This case study presents three distinct enterprise implementations of GenAI systems, each revealing important lessons about deploying LLMs in production environments. Let's examine each case and the key learnings about LLMOps implementation. ### Case 1: Manufacturing Maintenance Support System The first case involves a top 10 global automaker facing challenges with an aging workforce in their manufacturing maintenance operations. The critical nature of the problem stemmed from production line downtime directly impacting revenue, with younger workers reluctant to take on these challenging roles. Initially, the team approached the problem by: - Collecting and digitizing service manuals - Building a GenAI system to provide maintenance guidance - Training the system on technical documentation However, the initial deployment revealed a crucial gap between theoretical knowledge and practical application. The same experts who provided the service manuals reported that the system's answers, while technically correct, weren't practically useful. This led to what they termed "pilot paralysis." The LLMOps breakthrough came when they: - Incorporated 10,000 incident reports containing practical solutions - Implemented an agent-based architecture with two specialized agents: * A "theoretical expert" agent working from manuals * A "practical expert" agent drawing from incident reports - Integrated with existing reporting systems to reduce friction The technical implementation required significant data preparation for unstructured, non-standardized incident reports, resulting in higher initial costs but ultimately leading to successful adoption and ROI within 14 months. ### Case 2: Aviation Entertainment System Maintenance The second case involved a $25 billion company providing in-flight entertainment systems to airlines worldwide. They needed to reduce device failure rates affecting passenger satisfaction. The initial LLMOps approach involved: - Processing massive amounts of device log data (800GB-1TB per landing) - Implementing predictive maintenance - Analysis of logs from 20 different aircraft types The initial solution faced scalability issues due to processing individual log files for every seat on every aircraft. The technical breakthrough came through a multi-tiered approach: - Using traditional log analysis tools (Splunk/ELK) for initial pattern detection - Implementing context windowing to limit GenAI processing to relevant data - Creating a hybrid system combining classical AI with GenAI capabilities This lean workflow approach resulted in: - Reduced maintenance costs through optimized token usage - Processing only 5-7% of logs through GenAI - Pattern codification for common issues, further reducing GenAI usage ### Case 3: Telecom Sales Automation The final case involves an Asian telecom company with 40% market share looking to grow through improved sales to small and medium businesses. The initial chatbot implementation for product recommendations, while technically successful, failed to deliver business value as customers were using the information to purchase from competitors. The LLMOps solution evolved to include: - Real-time solution configuration alongside conversation - Infrastructure-as-code templates for immediate deployment - Dynamic scheduling and appointment booking - Automated upsell and cross-sell recommendations - Dynamic discount management Key Technical Implementation Details: - Integration with existing service catalogs - Template-based configuration system - Real-time pricing and availability checks - Dynamic response temperature adjustment General LLMOps Implementation Insights: 1. Cost and Timeline Expectations: - Initial investment: $200K to $1M for pilots - Timeline: 3-6 months for initial implementation - ROI timeline: 15-18 months typical 2. Technical Team Requirements: - Prompt designers and engineers - Infrastructure specialists - DevOps engineers - Business integration specialists - Project managers 3. Critical Success Factors: - Adoption triggers: Integration with existing workflows - Lean workflows: Optimal use of GenAI vs. traditional tools - Revenue accelerators: Built-in business process optimization 4. Implementation Challenges: - Managing user acceptance and trust - Balancing theoretical vs. practical knowledge - Scaling considerations for large-scale deployments - Integration with existing systems - Continuous updates and maintenance The case studies emphasize the importance of a balanced approach to LLMOps, combining technical excellence with practical business considerations. Success requires not just robust technical implementation but also careful attention to user adoption, workflow integration, and business process optimization.