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Systems & Reference Work

Real operational systems built in production environments, plus demonstration workflows showing modern automation architecture.

REAL SYSTEMS

Architecture Case Studies

These are real operational systems, built in real environments, with real constraints. They demonstrate system thinking, not tool usage.

Real System

Automated HR Reporting & Compliance System

Type: Internal Operations AutomationRole: System Design, Automation Logic, ImplementationExecution: Python-based workflow (production system)

Problem

HR reporting and document tracking were handled manually across multiple spreadsheets. Each week: employee documents had to be checked by hand, missing or expired files were often discovered late, weekly reports were compiled manually, and errors depended on human attention. This created compliance risk and wasted several hours every week.

System Architecture

The system was designed to fully replace manual checks with a structured automation pipeline. Core logic: 1. Scheduled data ingestion from HR records, 2. Validation of required documentation, 3. Expiration date comparison, 4. Automated report generation, 5. Delivery to stakeholders. The system runs automatically and requires no manual intervention.

Workflow Overview

  • Scheduled trigger (weekly)
  • Data extraction and normalization
  • Business-rule validation
  • Conditional logic for missing/expired documents
  • Automated report creation
  • Distribution via email
  • Error handling and logging were built into the workflow to ensure reliability

Tech Stack

PythonSpreadsheet-based data sourcesEmail automation

Outcome

Manual HR checks eliminated, weekly reporting automated, compliance visibility improved.

5–10 hours per week saved, depending on department size

Note

While this system was implemented in Python, the same architecture today would be deployed using n8n for orchestration and easier long-term maintenance.

Production

OCR → Excel Work Schedule Automation

Type: Internal Data Extraction SystemRole: System Architecture, OCR Pipeline DesignExecution: Python-based workflow (production system)

Problem

Work schedules were received as scanned PDF documents that required manual data entry into Excel spreadsheets. This process was time-consuming, error-prone, and created delays in schedule distribution. Staff had to manually transcribe dates, times, and employee assignments from scanned documents, leading to frequent mistakes and inconsistencies.

System Architecture

The system automates the extraction and validation of scanned documents into structured spreadsheets. The architecture includes: 1. OCR processing for scanned PDFs, 2. Data extraction and parsing, 3. Validation against business rules, 4. Structured data formatting, 5. Excel file generation with proper formatting. The system handles various document formats and quality levels.

Workflow Overview

  • Document ingestion (scanned PDFs)
  • OCR processing and text extraction
  • Data parsing and structure recognition
  • Validation against business rules
  • Error detection and flagging
  • Excel file generation with formatting
  • Quality checks and validation

Tech Stack

PythonOCR librariesExcel automationPDF processing

Outcome

Automated extraction and validation of scanned documents into structured spreadsheets, eliminating manual data entry.

Reduced processing time from hours to minutes per document

Note

This Python-based system demonstrates the architecture that would now be implemented using n8n's OCR nodes and Excel integration for improved maintainability.

Real System

Automated WhatsApp Alerts

Type: Internal Compliance AutomationRole: Workflow Design and LogicExecution: Production system

Problem

Employee documentation expiry dates were tracked manually, and notifications were sent inconsistently. Critical documents would expire without warning, creating compliance risks. The manual process required someone to remember to check dates and send reminders, which often failed under workload pressure.

System Architecture

The system automates document expiry tracking and notification delivery via WhatsApp. The architecture includes: 1. Document expiry date tracking, 2. Automated date checking on a schedule, 3. Notification logic for approaching and expired documents, 4. WhatsApp message formatting and delivery, 5. Delivery confirmation tracking. The system ensures no document expires without proper notification.

Workflow Overview

  • Scheduled document expiry check
  • Date comparison and threshold detection
  • Notification logic (30-day, 7-day, expired)
  • WhatsApp message formatting
  • Automated message delivery
  • Delivery confirmation and logging
  • Error handling for failed deliveries

Tech Stack

PythonWhatsApp APIDocument tracking systemScheduled tasks

Outcome

Automated WhatsApp alerts for expiring employee documentation, ensuring compliance and eliminating missed deadlines.

100% notification coverage, zero missed expiry dates

Note

This workflow architecture is now implemented using n8n's WhatsApp integration and scheduling nodes for more reliable and maintainable operations.

REFERENCE WORK

Demonstration Automations (n8n)

These are demonstration workflows designed to show how modern automation systems are built using n8n. They represent architectures I deploy for clients today.

n8n Demo

AI Lead Qualification System

Lead Processing Automation

Problem

Incoming leads from multiple sources require manual qualification and routing, creating delays and inconsistent follow-up.

System Design

  • AI-powered lead scoring and classification
  • Multi-source lead aggregation
  • Automated routing based on qualification criteria
  • CRM integration and enrichment

Workflow Overview

  • Lead ingestion from web forms, email, and APIs
  • AI classification and scoring
  • Conditional routing based on score
  • CRM update and notification delivery

Outcome

  • Automated lead qualification and routing
  • Reduced response time from hours to minutes
  • Consistent qualification criteria applied
Reference

Smart Email Inbox Parser

Email Processing Automation

Problem

Important emails get buried in inboxes, requiring manual sorting and action. Critical messages are missed or delayed.

System Design

  • Email parsing and content extraction
  • Intelligent categorization using rules and AI
  • Priority-based routing and notifications
  • Action item extraction and task creation

Workflow Overview

  • Email monitoring and ingestion
  • Content parsing and analysis
  • Categorization and priority assignment
  • Notification and task creation

Outcome

  • Automated email sorting and prioritization
  • Critical messages never missed
  • Reduced inbox management time
n8n Demo

Automated Invoice Processor

Document Processing Automation

Problem

Invoices arrive via email and require manual data entry into accounting systems, creating bottlenecks and errors.

System Design

  • OCR and data extraction from PDF invoices
  • Validation against business rules
  • Automated accounting system integration
  • Exception handling and approval workflows

Workflow Overview

  • Email monitoring for invoice attachments
  • PDF processing and data extraction
  • Validation and error checking
  • Accounting system update and notification

Outcome

  • Automated invoice processing end-to-end
  • Eliminated manual data entry
  • Faster payment processing cycles
Reference

HR Onboarding Automation

Employee Onboarding System

Problem

New employee onboarding involves multiple manual steps across different systems, creating delays and inconsistent experiences.

System Design

  • Multi-system workflow orchestration
  • Document generation and distribution
  • Automated account provisioning
  • Progress tracking and notifications

Workflow Overview

  • Trigger on new employee record creation
  • Document generation and email delivery
  • Account creation across systems
  • Progress tracking and completion notifications

Outcome

  • Streamlined onboarding process
  • Consistent experience for all new hires
  • Reduced administrative overhead
n8n Demo

Data Synchronization Pipeline

Data Integration Automation

Problem

Data needs to be synchronized between multiple systems, requiring manual exports, transformations, and imports that are error-prone.

System Design

  • Automated data extraction from source systems
  • Data transformation and validation
  • Scheduled synchronization workflows
  • Error handling and conflict resolution

Workflow Overview

  • Scheduled data extraction
  • Data transformation and mapping
  • Validation and error checking
  • Target system update and confirmation

Outcome

  • Automated multi-system data synchronization
  • Eliminated manual data transfers
  • Real-time data consistency across systems
Reference

Customer Support Ticket Router

Support Automation System

Problem

Support tickets arrive from multiple channels and require manual triage and assignment, leading to delays and misrouting.

System Design

  • Multi-channel ticket aggregation
  • Intelligent routing based on content and rules
  • Priority assignment and escalation
  • Team assignment and notification

Workflow Overview

  • Ticket ingestion from email, chat, and forms
  • Content analysis and classification
  • Routing decision based on rules
  • Assignment and notification delivery

Outcome

  • Automated ticket routing and assignment
  • Faster response times
  • Improved routing accuracy
n8n Demo

Meeting Scheduling Assistant

Calendar Automation

Problem

Coordinating meetings across multiple calendars requires back-and-forth emails and manual availability checking.

System Design

  • Calendar integration and availability checking
  • Automated meeting proposal generation
  • Response handling and confirmation
  • Calendar updates and reminders

Workflow Overview

  • Meeting request received
  • Availability check across calendars
  • Proposal generation and delivery
  • Confirmation and calendar update

Outcome

  • Automated meeting coordination
  • Reduced scheduling back-and-forth
  • Improved meeting efficiency

All systems are designed with:

  • Clear scope
  • Explicit logic
  • Error handling
  • Maintainability in mind

The goal is not automation for its own sake — but removing fragile manual processes.

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