1.1 Project Overview
Overview of Project ☁️
Scenario:
Customer Delight Co., a fast-growing e-commerce company, receives hundreds of customer messages every single day, ranging from urgent complaints to positive feedback.
Their support team currently reads and prioritizes each message manually, which slows down response times and frustrates customers waiting for critical help.
In an attempt to improve efficiency, the team experimented with AI chatbots and tools like ChatGPT to automatically classify and prioritize messages.
However, these solutions lacked real-time integration, had no connection to internal systems, and couldn’t handle message routing at scale.
To overcome these limitations, the company decided to build an in-house, serverless Smart Inbox powered entirely by AWS services, combining automation, scalability, and cost-efficiency into one intelligent workflow.
Our solution:
The goal is to design an intelligent Smart Inbox that automatically classifies incoming messages by sentiment and routes them to the appropriate priority queue, all in real time.
- Incoming messages are stored in Amazon S3.
- AWS Lambda is triggered automatically when new files arrive.
- Amazon Comprehend performs sentiment analysis (Positive, Negative, Neutral, Mixed).
- Based on sentiment:
- Negative messages → sent to HighPriorityQueue for urgent handling.
- Positive / Neutral messages → sent to NormalQueue for standard follow-up.
This ensures that the support team focuses first on critical issues while maintaining full automation and scalability.
About Project:
In this hands-on project, you’ll build a production-grade AI-powered sentiment routing pipeline, just like the ones used by enterprise support teams.
You’ll learn how to:
- Automate ingestion and event-driven processing with Amazon S3 and Lambda
- Detect sentiment using Amazon Comprehend
- Route messages intelligently through Amazon SQS
- Host a live results dashboard using S3 + CloudFront
- Apply IAM least-privilege policies for secure operation
By the end, you’ll have a fully functional AI-driven Smart Inbox Dashboard, a real-world, serverless, and scalable AWS application.
Steps To Be Performed 👩💻
We’ll build the Smart Inbox step by step:
- Create an S3 Inbox for incoming customer messages
- Set up Amazon SQS Queues for message routing
- Build the Lambda Function integrated with Amazon Comprehend
- Connect S3 Events to Lambda for automation
- Host the Frontend via S3 + CloudFront to display sentiment results
Services Used 🛠
- Amazon S3 → Store incoming customer message files (TXT)
- AWS Lambda → Serverless compute to process messages automatically
- Amazon Comprehend → Detect sentiment (Positive, Negative, Neutral, Mixed)
- Amazon SQS → Route messages to High Priority or Normal queues
- Amazon CloudWatch → Monitor logs and function execution
- AWS IAM → Manage least-privilege permissions securely
- Amazon CloudFront → Host and serve frontend securely
Estimated Time & Cost ⚙️
- Estimated time: 3-4 hours
- Cost: $0-$1 (under AWS Free Tier)
➡️ Architectural Diagram
This is the architectural diagram for the project:
➡️ Final Result
Once completed, you’ll have:
- An AI-powered Smart Inbox that automatically prioritizes customer messages
- Faster response times for critical issues and complaints
- A real-world AI/ML workflow that demonstrates event-driven design and AWS integration
The system detects if a message is positive, neutral, or negative and then routes it to different message queues based on priority.