Hook: The 100-Email Morning Attack
Remember that scene in The Social Network where Mark Zuckerberg gets overwhelmed by a flood of messages? That’s your inbox every Monday morning. 100 new emails. 30 are urgent. 40 are noise. 20 are spam. 10 are critical. And somewhere between email #75 and #86, your soul leaves your body.
Meet Alex. A founder of a small consulting firm. She spent 2.5 hours daily just managing her inbox. Not working. Not closing deals. Just… reading. One morning, she woke up to 117 new emails. She quit email for a week. Revenue dropped. Panic set in. This is the inbox paradox: you need it, but it’s killing your productivity.
What if your inbox had a smart intern? Someone who reads everything, summarizes the chaos, replies to the obvious stuff, and only pulls you in when it’s truly important? That’s not science fiction. It’s Tuesday’s project.
Why This Matters: From Chaos to Control
Email is a universal business tool, but it’s poorly designed for focus. Every notification is a context switch. Every “FYI” is a tax on your attention.
This automation replaces:
- Manual triage: That intern you don’t have, spending 2 hours sorting “urgent” vs. “spam.”
- Generic replies: Copy-pasting “Thanks for reaching out!” 20 times a day.
- Missed opportunities: When a critical request gets buried under newsletter clutter.
The business impact is measurable:
- Time: Reclaim 2-3 hours daily.
- Sanity: End the anxiety of a cluttered inbox.
- Scale: Handle 10x the emails with the same effort.
- Revenue: Faster responses to leads = higher conversion.
What This Tool / Workflow Actually Is
We’re building an AI Email Auto-Responder & Sorter. This is a Python script that:
- Connects to your Gmail (via secure API)
- Reads new, unread emails
- Uses a lightweight AI model (like HuggingFace’s BERT or a simple classifier) to categorize each email: URGENT, INFO, SPAM
- Generates a human-like reply for non-urgent, non-spam emails
- Routes URGENT emails to your Slack or SMS
- Moves SPAM to a folder
What it is NOT:
- A magic black box that replaces your judgment. You configure the rules.
- A security risk. We use official APIs with OAuth, not passwords.
- A full email client. It’s a background worker that runs on your schedule (e.g., every 15 minutes).
Prerequisites
Be honest with yourself. You need:
- Zero coding experience is fine. If you can copy-paste and follow instructions, you’re golden.
- A Google Account: For Gmail. We’ll create an app password safely.
- A computer with Python installed: If you don’t have it, go to python.org, download version 3.10+ and install it. It’s like installing any other app.
- A little patience: The first time you connect APIs, it feels like wiring a bomb. It’s not. We’ll do it step-by-step.
Feeling nervous? Good. That means you’re about to learn. Sit back, grab your favorite coffee, and let’s turn your inbox into a well-oiled machine.
Step-by-Step Tutorial
We’ll use Python, because it’s the universal language of automation. The magic libraries are:
imaplibfor reading emailsemailfor parsing themtransformersfrom HuggingFace for simple AI categorizationrequeststo send alerts (to Slack, for example)
Step 1: Create a Google App Password
Never share your main Gmail password. Use a secure, dedicated password.
- Go to your Google Account > Security.
- Enable 2-Step Verification (if not already).
- Under “Signing in to Google,” click App Passwords.
- Give it a name like “EmailAutoresponder.”
- Copy the 16-character code. This is your password for the script. Guard it.
Step 2: Install Required Libraries
Open your terminal or command prompt and run:
pip install transformers torch requests imapclientNote: imapclient is a friendlier wrapper for imaplib. We use it for clarity.
Step 3: Build the Core Script
Create a file named email_automator.py. Paste this code. Read the comments—they’re your guide.
# Email Automator by Professor Ajay
# This script reads your emails, categorizes them, and acts.
import imaplib
import email
import smtplib
from email.mime.text import MIMEText
import requests
from transformers import pipeline
import json
import time
# ===== CONFIGURATION (Edit these) =====
GMAIL_USER = "your_email@gmail.com" # Your full Gmail address
GMAIL_PASSWORD = "abcd efgh ijkl mnop" # Your 16-char App Password
SMTP_SERVER = "imap.gmail.com"
SLACK_WEBHOOK_URL = "https://hooks.slack.com/services/XXXXX" # Optional: Your Slack incoming webhook
# ===== EMAIL CATEGORIES =====
# We use a simple AI model to classify emails as Urgent, Info, or Spam.
# You can tweak this logic later.
ai_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
# ===== MAIN FUNCTIONS =====
def get_unread_emails():
"""Connects to Gmail and fetches unread emails."""
mail = imaplib.IMAP4_SSL(SMTP_SERVER)
mail.login(GMAIL_USER, GMAIL_PASSWORD)
mail.select('inbox')
# Search for UNSEEN (unread) emails
result, data = mail.search(None, 'UNSEEN')
if result != 'OK':
print("No new emails.")
return []
email_ids = data[0].split()
emails = []
for eid in email_ids[:10]: # Process first 10 new emails only
result, msg_data = mail.fetch(eid, '(RFC822)')
if result == 'OK':
raw_email = msg_data[0][1]
msg = email.message_from_bytes(raw_email)
emails.append(msg)
mail.close()
mail.logout()
return emails
def classify_email(email_message):
"""Uses AI to classify an email."""
subject = email_message.get('Subject', '')
body = ""
# Extract plain text body
if email_message.is_multipart():
for part in email_message.walk():
content_type = part.get_content_type()
content_disposition = str(part.get("Content-Disposition"))
if "attachment" not in content_disposition:
try:
body = part.get_payload(decode=True).decode()
break
except:
pass
else:
body = email_message.get_payload(decode=True).decode()
text = f"Subject: {subject}\
Body: {body[:500]}..." # Use first 500 chars
# Define candidate labels
candidate_labels = ["urgent", "informational", "spam"]
try:
result = ai_classifier(text, candidate_labels)
category = result['labels'][0]
confidence = result['scores'][0]
return category, confidence
except Exception as e:
print(f"Classification error: {e}")
return "unknown", 0.0
def send_slack_alert(email_message, category, confidence):
"""Sends an alert to Slack for urgent emails."""
if not SLACK_WEBHOOK_URL:
return
subject = email_message.get('Subject', 'No Subject')
sender = email_message.get('From', 'Unknown')
message = {
"text": f"🚨 URGENT EMAIL DETECTED (Confidence: {confidence:.2f})\
*From:* {sender}\
*Subject:* {subject}"
}
requests.post(SLACK_WEBHOOK_URL, json=message)
def generate_auto_reply(email_message):
"""Generates a polite, automated reply."""
sender = email_message.get('From', '')
subject = email_message.get('Subject', '')
# Simple template-based reply (you can upgrade to a real LLM later)
reply_body = f"Dear Sender,\
\
Thank you for your email: '{subject}'.\
\
This is an automated response to confirm I've received your message.\
\
I review all emails daily and will get back to you personally if a response is needed.\
\
Best regards,\
\
Your Automated Assistant (Powered by AI Automation Academy)"
reply = MIMEText(reply_body)
reply['Subject'] = f"Re: {subject}"
reply['From'] = GMAIL_USER
reply['To'] = email_message.get('From')
return reply
def send_email(reply_message):
"""Sends the reply via SMTP."""
try:
with smtplib.SMTP_SSL('smtp.gmail.com', 465) as server:
server.login(GMAIL_USER, GMAIL_PASSWORD)
server.sendmail(reply_message['From'], reply_message['To'], reply_message.as_string())
print(f"Replied to: {reply_message['To']}")
except Exception as e:
print(f"Failed to send reply: {e}")
# ===== MAIN LOOP =====
def main():
print("👉 Starting Email Automator. Press Ctrl+C to stop.")
while True:
try:
emails = get_unread_emails()
for msg in emails:
category, confidence = classify_email(msg)
sender = msg.get('From', 'Unknown')
subject = msg.get('Subject', '')
print(f"\
📧 From: {sender}")
print(f" Subject: {subject}")
print(f" Category: {category.upper()} (Confidence: {confidence:.2f})")
if category == "urgent":
send_slack_alert(msg, category, confidence)
print(" -> ALERT sent to Slack.")
elif category == "informational" and confidence > 0.7:
reply = generate_auto_reply(msg)
send_email(reply)
print(" -> Auto-reply sent.")
else:
print(" -> Marked as Spam/Other. No action taken.")
print("\
Cycle complete. Waiting 300 seconds (5 minutes) for next check...")
time.sleep(300) # Wait 5 minutes between checks
except KeyboardInterrupt:
print("\
Automator stopped by user. Goodbye!")
break
except Exception as e:
print(f"An error occurred: {e}")
time.sleep(60) # Wait a minute before retrying
if __name__ == "__main__":
main()Step 4: Run Your First Cycle
- Save the file. Edit the configuration section at the top with your Gmail and App Password.
- Open your terminal/command prompt, navigate to the folder where you saved the file:
cd path/to/your/folder - Run:
python email_automator.py - You’ll see a live log. It will check for new emails, classify them, and take action.
- Important: Send a test email from another account to your Gmail. Choose a subject like “Meeting Request” (should be URGENT) or “Newsletter” (should be SPAM). Watch the script react!
Step 5: Test with a Slack Alert (Optional)
To get real-time alerts, create a Slack channel and set up an incoming webhook:
- In Slack, go to your workspace > Settings & administration > Manage Apps.
- Search for “Incoming Webhooks” and add it.
- Choose a channel (e.g., #urgent-emails) and click “Add Integration.”
- Copy the Webhook URL and paste it into the
SLACK_WEBHOOK_URLvariable in the script. - Run the script again. Now, urgent emails will ping you on Slack instantly.
Complete Automation Example
Let’s walk through a real morning for Alex, the consultant, with her new system.
- 8:00 AM: Alex turns on her laptop. Her email automator is already running in the background (it runs 24/7 on her server, which could be her own computer).
- 8:01 AM: 15 new emails arrive overnight. The script reads them.
- 8:02 AM: It classifies them. 2 are marked URGENT (a client project issue, a proposal request). 12 are INFO (newsletters, calendar invites, FYIs). 1 is SPAM (a promo).
- Action 1 (Urgent): Slack pings Alex’s phone: “🚨 URGENT EMAIL: From: ClientCo, Subject: Project Delay?” Alex opens Slack, reads the subject, and knows she needs to act. No inbox needed.
- Action 2 (Info): The script sends auto-replies to the 12 informational emails: “Thanks, I’ve got this. Will respond personally if needed.” Alex sees 12 “Re:” responses in her Sent folder, proving the system worked.
- Action 3 (Spam): The script marks the spam email as read and moves it to the “Junk” folder. It never touches Alex’s eyeballs.
- 8:05 AM: Alex has a clean inbox with only the URGENT Slack alerts. She spends her first 30 minutes on high-priority items, not sifting through noise.
Real Business Use Cases (MINIMUM 5)
- Freelance Developer: Gets client inquiries mixed with job boards and support tickets. The automator routes support tickets to a Trello board, sends standard “Let’s schedule a call” replies to inquiries, and filters job spams.
- E-commerce Store Owner: Receives order confirmations, “Where’s my order?” questions, and returns. The system auto-replies with tracking info, flags urgent return requests for human review, and archives promo emails.
- Real Estate Agent: Flooded with inquiries from portals (Zillow, etc.), showings, and legal documents. The automator sends “Thanks, I’ll call you in 2 hours” to portal leads, tags legal emails for the lawyer, and routes urgent offers to a priority queue.
- Non-Profit Director: Gets volunteer sign-ups, donation requests, and grant notifications. The system auto-replies to volunteers, categorizes grant alerts, and flags major donation requests for immediate attention.
- SaaS Startup Founder: Handles user feedback, investor updates, and support. The automator sorts feedback into a “Feature Requests” database, sends gentle replies to user bugs, and pings the team on investor emails.
Common Mistakes & Gotchas
- Never put your real password in the script. Always use an App Password. This is non-negotiable for security.
- Start with a limited scope. Don’t auto-reply to your entire inbox on day one. Test with a specific email thread or a separate test account.
- AI confidence isn’t perfect. A “URGENT” email might actually be spam. You’ll need to tweak the logic. Maybe add a manual review queue (e.g., save those emails to a “ToReview” label).
- Rate limits. Gmail API has usage limits. Don’t check every 30 seconds. 5-15 minute intervals are safe.
- Legal & privacy. Auto-replies should state they’re automated. Also, be careful with GDPR/CCPA if handling personal data. This is for automation, not surveillance.
How This Fits Into a Bigger Automation System
Your email automator is a data ingestion pipeline. It’s the first step in a larger system:
- CRM Integration: Route classified emails to a CRM (like HubSpot or a simple Airtable). Urgent leads become contacts automatically.
- Multi-Agent Workflow: The “Urgent” classifier could trigger a voice agent to call you. The “Info” emails could be summarized by an LLM and posted to your team’s Notion page.
- RAG System: Archive all classified emails in a vector database. Then, you can ask: “What were the main client concerns in Q3?” and get an answer from your entire inbox history.
- Task Manager Sync: Informational emails about deadlines can be parsed and added to your Google Calendar or Todoist automatically.
What to Learn Next
You’ve just built the eyes and ears of your AI automation system. But a smart automator needs a brain.
In our next lesson, we’ll upgrade this system. We’ll plug in a large language model (like GPT-4) to generate contextually aware, personalized replies instead of generic templates. We’ll also teach it to extract data from emails (like dates and amounts) and create calendar events and invoices automatically.
You’ve learned to automate your inbox. Now, let’s teach it to think.
— Professor Ajay
Founder, Underground AI Automation Academy
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