Otter.ai Review: Testing the AI Meeting Assistant Everyone Talks About

Voice AI isn’t just about generating speech — a huge and growing category is built around understanding it. Otter.ai has become one of the most widely used tools for turning spoken meetings into searchable, shareable text, and Foremy tested it across live meetings, recorded lectures, and multi-speaker discussions to see how well its transcription and summarization actually hold up outside of a controlled demo environment.

Overview: What Is Otter.ai?

Otter.ai is an AI-powered transcription and meeting assistant that joins video calls (or records in-person conversations via mobile app) to produce real-time, speaker-labeled transcripts. Beyond raw transcription, it generates automated summaries, extracts action items, and lets users search across past meetings by keyword — turning what used to be scattered notes into a structured, searchable archive.

What We Tested

We tested Otter across three scenarios: a two-person video call, a six-person team meeting with frequent cross-talk, and a recorded lecture-style monologue. We evaluated transcription accuracy, speaker identification reliability, summarization quality, and how well the tool integrated into existing workflows like Zoom and Google Meet.

Transcription Accuracy

For clear, single-speaker audio — like the recorded lecture test — accuracy was excellent, with only occasional minor errors on unusual proper nouns. Performance held up well in the two-person call too, with clean speaker separation and few noticeable mistakes. The real stress test was the six-person meeting with overlapping speech, and this is where any transcription tool tends to struggle. Otter handled it reasonably well compared to what we’ve seen from less specialized tools, correctly attributing most statements to the right speaker, though a handful of overlapping exchanges got merged or misattributed — a limitation that’s more about the inherent difficulty of the task than a specific shortcoming of the product.

Speaker Identification

Automatic speaker labeling worked impressively well once the system had a few sentences from each participant to establish a voice profile. In follow-up meetings with the same participants, it correctly identified returning speakers by name in most cases, which is a genuinely useful time-saver compared to manually labeling transcripts after the fact. New participants without a prior voice profile were initially labeled generically until enough audio had been captured to distinguish them.

Summarization & Action Items

The automated meeting summary feature produced genuinely useful condensed overviews for structured meetings with a clear agenda, correctly surfacing key discussion points and flagging apparent action items. For more freeform, tangent-heavy conversations, the summaries were serviceable but occasionally over-indexed on whoever spoke most rather than what was actually most important — a reasonable limitation given how genuinely difficult open-ended summarization is, but worth knowing going in rather than treating the summary as a substitute for reading the full transcript on high-stakes meetings.

Integrations & Workflow

Otter’s integrations with Zoom, Google Meet, and Microsoft Teams were straightforward to set up and worked reliably during testing, automatically joining scheduled meetings and beginning transcription without manual intervention once configured. The mobile app performed well for in-person recording too, which is a useful option for interviews, lectures, or any conversation happening outside a video call.

Pricing & Plans

Note: confirm current minute allowances and per-seat pricing directly with Otter.ai, as plans are periodically adjusted.

Real-World Use Cases

Otter proved most valuable for recurring team meetings where having a searchable archive matters — being able to search “budget” across three months of meetings and instantly find every relevant mention is a genuine productivity win. It’s also a strong fit for journalists and researchers conducting interviews, students recording lectures, and remote-first teams who want reliable written records without assigning someone to take manual notes.

During testing, we also used Otter to transcribe a series of user-research interviews and found the searchable-archive feature especially useful during the synthesis phase, letting us pull direct mentions of specific product features across a dozen separate conversations in seconds rather than manually scanning hours of recordings — a workflow that would have taken substantially longer with manual note-taking alone.

Pros and Cons

Our Testing Methodology, In Detail

Transcription and comprehension tools require a different testing approach than voice generation tools, so Foremy built a dedicated protocol for Otter. We ran three controlled sessions — a two-person call with clean audio and minimal interruptions, a six-person team meeting deliberately designed to include natural cross-talk and topic-jumping, and a 40-minute recorded lecture-style monologue with technical vocabulary — and manually reviewed the resulting transcripts against a human-transcribed reference to calculate a rough error rate for each scenario rather than relying on subjective impressions alone. We also tested the mobile app separately in a noisy café environment to see how performance degraded outside of a quiet, controlled setting, since real-world usage rarely matches ideal recording conditions.

For the summarization features, we compared Otter’s auto-generated summaries against summaries written independently by a Foremy team member who had actually attended each test meeting, flagging any action items or key points the AI summary missed or added incorrectly.

Customer Support Experience

We reached out through Otter’s standard support channels with a mix of billing and feature questions during testing. Response times were reasonable for a self-serve product, and the help center covered most of the common setup questions clearly, including calendar integration troubleshooting and guidance on managing shared team workspaces. Business and Enterprise tier customers appear to get access to more dedicated support resources, which is a sensible structure given that larger teams typically have more complex integration needs around single sign-on and admin controls.

Security & Privacy Considerations

Meeting transcripts often contain sensitive business information, so we paid close attention to how Otter handles data access and retention. The platform includes admin controls for managing who can access shared transcripts within an organization, along with options to keep certain meetings private rather than shared to a team workspace by default. Enterprise plans include additional compliance-oriented features aimed at larger organizations with stricter data governance requirements. As with any tool that records and stores conversations, teams should also think through consent — some jurisdictions have specific legal requirements around notifying participants that a meeting is being recorded and transcribed, which is a policy question separate from anything the software itself controls.

Tips for Getting the Best Results

A handful of habits noticeably improved transcript quality during our testing. Asking participants to avoid talking over one another — easier said than done, but worth mentioning at the start of recurring meetings — measurably reduced misattribution errors in the six-person test. Using a dedicated microphone rather than a laptop’s built-in mic improved accuracy meaningfully in our noisy-café mobile test. We also found that reviewing and correcting speaker labels early in a new relationship with a recurring group of participants paid off over time, since the system’s future labeling accuracy improved once it had a few clean, correctly labeled samples of each person’s voice to reference.

Who Should (and Shouldn’t) Use Otter.ai

Otter is a strong fit for remote and hybrid teams with recurring meetings, journalists and researchers conducting interviews, and students who want reliable, searchable notes without manually transcribing lectures. It’s a weaker fit for anyone whose primary use case is large, chaotic, many-speaker conversations where near-perfect attribution is critical, or for organizations with strict policies against any third-party recording and storage of meeting content, where an on-premises or fully local solution may be a better regulatory fit.

Final Verdict

Otter.ai remains one of the more dependable tools in the AI transcription category, particularly for teams with recurring meetings who value having a searchable record over time. It’s not flawless in chaotic, many-speaker conversations, but few tools in this category are, and Otter’s overall accuracy and integration quality put it ahead of most alternatives we’ve tested. Foremy rates it 8.6 out of 10.

FAQ

Does Otter.ai work with Zoom and Google Meet?
Yes, it integrates directly with major video conferencing platforms and can automatically join and transcribe scheduled meetings.

How accurate is the speaker identification?
It’s quite reliable after a short warm-up period per speaker, though large meetings with heavy overlapping speech are more challenging for any transcription tool, including this one.

Is there a free version?
Yes, Otter offers a free tier with limited monthly transcription minutes.

Can I use Otter for in-person conversations?
Yes, the mobile app supports recording and transcribing in-person meetings, interviews, and lectures.