How a U.S.-based university used AI consulting, generative AI, retrieval-augmented search, and academic analytics to reduce repetitive student-service work, help advisors prepare faster, surface trusted policy answers, and improve course-demand planning across departments.
The client is a multi-school university serving more than 18,000 undergraduate and graduate students across business, technology, health sciences, liberal arts, and professional programs. Student services span admissions, academic advising, registrar operations, financial aid, course planning, and campus support.
Students and staff were working across the student information system, LMS, university website, policy documents, program catalogs, advising notes, knowledge bases, and departmental spreadsheets. Routine questions were repeatedly escalated, advisors spent time assembling context before appointments, and academic leaders lacked a consistent view of course demand and service bottlenecks.
JanBask designed and deployed CAMPUSIQ AI — an education AI solution combining retrieval-augmented generation, university knowledge, student-service workflows, academic analytics, and secure integrations. It helps students find trusted answers, gives advisors faster context, and helps academic leaders identify course-demand pressure without replacing human academic or admissions decisions.
A retrieval-augmented AI assistant that lets students and staff ask questions about programs, academic policies, registration, advising processes, deadlines, and campus services in plain English — with answers grounded in approved university sources.
AI classifies student-service requests by topic, urgency, department, and next-best destination so routine questions can be resolved faster and complex cases reach the appropriate human team with better context.
Summarizes relevant advising notes, degree requirements, registration status, outstanding tasks, and recent student interactions so advisors can prepare for appointments with less manual searching.
Indexes approved handbooks, catalogs, policy documents, FAQs, and departmental guidance so staff can locate the right rule or program requirement without searching across multiple university repositories.
Analyzes registration patterns, waitlists, historical enrollment, course capacity, and demand signals to help academic leaders identify sections that may require additional seats, staffing, or schedule adjustments.
Connects approved data and content from the SIS, LMS, university website, knowledge repositories, and student-service systems to create a governed AI layer across the university’s existing technology environment.
Performance was measured against pre-implementation student-service and academic workflows, including policy search, advisor preparation, request routing, student-support handling, and course-planning escalation cycles.
An enterprise-grade AI and data architecture designed for higher education, governed knowledge retrieval, student-service workflows, academic analytics, secure information access, and scalable university operations. AI outputs were designed to support — not replace — advisors, faculty, administrators, or admissions decisions.
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