Chatbots · 2026-01-22 · 11 min read

WhatsApp AI Chatbot Architecture

Reference architecture for a production WhatsApp AI chatbot: BSP, orchestration, RAG, CRM write-back, human handoff, and analytics.

A WhatsApp AI chatbot that works in production needs more than a prompt. It needs channel compliance, CRM write-back, retrieval, and escalation. ## Reference architecture 1. **WhatsApp Business API** via an approved BSP 2. **Orchestration layer** (n8n or custom service) for session state and tools 3. **LLM + retrieval** grounded in approved FAQs and product docs 4. **CRM / database** for identity, orders, and lead fields 5. **Human handoff** with full transcript into your inbox or helpdesk 6. **Analytics** for containment, escalation rate, and conversion ## Critical design choices - How do you identify the user (phone, login link, order ID)? - What can the bot promise without a human? - When must it refuse or escalate? - Which languages matter for your market (especially Nigeria and multilingual audiences)? - How do opt-outs and template messaging rules work? ## What breaks demos - Hallucinated prices or policies - No CRM sync, so sales cannot follow up - No human takeover path - Ignoring WhatsApp template and session constraints ## Our build pattern We start with a narrow job: FAQs + lead capture, or order status, or appointment booking. Then we expand tools once containment and accuracy are measured. ## Next step If WhatsApp is already your front desk, bring message volume and your CRM. We will propose a containment target and a phased rollout.

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