10 Days of Building AI Agents on AWS | Day 7
One of the most repetitive tasks in my daily workflow is replying to emails.
Whether it is acknowledging a meeting request, responding to a client, replying to a colleague, or sending a professional follow-up, I often spend more time thinking about how to phrase the message than actually writing it.
To solve this problem, I built ReplyPilot AI, an AI-powered email reply assistant that generates professional email responses within seconds.
Instead of manually drafting every response, users simply:
- Paste the email they received
- Choose the desired tone
- Generate a polished AI reply
The application also keeps a history of generated replies, allowing users to revisit previous responses without generating the same reply again.
The Problem
Writing an email is usually not technically difficult.
The difficult part is deciding how to write it.
Questions such as these can slow down even a simple reply:
- How formal should I be?
- Should I sound friendly or strictly professional?
- How do I politely decline?
- How should I follow up?
- How can I acknowledge a request professionally?
- What is the best way to phrase my response?
These small decisions happen repeatedly throughout the day.
ReplyPilot AI was built to reduce that friction.
The goal is simple:
Received Email → Select Tone → AI Generates Reply
Instead of starting from a blank page, the user receives a polished draft within seconds.
What ReplyPilot AI Does
ReplyPilot AI is a web application that uses generative AI to create professional email replies.
Users provide the received email and select a desired writing style or tone.
The AI analyzes the content and generates an appropriate response.
The application allows users to:
- Paste a received email
- Choose a writing tone
- Generate a professional reply
- Copy the generated response
- Browse previously generated replies
- Revisit reply history
- View simple usage statistics
Every generated reply is stored in Amazon DynamoDB.
This provides a searchable history and prevents users from losing previous AI-generated drafts.
The application also tracks statistics such as:
- Total replies generated
- Processed emails
- Application usage
How I Built It
I wanted the application to be:
- Lightweight
- Serverless
- Responsive
- Scalable
- Inexpensive to operate
Frontend
The frontend was developed using:
- React
- Vite
The interface allows users to generate replies, browse previous replies, and view application statistics.
Backend
The backend is built using:
- Node.js
- Express
- serverless-http
- AWS Lambda
Express runs inside AWS Lambda using serverless-http, allowing the API to operate in a serverless environment.
The frontend communicates with the backend through Amazon API Gateway.
AI Generation
For AI inference, I integrated the Groq API.
The Lambda backend sends the received email and selected tone to the AI.
Groq generates a professional response based on that context.
The workflow is:
Received Email
↓
Choose Tone
↓
React Frontend
↓
Amazon API Gateway
↓
AWS Lambda
Express.js Backend
↓
Groq API
↓
Generated Reply
↓
Amazon DynamoDB
↓
Reply History
AWS Architecture
ReplyPilot AI uses a fully serverless AWS architecture.
The main AWS services used are:
- AWS Amplify
- AWS Lambda
- Amazon API Gateway
- Amazon DynamoDB
- AWS CloudFormation
- AWS SAM
- Amazon CloudWatch
The overall architecture is:
User
│
▼
AWS Amplify
React Frontend
│
▼
Amazon API Gateway
│
▼
AWS Lambda
Express API
│
┌────┴────┐
▼ ▼
Groq API Amazon DynamoDB
│ │
└────┬────┘
▼
Generated Reply and History
How the Workflow Works
The complete workflow follows these steps:
- The user opens ReplyPilot AI on AWS Amplify.
- The user pastes the email they received.
- The user selects the desired tone.
- The React frontend sends the request to Amazon API Gateway.
- API Gateway invokes AWS Lambda.
- Lambda processes the request through the Express backend.
- Lambda sends the prompt to the Groq API.
- Groq generates a professional email reply.
- The generated reply is returned to the frontend.
- The reply is also stored in Amazon DynamoDB.
- The user can later access the reply from the history section.
This creates a simple but useful workflow:
Email → AI → Professional Reply → Stored History
Infrastructure as Code
The backend infrastructure is managed using:
- AWS SAM
- AWS CloudFormation
Instead of manually creating resources through the AWS Console, the infrastructure is defined as code.
This makes the deployment:
- Repeatable
- Easier to reproduce
- Easier to update
- More consistent
The complete serverless backend can be recreated through the deployment workflow.
Challenges I Faced
Building ReplyPilot AI involved several deployment and infrastructure challenges.
CloudFormation Parameter Validation
One of the early issues involved CloudFormation parameter validation.
Infrastructure templates must match expected configurations precisely, and incorrect parameter definitions can prevent deployments before the application even starts.
This helped me better understand the importance of validating infrastructure configuration.
Lambda Configuration Problems
I also encountered Lambda configuration issues during deployment.
These required reviewing the function settings and ensuring the deployed environment matched what the application expected.
API Gateway Routing Issues
Another challenge involved API Gateway routing.
The frontend and backend both worked individually, but requests needed to be correctly routed from API Gateway to the Express application running inside Lambda.
Debugging this helped me understand how API Gateway and Lambda integrate in a serverless API.
Out of Memory Error
After deployment, I encountered an out-of-memory error.
The application required adjustments to its Lambda memory configuration during testing.
Using logs and deployment tools, I identified the issue, updated the configuration, and redeployed successfully.
This was an important reminder that serverless applications still require resource planning.
Even though there are no servers to manage, memory and runtime configuration still directly affect application performance.
Debugging with CloudWatch
One of the most valuable tools during development was Amazon CloudWatch.
CloudWatch logs helped me diagnose Lambda runtime issues and understand what was happening after deployment.
When something worked locally but failed in the cloud, logs provided the information needed to investigate the actual serverless execution environment.
The development workflow involved:
Deploy → Test → Check CloudWatch Logs → Identify Issue → Update Configuration → Redeploy
This gave me practical experience debugging production-style serverless applications.
What I Learned
Building ReplyPilot AI gave me hands-on experience with several AWS services and modern application development concepts.
AWS Serverless Development
I gained practical experience using:
- AWS Lambda
- Amazon API Gateway
- Amazon DynamoDB
- AWS Amplify
These services work together to create a complete application without managing traditional servers.
Infrastructure as Code
Using AWS SAM and CloudFormation helped me understand the value of defining infrastructure as code.
Resources can be deployed consistently instead of manually recreating them through the AWS Console.
Connecting Frontend and Serverless Backend
I learned more about connecting a modern React application with a serverless backend.
The flow between Amplify, API Gateway, Lambda, and DynamoDB became a complete end-to-end application architecture.
Debugging Serverless Applications
CloudWatch logs, AWS SAM, and repeated testing helped me understand how to troubleshoot problems in deployed serverless environments.
Building Around Everyday Problems
Most importantly, this project reinforced a simple lesson:
A small everyday annoyance can become a genuinely useful application.
Writing professional email replies is something many people do every day.
Reducing the time spent drafting them can make a small but meaningful difference.
The application demonstrates how:
- AI can automate repetitive communication tasks
- Serverless architecture can keep infrastructure simple
- DynamoDB can provide persistent reply history
- AWS SAM and CloudFormation can make deployments repeatable
- CloudWatch can help debug real deployment issues
The core workflow remains simple:
Received Email → Choose Tone → AI Generates Reply → Save to History
Sometimes the most useful AI applications are not the most complicated ones.
They simply remove friction from tasks we already do every day.
Try ReplyPilot AI
Live Demo:
Open ReplyPilot AI
Top comments (1)
The out-of-memory failure is a useful reminder that "serverless" removes server management, not capacity planning, and your deploy-test-CloudWatch-redeploy loop captures that well. Routing a React/Vite frontend through API Gateway to Express on Lambda is pragmatic, while DynamoDB history makes the tool more than a one-shot prompt box. The founder-level tradeoff I'd address next is privacy: storing received emails alongside generated replies creates real retention and access-control obligations, so redaction, encryption, and user-controlled deletion may matter more than additional tone options.