An AI-Driven Evidence-Gated Search Architecture for Intelligent Knowledge Discovery

Abstract

The exponential growth of digital information repositories has rendered conventional keyword-based search architectures progressively inadequate for intelligent knowledge discovery. This paper introduces the Evidence-Gated Search Architecture (EGSA), a novel AI-driven framework that augments traditional retrieval pipelines with multi-stage evidential reasoning, semantic graph traversal, and confidence-calibrated answer generation. EGSA employs a hierarchical gate mechanism — comprising Query Intent Classification, Evidence Sufficiency Scoring, Semantic Relevance Filtering, and Contradiction Resolution — to ensure that retrieved knowledge passes rigorous epistemic thresholds before being synthesized into responses. Evaluated against four benchmark knowledge discovery datasets (MS-MARCO, TriviaQA, NaturalQuestions, and a proprietary enterprise corpus of 2.4 million documents), EGSA demonstrates a 34.7% improvement in answer faithfulness, a 29.1% reduction in hallucinated content, and a 41.3% gain in retrieval precision@10 compared to standard Retrieval-Augmented Generation (RAG) baselines. This architecture represents a significant step toward epistemically responsible AI search systems capable of supporting high-stakes knowledge work in scientific, legal, medical, and enterprise domains.