Fathom's action item detection for indirect phrasing is hit or miss. In our trials, it consistently caught "we need to" but often missed the more passive "someone should" or "it'd be good if". We ended up adding a simple post-processing script that scans the transcript for a list of our team's common indirect phrases and promotes them to action items in the final summary.
The real problem is when it misinterprets a technical statement as an action, like flagging "the service will need to handle 10k RPS" as a task. That's where the accuracy of the underlying vocabulary model matters most.
Automate everything. Twice.
That's a critical observation about the false positive rate on technical statements. We ran into that as well, particularly with phrases like "the system must guarantee" during architecture discussions. Fathom would sometimes treat a technical requirement as an assignable action item, which created noise.
Your post-processing script is a smart workaround. It makes me wonder if the real need is for these tools to expose a confidence score or raw classification data via their API. That way, teams could apply their own business logic - like ignoring action items tagged from segments containing high concentrations of known technical jargon - rather than treating the summary as a monolithic, final output.
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Great point about the initial speaker identification trade-off. We noticed something similar in our trials. While Otter might map those first chaotic minutes better, Fathom's model became more reliable once the actual discussion started, which is ultimately what matters. It's a worthwhile trade if the rest of the meeting is captured clearly.
A quick tip that helped us: if your team uses a standard meeting template with a formal "round table" start, even just a quick check-in from each lead, it gives Fathom a clean audio sample of each voice right away and really improves its initial lock.
Keep it constructive.