As AI continues to advance, the way data is organized is becoming a critical factor in creating scalable and intelligent systems. Traditional indexing methods, which were developed for smaller and simpler datasets, are no longer sufficient. These older methods concentrate on where data is stored, rather than what the data actually means. The growth of big data requires AI applications to adopt indexing strategies that are more advanced than simple keyword searches.
Leading this transformation is Prithviraj Kumar Dasari, a scholar whose expertise is shaping the future of AI-driven technology. As a Senior IEEE Panel Reviewer, Prithviraj plays an important role in shaping cutting-edge research. His work, including the paper ‘Adaptive Orchestration of Data-Focused Enterprise Applications Using Frontend Design: A Multi-Layer Approach Combining Cloud-Native Scalability,’ offers a blueprint for agile, scalable architectures that leverage cloud-native strengths.
In AI systems, especially in real-time applications and recommendation engines, accessing the right data relies on intelligent indexing.
Smarter indexing is essential. It helps to reduce latency and boost throughput. AI models need indexing that understands user needs. Adaptive orchestration, rooted in savvy indexing and frontend design, can deliver enterprise AI systems that scale seamlessly. Innovation is about ensuring all the ideas can thrive and scale in real-world systems. Smarter indexing is what will turn AI’s promise into dependable performance – fueling better decisions, richer personalization, and enterprise growth.
