Student discount
Verified students get 40% off monthly Explore or Studio for 12 months - full AI credits included. Re-verify each year to renew.
Academic Program
Explore agentic AI in architecture.
For professors, students, and researchers who want AI to participate in design reasoning - not only produce isolated outputs. Students: apply below and upload enrollment proof for 40% off monthly Explore or Studio.
01
Studios & seminars
Seats and shared workspaces sized for a course cohort - not a single login.
02
Compose, do not just prompt
Students author agentic processes: analysis, generation, critique, and iteration as one workflow.
03
Research labs
Prototype how design reasoning moves across agents, tools, and memory - then reuse what works.
Prefer email? academic@versur.ai
Apply
Join the Academic Program
Tell us about your course, lab, or thesis - we will map seats and onboarding that fit.
What you get
Value for the studio, seminar, and lab.
The program is built for teaching and research - cohort access, onboarding, and tools that keep design process in the center.
Cohort-ready access
Instructor workspace plus student seats for a studio or seminar - sized to the class, not a freelance seat.
Teaching onboarding
Get the course live fast: starter agents, assignment framing, and a setup path that stays out of the way of critique.
Bridge into studio tools
Rhino and Grasshopper stay in the loop. Agentic work happens where computational design already lives.
Process you can assess
Workflows and reasoning trails become the deliverable - grade how the process thinks, not one final image.
Who it is for
Teaching and research, same platform.
The shift is from fixed assistants toward composed agents - so a studio can define how a process should think.
Professors
Run a studio or seminar on agentic design without drowning in setup. Shared workflows, cohort seats, teaching kit.
Students
Author agents for site reading, precedent, massing, or critique - a process you can show, not a chat log.
Researchers
Treat agent configurations as experimental variables. Keep methods reproducible with Bridge, memory, and callable workflows.
Academic data stays yours
Course and lab work in Academic Program workspaces is not used to train models. You keep the IP; we keep the infrastructure.