TwinMind Reference Architecture

Overview

Production architecture for meeting AI systems using TwinMind: transcription pipeline, memory vault, action item workflow, and calendar integration. TwinMind uses the Ear-3 speech model (5.26% WER, 3.8% DER) for transcription, with GPT-4, Claude, and Gemini for AI summarization.

Prerequisites

• TwinMind account (Free, Pro $10/mo, or Enterprise)
• Chrome extension installed and authenticated
• Understanding of TwinMind workflow

Instructions

Step 1: Setup

TwinMind operates as a Chrome extension and mobile app with optional API access for Pro/Enterprise users.

// TwinMind configuration
const config = {
  apiKey: process.env.TWINMIND_API_KEY,
  model: "ear-3", // Transcription model
  aiModels: ["gpt-4", "claude", "gemini"], // Summary models
};

Step 2: Implementation

// TwinMind Reference Architecture implementation
// Core TwinMind integration
const twinmind = {
  transcriptionModel: "ear-3",
  languages: ["en", "es", "ko", "ja", "fr"],
  features: ["transcription", "summary", "action-items"],
  privacyMode: "on-device", // Audio never stored
};

// Check transcription capabilities
async function verify() {
  const health = await fetch("https://api.twinmind.com/v1/health");
  console.log("TwinMind status:", await health.json());
}

Step 3: Verification

# Verify TwinMind integration
curl -H "Authorization: Bearer $TWINMIND_API_KEY" https://api.twinmind.com/v1/health | jq .

Key TwinMind Specifications

Output

• TwinMind Reference Architecture configured and verified
• TwinMind integration operational
• Meeting transcription workflow ready

Error Handling

Resources

• TwinMind Website
• Chrome Extension
• Ear-3 Model
• iOS App

Next Steps

See twinmind-prod-checklist for production readiness.

Examples

Basic: Configure reference architecture with default TwinMind settings for standard meeting workflows.

Enterprise: Customize for high-volume meeting transcription with monitoring and alerting.