Architecting an AI-Powered Customer Support Chatbot

Example: Architecting an AI-Powered Customer Support Chatbot

1. Objective

Design an AI chatbot to automate customer support for a financial services firm, capable of handling queries about account balances, transactions, loan eligibility, and FAQs.

2. Architecture Overview

pgsql
CopyEdit
                    +--------------------------+
                    |   Customer Interfaces    |
                    | (Web, Mobile, WhatsApp)  |
                    +-----------+--------------+
                                |
                                v
                +---------------+----------------+
                |       API Gateway / Router     |
                +---------------+----------------+
                                |
             +-----------------+------------------+
             |                                    |
             v                                    v
+--------------------------+      +-------------------------------+
|  Intent Recognition (NLP)|      |      Authentication Service   |
| (LLM, Rasa, Dialogflow)  |      | (OAuth2, JWT, Identity Server)|
+------------+-------------+      +-------------------------------+
             |
             v
+-------------------------------+
|   Dialogue Manager / Orchestrator |
| (State Tracking, Context Mgmt)    |
+-------------------------------+
             |
             v
+------------------------------+
|   Business Logic Layer       |
| (Loan, KYC, Payments Modules)|
+------------------------------+
             |
             v
+-----------------------------+
|   Data Access Layer         |
| (Banking DBs, User History) |
+-----------------------------+

3. Technology Stack

• Frontend: React (web), Flutter (mobile), WhatsApp Business API
• API Gateway: AWS API Gateway / NGINX
• NLP Layer: OpenAI GPT-4, Rasa NLU, or Google Dialogflow
• Authentication: Auth0 / AWS Cognito
• Orchestration: Node.js with Redis for session storage
• Business Logic: Microservices using Python (FastAPI)
• Data Layer: PostgreSQL, Redis (cache), MongoDB (logs)

4. Key Features

• Multi-language support using multilingual models
• Human handoff system integrated with Zendesk
• Real-time transaction status lookup
• Adaptive learning from customer feedback

5. Considerations

• Security: Encrypt sensitive data, follow GDPR/PCI DSS
• Scalability: Use serverless functions or containers (Kubernetes)
• Monitoring: Prometheus, Grafana, Elastic Stack for logging
• Performance: Use embeddings or vector databases (like Pinecone) for context memory