Meta Reality Labs has developed a comprehensive production AI system for Ray-Ban Meta smart glasses that represents a significant advancement in edge AI deployment for wearable devices. The project, led by Alexander Petrescu from the wearables platform team, addresses the fundamental challenge of bringing AI capabilities as close as possible to users' perceptions through a sophisticated four-part architecture that balances performance, power efficiency, and user experience. The core philosophy driving the system is that for AI to be truly useful, it must be contextually aware - able to see what users see and hear what they hear. This approach enables practical applications such as remembering parking locations, reading QR codes and signs, providing real estate information based on visual context, and capturing spontaneous moments through voice commands. The system demonstrates how production AI can be successfully deployed in highly constrained environments while maintaining reliability and user privacy. The technical architecture represents a carefully orchestrated balance between four key components: the glasses hardware, smartphone connectivity, cloud-based AI services, and specialized optimizations for real-time performance. The glasses themselves contain sophisticated edge computing capabilities including a microcontroller for wake word detection, a system-on-chip (SOC) for local processing, specialized accelerators for power-efficient model execution, and multiple connectivity options including Bluetooth and Wi-Fi. The hardware design incorporates a 4-hour battery in the frames supplemented by an additional 36 hours of charging capacity in the case, addressing one of the most critical constraints in wearable AI deployment. The smartphone component serves as a crucial intermediary, running the Meta AI app that provides connectivity to Meta's servers while also enabling integration with communication applications like Messenger and WhatsApp, music services like Spotify and Apple Music, and accessibility applications such as Be My Eyes. This architecture allows for computationally expensive operations like HDR processing and image stabilization to be offloaded from the power-constrained glasses to the more capable smartphone, demonstrating intelligent workload distribution in edge AI systems. The cloud component leverages Meta's advanced multimodal models to provide sophisticated AI capabilities including conversational AI, current information retrieval through plugins, sports scores, and extensible agent capabilities. Importantly, the system includes personalized user memory features that can remember user preferences and handle reminders, showing how production AI systems can maintain state and context across interactions while preserving privacy. The deployment challenges faced by the team illustrate the complexity of production AI in constrained environments. Design challenges include ensuring the glasses are fashionable and wearable across diverse scenarios while accommodating different head shapes and aesthetic preferences through Meta's partnership with EssilorLuxottica. The hardware miniaturization challenge involves carefully positioning components like speakers, microphones, and cameras within the limited space while maintaining proper weight distribution to ensure comfort during extended wear. Power management represents one of the most critical aspects of the system's operation. The team had to carefully balance battery size, chemistry, and thermal constraints while ensuring peak power availability for concurrent operations. The system must handle real-world conditions including temperature variations that affect battery performance, similar to challenges faced by electric vehicle manufacturers. This includes scenarios where certain operations like simultaneous photo capture and Wi-Fi transfer may not be possible due to power constraints, requiring intelligent orchestration of features. Thermal management presents another significant challenge, as the miniaturized components generate heat that must be safely dissipated while maintaining user comfort. The system incorporates heat dissipators and carefully selected materials while implementing dynamic power management including component downclocking when necessary. External temperature conditions further complicate thermal management, with the system designed to communicate thermal status to users when safe operation limits are approached. Connectivity optimization demonstrates sophisticated engineering in production AI systems. The team balanced power consumption against bandwidth requirements across multiple connectivity options: 4G cellular for high-speed connectivity but with significant power drain, Wi-Fi for high-bandwidth applications when available, and Bluetooth Low Energy for power-efficient communication with bandwidth limitations. The system must also gracefully handle scenarios with no connectivity, ensuring continued functionality in offline modes. Performance optimization represents a key innovation in the system's design through predictive processing techniques. Rather than simply optimizing individual components, the team developed methods to reduce user-perceived latency by starting to understand and process user requests before they finish speaking. This approach enables speculative operations like photo capture, OCR processing, and model loading to occur in parallel with speech recognition, significantly reducing response times without necessarily making individual operations faster. The optimization strategy demonstrates sophisticated understanding of production AI performance requirements. On-device commands like photo capture achieve sub-second response times with high reliability, while complex AI interactions requiring multiple hops through the phone and cloud infrastructure maintain average response times under 3 seconds. This performance is achieved through careful resource management, model pre-loading, and predictive processing that begins analysis of user intent before complete utterance. Privacy preservation is woven throughout the system architecture, with careful consideration of when and how data is transmitted to servers. The system waits for complete user utterances before sending sensitive information to preserve privacy while still enabling predictive processing for performance optimization. This approach demonstrates how production AI systems can balance performance requirements with privacy considerations. The system's multimodal capabilities represent a significant achievement in production AI deployment. The integration of computer vision, natural language processing, and speech recognition within the constraints of wearable hardware showcases advanced model optimization and hardware acceleration techniques. The ability to process visual information through OCR, understand spoken commands, and provide contextual responses demonstrates the maturity of the underlying AI infrastructure. Looking toward future developments, the team is expanding agent capabilities, international market deployment, and advanced features like real-time translation including offline capabilities. These additions will introduce new challenges in terms of power utilization, competing workloads, and model accuracy across diverse linguistic and cultural contexts, highlighting the ongoing evolution required for production AI systems. The case study provides valuable insights into the engineering challenges of deploying sophisticated AI systems in highly constrained environments. The success of the Ray-Ban Meta glasses demonstrates that with careful architectural design, intelligent resource management, and innovative optimization techniques, it is possible to deliver advanced AI capabilities in wearable form factors while maintaining user experience standards and privacy requirements. The four-part architecture serves as a model for future wearable AI deployments, showing how edge computing, smartphone integration, and cloud services can be orchestrated to create compelling user experiences within the constraints of current technology.