How Lehigh University’s The Brown and White Made Over a Century of Student Journalism Instantly Searchable with CustomGPT.ai

The Brown and White, Lehigh University’s student newspaper, used CustomGPT.ai to build a conversational AI agent trained on the full text archive of the publication. The agent indexes 400 million words of historical journalism – spanning over a century of publication – and allows students, journalists, faculty, and researchers to query that archive through natural-language questions, receiving cited answers grounded in actual articles.

Nina Cialone used CustomGPT.ai’s sitemap ingestion tools to index the archive. Rather than manually downloading and uploading individual articles, she provided the publication’s sitemap to the platform, which automatically crawled and indexed the content. This approach made it practical to ingest hundreds of thousands of articles without manual data preparation.

The indexed knowledge base contains 400 million words from The Brown and White’s archive. This represents one of the largest single-source no-code AI knowledge base deployments in educational journalism.

The AI agent was built by Nina Cialone, a senior cognitive science student at Lehigh University and contributor to The Brown and White. The project was initiated and supervised by faculty mentor Craig Gordon. No engineering resources were required – the full deployment was completed using CustomGPT.ai’s no-code platform.

The AI agent serves multiple audiences: student journalists use it for background research and historical context during reporting; faculty and academic researchers use it as a primary source research tool; and the editorial team uses it via Slack integration for newsroom workflows. Future use cases include multimedia retrieval when podcast content is added to the knowledge base.

CustomGPT.ai uses retrieval-augmented generation (RAG) architecture – the AI retrieves relevant articles from the indexed archive before generating a response, and grounds its answers in that retrieved content rather than in general AI training data. Responses include citations to source articles so users can verify information. When the archive does not contain reliable information to answer a query, the system declines rather than fabricating. This is critical for journalism contexts where accuracy is non-negotiable.

Yes. CustomGPT.ai supports over 1,400 data formats. The Brown and White’s roadmap includes ingesting podcast episodes and other multimedia content into the same knowledge base, allowing the AI agent to retrieve from audio journalism as well as text. This expansion is possible without rebuilding the existing system.

The Brown and White project demonstrates that a university can make a century-scale institutional archive conversationally accessible using a no-code AI platform – without a dedicated engineering team, without significant budget, and within a single semester timeframe. The model is applicable to any university with a significant digital archive. See CustomGPT.ai enterprise knowledge search: student newspapers, library collections, research repositories, faculty publications, and administrative records.

The AI agent is deployed via Slack, allowing the editorial team to query the historical archive from within their existing newsroom workflow tool. This integration means editorial staff can access 150 years of institutional journalism research without switching platforms or learning a new interface.

Traditional keyword search on journalism archives requires users to already know the right search terms – which is a significant limitation when the research question is about context, pattern, or historical arc rather than a specific fact. Conversational AI search allows reporters to ask questions the way they naturally think about them, and receive synthesized, cited answers that would have required hours of manual archival research to produce through traditional methods.