Resilient Real-Time Data Delivery for AI Summarization in Conversational Platforms: Ensuring Low Latency, High Availability, and Disaster Recovery

Abstract

As conversational artificial intelligence (AI) agents become integral components of communication platforms the need for reliable and timely data delivery to AI summarization engines is paramount. This is true in the case of many domains from real-time customer support to interactive tutoring systems. Ensuring that conversational transcripts, user queries, and contextual metadata flow to summarization models with minimal latency and high fault tolerance is critical to maintaining seamless user experiences. This paper presents a comprehensive system design and technical strategies for achieving resilient real-time data delivery. We focus on architectural principles, low-latency data pipelines, fault-tolerant components, and disaster recovery mechanisms. By combining scalable streaming frameworks, distributed consensus protocols, geo-redundant storage, and active-active failover techniques, we demonstrate that it is feasible to maintain continuous availability, even in the face of network partitions and data center outages. Experimental evaluations on a prototypical testbed show our approach can maintain sub-100ms latency targets, minimize downtime under failure scenarios, and recover state swiftly and accurately.