Case Study · Conversational AI Framework
Conversational Operations Engine
Multi-channel conversational AI framework for customer qualification, routing, follow-up and human handoff across WhatsApp, Telegram and web channels. Stateful conversation memory, multilingual message handling, and autonomous decision logic — from inquiry triage to qualified lead routing — with graceful escalation to human operators when needed. Designed as a deployable operational system for any business with inbound conversational volume.
Focus
Systems Architecture · Automation Design · Customer Operations · AI Implementation
Stack
n8n · Claude API · Notion · WhatsApp Cloud API · Telegram Bot API · Web Chat
Status
Production · Deployable
Problem
Inbound customer conversations land across WhatsApp, Telegram and web — each demanding instant, contextual, multilingual response. Manual handling caps growth; rigid bots damage the brand. Businesses need a deployable conversational operations framework that scales across channels and industries.
Approach
Multi-channel conversational AI framework for customer qualification, routing, follow-up and human handoff across WhatsApp, Telegram and web channels. Stateful conversation memory, multilingual message handling, and autonomous decision logic — from inquiry triage to qualified lead routing — with graceful escalation to human operators when needed. Designed as a deployable operational system for any business with inbound conversational volume.
What I built
- Multi-channel deployment framework: WhatsApp, Telegram, Web
- Autonomous customer qualification and routing logic
- Stateful conversation memory with persistent context across sessions
- Human handoff with full context preservation and operator dashboard
Outcome
A deployable conversational AI framework running 24/7 across WhatsApp, Telegram and web — with stateful memory, autonomous qualification and clean handoff to humans when nuance is required. Ready to deploy into any business with inbound conversational volume.
Architecture notes
Built to run autonomously, fail loudly, and be handed off to a team — not held together by a single operator. Conversation state, business logic and integration adapters are separated so any layer can be swapped without rebuilding the system.
Next case study
Customer Journey Automation System