Deploy purpose-built AI agents that handle reference queries, guide research instruction, and streamline collection workflows — all on your infrastructure, with full data ownership.
Research university libraries serve tens of thousands of students and faculty across dozens of disciplines, yet most operate with the same staffing models built for a fraction of that scale.
Reference desks are overwhelmed during peak periods, research instruction is inconsistent across departments, and digital repositories remain underutilized because discovery is too complex for most users.
Legacy systems, siloed data, and compliance requirements make it nearly impossible to adopt off-the-shelf AI tools — leaving librarians stretched thin and researchers underserved at the moments that matter most.
Library staff field thousands of repetitive reference queries each semester, leaving little capacity for complex research consultations that require expert human judgment.
Up to 70% of reference questions are repeat or routine queriesResearch instruction quality varies widely by librarian, department, and time of year, creating inequitable learning experiences for graduate and undergraduate researchers.
Only 34% of undergraduates report receiving adequate research skills trainingInstitutional repositories hold vast collections of research outputs, but poor discoverability and complex metadata structures mean most users never find relevant materials.
Average repository discovery rate is under 12% of available holdingsEvaluating usage data, managing renewals, and identifying collection gaps across hundreds of databases and journals consumes enormous staff time with limited analytical support.
Librarians spend 40%+ of time on administrative collection tasksGeneric AI tools violate FERPA when processing patron records or research queries, forcing libraries to block adoption or accept unacceptable legal exposure.
FERPA violations can result in loss of federal fundingA purpose-built reference agent handles routine queries, citation guidance, database navigation, and research starting points at any hour — escalating complex needs to human librarians with full context.
AI-powered instruction agents deliver tailored research skills guidance aligned to specific disciplines, assignment types, and student skill levels — ensuring consistent, high-quality support at scale.
Semantic search and AI-guided discovery agents surface relevant theses, datasets, preprints, and publications from institutional repositories based on natural language research queries.
AI agents analyze usage patterns, cost-per-use metrics, citation overlap, and faculty research trends to generate actionable collection development and renewal recommendations.
Agents integrate with existing systems — Canvas, Blackboard, Banner, and library ILS platforms — to embed library support directly into course workflows and student research journeys.
AI-powered assessments verify and credential student research competencies — from database literacy to citation management — providing faculty and accreditors with verifiable skill evidence.
Audit existing library systems, ILS integrations, repository platforms, and reference workflows. Map FERPA data flows and identify priority use cases with library leadership.
Deploy and configure the Reference AI Agent and Repository Discovery Agent on university infrastructure. Integrate with ILS, LMS, and authentication systems. Train agents on library-specific knowledge bases.
Launch personalized research instruction agents aligned to key disciplines. Activate collection intelligence dashboards with usage data feeds. Pilot with graduate programs and high-enrollment undergraduate courses.
Expand deployment across all library service areas. Launch research skills credentialing for undergraduate and graduate programs. Review performance metrics and optimize agent responses.
Students wait hours or days for responses; staff overwhelmed during midterms and finals
AI agent provides instant, accurate responses 24/7; staff focus on complex consultations
One-size-fits-all library instruction sessions, often disconnected from actual assignments
Personalized, discipline-specific AI instruction embedded directly in course workflows
Complex metadata interfaces deter users; institutional research outputs go undiscovered
Natural language AI discovery surfaces relevant institutional research instantly
Manual analysis of spreadsheets and vendor reports; decisions made with incomplete data
AI-generated collection intelligence reports with usage trends, gaps, and renewal recommendations
Generic AI tools create FERPA risk; library blocked from adopting modern AI solutions
FERPA-compliant AI agents running on university infrastructure with full institutional data ownership
Powers the 24/7 AI reference agent and personalized research instruction experiences, providing students and researchers with expert-level guidance tailored to their discipline and query context.
The core platform for building, deploying, and managing all library AI agents — including reference, repository discovery, and collection intelligence — on university-owned infrastructure with full ILS and LMS integration.
Enables library services to design and issue verifiable research skills credentials for undergraduate and graduate students, supporting information literacy programs and accreditation evidence.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.