# Alaska Airlines Natural Language Flight Search Implementation ## Company Background and Context Alaska Airlines, the fifth-largest US airline serving 45 million customers annually, has demonstrated a strong commitment to digital innovation and AI implementation. The company has previously established a real-time data foundation connecting guest data with operations data, laying the groundwork for more sophisticated AI applications. ## AI Strategy and Implementation Approach - Developed an 8-step process from idea selection to production deployment - Adopted a multi-model approach rather than relying on a single AI solution - Started with internal-facing opportunities before moving to customer-facing applications - Maintained strong focus on maintaining customer trust and care in AI implementations ## Technical Architecture and Integration ### Core Infrastructure - Modernized tech stack from monolithic to microservices-based cloud-native solutions - Built on existing Google Cloud infrastructure including: ### Gemini Implementation - Primary use case: Natural Language Destination Search - Key technical components: ### Data Integration Points - Customer profile data - Flight availability and pricing - Destination information - Historical booking patterns - Real-time inventory ## Development Process and Methodology - Rapid prototyping and iteration based on customer feedback - Initially started with chatbot approach but pivoted based on user preference for immersive experience - Development from concept to production in matter of weeks - Strong emphasis on testing and validation before customer-facing deployment ## Production Considerations ### Quality and Safety Measures - Implementation of factuality checks through function calling - Verification of flight availability before making recommendations - Price validation in real-time - Multi-language support validation ### User Experience Design - Focus on natural conversation rather than traditional search parameters - Integration of visual elements (maps, images) for improved user experience - Dynamic content generation tailored to user queries - Support for complex multi-constraint queries ## Technical Challenges and Solutions - Ensuring real-time availability of flight data - Managing multiple constraint satisfaction in search queries - Integrating multiple data sources seamlessly - Maintaining system performance at scale - Handling multiple languages and cultural contexts ## Production Infrastructure ### Core Components - Microservices architecture - Cloud-native deployment - API integration layer - Customer data platform - Machine learning recommendation system ### Integration Points - Google Cloud Platform services - Existing airline booking systems - Customer database - Pricing systems - Maps and location services ## Monitoring and Maintenance - Real-time verification of recommendations - Price accuracy monitoring - Language quality assurance - System performance metrics - User interaction tracking ## Results and Impact - Successfully launched first customer-facing AI travel search capability - Improved customer experience through natural language interaction - Enhanced ability to handle complex travel queries - Support for multiple languages and cultural contexts - Integration with existing airline systems and data ## Future Development Plans - Expansion of use cases beyond initial search functionality - Integration with additional customer service touchpoints - Enhanced personalization capabilities - Broader language support - Integration with merged airline systems (pending Hawaiian Airlines merger) ## Key Learnings - Importance of starting with internal applications before customer-facing ones - Value of rapid prototyping and customer feedback - Need for strong data infrastructure before AI implementation - Importance of maintaining trust while innovating - Benefits of multi-model approach to AI implementation ## Production Best Practices - Maintain strong focus on factuality and accuracy - Ensure real-time validation of recommendations - Integrate closely with existing systems - Build on solid data infrastructure - Prioritize user experience over technical sophistication - Implement robust testing and validation processes - Maintain flexibility for future expansion