Otter.ai is a strong product for what it was built for: transcribing virtual meetings. It auto-joins Zoom, Teams and Google Meet, produces accurate transcripts and useful summaries, and has a mature collaboration workflow around them.
In-person conversations are a different problem, and the difference is mostly physics.
Virtual meetings are an easier input
In a video call, every participant has:
- Their own microphone, close to their mouth
- A separate audio stream
- Automatic gain control and noise suppression applied at source
- Speaker identification handled by the platform
That is close to laboratory conditions for transcription. Speaker separation is solved before any transcription happens, because the platform hands over already-separated streams.
In person, one microphone must:
- Pick up one speaker among many
- Reject ambient noise that may be louder than the speaker
- Separate speakers who overlap
- Handle varying distances and orientations
These are not comparable engineering problems, and a product optimised for the first will not automatically be good at the second.
Where this shows up at a trade show
Exhibition halls are among the noisiest environments any recorder encounters — demo audio, stage presentations, thousands of simultaneous conversations.
A phone microphone in that setting captures the hall, not the conversation. Transcription accuracy degrades sharply, and it degrades exactly where accuracy matters, because at a booth the transcript is the lead.
The fix is hardware, not software: a directional or beamforming microphone array that isolates the intended speaker spatially. Noise reduction applied afterwards cannot separate what was never separated at capture.
The workflow difference
Beyond acoustics, three workflow assumptions differ.
Session model. Otter assumes a meeting — one session, one start, one stop, consent established once at the beginning. A booth has dozens of separate conversations with different people, and groups that change mid-conversation.
In all-party consent jurisdictions that matters legally, not just practically. Consent is needed per conversation and again when someone joins.
Output format. Otter produces a transcript and summary — designed for a human to read. Lead capture needs structured CRM fields. A transcript of a booth conversation still requires someone to read it and extract name, company, budget, timeline and next step.
At 40 conversations a day, that reading does not happen. The transcripts accumulate and nobody opens them.
Hands and attention. Otter's mobile workflow assumes you can hold a phone and look at a screen. A rep at a booth is shaking hands, gesturing at a product, and moving. Anything requiring a screen between conversations gets skipped by mid-morning.
An honest comparison
| Otter.ai | Purpose-built event capture | |
|---|---|---|
| Zoom / Teams / Meet | ✅ Excellent | Not the purpose |
| Quiet in-person meetings | ✅ Good | ✅ |
| Noisy exhibition halls | ⚠️ Phone mic struggles | ✅ Directional array |
| Consent model | Per session | Per conversation |
| Output | Transcript + summary | Structured CRM fields |
| CRM sync | Via integrations | Direct, automatic |
| Collaboration on transcripts | ✅ Strong | Not the purpose |
If most of your meetings are virtual, Otter is the better tool. That is a genuinely large share of B2B selling, and it does that job well.
What to evaluate for in-person use
- Test in real noise. Play crowd audio at realistic exhibition levels and check the transcript. Vendor demos happen in quiet rooms.
- Check the consent flow. Can a rep obtain and log consent in under five seconds, per conversation, without a menu?
- Look at the output. Transcript, or fields? If transcript, who reads 40 of them?
- Time the sync. Before the next visitor, or that evening?
- Check whether a screen is required between conversations.
Where Confee sits
Confee is built for the in-person case: a 3-microphone beamforming array, a visible LED trust light with per-conversation consent, extraction into structured CRM fields rather than a transcript, and sync in under 30 seconds.
It is not a better virtual meeting transcriber than Otter, and does not attempt to be.
The short version
Otter solves virtual meetings well, helped by an input that is already separated and clean. In-person capture is a harder acoustic problem needing directional hardware, a per-conversation consent model, and field extraction rather than transcripts.
Test any candidate in actual noise. That single test predicts trade show performance better than any feature list.
Related reading:
- Best AI Meeting Recorder for In-Person Meetings — the broader category
- AI Note-Taker vs Voice Recorder vs AI Wearable — the full comparison
- Plaud Alternative for Trade Shows — the hardware comparison
FAQ
Does Otter.ai work for in-person meetings?
It can record in person via mobile, but it is built around virtual meetings with clean per-participant streams. In-person recording relies on a phone microphone capturing a room, which is a materially harder acoustic problem.
Why do virtual meeting recorders struggle at trade shows?
They were designed for an easier input. In a video call each participant has a separate clean stream; at a booth one microphone must isolate one speaker from thousands of competing conversations, which requires directional hardware.
What should you look for in an in-person alternative to Otter?
Directional or beamforming microphones, per-conversation consent, output as structured CRM fields rather than transcripts, and sync completing before the next conversation.
Is a transcript enough for sales lead capture?
Rarely. A transcript still requires someone to read it and extract the facts. At dozens of conversations a day that reading never happens — what teams need is the extraction itself.