# Lime's AI-Powered Customer Support Transformation ## Company Background and Initial Challenges Lime is a global leader in micromobility, providing electric bikes and scooters across 200+ cities worldwide. Their service has enabled over 250 million rides, replacing 60 million car rides with sustainable transportation options. The company faced significant challenges in scaling their customer support operations to match their rapid growth: - Manual ticket handling without prioritization - Language barriers requiring constant translation - No automated self-service options - Critical compliance and safety issues mixed with routine inquiries - Growing ticket volume due to business expansion ## Technical Implementation ### AI Solution Components The implementation consisted of multiple AI-powered components working together: - **Forethought Triage System** - **Forethought Solve** - **Workflow Builder with RPA** ### Technical Architecture and Integration The solution was built with several key technical considerations: - **Intelligent Routing System** - **Knowledge Management** - **Process Automation** ## Implementation Results and Metrics The deployment of these AI solutions resulted in significant improvements: ### Automation Metrics - 27% case automation rate for email and web channels - Processing of 1.7+ million tickets annually - 2.5 million automated language and category tags - 98% automatic ticket tagging accuracy - 77% reduction in first response time ### Operational Improvements - Enhanced handling of critical safety cases - Improved compliance management - Better language-specific support - Reduced agent workload - Increased customer satisfaction ## Technical Challenges and Solutions ### Language Processing - Implementation of multi-language support - Accurate language detection - Proper routing to language-specific agents - Automated translation integration ### Classification and Routing - Development of custom tagging system - Priority-based routing rules - Integration with existing support platforms - Handling of complex case scenarios ### Automation Rules - Creation of specific automation workflows - Integration with internal systems - Development of RPA processes - Error handling and fallback procedures ## Best Practices and Lessons Learned ### Implementation Strategy - Focus on ROI and performance metrics - Importance of partnership approach - Gradual expansion of capabilities - Regular system optimization ### Technical Considerations - Need for robust integration capabilities - Importance of accurate language detection - Priority for critical case handling - Balance between automation and human intervention ## Future Development Plans The implementation continues to evolve with planned improvements: - Expansion of language support - Additional automated workflows - Enhanced RPA capabilities - Scaling of global support operations ### Technical Roadmap - Development of new automated workflows - Integration with additional systems - Expansion of language capabilities - Enhancement of existing automations ## Impact on Support Operations The implementation has transformed Lime's support operations: ### Efficiency Improvements - Reduced manual workload - Faster response times - Better handling of critical cases - Improved language support ### Customer Experience - Faster issue resolution - Better self-service options - Improved critical case handling - Consistent multi-language support The case study demonstrates the successful implementation of AI-powered support automation in a global transportation company, showing significant improvements in efficiency, response times, and customer satisfaction through the strategic use of multiple AI components and careful integration with existing systems.