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AI-Powered Knowledge Management System for Enhanced Customer Support Efficiency

eleks.com

Information technology

Business services

Software development

Challenges in Support Operations and Knowledge Management

The client struggled with inefficient handling of thousands of customer-submitted bug reports and issue resolutions. Manual documentation processes led to knowledge silos, prolonged root cause analysis times, and reduced support engineer productivity. Critical historical resolution data was difficult to retrieve, impacting service quality and operational efficiency.

About the Client

Global IT services company specializing in support services and digital transformation solutions

Objectives for AI-Driven Support Optimization

Reduce root cause analysis time by 20% through AI automation
Improve knowledge retrieval accuracy from historical data
Enhance support engineer productivity through intelligent workflows
Streamline cross-system information exchange between support tools
Establish foundation for future AI capabilities in issue resolution

Core System Functionalities

Smart issue categorization using natural language processing
Historical resolution pattern recognition and retrieval
Root cause analysis acceleration through AI insights
Integration with collaboration and ticketing systems
Secure data normalization and vectorization pipeline

Technology Stack Requirements

Microsoft Copilot Agent

Azure OpenAI Services

Microsoft Teams integration framework

Atlassian API connectors

Azure cloud infrastructure

System Integration Needs

Microsoft Teams real-time collaboration
Jira Service Management ticketing system
Confluence knowledge base
CI/CD pipeline monitoring tools
Azure Active Directory authentication

Operational Requirements

Enterprise-grade data encryption and security
High-availability cloud architecture
Low-latency query response times
GDPR and compliance adherence
Horizontal scalability for global operations

Expected Business Impact of AI Implementation

The solution is expected to reduce root cause analysis time by 20%, accelerate issue resolution cycles, and improve knowledge retention across support teams. Enhanced AI-driven workflows will increase engineer productivity, reduce operational costs, and enable future AI capabilities like visual recognition for complex issue diagnosis.