Every major research university sits on top of a knowledge access problem it has never fully solved. The archive is digitised. The content exists. Researchers still cannot find what they are looking for - because keyword search was designed to locate documents, not answer questions. A graduate researcher asking how campus attitudes toward a specific issue evolved across three decades cannot get that answer from a keyword search. They get a list of documents, read manually, and synthesise independently - a process that takes days and frequently gets skipped under deadline pressure. RAG-based AI archive search changes this entirely: semantic retrieval bridges 50-year vocabulary gaps, grounded generation produces cited answers rather than document lists, and synthesis questions that once took days now take seconds. Lehigh University indexed 400 million words of student journalism history using CustomGPT.ai in one semester with zero engineers.
Explore the education solution at https://customgpt.ai/industry/education/ and the anti-hallucination architecture at https://customgpt.ai/anti-hallucination/
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