Deploy purpose-built AI agents that deliver 24/7 reference support, personalized research instruction, and scalable library services to every online student — no matter the time zone.
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Online university students rarely visit a physical library, yet their research and information needs are just as complex as on-campus peers.
Library staff are stretched thin supporting thousands of asynchronous learners across time zones, with limited hours and no in-person touchpoints to catch struggling students early.
Without scalable, always-on library support, students disengage from research, submit lower-quality work, and face higher attrition — while librarians spend hours on repetitive reference queries instead of high-value instruction.
Online students submit reference requests at all hours, but most library teams operate on business-hours schedules, leaving students without support when they need it most.
Over 60% of online student library interactions occur outside standard business hoursWithout physical library spaces or librarian walk-ins, online students underutilize databases, research guides, and digital collections, weakening academic outcomes.
Online students use library resources 40% less frequently than on-campus peersStaff spend the majority of reference hours answering the same foundational questions about citations, database access, and search strategies instead of delivering advanced instruction.
Up to 70% of reference queries are repetitive and answerable without librarian expertiseTraditional library instruction sessions are synchronous and campus-centric. Adapting them for thousands of async online learners requires resources most library teams don't have.
Less than 15% of online students complete optional library instruction modulesWithout guided research support, online students are more likely to rely on unvetted sources, misuse AI tools, or inadvertently plagiarize — increasing institutional risk.
Academic integrity violations are 2x more likely when students lack research guidanceA purpose-built AI reference librarian agent answers student questions about databases, citations, research strategies, and library policies at any hour — escalating complex queries to human librarians with full context.
AI agents deliver adaptive, course-aligned research instruction modules that guide students through source evaluation, database selection, and citation practices based on their assignment and skill level.
AI agents help students and faculty discover, access, and navigate digital repository assets — surfacing relevant institutional research, theses, and open-access materials aligned to their coursework.
AI-powered analytics surface underutilized collections, identify high-demand resources, and generate actionable reports to support data-driven collection development decisions.
Embedded AI coaching guides students through ethical research practices, proper attribution, and source verification — reducing unintentional plagiarism before submissions reach faculty.
Library AI agents integrate directly into Canvas, Blackboard, or your existing LMS — delivering contextual research support inside the courses where students are already working.
Map existing library systems, reference workflows, digital repository structure, and LMS integrations. Identify top reference query categories and instruction gaps.
Configure the AI reference agent with institutional knowledge — library policies, database access guides, citation standards, research guides, and digital repository metadata.
Embed AI library agents into the LMS environment. Deploy adaptive research instruction modules aligned to high-enrollment courses and academic integrity workflows.
Go live with full student access. Train library staff on agent management, analytics dashboards, and escalation handling. Establish feedback loops for ongoing improvement.
Business-hours email and chat support, leaving evening and weekend students without help
24/7 AI reference agent with instant responses and seamless human escalation during staffed hours
Optional synchronous webinars with low attendance and no personalization by course or skill level
Adaptive, asynchronous AI instruction modules embedded in courses and tailored to each student's assignment
Students navigate complex repository interfaces independently, often failing to find relevant institutional resources
AI agent surfaces relevant repository assets contextually based on course topic and student query
Staff overwhelmed by high volumes of basic queries, limiting capacity for advanced research consultations
AI handles routine queries automatically; librarians focus on complex consultations and collection strategy
Students receive integrity guidance only after a violation is flagged — reactive and punitive
Proactive AI coaching guides students through ethical research practices before submission
The platform for building, deploying, and managing library AI agents with deep integrations into existing library systems, LMS platforms, and institutional repositories.
Enables rapid creation and adaptation of research instruction modules, library guides, and academic integrity coaching content — keeping library resources current and course-aligned.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.