AI-powered email triage system analyzing local inbox storage to surface urgent items, identify stale threads, and generate actionable reports.
The Challenge
Managing email across multiple accounts with hundreds of thousands of messages had become overwhelming. Urgent items got buried under newsletters and notifications, stale threads awaited responses that never came, and there was no unified view across accounts. Manual triage wasn't sustainable—the inbox always won.
Our Approach
We built an automated email intelligence system that analyzes the local Mimestream database directly via SQLite queries. Rather than relying on server-side processing, the system reads the already-synced local mail storage, categorizes messages by urgency and status, and generates actionable reports in seconds. AI-powered content analysis identifies patterns and drafts contextual responses for high-priority items.
Key Features
- Multi-Account Analysis: Processes all connected email accounts simultaneously from local Mimestream storage, providing unified visibility across inboxes
- Smart Urgency Classification: Four-tier system (URGENT, IMPORTANT, UNREAD, read) based on flags, read status, sender patterns, and content analysis
- Stale Thread Detection: Identifies conversations awaiting your response by analyzing reply patterns and message flow—surfaces missed follow-ups before they become problems
- Automated Reporting: One-command workflow generates detailed Markdown reports and interactive HTML dashboards for visual triage
- AI-Powered Insights: Content analysis suggests filtering rules, identifies recurring patterns, and drafts contextual responses for urgent items
- Reduced email triage time from hours to under 30 seconds
- Surfaced missed reply opportunities that would have otherwise been lost
- Provided per-account efficiency metrics to prioritize inbox cleanup efforts
- Automated identification of actionable items across 160K+ messages
The Results
Technologies Used
Bash scripting, SQLite, Mimestream integration, Claude AI, HTML5/CSS3, Markdown