Customer Challenges

Rizz Wireless needed to provide round-the-clock customer support without incurring the high operational costs and staffing requirements of traditional contact centers. Their customer service teams relied on scattered documents and inconsistent knowledge sources, resulting in slow responses, variable service quality, and frustrated customers.

The existing setup created three major challenges:

1. Limited Scalability

Handling thousands of daily inquiries—ranging from simple questions to complex technical issues—was becoming unsustainable. Scaling human agents to match customer demand, especially during peak hours, was costly and inefficient.

2. Rising Operational Costs

Maintaining 24/7 human agent coverage would cost more than $500,000 annually. Hiring, training, and supporting large teams was no longer viable as customer support volumes continued to grow.

3. Inconsistent & Slow Responses

Information was dispersed across multiple systems. Agents lacked real-time access to updated documentation, causing delays, inconsistent responses, and error-prone support interactions. Average response times during peak hours stretched beyond 30 minutes.

Without intervention, Rizz Wireless risked increased churn, lower customer satisfaction, compliance issues from incorrect information, and mounting operational costs. To overcome these challenges, the company identified generative AI as a strategic solution to deliver accurate, scalable, 24/7 customer support.

Ibexlabs guided Rizz Wireless through AWS generative AI adoption strategy, RAG architecture design, Bedrock model selection, serverless implementation, and production deployment—all with security, observability, and operational excellence.

By replacing manual support workflows with an AI-powered, serverless solution built on Amazon Bedrock, AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon S3, and AI-powered Retrieval-Augmented Generation (RAG) retrieval, Ibexlabs enabled Rizz Wireless to achieve scalable, accurate, and cost-effective customer support.

This case study highlights Ibexlabs’ expertise in designing and managing secure, production-ready, generative-AI-powered support systems on AWS.

Partner Solution

1. Real-time Mobile Chat Interface

Customers using iOS, Android, React Native, or Flutter apps connect directly to the chatbot through a secure WebSocket connection. The mobile app connects to Amazon API Gateway (WebSocket API) using TLS, enabling instant, bidirectional messaging—typing indicators, quick responses, and seamless chat continuity.

2. Secure API Gateway Layer

The WebSocket API acts as the entry point and manages all client connections.
It handles:

• • Connecting and disconnecting events
  • Routing customer messages to backend services
  • Secure message delivery back to devices

3. Serverless Compute with AWS Lambda

All incoming messages are processed by a single Lambda function that performs the core logic of the system:

• • Stores and retrieves chat history
  • Calls the Knowledge Base for relevant documents
  • Sends context-rich prompts to Amazon Bedrock
  • Pushes AI responses back over WebSocket

Because Lambda is serverless, the system automatically scales to handle spikes in chat volume without managing infrastructure.

4. AI-Driven Response Generation with Amazon Bedrock

To generate natural, human-like responses, the solution uses Amazon Bedrock with Claude 3.7 Sonnet. The model receives:

• • Recent chat history
  • Relevant knowledge base content
  • Customer’s latest query

Using this combined context, it produces accurate, helpful, real-time responses tailored to Rizz Wireless’ support needs.

5. RAG Using Knowledge Base

To give the model factual grounding, the solution integrates Amazon Bedrock Knowledge Base, which performs vector search over Rizz Wireless’ documents.
This allows the chatbot to:

• • Pull the most relevant policy or troubleshooting content
  • Use S3-stored PDFs and manuals as truth sources
  • Generate answers supported by real documentation

The RAG pattern ensures responses are not only fluent but also accurate and trustworthy.

6. Chat History Storage in DynamoDB

The chatbot stores every message in an Amazon DynamoDB table with a 30-day TTL. This allows the system to:

• • Retrieve the last 5 messages for context
  • Maintain continuity across messages
  • Automatically clean up old conversations

DynamoDB’s on-demand capacity ensures performance at any scale.

7. Secure Storage of Documents in Amazon S3

All knowledge base documents—including PDFs, troubleshooting guides, and SOPs—are stored in Amazon S3. These documents are fed into the Knowledge Base for embedding and retrieval, enabling accurate customer support grounded in Rizz Wireless’ policies and device manuals.

8. Enterprise-Grade Security & Access Control

Every interaction between services is secured using IAM roles and policies.
This enforces least privilege access for:

• • AWS Lambda
  • Amazon Bedrock
  • Amazon DynamoDB
  • Amazon API Gateway
  • Amazon S3

Customer data is encrypted in transit (TLS), and at rest through AWS managed encryption.

9. Monitoring & Observability With CloudWatch

The entire system is monitored using Amazon CloudWatch, including:

• • Lambda logs
  • API Gateway metrics
  • Bedrock usage
  • DynamoDB performance

Alerts and dashboards help Rizz Wireless track chatbot health, latency, and performance in real time.

Solution Optimality

Ibexlabs’ serverless, Amazon Bedrock-powered architecture gave Rizz Wireless a real-time AI support assistant that scales automatically, delivers accurate responses, and requires minimal operations effort.

The combination of WebSockets, AWS Lambda, and Amazon Bedrock ensures instant responses, while the RAG-based Knowledge Base keeps answers grounded and up-to-date with company policies. Amazon DynamoDB enables fast context retrieval, and Amazon CloudWatch ensures complete observability.

Alternative approaches—such as building proprietary LLM pipelines, hosting custom GPU models, or using traditional rules-based chatbots—were evaluated but rejected due to higher costs, limited scalability, slower response times, and increased maintenance overhead.

The final solution gives Rizz Wireless a reliable, self-improving AI assistant that reduces customer support load, improves accuracy, and enhances mobile user experience.

Faster Customer Support

Response times dropped from 15 minutes (human agents) to 3 seconds with AI-driven support. Customers now receive instant, context-aware answers across all channels.

Lower Operational Costs

AI support eliminated the need for 24/7 human staffing, saving over $350,000 annually while increasing support capacity by 300%.

Built for Scalability

The serverless architecture seamlessly handles 10,000+ daily inquiries, automatically scaling during peak hours without performance degradation.

More Accurate, Consistent Answers

RAG-based knowledge retrieval improved accuracy to 95%, dramatically reducing misinformation compared to human agents (78%). Auto-reconciliation keeps answers continuously up-to-date.

Higher Customer Satisfaction

Customer satisfaction improved by 35%, with 92% of users rating responses “helpful” or “very helpful.”