The evolution of AI is driving the need for more intelligent data structures to support scalable, responsive, and smarter systems. Traditional indexing approaches, designed for smaller and more predictable datasets, are inadequate. They focus on where data resides rather than its meaning. With the explosion of big data, AI applications require sophisticated indexing strategies that move beyond basic keyword retrieval.
Driving this shift is Prithviraj Kumar Dasari, a leading expert in the field. His work in indexing and enterprise application architectures is central to shaping the future of AI-driven tech. As a Senior IEEE Panel Reviewer, Prithviraj contributes to cutting-edge research. His publication, ‘Adaptive Orchestration of Data-Focused Enterprise Applications Using Frontend Design: A Multi-Layer Approach Combining Cloud-Native Scalability,’ provides a practical model for creating flexible and scalable architectures that combine cloud-native advantages with enhanced frontend orchestration.
AI systems, especially real-time applications and recommendation engines, rely on intelligent indexing to access the right data efficiently.
Smarter indexing is critical for success. It increases efficiency at scale. AI models need semantic recall: indexing that understands what a user needs. Indexing schemes must be adaptable to dynamic workloads.
Prithviraj’s work emphasizes how adaptive orchestration can deliver enterprise AI systems that scale without compromising accuracy. Innovation is about ensuring ideas can thrive in real-world systems. Smarter indexing will turn AI’s potential into reliable performance.