Otter.ai
Meeting transcription and searchable conversation knowledge
- Overview
- Otter.ai joins your meetings, transcribes in real time, and generates summaries with action items across Zoom, Google Meet, and Teams. In April 2026 it launched a Conversational Knowledge Engine, repositioning from a note-taker to a searchable meeting knowledge layer with agentic capabilities. AI Chat lets users query single meetings or the full meeting history, with sources shown, and the tool now acts as both an MCP client and server.
- Why it matters
- Otter.ai is moving beyond transcription into a meeting-data layer that feeds downstream automation — a direct response to Microsoft Copilot and similar in-platform note-takers bundled into productivity suites. The April 2026 Conversational Knowledge Engine and MCP server position it as middleware between meetings and enterprise systems. Watch the Pro-tier minute cut (6,000 → 1,200/mo, no price change): heavy users are being pushed toward the $30/seat Business plan, which compresses the value proposition for smaller teams. Data residency and privacy governance remain enterprise procurement blockers.
- In practice
- Sales teams commonly run Otter's bot on every customer call, then use AI Chat to surface objections and decisions across a deal's entire call history before a renewal or QBR. RevOps leads route post-meeting summaries automatically into Salesforce or HubSpot via the native integration. The 90-minute per-meeting cap on Pro is a real friction point for workshops or half-day sessions, which silently stop recording mid-meeting without a Business plan.
- Best for
- Revenue and operations teams that need a searchable archive of meeting decisions and want to push action items directly into CRM or project tools
- Pricing
- Free (300 min/mo, 30 min/meeting cap, 3 lifetime imports); Pro $16.99/user/mo or $8.33/user/mo billed annually (1,200 min/mo, 90 min/meeting, 10 imports/mo); Business $30/user/mo or $19.99/user/mo annually (unlimited recording, 4-hr meeting cap); Enterprise custom. Note: Pro minutes were cut from 6,000 to 1,200/mo with no price reduction.
Key features
- Real-time transcription with automated summaries and action items
- Conversational Knowledge Engine: query your full meeting history via AI Chat
- MCP client and server support for agentic workflow integration
- CRM and storage integrations: Salesforce, HubSpot, Notion, Google Drive, OneDrive
- Zoom, Google Meet, and Teams bot with speaker diarization
Quick answers
- What is Otter.ai?
- Otter.ai joins your meetings, transcribes in real time, and generates summaries with action items across Zoom, Google Meet, and Teams. In April 2026 it launched a Conversational Knowledge Engine, repositioning from a note-taker to a searchable meeting knowledge layer with agentic capabilities. AI Chat lets users query single meetings or the full meeting history, with sources shown, and the tool now acts as both an MCP client and server.
- Why does Otter.ai matter?
- Otter.ai is moving beyond transcription into a meeting-data layer that feeds downstream automation — a direct response to Microsoft Copilot and similar in-platform note-takers bundled into productivity suites. The April 2026 Conversational Knowledge Engine and MCP server position it as middleware between meetings and enterprise systems. Watch the Pro-tier minute cut (6,000 → 1,200/mo, no price change): heavy users are being pushed toward the $30/seat Business plan, which compresses the value proposition for smaller teams. Data residency and privacy governance remain enterprise procurement blockers.
- What is Otter.ai best for?
- Revenue and operations teams that need a searchable archive of meeting decisions and want to push action items directly into CRM or project tools
- How much does Otter.ai cost?
- Free (300 min/mo, 30 min/meeting cap, 3 lifetime imports); Pro $16.99/user/mo or $8.33/user/mo billed annually (1,200 min/mo, 90 min/meeting, 10 imports/mo); Business $30/user/mo or $19.99/user/mo annually (unlimited recording, 4-hr meeting cap); Enterprise custom. Note: Pro minutes were cut from 6,000 to 1,200/mo with no price reduction.
Know the terms
Glossary entries that explain what Otter.ai is built on.
Agentic workflow
A multi-step process where an AI agent plans, executes, evaluates, and iterates on tasks with minimal human intervention. Unlike single-turn prompts, agentic workflows involve loops, branching logic, and tool calls that unfold over minutes or hours.
MCP (Model Context Protocol)
An open standard (created by Anthropic) that lets AI models connect to external tools, data sources, and services through a unified interface. MCP is becoming the USB-C of AI integrations.
RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents from an external knowledge base and feeds them to a model alongside the user's query. RAG reduces hallucination and keeps responses grounded in current, factual data.
LLM (Large Language Model)
A neural network trained on massive text corpora to predict and generate language. LLMs like GPT-4, Claude, and Gemini are the foundation of the current AI wave, powering chatbots, coding tools, and enterprise automation.
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