There is a gap at the centre of most university knowledge systems that nobody discusses directly: the archive exists, the content is digitised, the information is technically accessible, and researchers still cannot find what they are looking for. Keyword search was designed to answer "which documents contain these words?" - not "how did institutional policy on this issue evolve between 1960 and 2000?" At university archive scale this mismatch produces five compounding failure modes: a temporal vocabulary gap where modern queries miss historical content, a synthesis barrier where document lists cannot answer cross-decade questions, cross-system fragmentation where relevant content lives across six separate unconnected systems, no intent modelling, and a scale cost where complex questions take hours. RAG-based enterprise AI search - semantic retrieval plus grounded generation plus source citations - fixes all five. CustomGPT.ai deployed this at Lehigh University at 400 million words in one semester with zero engineering resources.
Explore the platform at https://customgpt.ai/solutions/enterprise-knowledge-search/ and see it in action at https://customgpt.ai/customer/lehigh-university-the-brown-and-white/
https://pollthepeople.app/enterprise-ai-search-university-archives/
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