Deploy purpose-built AI agents that extend your library staff's reach—answering reference questions, guiding research, and managing collections 24/7. Built for lean budgets and high student demand.
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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Community college libraries serve diverse, high-need student populations with limited staff and shrinking budgets.
Librarians spend hours on repetitive reference questions, leaving little time for research instruction or collection development.
Students—many working adults or first-generation learners—need research support outside business hours, and a single librarian can't be everywhere at once.
Most community college libraries operate with 1–3 professional librarians serving thousands of students, making consistent reference support nearly impossible.
Average ratio: 1 librarian per 2,000+ students at community collegesWorking adult students—a majority at community colleges—need research help evenings and weekends when library staff are unavailable.
Over 60% of community college students work while enrolledStaff spend up to 70% of reference time answering the same foundational questions about databases, citations, and research processes.
Up to 70% of reference queries are repeat or routine questionsCommunity colleges lack dedicated IT staff to implement and maintain complex library technology, making vendor-dependent solutions risky and costly.
Community colleges spend 30–40% less per student on IT than 4-year institutionsValuable institutional content—course materials, OER, local research—sits undiscovered because students lack guidance navigating digital repositories.
Studies show fewer than 15% of students regularly use institutional repositoriesA purpose-built reference agent answers student questions about databases, citations, research strategies, and library policies at any hour—trained on your library's specific resources and FAQs.
Guide students step-by-step through the research process—from topic development to source evaluation—aligned with ACRL information literacy frameworks and your institution's curriculum.
AI agents surface relevant library resources, OER, and digital repository items based on student course enrollment, assignment context, and search behavior.
Analyze circulation data, usage trends, and curriculum alignment to recommend acquisitions, identify gaps, and flag underused resources—helping librarians make data-driven collection decisions.
Help students and faculty discover, deposit, and cite materials in your digital repository with an AI agent that understands metadata, access policies, and submission workflows.
Specialized agents assist students researching career pathways, transfer requirements, and industry credentials—connecting library resources to workforce and transfer goals.
Map existing library workflows, reference query logs, database subscriptions, and digital repository structure. Identify the highest-impact AI agent use cases for your specific student population.
Configure and train the AI Reference Agent and Research Instruction Companion using your library's resources, policies, and FAQs. Connect to existing systems including your ILS, Canvas or Blackboard, and student portal.
Soft-launch agents with a pilot student cohort. Train library staff to monitor agent interactions, review escalations, and refine agent responses. Establish feedback loops for continuous improvement.
Roll out agents institution-wide. Activate collection management intelligence and digital repository assistant. Establish monthly review cadence with librarians to optimize agent performance.
Students wait in line or submit email tickets for basic research questions during limited staffed hours.
AI Reference Agent handles routine queries instantly at any hour, escalating complex needs to librarians.
One-shot library instruction sessions reach a fraction of students; most never receive personalized research guidance.
AI Research Companion delivers personalized, on-demand instruction aligned to each student's assignment and course.
Librarians rely on vendor reports and intuition to make acquisition decisions with limited usage data.
AI analyzes circulation, curriculum alignment, and usage trends to surface actionable collection recommendations.
Students rarely discover institutional repository materials; content sits unused due to poor discoverability.
AI agents proactively surface relevant repository content based on student course context and search intent.
Students must navigate multiple offices to connect library resources with career or transfer research needs.
Specialized AI agents connect library resources directly to workforce pathways and transfer articulation research.
The platform for building, deploying, and managing all library AI agents on your own infrastructure—integrating with your ILS, LMS, and SIS with zero vendor lock-in and full institutional data ownership.
Enables librarians to create, adapt, and deliver AI-powered research guides, information literacy modules, and collection discovery content tailored to community college student needs and course contexts.
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.