My passion lies at the intersection of purposeful technology and captivating experiences.

Don't just predict – understand and influence!

This article breaks down how to build an Agentic RAG system using LangChain, LangGraph, Cursor, MCP, and more. Get hands-on with practical code examples for AST parsing, vector database integration, and IDE plugins.

I share how Nixtla's TimeGPT (and its potential synergy with Neo4j) is impacting customer behavior analysis in banking. Forget linear regressions, detects anomalies, and predicts future trends.

By 2027, a significant shift in the financial sector will see 70% of leading institutions adopting graph database technology like Neo4j to overcome relational database limitations and enhance customer experiences.

Bank executives, are you leaving revenue on the table? "Beyond Transactions" reveals how mastering product holding ratios, advanced CLV modeling, and AI-powered customer lifetime optimization isn't just theory.

Looking beyond 2028, I anticipate the rise of sentient search agents, capable of not only interpreting but also anticipating and even influencing user needs.

The image below from Harvard Business Review depicts a Customer Value calculation framework. This framework directly connects to our CLO solution (AI Agent driven CLV optimizer) at Backbase.

The AI-augmented leader strategically integrates AI to amplify human strengths. They cultivate awareness, wisdom, and compassion, leveraging AI to enhance each.

Is your bank ready to move beyond basic automation and truly enhance customer engagement and profitability?