Case Study
A Multi-Campus AI Partnership Across SUNY
How faculty and administrators across SUNY campuses adopted ibl.ai for a multi-campus initiative — choosing a platform for its institutional data ownership and, above all, the quality of support behind it.
In Their Words
ibl.ai has been an outstanding partner on our multi-campus SUNY IITG project. From day one, their team took the time to understand our specific needs and worked alongside us at every stage of the project.
The platform itself is robust and thoughtfully designed, giving administrators the tools, flexibility, and confidence needed to manage AI across an institution.
Just as importantly, ibl.ai's data ownership model provides reassurance that institutional information remains secure and appropriately governed.
What has impressed me most, however, is the quality of their support. Questions are answered promptly, feedback is taken seriously, and it is clear that the ibl.ai team genuinely cares about the institutions they serve.
For anyone evaluating AI platforms, ibl.ai stands out not only for what their platform enables, but also for the partnership, responsiveness, and care they bring to work.
Deepa Deshpande, Ph.D.
Associate Director, Center for Innovation and Teaching Excellence
SUNY College of Technology, Alfred State
Working with ibl.ai has been a very positive experience.
The platform offers something that is difficult to find with other AI vendors: institutional ownership and control of AI infrastructure and data, which is critical in higher education, especially for protecting student and faculty work.
The administrative tools are also well designed, providing visibility and control over AI use at both the university and course levels.
What truly sets ibl.ai apart, though, is the people behind the platform. Their team is responsive, genuinely invested in our success, and consistently willing to go beyond what would normally be expected to help think through institutional questions and implementation challenges.
In a space where many AI vendors make broad promises, ibl.ai has been a reliable partner that follows through and remains engaged throughout the process.
Audeliz (Audi) Matías, Ph.D.
Professor, Department of Natural Sciences
College of Arts and Sciences, SUNY
The Pricing Advantage
One LLM-agnostic platform — not four separate subscriptions
ibl.ai governs access to ChatGPT, Claude, Gemini, Perplexity, and other models from a single platform. Institutions are never locked into one provider — they mix, match, and compare models, with API keys, seats, and usage managed centrally.
Stacking individual subscriptions
- ChatGPT — $20 / user / month
- Claude — $20 / user / month
- Gemini — $20 / user / month
- Perplexity — $20 / user / month
$80 / user / month
Each provider is a separate per-seat license. For a campus of 1,000 students, four stacked subscriptions run $80,000 per month — roughly $960,000 per year — before faculty can even compare models side by side.
ibl.ai
- A single platform for every major LLM
- API keys, seats, and usage governed in one place
- A fixed platform fee — orders of magnitude lower
- LLM usage billed on actual consumption, never per seat
Over 90% lower
Institutions pay a fixed platform fee plus usage-based LLM charges — paying only for the tokens actually used. For a 1,000-student campus, that turns a near-$1M annual bill into a fraction of the cost.
The takeaway: per-seat AI subscriptions multiply with every model and every student. A usage-based, LLM-agnostic platform does not — which is why a multi-campus initiative can run several models at once without a runaway bill.
Multi-campus
SUNY IITG initiative
Institution-owned
Data & governance model
Hands-on
Support at every stage
Over 90%
Lower cost vs. per-seat LLM plans
Adopting AI across an institution is not a software problem
A platform demo takes an hour. Running AI responsibly across departments, courses, and campuses takes a partner who stays engaged long after the contract is signed.
Data and governance questions
Institutions need to know where student and faculty work lives, who can see it, and how AI use is governed at both the university and the course level.
Vendors that over-promise
Many AI vendors make broad promises during the sales cycle, then disappear once the real implementation questions begin. Higher education cannot run on that.
A multi-campus rollout
A SUNY IITG project spanning multiple campuses meant coordinating faculty, administrators, and IT teams — each with their own systems, needs, and timelines.
A multi-campus SUNY IITG initiative
SUNY’s Innovative Instruction Technology Grant (IITG) program funds faculty-led projects that test new approaches to teaching and learning. A multi-campus team chose ibl.ai as the platform for managing AI across their institutions.
Worked alongside SUNY teams
From day one, ibl.ai took the time to understand each campus's specific needs and worked alongside faculty and administrators at every stage of the project.
Administrative tools
Administrators received the tools, flexibility, and confidence needed to manage AI across an institution — with visibility at both the university and course levels.
A data ownership model
Institutional information stays secure and appropriately governed — reassurance that AI infrastructure and data remain under the institution's control.
What set ibl.ai apart
Across every conversation with SUNY faculty, the same theme came back: the platform matters, but the team behind it matters more. Support was not an add-on — it was the reason ibl.ai stood out.
Prompt answers
Questions are answered promptly — not routed into a ticket queue and forgotten.
Feedback taken seriously
Feedback from faculty and administrators is heard and acted on, not filed away.
Beyond what is expected
The team consistently goes beyond what would normally be expected to help think through institutional questions.
Engaged throughout
ibl.ai follows through and remains engaged for the full length of the engagement.
Both Deepa Deshpande and Audeliz Matías — quoted above — pointed to the same thing: the people behind the platform are what set ibl.ai apart.
Institutional control, not a black box
In higher education, protecting student and faculty work is non-negotiable. ibl.ai is built so the institution — not the vendor — holds the controls over how AI is configured, governed, and used.
Typical AI vendor
- Student and faculty work lives on vendor servers
- Governance set by the vendor's defaults
- One-size-fits-all chatbot behavior
- Broad promises, limited follow-through
ibl.ai with SUNY
- Institutional ownership and control of AI data
- Governance set at the university and course level
- Instructors define behavior, limits, and resources
- A reliable partner that stays engaged
ibl.ai gives instructors far more control than ChatGPT… I can decide what it won't answer, define the personality, and point students to our own campus resources.
Ken Fujiuchi
SUNY
Why the partner matters as much as the platform
AI tools are converging on similar features. What separates a successful institutional rollout from a stalled one is the team standing behind the software.
Support is the real differentiator
When platforms look alike on a feature list, responsiveness and follow-through decide whether faculty actually adopt the tool. SUNY chose ibl.ai for exactly that reason.
Data control earns institutional trust
An ownership model that keeps student and faculty work secure and governed gives administrators the confidence to expand AI use rather than restrict it.
Proven across multiple campuses
A multi-campus initiative is a hard test of any platform. ibl.ai coordinated faculty, administrators, and IT teams across SUNY campuses — and earned faculty endorsements doing it.
A partner that follows through
In a space full of broad promises, a vendor that stays engaged from kickoff to rollout is rare. That reliability is what SUNY faculty highlight most.
Looking for an AI partner that actually shows up?
Adopt a platform built for institutional data ownership — backed by a team that answers questions promptly, takes feedback seriously, and stays engaged at every stage.