Email Triage Automation

The Challenge

Growing companies often struggle with overflowing shared inboxes (sales@, support@, info@). Manual sorting, categorizing, and assigning emails to different departments causes response delays. Even worse, high-priority issues—such as urgent billing complaints or system failures—frequently get buried in the noise, leading to missed deadlines and unhappy clients.

The Solution & Architecture

I engineered an enterprise-grade inbox management and automation architecture using n8n, Gmail API, and OpenAI. The system workflow functions as follows:

Ingestion & AI Classification: The workflow triggers instantly upon receiving a new message (Gmail Trigger), sanitizes the payload, and passes the text to an OpenAI Chat Model for sentiment analysis and intent classification.

Smart Categorization & Labeling: Based on the AI's output, a routing node assigns the email to the correct department (Finance, Sales, Technical Support, Partnerships) and automatically applies matching Gmail labels in real time.

Priority Escalation Routing: If the system flags an email as high-priority, it immediately triggers a secondary logic line. This packages the case details and shoots an urgent alert card directly into a dedicated Discord channel, complete with a summary and recommended actions.

AI-Assisted Contextual Response: Simultaneously, the system evaluates if an instant acknowledgement is needed, generates a contextual, brand-aligned draft reply using LLMs, and sends it directly within the original Gmail thread to reassure the client.

Business Results

Zero Sorting Overhead: Fully eliminated the manual labor required to filter, read, and forward inbound corporate emails.

Accelerated Escalation: Critical, high-priority issues (like duplicate billing charges) are delivered to managers' chat screens within seconds.

Improved Client Retention: Automated contextual auto-replies ensure clients receive an immediate confirmation that their issue is prioritized and routed to the correct desk.

I'd test calm messages about a service outage alongside angry messages about a routine request, so sentiment doesn't become a proxy for urgency. When the model is unsure of the department or priority, does the email go to a human review queue rather than being silently assigned?

Quran Reel Automation: give it a recitation and a verse range, and it makes the finished reel with no editing. It finds the verses inside a long recording (Whisper + fuzzy matching), times every Arabic word to the audio with forced alignment, has AI classify each verse's mood to pull matching footage from Pexels and Pixabay, and renders correctly shaped Arabic captions with Remotion. Runs from a web app with live progress and a waveform trimmer. Sample: Surah Ibrahim 14:40-41, recited by Jaber Alqaytan. Code: github.com/khan-abdullah-aidev/Quran-reel