Enterprise Architecture & System Integration

Microservices Enterprise Architecture Design with AI-Powered OCR

Jul 2024 - Aug 2024

3 People

Lead Enterprise Architect & System Designer

TOGAF

Microservices

OCR

AI Integration

Enterprise Architecture

System Design

Overview

Led the design of a comprehensive microservices-based enterprise architecture integrating AI-powered OCR and Natural Language Processing (NLP) with Integrated Customer Management System (ICMS) and Knowledge Management System (KMS). This research utilized TOGAF framework to create a scalable solution that automates complaint category identification and solution retrieval through intelligent document processing. The OCR system works by automatically extracting text from Standard Operating Procedure (SOP) documents, converting them into machine-readable format, and feeding this data into the knowledge management system for instant retrieval during customer service interactions.

Key Objectives

Design scalable microservices architecture using TOGAF framework

Integrate AI-powered OCR for automated SOP document processing and data extraction

Implement NLP-based complaint categorization system with intelligent text analysis

Optimize customer complaint handling processes through automated knowledge retrieval

Problem

The telecommunications company faced significant operational challenges in handling customer complaints through newly designed touchpoints following Fixed Mobile Convergence (FMC) strategy adoption. Performance reports showed critical inefficiencies in customer service operations.

Key Challenges

Average social media response time reached 19.28 minutes (target: 5 minutes)

Fragmented systems causing operational inefficiencies

Manual processes for retrieving knowledge base information from SOP documents

Lack of integration across customer service platforms

Inconsistent complaint handling procedures across touchpoints

Time-consuming manual document processing and information extraction

Business Impact

These inefficiencies were causing customer dissatisfaction, increased operational costs, and undermining the company's digital transformation goals in the competitive telecommunications landscape.

Process

Implemented Design Science Research Methodology (DSRM) combined with TOGAF Architecture Development Method (ADM) to systematically design and validate the enterprise architecture solution. The OCR integration involved creating an intelligent document processing pipeline that automatically scans, extracts, and indexes SOP content for real-time access during customer service operations.

Phase 1: Problem Identification & Requirements Analysis

2 weeks

Conducted stakeholder mapping using Mendelow matrix classification

Analyzed existing business processes and identified manual document handling inefficiencies

Developed Goal/Objective/Requirement diagrams for OCR integration

Created comprehensive requirement catalog for automated document processing system

Phase 2: Architecture Design & TOGAF Implementation

4 weeks

Designed business architecture with optimized OCR-enabled process flows

Created data architecture with entity relationship modeling for document management

Developed application architecture using microservices patterns with OCR service integration

Designed technology architecture with cloud-based OCR and document processing capabilities

Phase 3: AI Integration & OCR Implementation

1 week

Integrated Google Vertex AI for advanced NLP processing and text analysis

Implemented OCR technology for automated SOP document scanning and text extraction

Developed AI-powered recommendation system using processed document data for complaint resolution

Created API ecosystem enabling real-time document processing and knowledge retrieval

Phase 4: Validation & Architecture Roadmap

1 week

Validated OCR-enhanced architecture using Enterprise Architecture Benefits Model (EABM)

Conducted stakeholder evaluation with structured assessments of document processing efficiency

Developed 3-year implementation roadmap including OCR system deployment phases

Created comprehensive project catalog for phased OCR and microservices deployment

Results

The microservices-based enterprise architecture successfully addressed critical gaps in customer service operations, delivering a scalable and efficient solution that aligns with organizational goals and industry best practices. The OCR integration significantly improved document processing efficiency and knowledge retrieval speed.

Key Metrics

Stakeholder Validation Score: N/A → 4.25/5

Excellent

System Integration: Fragmented → Unified

100%

Process Automation: Manual → AI-Powered OCR

Automated

Architecture Compliance: Ad-hoc → TOGAF-Aligned

Standardized

Outcomes

Designed comprehensive enterprise architecture using TOGAF ADM methodology

Successfully integrated AI-powered OCR with microservices architecture for automated document processing

Created automated complaint categorization using NLP and machine learning algorithms

Developed scalable API ecosystem supporting real-time OCR data processing and knowledge retrieval

Established 3-year implementation roadmap with 5 strategic projects including OCR deployment phases

Achieved 4.25/5 stakeholder validation score across all evaluation criteria

Created reusable architecture framework with OCR capabilities applicable across telecommunications industry

Published research findings in IEEE conference proceedings for academic and industry reference