DEV Community: Jagadeesh

# ✉️ SmartSupportAI – AI-Powered Email Support for OnlineStore

Jagadeesh — Sun, 08 Jun 2025 10:12:59 +0000

🧠 What I Built

SmartSupportAI is an intelligent customer support automation system for e-commerce platforms like OnlineStore, where users can send feedback or support queries via email — and receive a personalized, human-like AI response within seconds.

It uses Postmark's inbound email parsing feature to receive emails and triggers a webhook to:

Log the email into a database
Analyze the content with Gemini 2.0 Flash
Classify and respond using AI
Send a formal, contextual, and human-sounding reply using Nodemailer

✨ Demo

📬 Send a feedback email to:

feedbacksonlinestore@gmail.com

Example:

"I'm facing an issue placing a mobile order."

You'll get a smart reply like this:

🔧 How It Works

Here’s the flow architecture:

User sends feedback to feedbacksonlinestore@gmail.com
Email is auto-forwarded to Postmark Inbound
Postmark triggers a webhook to my Express server
Server logs the message in MongoDB
It checks if this message is a follow-up to a previous query
Sends the data to Gemini AI (2.0 Flash) for smart classification and response generation
The AI's response is sent back to the user using Nodemailer
All feedback and responses are stored for future context

📦 GitHub Repository

👉 https://github.com/Jagadeesh961982/postmark

🤖 AI Deep Dive

Uses Gemini 2.0 Flash for generating formal, empathetic, and context-aware replies
AI checks similarity with previous feedback (via prompt-based comparison)
Example: If a user sends "I’m still facing payment issues", Gemini picks up the thread from the earlier conversation

🧠 Postmark Integration

Inbound email goes to a Postmark inbound stream
Webhook forwards full structured data to Express
Handles attachments, raw text, and reply chains
Logs feedback in MongoDB for traceability

🔐 Tech Stack

Backend: Node.js + Express
Email Inbound: Postmark Webhooks
AI Engine: Gemini Flash via Google Generative AI API
Email Outbound: Nodemailer with Gmail App Password
Database: MongoDB with Mongoose

💡 Why I Built It

Many customers still prefer to send support requests via email — especially older users or those in low-connectivity areas. Instead of forcing users into live chat bots or apps, this project turns traditional support mail into a fully automated yet human-sounding experience.

This also:

Saves support time
Offers consistent and structured replies
Enables easy record-keeping for admins

📈 Potential Use Cases

E-commerce customer support
SaaS support inbox
HR feedback handling
Complaint resolution bots
Feedback-based AI analytics

📣 Want to Try It?

✅ Just send a feedback email to:

📨 feedbacksonlinestore@gmail.com

🧪 You’ll receive an AI-generated response like this:

Dear Customer,

We understand you're facing issues placing your mobile order...

Please provide the following details...

✅ AI decides if it’s a follow-up, and builds context-aware responses.

🙌 Shoutout to Postmark

Postmark's inbound email parsing made this flow clean and reliable. Setting up inbound streams and webhooks was painless. And the structured payload helped feed AI models directly.

Huge thanks for this challenge — it pushed me to explore how email + AI can reinvent everyday support workflows.

Stock Price Predictor

Jagadeesh — Sun, 26 Jan 2025 16:37:34 +0000

This is a submission for the Agent.ai Challenge: Productivity-Pro Agent (See Details)

What I Built

I developed a Stock Prediction Agent designed to empower traders with AI-driven insights. My agent, Stock Price Predictor, analyzes historical stock data, leverages technical indicators (Bollinger Bands, MACD, RSI), and predicts actionable trading signals (Buy/Sell/Hold) alongside next day price targets (Open, Close, Buy/Sell levels). Built with Flask and machine learning, it transforms complex market data into digestible, confidence-scored recommendations.

Demo

https://agent.ai/agent/stock-price-predictor

How It Works

Step 1: Enter a stock symbol (e.g., RVNL.NS).
Step 2: The Agent fetches historical data, calculates technical indicators, and runs ML models.
Output:

Action: Buy/Sell/Hold recommendation with confidence score.
Price Forecasts: Predicted Buy (Low), Sell (High), Open, and Close prices for the next trading session.

Challenge Experience

Building this agent with Flask, XGBoost, and Random Forest was a rewarding deep dive into Stock Price Prediction. Key learnings:

Balancing imbalanced classes with SMOTE drastically improved prediction accuracy.
Integrating Yahoo Finance data via yfinance streamlined real-time analysis.
Deploying the model as an API with error handling ensured scalability.
While hyperparameter tuning with GridSearchCV was computationally intense, the performance gains justified the effort.

Next step? Adding real-time news for sentiment analysis!
Disclaimer: Predictions are educational and not financial advice. Trade responsibly!

Agent.ai Experience

Agent.ai simplified building and deploying Stock Price Predictor, letting me focus on refining ML models instead of infrastructure. Its intuitive tools made API integration and scaling effortless.

A Smarter Stock Predictor

Jagadeesh — Sun, 26 Jan 2025 14:17:06 +0000

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)