Interactive Otter.ai Demo
Otter.ai is an AI meeting assistant that records, transcribes, and summarizes conversations in real time. Teams use it to capture meetings automatically, get searchable transcripts, and pull out action items without anyone taking notes by hand.
What is Otter.ai?
Otter.ai is an AI-powered transcription and meeting assistant. It joins your calls or records in-person conversations, produces a live transcript as people speak, and identifies who said what. The transcript is searchable, so a meeting becomes something you can scan and quote later rather than a memory that fades by the afternoon. It has been doing this since 2016 and is one of the more recognized names in the category.
The assistant side is what has grown most recently. Otter generates a summary of each meeting, pulls out action items, and can answer questions about what was discussed. Otter can join Zoom, Google Meet, and Microsoft Teams calls automatically when they are on your calendar, capture the audio, and have the notes ready shortly after the call ends. That removes the job of designating a notetaker.
Otter integrates with calendars to know when to join, and with tools like Slack and CRMs to push summaries where the team already works. Pricing runs from a free tier with a monthly transcription limit up to paid plans with more minutes and features. Accuracy is good on clear audio with distinct speakers and drops with heavy crosstalk, accents the model struggles with, or poor microphones, which is worth knowing before relying on it for a critical record.
How to get started with Otter.ai
- 1
Create an account and connect your calendar
Sign up for Otter and link your Google or Microsoft calendar. This lets Otter see your scheduled meetings and know which ones to join. Connecting the calendar up front is what enables the automatic join behavior, rather than starting a recording manually each time.
- 2
Set which meetings Otter joins
Configure whether Otter joins all calendar meetings or only ones you choose, and on which platforms. Starting selective is sensible while you learn how it behaves and confirm participants are comfortable with it. You can broaden the rule once the team has agreed on norms.
- 3
Run your first recorded meeting
Let Otter join a real call and watch the live transcript build as people speak. After the meeting, open it to see the summary and the action items it pulled out. This first run shows you how accurate it is on your typical audio and where you might need to clean up names or terms.
- 4
Search, edit, and share
Use search to jump to a moment in any past meeting, correct any misheard words, and assign speaker names so future transcripts label them automatically. Share the transcript or summary with people who missed the call so they get the decisions without sitting through a replay.
- 5
Connect the tools your team uses
Push summaries and action items into the places work happens, such as Slack or your CRM, so notes do not stay trapped in Otter. Setting up these integrations means the meeting record lands where people will actually see it, which is what turns captured notes into follow-through.
Who is Otter.ai most useful for?
Otter.ai is most useful for people who sit in a lot of meetings and need a record without doing the recording. Sales teams capture discovery calls so details do not get lost, project managers keep an accurate account of decisions, and anyone running back-to-back calls can stay present in the conversation instead of typing through it. The action-item extraction is what turns a transcript into something a team follows up on.
Students and journalists were an early audience and still use it heavily, for lectures and interviews respectively, where having the spoken word as searchable text changes how you work with the material. Accessibility is another genuine use: live captions help people who are deaf or hard of hearing follow a meeting in real time. For teams that record product walkthroughs or customer calls, pairing the transcript with an interactive Supademo of the product gives a fuller record of what was shown and said.
It is less suited to highly confidential conversations where having an AI assistant join and store the audio is a non-starter, or to settings with consistently poor audio where accuracy suffers. Consent also matters: recording people has legal and cultural expectations that vary by region, so teams should set norms about when Otter joins and tell participants it is there.
AI meeting assistants and transcription tools differ in whether they focus on raw transcription, video meeting workflows, or sales coaching, so the right fit depends on what you do with the recording afterward.
Fathom focuses on recording and summarizing video meetings and is known for a generous free tier and tight CRM integration. It is built around the meeting use case rather than general transcription, which keeps it simple for sales and customer teams. Compared with Otter it does less outside of meetings but is very polished at the meeting job itself.
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Fireflies records and transcribes meetings and emphasizes building a searchable knowledge base across all of a team's calls, with integrations into many CRMs and tools. It leans toward teams that want a shared record of every conversation. The breadth of integrations is its selling point, where Otter leans more on its standalone transcription quality.
Grain records calls and makes it easy to clip and share key moments, with a clear lean toward sales teams reviewing and coaching on customer conversations. The highlight-and-share workflow is its distinctive feature. Teams that mainly want to capture and revisit moments from sales calls find it more targeted than Otter's general-purpose transcription.
Rev offers both automated and human-powered transcription, with the human option reaching accuracy that AI alone does not, for a higher price per minute. It suits cases like legal or media work where a near-perfect transcript matters more than instant turnaround or meeting summaries. Otter is faster and cheaper for everyday meetings; Rev wins when accuracy is the priority.
FAQs on Otter.ai
Commonly asked questions about Otter.ai. Have more? Reach out and our team will be happy to help.
How accurate is Otter.ai's transcription?
Otter is accurate on clear audio with distinct speakers who are not talking over each other, and it handles common vocabulary well. Accuracy drops with heavy crosstalk, strong background noise, poor microphones, or accents the model handles less well. For most standard meetings the transcript is reliable enough to search and quote, but for a legal or otherwise critical record it is worth reviewing the output rather than trusting it blindly.
Can Otter join my meetings automatically?
Otter can connect to your calendar and join Zoom, Google Meet, and Microsoft Teams calls on its own when they are scheduled. It captures the audio, transcribes live, and has a summary with action items ready shortly after the call ends. This is the feature that removes the need to assign someone to take notes, since the assistant handles it for every meeting on the calendar.
How does Otter.ai compare to Fathom?
Otter.ai and Fathom are both AI meeting assistants that record, transcribe, and summarize calls. Otter has a longer history and a strong standalone transcription product used well beyond meetings, including lectures and interviews. Fathom is tightly focused on video meetings and CRM workflows and is known for a generous free tier. The choice often comes down to whether you want broad transcription or a meeting-and-sales-focused tool.
Is there a free version of Otter.ai?
Otter offers a free plan with a monthly cap on transcription minutes and a limit on how many conversations you can import. It is enough to evaluate the product and cover light use. Paid plans raise the minute limits and add features like more advanced summaries and admin controls, which is where regular team use tends to land.
Do I need to tell people Otter is recording?
You should. Recording laws vary by region, and some require consent from everyone on the call. Beyond the legal side, people generally expect to be told when an AI assistant is capturing a conversation. The practical approach is to set a team norm about when Otter joins and to make sure participants know it is there, rather than having it appear silently.