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High-volume digital content repositories face a critical systemic trade-off between operational curation velocity and algorithmic search discoverability. As repositories scale past tens of thousands of digital assets, conventional manual content engineering yields acute cognitive fatigue, severe time-to-market delays, and structural information architecture pathologies—such as crawl-budget leakage, orphan entity isolation, and broken internal graph linkages. This paper addresses these challenges through a Design Science Research (DSR) approach, designing and instantiating an integrated IT artifact: an Automated Semantic Content Orchestration Engine (ASCOE). The artifact synthesizes programmatic entity clustering, in-place zero-latency HTML mutation via authenticated REST endpoints, dynamic phonetic transliteration, and rich ontological schema construction (MusicPlaylist and MusicRecording). Furthermore, the system incorporates an adaptive, two-stage crawl-budget preservation mechanism that systematically decouples latent CMS shortlink signals at both the Document Object Model (DOM) and HTTP protocol header layers.The artifact was deployed and evaluated through an empirical, quasi-experimental field study on an enterprise-scale digital media repository comprising 48,628 published entities over an active 30-day evaluation window. Empirical findings demonstrate a transformative operational and business impact: curation latency for batch topic generation collapsed by 98.7% (from 45–75 minutes to < 1 second), and incremental track injection latency was reduced from 180 seconds to < 2 seconds. Concurrently, resolving architectural crawl drains and propagating bidirectional link equity across legacy nodes triggered a marked expansion in organic audience acquisition. Daily active users surged by 67.7% (from an established baseline of 12,226 to a peak of 20,500 active users/day), while daily organic search clicks expanded by 87.1% (reaching a peak of 26,201 clicks/day, aggregating to 635,097 total web search clicks within the evaluation period). This study articulates nascent design principles for autonomous content engineering and enterprise search retrieval in data-intensive media ecosystems.
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Reza Kamaliniya (2026) studied this question.
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