3 posts about behavioral analytics by 2 contributors.
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Unifying transactional and NLP-derived signals within a feature store transforms raw data into actionable intelligence. By integrating this store with a Multi-Agent LangGraph RAG system, enterprises can move beyond static rules to dynamic, context-aware decision-making. This POC illustrates how hybrid features enable more nuanced outcomes, such as empathetic support routing.
Predictive clickstream analytics requires moving beyond descriptive dashboards to prescriptive, real-time systems. The most valuable signals—session velocity, error loops, and cross-session re-engagement—are inherently contextual and benefit from multi-agent reasoning. By implementing a LangGraph-based RAG architecture with persistent state, enterprises can transform raw telemetry into intelligent interventions.
Discover how AI detects fraud in real time using behavioral analytics. In this session, Sharath Reddy Polu explains practical approaches, real-world use cases, and advanced techniques for identifying suspicious activities and preventing fraud using intelligent data analysis.
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