
AI-driven recommendation engines require seamless integration of historical records and current data to prevent the generation of inaccurate or risky information. As enterprises increasingly rely on these complex systems, the discrepancy between development environments and live production settings has become a glaring vulnerability. During the design phase, applications often function within sanitized, controlled datasets where latency is low and relationships










