Enriching Clickstream Analytics with Generative AI: A Scalable Serverless Architecture for Real-Time Session Intelligence

Abstract- In large-scale digital ecosystems, understanding user behavior through clickstream data is essential for optimizinguser experience, marketing strategy, and conversion funnels. Traditional approaches to session analysis often rely onstatic rules or manual reconstruction, which are difficult to scale and lack contextual nuance. In this paper, we presenta scalable, serverless architecture that uses generative AI to extract session-level intent and behavioral summariesfrom Adobe Analytics clickstream data. The system integrates AWS Glue for session aggregation, Lambda for orchestration, and Claude via AWS Bedrock for large language model inference. Developed in a Fortune 500 financial company, the solution processes over 200,000 sessions daily, achieving 87% precision in manual QA validation, sub second inference latency and a 40% reduction in enrichment cost through prompt optimization. Our results demonstrate that AI-enriched session data significantly enhance analyst workflows by automating narrative generation, improving segmentation, and enabling intent-aware dashboards. We discuss key architectural components, prompt design strategies, and use cases in support detection, conversion analysis, and content engagement. This work establishes a blueprint for real-time behavioral intelligence using modern cloud-native and AI-driven technologies.Keywords:- Clickstream analytics, generative AI, user session enrichment, large language models, behavioralintelligence, serverless architecture, AWS Glue, Adobe Analytics, real-time inference, data engineering, cloudcomputing.