Otter AI 評測：兩段錄音共 802 詞只錯 3 詞

You finish a 45 minute call and realize you only wrote down half of what mattered. Who agreed to send the proposal? Was the deadline the 10th or the 12th? Did the client say $29 or $34? Now you’re scrubbing through a recording or emailing everyone to double-check.

That’s the problem Otter AI promises to fix. It records your meetings, turns them into searchable transcripts, and writes the summary and action items for you. It also lets you ask questions across your meetings with AI Chat, supports 19 transcription languages, and offers AI agents for sales teams.

So I tested it the hard way. I gave it two scripted meetings packed with names, prices, and deadlines. From there, I checked every word, summary, and AI chat answer Otter produced against my original scripts.

In this Otter AI review, I’ll show you how it worked, where it impressed me, and where I think it still falls short. I’ll finish the article by comparing it to my top three alternatives: MeetGeek, Fathom, and Granola. By the end, you’ll know which tool is right for you!

Verdict

Otter AI delivered excellent transcription accuracy in my tests, missing just three words across two recordings. Its AI Chat never made anything up even when I asked a misleading question, though it did assign tasks to the wrong person and misread a deadline. However, speaker identification struggled on a noisy call, and the free plan’s limits (a maximum of three file imports and 30 minutes of transcription per conversation) make it difficult to use regularly without upgrading.

Pros and Cons

• Excellent transcription accuracy, even in noisy environments.
• Correctly captures numbers, prices, and dates.
• Custom vocabulary helps get names and technical terms right.
• AI Chat gives accurate answers and admits when information is missing.
• Clear summaries that highlight the important details.
• Automatically identifies relevant action items.
• Supports 19 transcription languages.
• Works with Zoom, Google Meet, and Microsoft Teams.
• Available on iOS, Android, Mac, and Windows.
• MCP integration lets ChatGPT and Claude access meeting history.
• Free plan includes 300 transcription minutes per month.
• 19 transcription languages.

• Speaker identification struggled with noisy audio, assigning a large chunk of the conversation to one person.
• Speakers aren’t named automatically, which can lead to incorrect task assignments.
• Free plan only allows three file imports.
• Free plan limits AI Chat to 20 questions per user and three per conversation.
• PDF, DOCX, and SRT exports require a paid plan.
• Only 5 of the 19 supported languages are out of beta.
• Faces a federal privacy lawsuit over recording meetings without everyone’s consent.
• AI Chat was slow, taking around 30 seconds to answer questions.

What is Otter AI?

Otter AI is an AI meeting assistant that records conversations, transcribes them in real time, and turns them into summaries, action items, and a searchable archive.

Founded in 2016, Otter AI is still one of the best-known names among AI note-taking apps. Otter now reports over 25 million users and $100 million in annual recurring revenue.

What Happens When You Use It

There are three ways to get a conversation into Otter:

• The Notetaker joins your meeting. Connect your calendar, and Otter’s bot joins Zoom, Google Meet, or Microsoft Teams calls, then transcribes them live.
• You record it yourself. Press Record in the browser, mobile app, or desktop app. The Mac and Windows apps can capture meeting audio without sending a bot into the call.
• You upload a file. Import an MP3, WAV, MP4, or another common audio or video format, and Otter transcribes it.

Every conversation becomes a transcript with timestamps and speaker labels, a summary broken into topics, and a list of action items. You can play audio back from any word, edit the transcript, and share it with your team.

From Transcripts to Answers

One of my favorite features is Otter AI Chat. Instead of rereading transcripts, you ask it questions like “What did we promise the client?” and get an answer with timestamps linking back to the moment it was said. In April 2026, Otter expanded this to search across connected apps like Gmail, Google Drive, Notion, Jira, and Salesforce.

Otter has also added role-specific agents for sales (live coaching and CRM updates), recruiting (Greenhouse sync), and lead qualification. I didn’t test these, and they’re aimed mostly at Business and Enterprise teams.

Languages

For the longest time, Otter was English-only. Today, Otter’s upload screen now lists 19 languages, including Spanish, French, German, Japanese, Korean, and Portuguese. However, 14 of them are still labeled “beta,” so I’d test your language before relying on it.

The Privacy Question

Once you connect your calendar, the Notetaker can join meetings automatically.

However, a 2025 class-action lawsuit claims Otter recorded participants without consent and used the recordings to train its AI. On August 13, 2026, a federal judge allowed the core wiretap and privacy claims to proceed.

In my account, the setting that emails guests before Otter records was off by default, so I’d turn it on for client calls.

Who is Otter AI Best For?

Here’s who Otter AI is best for:

• Freelancers and consultants can record client calls so they have a record of agreed prices, scope changes, and deadlines without having to take manual notes.
• Content teams can turn planning meetings into task lists with assigned people and deadlines, then ask AI Chat to draft the follow-up email instead of using a separate AI email generator.
• Journalists and podcasters can upload interviews and get accurate transcripts with timestamps, as long as the recording is fairly clean.
• Sales teams can use the Sales Agent and CRM sync to log calls in Salesforce or HubSpot. It works well alongside AI SDR tools.
• Students and educators can record lectures and search them later (check for back to school deals).
• Teams already using ChatGPT or Claude can connect Otter’s MCP server so their AI assistant can answer questions from meeting history.

Otter AI Key Features

Here are the key features that stood out to me:

• Live transcription: Transcribes meetings and in-person conversations as they happen, with timestamps and speaker labels.
• Otter Notetaker: A bot that joins Zoom, Google Meet, and Microsoft Teams calls from your calendar and records them automatically.
• Bot-free desktop recording: The Mac and Windows apps capture system audio so a bot doesn’t need to join the call.
• File import: Upload AAC, MP3, M4A, WAV, WMA, MOV, MPEG, MP4, or WMV files for transcription.
• AI summaries: Organizes each conversation into topics with bullet points, a keyword list, and an outline.
• Action items: Pulls tasks and deadlines out of the conversation into a checklist you can assign and check off.
• Otter AI Chat: Ask questions about one meeting or all of them, draft emails, and get answers with timestamps.
• Custom vocabulary: Teach Otter names, brands, and jargon. The Basic plan allows 5 terms, Pro allows 100 names and 100 other terms, and Business allows up to 800 of each.
• Integrations & MCP: Connects to Slack, Salesforce, HubSpot, Notion, Jira, Google Drive, and Zapier, plus an MCP server for ChatGPT and Claude.
• 19 transcription languages: Includes English, Spanish, French, and Japanese, plus 14 more in beta.

Hands-On Testing: What Otter AI Produced

To measure Otter’s accuracy, I gave it two meeting scripts full of unusual names, prices, percentages, and dates, and recorded them with AI voices. That gave me the exact wording to count every mistake against. I uploaded both files to Otter’s free plan, set to English (US).

One thing to note: AI voices are cleaner than real people, so treat my accuracy numbers as a best case. Background noise, bad microphones, and mumbling will produce more errors.

Test 1: A Three-Person Team Meeting

For my first test, I uploaded a three minute content team meeting between three people: Maya and Dev (American voices) and Oliver (a British voice). They discussed traffic numbers, a product launch with uncertain pricing, and who would write an upcoming roundup.

The script contained 498 words, five deadlines, and names like “Vantora,” “Quillbase,” “Daniel Okafor,” and “MeetGeek.” I didn’t add any custom vocabulary first.

When I compared the transcript against my script, Otter had gotten just two words wrong:

• “Okafor” became “Ochafer.”
• “MeetGeek” became “MeatGeek.”

Everything else was correct, including “Vantora,” “Quillbase,” “Semrush,” “Surfer SEO,” “42,000 visits,” “1.9 percent,” and every date. That’s a word accuracy of 99.6%.

Speaker identification was almost as good. Out of 24 speaking turns, Otter labeled 23 correctly as Speaker 1, 2, or 3. The only mistake was at the very end, where Dev’s “Nope, that’s everything from me” and Oliver’s “All good here” were merged into one turn.

The summary was organized into “Traffic Performance” and “Project Updates,” and the facts were right. However, it credited Dev with noticing the meta description problem, when it was actually Oliver who said it.

Otter pulled out five action items, and all five were actual tasks from the meeting with the right deadlines.

However, it did miss one: approving the article by November 10. That deadline appeared in the summary but not in the action item list. Only one action item had an owner, and Otter assigned it to me (the account owner), even though it was Maya who said she’d email Daniel.

My Take

The transcript itself was one of the cleanest I’ve gotten from an AI notetaker. I would publish quotes from it with little editing. And the numbers were correct, which is what matters most when you go back to check a price or deadline.

The weak spot is attribution. Since Otter doesn’t know who “Speaker 2” is until you tag them, the summary and action items can give tasks to the wrong person. If you’re using Otter to keep a team accountable, plan to spend a minute tagging speakers after each meeting.

Test 2: A Noisy Client Call with Custom Vocabulary

For my next test, I wanted to make things harder.

I uploaded a 1.5-minute client call between Priya (a British voice) and Marco (a voice with an Italian accent), then mixed in café chatter, background noise, and the clink of cups. The script mentioned budgets, a ten-day legal review, a Monday deadline, and tricky names like “Elena Kowalczyk.”

Before uploading, I added five terms to Otter’s custom vocabulary: Daniel Okafor, Elena Kowalczyk, MeetGeek, Quillbase, and Vantora. That’s the free plan’s limit, and it included the two names Otter got wrong in Test 1.

Despite the noise and the accent, Otter got 303 of 304 words right (99.7% accuracy). The only mistake was “send” becoming “sent.”

All five vocabulary terms were spelled correctly, including “Okafor” and “MeetGeek,” which it had gotten wrong in the first test. It also correctly caught “$7,500,” “12%,” “25%,” and “Mailchimp to HubSpot.”

However, the speaker labels were a mess. Otter put most of the conversation under “Speaker 1,” lumping Priya and Marco together in one long block. The first block alone mixes Priya’s greeting, Marco’s reply, and both of their next lines. If you were reading this transcript to figure out who promised what, you’d struggle.

Surprisingly, it didn’t seem to affect the quality of the summary.

Otter correctly named “Priya and Marco,” broke the call into the current situation, the proposed solution, budget and timeline, and approvals, and got every figure right. It created one action item, which was correct: send the proposal by Monday morning and copy Elena.

My Take

The background noise didn’t hurt Otter’s word accuracy at all, which surprised me. And the five vocabulary terms fixed both errors from Test 1.

However, speaker separation is where noisy audio caught Otter out. The summary still worked because the speakers used each other’s names, but a raw transcript with two people merged into one is something I’d hold off on sharing with others. If your calls happen in cafés, cars, or open offices, use a headset to keep speakers easier to separate.

Test 3: Asking Otter AI Chat About the Meeting

For the final test, I used Otter AI Chat on the team meeting from Test 1.

The free plan only allows three questions per conversation, so I made each one count:

1. A simple question: “List every deadline from this meeting, who owns each one, and the exact date.”
2. A writing task: “Draft a short follow-up email from Maya to Daniel asking him to confirm the Quillbase pricing and send the dashboard screenshots. Include the dates that depend on his reply.”
3. A trick question about things that never happened: “What budget did the team approve for the AI notetaker roundup, and why did they decide to drop Notta from it?”

For the deadlines, Otter returned a table with eight dates, owners, and tasks. It turned “Friday” into “November 6, 2026,” which was correct, and it noted that it had worked out the dates based on the day I asked. It was also honest when the meeting didn’t name an owner, writing “No owner specified” for the launch date.

But there were two problems.

• It assigned Maya’s tasks to me because I own the account.
• It read “by Wednesday” as the same day as the meeting, when the team almost certainly meant the following Wednesday.

For the email, Otter didn’t show the draft in the chat. Instead, it gave me an “Open the Gmail draft” link and noted that it couldn’t find Daniel’s email address.

Inside the link was a short email asking Daniel to confirm whether the price would be $29 or $34 and listing the review, approval, and launch dates. The only issue was that it asked Daniel to reply “by Wednesday, November 4,” which was the same day.

The trick question was the most impressive answer. Otter said the team “didn’t discuss or approve a budget,” that the transcript “doesn’t mention Notta,” and that the tool actually proposed was tl;dv, which the team decided not to add. It linked to the exact moment in the recording (2:32 to 2:42).

My Take

Many AI tools will invent an answer to a leading question, and Otter didn’t. Its timestamps also made it easy to verify the transcript in seconds.

The chat’s weaknesses mirror the transcript’s: it doesn’t know who people are unless you tag speakers, and it makes assumptions about specific dates. Each answer also took roughly thirty seconds, and with only three questions per conversation on the free plan, you’ll hit the limit quickly.

Results & Output Quality

Across both recordings, Otter made a total of 3 word errors out of 802 words:

• Two were names it hadn’t been taught.
• One was a single letter (“send” vs. “sent”).

For the facts that matter when you revisit a meeting (prices, percentages, dates, and names), it was correct every time once custom vocabulary was set up. That’s a strong result compared to most AI transcription tools I’ve used.

The summaries were also genuinely useful. They grouped the discussion by topic, included the right numbers, and took under a minute to scan. Test 1 only needed one correction for a misattributed point.

But attribution is Otter’s weak spot. On the clean recording, speaker detection was nearly perfect. On the noisy one, it merged two people into one speaker for most of the call.

Because speakers start out as “Speaker 1” and “Speaker 2,” tasks can also land on the wrong person (in my case, me). Tagging speakers after each meeting fixes most of this, but it’s a manual step.

The speed was mixed. Transcripts and summaries were ready almost as soon as each upload finished, but AI Chat took about thirty seconds to answer each question.

How to Use Otter AI

Here’s the workflow that gave me the best results:

1. Add custom vocabulary first. Add client names, products, and jargon under “Manage Vocabulary” in Account Settings. In my tests, this was the difference between “Ochafer” and “Okafor.”
2. Capture the meeting. Let the Notetaker join from your calendar, record with the desktop app to avoid a bot, or import a file. Pick the transcript language in your Account Settings before uploading.
3. Tag your speakers. Assign a name to each “Speaker” label so the summary, action items, and AI Chat attribute tasks to the right people.
4. Check, then ask. Make sure each action item is assigned to the right person and has the right due date. Then ask AI Chat for a recap or follow-up email, and click any timestamps it provides to verify anything important.

Top 3 Otter AI Alternatives

Here are the best Otter AI alternatives I’ve tried that I’d recommend.

MeetGeek

Among the Otter AI alternatives I’d recommend, MeetGeek is the closest match to Otter. Both send a bot into Zoom, Google Meet, and Teams calls, transcribe them, and produce summaries, action items, and a searchable meeting library.

The biggest difference is language support. MeetGeek transcribes in 100+ languages with automatic detection, while Otter supports 19 languages and labels 14 of them as beta. If your team regularly meets in languages other than English, MeetGeek is the safer choice.

MeetGeek’s free plan also gives you a little more breathing room: 3 hours of transcription a month and up to 2 hours per recording, compared with Otter’s 30 minute cap per conversation. However, MeetGeek only keeps free plan transcripts for 3 months.

Choose Otter if you work mostly in English. Otherwise, choose MeetGeek if you need multilingual meetings covered and more transcription time on the free plan.

Read my MeetGeek review or visit MeetGeek!

Fathom

The next alternative I’d recommend is Fathom. It’s main advantage is the free plan, where it offers unlimited recordings and transcriptions at no cost. Meanwhile, Otter’s free plan stops at 300 minutes a month and 30 minutes per conversation. If you take a lot of calls and don’t want to pay, Fathom will go much further.

Like Otter, Fathom joins Zoom, Google Meet, and Teams as a bot, writes summaries, and answers questions across your calls with Ask Fathom.

However, Fathom is built around live meetings. You can’t upload an existing audio or video file, which is how I ran both of my Otter tests. If you need to transcribe interviews, lectures, or podcasts after the fact, you’ll need a tool like Otter. Also, Fathom’s paid plans cost more than Otter Pro, even with annual billing.

Read my Fathom review or visit Fathom!

Granola

Granola takes a different approach to Otter. Instead of sending a bot into the meeting, it records audio from your computer, so other participants don’t see a notetaker join. You type rough notes during the call, and Granola fills them in with context from the transcript afterward.

That makes Granola feel more like an AI notebook than a transcription service. It’s the most useful when you want clean notes in your own structure, while Otter is best when you need an entire transcript to quote, search, or share.

Granola’s bot-free approach also feels less intrusive, which matters given the lawsuit around Otter’s Notetaker. You’re still recording people, so you still need their consent, but nobody gets an unexpected bot in their call. Keep in mind that Granola’s free plan only keeps 30 days of meeting history.

Read my Granola review or visit Granola!

Otter AI Review: The Right Tool For You?

What impressed me most about Otter AI was how accurate it was where it counts. Across two recordings, it missed just three words.

After I added five vocabulary terms, it also got every name, price, and date right despite café noise and an accented speaker. The AI Chat was just as reliable. When I asked about a budget and a tool that were never mentioned, Otter told me so instead of making something up.

What disappointed me most was how it handled the noisy call. It merged two speakers into one for most of the conversation. Meanwhile, on the clean call it gave one person’s tasks to me. You’ll need to tag speakers after every meeting to avoid this.

Otter’s free plan is fine for testing, but three lifetime imports and 30-minute conversations aren’t enough for weekly use. Pro is good value for freelancers, students, and anyone who mainly needs accurate transcripts. Teams that want unlimited meetings and longer calls should look at the Business plan.

However, I’d choose a different tool altogether if you meet in languages other than English (MeetGeek), want unlimited free recordings (Fathom), or don’t want a bot in your calls (Granola). Whatever you choose, tell participants you’re recording, especially while Otter’s privacy lawsuit is ongoing.

Thanks for reading my Otter AI review! I hope you found it helpful. If you want to try Otter AI for yourself, you can sign up for free and see what it can build for you.

Frequently Asked Questions

Is Otter AI free?

Yes, Otter’s Basic plan is free and includes 300 transcription minutes a month, up to 30 minutes per conversation, three lifetime file imports, and 20 AI Chat questions a month.

How accurate is Otter AI?

In my tests, Otter transcribed 799 of 802 words correctly, including a noisy café recording with an accented speaker. My recordings used clear AI voices, so expect lower accuracy with poor microphones or people talking over each other. Adding names and jargon to custom vocabulary fixed every name it initially got wrong.

Is Otter AI safe to use?

Otter lets you control who can access each conversation, and Enterprise adds SSO and an optional HIPAA compliance add-on. However, it’s facing a class-action lawsuit claiming its Notetaker recorded participants without their consent. To be safe, tell participants when you’re recording and turn on Otter’s pre-meeting notification emails.

What languages does Otter AI support?

Otter now transcribes 19 languages. English (US and UK), Spanish, French, and Japanese are fully supported, while languages like German, Italian, Korean, Portuguese, and Mandarin are still in beta.

What is the best alternative to Otter AI?

It depends on what you need. MeetGeek is better for multilingual teams, Fathom has the most generous free plan with unlimited recordings, and Granola records without a bot joining your calls. If you need accurate transcripts of uploaded files, I’d still choose Otter.