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How we helped a major research university transform literature discovery,
reducing review time by 47% while maintaining academic rigor.
A tier-1 research institution with 15,000+ graduate students across 120 departments. The university produces over 4,000 peer-reviewed publications annually and manages $800M in active research funding.
Their library system serves 2,000+ active researchers with access to 12 million journal articles and 500+ specialized databases.
We conducted 24 in-depth interviews and shadowed 12 researchers across three months. Here are the primary personas that emerged from our research.
Principal Investigator | Computational Biology
Automated weekly digests, cross-domain paper discovery
"I need an assistant that understands the nuance of protein folding research."
PhD Candidate | Applied Mathematics
Smart organization, gap analysis in literature review
"I want to know what I am missing before my committee asks."
Department Chair | Environmental Science
Team collaboration, grant proposal support, cross-lab synthesis
"Our department needs a shared brain for climate research."
Natural language queries across the entire corpus. Find papers by concept, not just keywords.
Interactive knowledge graph showing paper relationships, citations, and concept clusters.
Full visualization available in the deployed application.
Evaluated against 500 expert-annotated queries
Correct paper attribution in generated text
Claims verified against source documents
Unsupported statements per 100 responses
Seconds for end-to-end response
"This tool has fundamentally changed how our researchers approach literature review. The time savings alone justify the investment."
"The cross-disciplinary discovery feature helped us identify three collaborative opportunities we would have missed otherwise."
"Finally, an AI tool that respects academic rigor. Every claim is traceable back to its source."
All research data processed locally. No papers sent to external APIs. FERPA compliant.
AI-generated summaries always cite sources. Plagiarism detection built-in.
Regular audits for citation bias. Gender and geography diversity metrics tracked.
Confidence scores on all AI outputs. Clear labeling of generated content.
We released several components as open source to give back to the research community. These tools are now used by 50+ institutions worldwide.