DocuTracker
DocuTracker is a WIL prototype task efficiency and workflow tracking system designed for document digitization environments. Developed as part of my WIL at the DHA Digitization Hub, it improves visibility, accountability, and operational performance by automating task assignment, tracking progress, and providing AI-powered user guidance.
PROBLEM STATEMENT
During my WIL at the DHA Digitization Hub, I observed that tasks move through multiple stages such as batch creation, indexing, scanning, assembly, and quality checks. Errors occurred frequently, and managers relied on verbal interventions to correct mistakes.
There was no centralized system to track task progress, staff performance, or efficiency metrics, making accountability and operational visibility difficult. DocuTracker was designed as a prototype for this specific hub.
SOLUTION OVERVIEW
DocuTracker addresses these challenges by providing:
-Task CRUD with status updates visible to both managers and assigned staff
-Automated task assignment via n8n workflows using manager text input
-Efficiency and performance tracking for each staff member, including error rates, average completion time, and availability
-AI-powered ChatGPT assistant to help users with guidance and questions
CORE FEATURES
Task Management
-Create, update, and track tasks
-Role-based interaction between managers and staff
-Live dashboards
-Status updates trigger notifications to managers
Task Assignment Automation (n8n)
-Assigns tasks automatically based on efficiency, error rates, and availability
-Reduces manual administrative workload
Efficiency & Performance Tracking
-Measures task completion time, error rates, and overall efficiency
-Provides actionable insights for managers
AI Assistance (ChatGPT)
-Guides users through workflows and system navigation
-Answers task-related questions
-Optional support layer for staff and managers
TARGET USERS
-Managers overseeing document digitization workflows
-Staff performing document processing tasks
SYSTEM ARCHITECTURE(high level)
Manager → Task Input → n8n Automation → Assigned Staff → Task Completion → Efficiency Metrics → Manager Dashboard → (Optional) ChatGPT Assistant
Decoupled layers for task logic, automation, AI, and performance tracking Modular and extensible design
TECH STACK
frontend: html, css, JavaScript, bootstrap
Backend: Express.js, Node.js
Automation: n8n workflows, JavaScript & Python scripts
AI / Chatbot: ChatGPT API
Database: MySql
FUTURE IMPROVEMENTS
1 Expand efficiency scoring algorithms for more accurate task assignment
2 Enhance AI assistant with contextual guidance and dynamic suggestions
4 Modularize architecture for multi-hub deployments
5 Add automated unit and integration tests
6 Improve automation rules for adaptive task distribution
PROJECT STATUS
-WIL prototype for DHA Digitization Hub
-Demonstrates workflow analysis, automation, and AI integration
-Not yet deployed in production
AUTHOR
Developed by Lekoloane Nape Percy Computer Science Graduate