A prospective student in another hemisphere calls about application requirements. The AI answers in one ring, in their language, from YOUR approved information — and books the virtual visit.

Transcript requests, enrollment verification steps, add-drop deadlines: answered instantly and identically every time — while your registrar staff works the queue instead of the phone.

Campus tours, advising appointments and department visits booked into real synced calendars, confirmed and reminded in the visitor’s language.

Progress report drafts, pipeline summaries, budget-versus-actual reviews — assigned to the AI like to a research administrator, executed on schedule, fully auditable.

“What indirect rate did we use on the 2021 proposal?” “Which aims did the reviewers score highest?” The grants archive answers with the document attached.

Every AI-drafted section lands in Output awaiting review. Nothing reaches a sponsor, a journal or the provost without a human decision.

PDFs, Word, spreadsheets, scanned agreements, images and media — the ingestion pipeline turns what the institution actually holds into queryable knowledge.

No source in your library, no claim: the AI cites the paper, the policy, the award document — or says it cannot answer. Critical for work that gets peer-reviewed.

A grad student asks about lab procedures; the department chair asks about budgets: each gets answers scoped to their role, enforced server-side.

Proposal section, progress report, pipeline summary, policy digest: work types molded to your institution, with default libraries and examples per type.

The AI’s workload is managed like the team’s: one-click views show what is urgent, late, today and tomorrow — nothing slips silently in submission season.

Every work is a full session: reasoning streamed, sources cited, milestones tracked, linked to the award record and the calendar.

Lab safety procedures, data-retention rules, submission checklists, travel policies: one library, approved versions on top, superseded drafts outranked.

“What is the procedure for a chemical spill in the wet lab?” — answered in seconds with the document attached, at 2 PM or 2 AM, to any authorized member.

Work types carry their default policies: the AI drafting a progress report reads from the sponsor’s reporting guidelines — by construction.

New grad students and staff learn your lab procedures, purchasing rules, systems and policies through guided training — productive in days, not a semester.

“How do I submit a reimbursement for conference travel?” — asked to the assistant, answered from the SOP, without pulling the lab manager off an experiment.

Private chats work on any device with a login — the how-to knowledge follows the researcher to the lab bench, the library or the field site.

Sponsor deadlines, report due dates and renewal windows drawn from your records trigger reminders and draft the required documents ahead of time — queued for review.

Award notices, sponsor correspondence and signed agreements routed to the right grant record with handling instructions — no lost-email archaeology.

The weekly pipeline summary, the monthly department report, the semester teaching digest: compiled by the AI, filed to Output, on time every time.

Engineering alumni of the last decade, lapsed annual donors, event attendees who never gave: audiences built from your own categories — no exporting lists to a third-party tool.

Stewardship notes and newsletters written the way alumni actually read them — warm, specific, in your institution’s voice, in the recipient’s language.

Opt-outs and contact preferences are enforced by the system, not by memory. Every send is on the record.

Proposals, awards and documents mapped field by field, with a dry-run preview before anything writes — push, pull or two-way, your choice.

Enrollment data, course materials and calendars sync with the AI layer — so the assistant answering a question reads the same systems the campus runs on.

A full API for whatever else the institution runs: library systems, facilities tools, department databases — data in, results out, under scoped keys.

The server redacts what a role cannot see — a student worker scheduling appointments sees the slot, not the record. FERPA-conscious handling, enforced in code.

A searchable log of every audited action, with configurable categories: who saw what record, who changed what, when.

Five-level workgroups map colleges, departments and labs; the on-premises option puts the whole system inside your facilities, air-gap capable, multi-campus ready.

Ask the chat: “what are the requirements for the graduate program?” — watch it answer from approved information and offer to book the visit.

Ask: “how would you draft a budget justification?” — it walks the workflow it would run in your tenant, grounded, cited, sign-off included.

The full demo loads a university tenant with sample records: proposal drafting, pipeline reports, policy answers — your world, working.