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Conversational Knowledge Engine

8 posts · Jun 2026 – Sep 2026

What this trail covers

The Conversational Knowledge Engine is evolving into a foundational infrastructure layer for organizational AI. It connects and enriches existing enterprise systems (CRM, HRIS, ERP) by capturing and organizing spoken knowledge from conversations. New AI Chat Connectors enable automated pushing of summaries and action items to tools like Notion and Gmail, and allow real-time data pulling from tools like Gmail, Google Drive, Jira, and Salesforce into Otter's AI Chat. It also acts as an MCP server. Live Assist is a new real-time AI coaching agent that joins calls and provides live guidance to users based on configurable playbooks and existing company knowledge. It integrates with common conferencing tools (Zoom, Google Meet, Microsoft Teams) and enterprise systems (Claude, Salesforce) to provide context-aware prompts and track call objectives.

All Otter.Ai trails
  1. 7 AI Agents That Can Attend Your MeetingsThis post details Otter.ai's functionality as an AI meeting agent, focusing on its ability to join calls (Zoom, Google Meet, Microsoft Teams) and provide live transcription. It highlights the logging of action items flagged mid-call and the integration capabilities via MCP with LLMs (Claude, ChatGPT) and other tools (Salesforce, HubSpot, Slack, Notion, Jira). The post also mentions Otter's bot-free audio capture via desktop and mobile apps, and the Live Assist feature for real-time coaching on Enterprise plans. It contrasts Otter with other meeting AI agents based on join method, real-time participation, action item handling, and admin controls.
  2. How to Convert MP4 to Text in 2026 (Free, Fast, and Accurate)This post details the technical process of converting MP4 video files to text transcripts using Otter.ai's AI notetaker and Conversation Intelligence Platform. It outlines a five-step workflow: importing MP4 files, the transcription process, editing the generated transcript, sharing with team members, and exporting in various formats (TXT, DOCX, PDF, SRT, VTT). It also discusses the underlying technology of MP4 container format and audio stream extraction, the benefits of transcription for searchability, AI integration, and accessibility, and compares this method to using YouTube auto-captions, highlighting technical limitations of the latter such as lack of custom vocabulary, limited export formats, and absence of speaker attribution. The post also touches upon considerations for choosing an MP4 to text converter, including speaker recognition, language coverage, custom insight extraction, privacy, and export format suitability for different use cases.
  3. Your Calls Have a Coach Now: How to Use Live AssistIntroduces Live Assist, a real-time AI coaching agent that joins calls to provide live guidance. This feature leverages the Agent Builder to create custom coaching agents by feeding them playbooks, SOPs, and call transcripts. Live Assist integrates with conferencing platforms (Zoom, Google Meet, Microsoft Teams) and enterprise tools (Claude, Salesforce) to pull context and provide real-time prompts, acting as a private coach visible only to the user. It also tracks call objectives live.
  4. How to Build an AI Sales Workflow With OtterThis post details the integration of Otter's speech-to-text transcription and AI capabilities into a sales workflow. It describes how Otter captures sales calls, provides live coaching based on conversation signals (BANT, MEDDIC), automatically extracts deal fields (budget, timeline, etc.) and syncs them to CRMs like Salesforce and HubSpot, drafts follow-up emails, and makes past conversations searchable via Otter AI Chat. The bidirectional MCP integration is highlighted, allowing external AI models to access Otter's meeting data and Otter AI Chat to query external tools like Notion, Gmail, and Jira. The post also mentions the use of proprietary speech recognition technology and a large voice data set for transcription accuracy and speaker attribution.
  5. 7 Ways to Build Agentic AI Workflows for SalesThis post details how agentic AI workflows can be built and applied to sales processes. It outlines the core components of an agentic workflow (trigger, access to conversation record, connected system to act in) and provides seven specific examples: automated post-call CRM enrichment, automatic follow-up email drafting, live deal coaching and signal capture, autonomous inbound qualification and routing, pipeline hygiene and deal updates, cross-meeting deal intelligence retrieval, and AE-to-CS handoff briefs. The post emphasizes the transition from assistive AI to agentic AI, where the agent acts autonomously based on conversation data to drive sales outcomes.
  7. From AI Notetaker to $100B Category: Otter’s Conversational Knowledge EngineThis post announces the evolution of Otter's Conversational Knowledge Engine (CKE) into a new category of enterprise infrastructure. It details the integration of AI Chat Connectors that push summaries and action items to external tools (Notion, Gmail) and pull live data from various enterprise systems (Gmail, Google Drive, Jira, Salesforce) into Otter's AI Chat. The post also highlights Otter's role as an MCP server, allowing external AI tools to access meeting history as live context. The core technical contribution is the architectural shift towards an intelligence layer that connects and feeds CRM, knowledge bases, and AI agents by leveraging transcribed conversations as a persistent, accessible data source.
  8. How to Create a Sales Call Automation WorkflowThis post details the implementation of sales call automation as an extension of the Conversational Knowledge Engine. It outlines a phased rollout strategy starting with post-call CRM logging, followed by AI transcription, pre-call briefs, and real-time coaching overlays. The technical contribution lies in the integration of Otter's transcription and AI capabilities with CRM systems (Salesforce, HubSpot) to automate data extraction for deal stage updates, action item assignment, and follow-up email generation. It emphasizes the importance of defining CRM logging schemas and configuring templates for different deal stages. The post also highlights the use of real-time transcription with speaker identification and keyword-based live coaching prompts to reduce rep cognitive load during calls.