Log Analysis with Gen AI

[Audio] Log Analysis with Gen AI GenAI Team Aegis.

[Audio] Idea / Solution in a Nutshell Generative AI revolutionizes log analysis by automating the processing of vast log data, identifying patterns and anomalies in real-time, and providing deep contextual insights. This intelligent log analyzer enhances system monitoring and maintenance, offering predictive capabilities and instant alerts for rapid issue resolution. Its scalability ensures consistent performance as data volumes grow, significantly improving operational efficiency and reducing downtime. Solution in Brief The problem involves managing large volumes of complex log data, detecting patterns and anomalies, providing real-time analysis and alerts, and offering contextual understanding. Traditional tools are often time-consuming, error-prone, and lack scalability. We aim to solve these issues using generative AI for enhanced efficiency and accuracy. Problem You're Trying to Solve Natural Language Understanding Scalability Real-Time Analysis Cost Savings Risk Mitigation Improved Productivity Benefits to Customers Key Features How do you plan to implement it? Implementing a generative AI-powered log analyzer involves feeding log data into models like ChatGPT or Gemini for automated analysis, anomaly detection, and real-time monitoring, while allowing user interaction for specific queries or insights, and continuously optimizing the system based on operational feedback. Operational Efficiency Customer Satisfaction Benefits to Virtusa.

[Audio] Solution Frequent Incident Tracking: The ServiceNow Log Analyzer diligently monitors both existing and new incidents on a regular basis, ensuring timely response and resolution. Automated Log Analysis: By harnessing Language Model AI (LLM), this system swiftly extracts insights from incident attachments, generating concise summaries that are promptly integrated into work notes. Time-Saving Efficiency: This solution revolutionizes incident management by automating tedious tasks, thereby empowering teams to focus more on strategic initiatives and less on routine operations..

[Audio] Technologies Used Python: Used as the primary programming language for backend logic and automation. Requests: Facilitates HTTP requests, including authentication through HTTPBasicAuth. Flask: Provides the framework for building and serving the RESTful API endpoints. Flask-SocketIO: Enables real-time communication through WebSocket protocol. SQLite: Utilized as the relational database management system for data storage and retrieval. GoogleGenerativeAI (LangChain): Integrated for natural language processing and generation capabilities. Threading: Implemented for concurrent execution of tasks to optimize performance..

[Audio] Key Features Seamless ServiceNow Integration Advanced Log Analysis with LLM Automatic Work Note Updates Real-time Dashboard Metrics Enhanced Monitoring and Control.

[Audio] Log Analyzer - Dashboard. Log Analyzer - Dashboard.

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[Audio] . Log Analyzer - Dashboard.

[Audio] Log Analyzer - Work Notes SNOW LLM Summary.

[Audio] Team Details Vignesh Kalavakuri Siva Vemasani Dhanaraj Marpu – Team Lead Moode Hari Prakash Naik Ponnana Rakesh.

[Audio] Thank You. Thank You.