AI Inbox Automation Agent Suite

The Challenge

Managing inboxes at scale is inefficient: slow replies, missed follow-ups, and overwhelming email volume reduce productivity. This project solves that using AI-powered autonomous agents.

The Solution

Developed a multi-agent system that reads incoming emails, categorizes them by priority, drafts context-aware replies using RAG (Retrieval-Augmented Generation), and schedules follow-ups automatically.

System Architecture

Key Results & Impact

What’s Next

• 🚀 Add vector memory for cross-thread context
• 🚀 Auto-learning classifier using feedback loops
• 🚀 Multi-account inbox merging
• 🚀 Slack notifications
• 🚀 Automatic follow-up sequencing

Testimonials

Under the Hood

def analyze_email_priority(email_content):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are an email priority analyzer."},
            {"role": "user", "content": f"Analyze priority (High/Medium/Low): {email_content}"}
        ]
    )
    return response.choices[0].message.content