Monitor AI Voice & Chat Performance with SEBVM Dashboard
Track calls, conversations, costs, and customer insights in real-time using SEB Voice Monitoring (SEBVM).
This system provides a complete analytics layer for AI voice agents and chatbots — helping businesses understand performance, optimize conversations, and reduce operational costs.
SEBVM collects data from voice (Vapi) and chat systems, processes it through intelligent pipelines, and presents actionable insights via a powerful dashboard.
Perfect for AI-driven businesses, call centers, and automation-focused teams.
What This Template Does?
- Tracks total calls, call duration, and AI usage cost
- Monitors chatbot sessions and engagement metrics
- Captures full transcripts and summaries of conversations
- Analyzes call outcomes (completed, failed, user dropped, etc.)
- Displays real-time KPIs for business decisions
- Stores and processes webhook data from Vapi & chat systems
- Tracks user behavior across sessions
- Provides cost-per-call and efficiency insights
- Enables deep dive into individual call logs
Step-by-Step Setup Instructions
- Connect Vapi webhook to /api/webhook/vapi
- Configure server environment (MongoDB + Express API)
- Set up call and chat data storage collections
- Enable metrics endpoints (/api/calls, /api/call-metrics, /api/chat-metrics)
- Configure frontend dashboard (React + Vite)
- Set date filters and KPI aggregation logic
- Deploy using Docker (client + server + MongoDB)
- Configure domain + Nginx reverse proxy
- Activate real-time monitoring
Features
- Real-time call tracking (Vapi integration)
- AI conversation transcript storage
- Call outcome classification (success, failed, dropped)
- Cost tracking (total spend, avg cost per call)
- Chat + voice unified analytics
- KPI dashboard (sessions, users, engagement)
- Secure admin login (JWT-based authentication)
- API-driven architecture for scalability
- Filterable insights (date range, bot, session type)
- High-performance MongoDB aggregation
Key Benefits
- Full visibility into AI agent performance
- Optimize cost vs performance of voice AI
- Identify drop-offs and failed conversations
- Improve conversion rates using insights
- Centralized analytics for voice + chat
- Faster decision-making with real-time data
- Scalable architecture for enterprise use
Requirements
- Vapi account (for voice AI data)
- Node.js backend (Express server)
- MongoDB database (local or cloud)
- React frontend dashboard
- Webhook configuration for data ingestion
- Environment variables (JWT, DB URI, API configs)
- VPS / Cloud deployment setup
Target Audience
- AI SaaS companies
- Call center automation teams
- Mortgage / finance AI systems (like SEB Mortgage)
- Customer support automation platforms
- Agencies building AI solutions
- Businesses using voice/chat AI at scale
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