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AI for research institutions, on their terms.

Research-grade flexibility. Institutional-grade compliance. A platform that serves faculty, researchers, and students without exporting institutional governance to a foreign vendor. Ibero built the template. Your institution is the next one.

002 / The Problem

Higher ed doesn't need an AI tool. It needs an AI institution.

Universities have access to AI tools. What they mostly lack is AI capability that operates the way an institution operates — with research flexibility, student privacy protection, role-based governance across faculty and staff, and the data sovereignty stance a serious academic institution maintains over its own research and records.

The gap between "our students use ChatGPT" and "our institution operates AI under our own governance framework" is the gap between consuming AI and having AI capability. Saptiva AI is built to close it.

01 / GOVERNANCE
Research ethics, under your framework.
Universities have ethics review boards, data governance policies, and research integrity standards. AI infrastructure should obey those — not the other way around.
02 / FLEXIBILITY
Research workloads are not production workloads.
Academic research requires room for novel models, experimental configurations, and capability that does not exist yet. The platform must allow for that without breaking.
03 / CONSOLIDATION
One platform, not a portfolio of pilots.
Most universities have a sprawl of disconnected AI experiments across departments. One institutional platform replaces the sprawl without shutting down the research.
003 / Use Cases

The workloads a serious research institution actually runs.

Higher education spans research, teaching, institutional administration, and student-facing services. Saptiva AI operates across the full surface — one platform, different policies, shared governance.

01

Institutional AI lab infrastructure.

A research-grade compute backbone governed by institutional data policy. Faculty and research groups access shared infrastructure for model training, experimentation, and applied research. Ibero operates Mexico's largest private AI lab on this foundation.

RESEARCH-GRADESHARED COMPUTEROLE-BASED
02

Institutional knowledge copilots.

Private RAG systems trained on institutional policies, historical administrative records, academic procedures, and library collections. Faculty, staff, and authorized students access what their role permits. The institution's knowledge stays the institution's knowledge.

PRIVATE RAGROLE-BASED ACCESSFULL AUDIT
03

Student services automation.

First-tier student inquiries — enrollment status, procedural questions, financial aid basics — resolved with clear escalation paths to the actual staff who handle complex cases. Compliance with student-privacy frameworks enforced at the policy layer.

STUDENT-FACINGPRIVACY ENFORCEDESCALATION
04

Research administration workflows.

Grant assembly, compliance documentation, institutional reporting, and the long back-office tail of running research programs. The workflows that consume faculty and administrator time without actually being the research.

GRANTSIRB / ETHICSINSTITUTIONAL REPORTING
05

Teaching and curriculum support.

Faculty copilots for course design, assessment calibration, and teaching-facing administrative work. Enhancement for the human teacher, not replacement of them. Academic integrity and evaluation fairness remain faculty-owned decisions.

FACULTY ASSISTHUMAN-AUTHOREDINTEGRITY FIRST
06

Research partnerships with industry and government.

The connective infrastructure that makes joint research feasible across institutional boundaries. Data rooms with role-based access, policy enforcement on derived artifacts, audit surface visible to both sides. The governance infrastructure that makes collaborations actually work.

CROSS-INSTITUTIONALDATA ROOMSPOLICY ENFORCED
004 / Engagement Modes

How a university actually gets started.

Universities do not usually adopt institutional infrastructure in one step. Saptiva AI supports three typical entry points — each one a legitimate starting place, each one a valid end state depending on how far the institution wants to take it.

MODE 01

Research lab infrastructure.

The university anchors with a specific research lab or initiative. The AI capability expands from there as adjacent programs come online. This is how Ibero started.

MODE 02

Institutional knowledge and admin backbone.

The university anchors on back-office and institutional-knowledge applications first — less dependent on research-grade flexibility, more about operational load reduction for admin staff.

MODE 03

Whole-institution infrastructure.

The university treats AI as institutional infrastructure from the beginning. Single platform, single governance layer, applications deployed progressively across research, administration, and student services.

005 / Customer

Anchored by Universidad Iberoamericana.

HIGHER EDUCATION · MEXICO · MULTI-YEAR

Universidad Iberoamericana

Mexico's largest private AI lab, built with Saptiva AI on a multi-year commitment. Selected over global solution partners.

Ibero evaluated Saptiva AI against global solution partners — incumbents with established procurement relationships in higher education — and selected Saptiva AI for three reasons: architectural fit and execution speed.

A Forward Deployed Engineer in the room — not an account manager routing through a partner's partner — was not a soft differentiator. It was the differentiator. Research institutions across Latin America evaluating AI infrastructure partners now have a reference point.

Read the deployment →
006 / Get In Touch

AI infrastructure for your university.

If you run a research institution, university administration, or individual lab evaluating AI partners, a Forward Deployed Engineer responds within 48 hours.

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