ZANUS AI FOR UNIVERSITY & RESEARCH — AI WORKFORCE

A research administrator that never falls behind.

Assign work to the AI the way you assign it to staff: type, sources, deadline, priority. Progress report drafts, grants pipeline summaries, committee-minutes digests — executed on schedule, with citations, under review.

Get started

Private tenant or your own servers · unlimited users · FERPA-conscious handling

Assigning AI work with the four-step wizard

Four steps: work type, records and libraries, tags, schedule — the AI takes it from there.

What it does

Work types for a campus

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

Urgent / Overdue / Today

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.

A postdoc’s diligence, on the record

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

Monday, 7 AM: the pipeline report is done

Set “Weekly Grants Pipeline Review” once: every Monday the AI summarizes proposal statuses, upcoming deadlines and outstanding items from your records, and files the report for the research office before the first meeting.

Recurring schedules for periodic admin work
Findings cited to award and proposal records
Report in Output, awaiting sign-off

Roster of scheduled AI works for a research office

Higher-ed software is procured module by module and seat by seat, research computing sits behind request queues — and the grants office still rebuilds every budget justification from scratch the week the proposal is due. Zanus AI: one AI operating system for grants, papers, policies and student services — unlimited users, FERPA-conscious handling, hosted in your private tenant or on servers the institution owns.

Questions, answered

Can we supervise mid-work?

Yes — open any session, read the reasoning, redirect it, or stop it. The AI works like staff: visible, accountable, interruptible.

What volume can it handle?

It does not get tired: the constraint is your review capacity, not the AI’s. Configurations size from one lab to a multi-campus institution.

A research administrator that never falls behind.

What it does

Monday, 7 AM: the pipeline report is done

Questions, answered

See it working on YOUR campus — the demo IS the product.