AI-Driven Knowledge Management System for Support Efficiency

A support organization with over 20 years of experience faces difficulties managing large volumes of customer-submitted bug reports and issue resolutions. Critical knowledge is hard to retrieve, leading to prolonged resolution times. Manual documentation hampers data utilization, and root cause analysis efficiency is crucial for maintaining high service quality.

A large support services organization managing extensive customer issue data and seeking to enhance operational efficiency through AI-powered solutions.

• Reduce root cause analysis time by approximately 20%.
• Improve knowledge retrieval efficiency and accuracy.
• Increase support engineer productivity and job satisfaction.
• Streamline access to historical issue resolutions across multiple systems.
• Lay foundation for future AI enhancements such as multimedia recognition capabilities.
• Enhance overall customer support quality and speed.

• Issue content analysis and categorization using AI models.
• Smart retrieval of historical issues and solutions based on content similarity.
• Automated identification of root causes from accumulated data.
• Generation of enriched issue resolution descriptions to improve knowledge sharing.
• Integration with communication platforms (e.g., Teams) and issue tracking tools (e.g., Jira, similar).
• Secure data storage leveraging cloud-based AI services with strict data privacy and security protocols.
• Utilization of large language models for open-ended support queries, trend analysis, and insights.

• Communication platforms (e.g., chat tools, collaboration software).
• Issue management systems (e.g., ticketing, bug tracking tools).
• Source data repositories including chat logs, CI/CD tools, and historical support data.

• Data security compliance ensuring customer data is protected and never used for model retraining.
• High availability with minimal downtime.
• Fast response times critical for real-time support assistance.
• Scalability to handle large datasets and concurrent user queries.

Anticipated to achieve a 20% reduction in root cause analysis time, resulting in faster issue resolution. The system will improve support engineer productivity, enhance knowledge retention, and streamline workflows, culminating in higher customer satisfaction and laying the groundwork for future AI capabilities such as multimedia recognition and advanced troubleshooting support.