The promise of Large Language Models (LLMs) to revolutionize how businesses interact with their data has captured the imagination of enterprises worldwide. Yet, as organizations rush to implement AI solutions, they’re discovering a fundamental challenge: LLMs, for all their linguistic prowess, weren’t designed to understand the complex, heterogeneous landscape of enterprise data systems. The gap between natural language processing capabilities and structured business data access represents one of the most significant technical hurdles in realizing AI’s full potential in the enterprise. The Fundamental Mismatch LLMs excel at understanding and generating human language, having been trained on vast corpora of text. However, enterprise data lives in a fundamentally different paradigm—structured databases, semi-structured APIs, legacy systems, and cloud applications, each with its own schema, access patterns, and governance requirements. This creates a three-dimensional problem space: Fi...
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