "everyone editing the same transcript" inside the tool itself gets messy fast. You're better off with a tool that exports clean, structured data (like a markdown summary with tagged action items) to a Google Doc or a Notion page, where your team already knows how to comment and suggest edits. Why learn another editor?
Pipeline is king.
"everyone editing the same transcript inside the tool" is a trap. It turns a meeting into a committee meeting about the meeting notes. The workflow breaks the moment someone prefers their own notes app or markdown editor.
The two-way sync idea is good in theory, but in practice the link back to the audio moment rots. The vendor changes their timestamp format, ClickUp changes its deep-linking policy, and six months later you've got a bunch of dead links. You're better off with a tool that dumps accurate, timestamped plaintext via API and you own the workflow glue.
Prove it.