No Vendor Lock-In, Full Code & Data Ownership with ibl.ai

When campus leaders ask what it really takes to use AI at scale, the conversation inevitably lands on control. Not just feature control, but control over where the software runs, how it evolves, and who ultimately owns the code and the data. Closed, per-seat AI products make a lot of decisions for you—pricing, roadmaps, even what you’re allowed to integrate. That’s convenient until it isn’t. Our approach is different: we ship the whole stack so universities can run the platform on their own terms. The institution decides the cloud, the model mix, the integrations, and the governance. If priorities change next semester (or next week), your platform doesn’t need to.

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What “Owning It” Looks Like in Practice

Owning the stack isn’t a slogan; it’s a deployment stance. We deliver the full application codebase, ready to run in your environment, with support for cloud options like AWS ECS and easy fit in GCP, AWS, Azure, or Oracle environments. Your data stays inside your compliance perimeter; logging and guardrails align to your policies. And if we part ways, your system keeps running—no claw-backs, no lock-outs. It’s also multi-tenant by design. One deployment can safely serve multiple schools or programs—Continuing Ed, the College of Arts & Sciences, professional certificates—each isolated as its own tenant. That lets IT standardize infrastructure while giving local admins the autonomy to manage users, roles, content sources, and policy settings without crossing any data boundaries.

The Model Layer: Choice Without Churn

Higher ed shouldn’t be forced into a single LLM. We abstract over leading providers (e.g., OpenAI, Google, Anthropic) so teams can choose a model per assistant or workflow and swap later as needs evolve—without rewriting the application layer. Because SDKs and capabilities differ, some advanced features are model-specific; we surface those trade-offs clearly in templates and settings so faculty aren’t surprised mid-course. The result is practical flexibility: keep the UX and governance stable while the model mix improves underneath.

Integrations on Your Terms

Faculty expect AI to live where they teach. We embed natively via LTI 1.3 in any LMS, including Canvas, Blackboard, and Brightspace. We also provide standalone web and mobile apps for departments that want to move fast. For content grounding (RAG), instructors can drag-and-drop materials, and IT can approve API-based connectors to LMS content libraries or other sources. Permissions are enforced at the course or tenant level, so context stays appropriate and auditable.

Defaults That Work, Controls When You Want Them

The fastest way to lose faculty is to hand them a blank configuration screen. Out of the box, everything “just works”—sensible defaults, clear citations, safety rails. When instructors want more say, they can tune prompt and pedagogy settings to steer tone, scope, and behavior. Institution-level guardrails sit above any vendor model, so responses remain aligned with your academic standards and policies.

Why This Matters to Universities

Ownership changes the risk calculus. With your own application layer, you can respond to pricing changes, vendor shifts, and new compliance requirements without ripping and replacing tools mid-semester. IT gets a modern, containerized deployment that matches existing cloud and security practices. Faculty get dependable interfaces they can shape to their courses. And budgets stretch further because you’re paying for usage (via APIs), not seats in a black-box product. In short, you gain:

• Governance & compliance that travel with you across departments, models, and semesters.

• Academic continuity when a provider changes terms or features.

• Strategic flexibility to adopt new models and integrations without retraining your campus.

• IT alignment through containerized deployments and policy-driven tenants.

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In Conclusion

If you want AI that reflects your curriculum, your governance, and your infrastructure, let’s talk. We’ll show how shipping the whole stack—multi-tenant and model-agnostic—keeps your campus in control today and adaptable tomorrow. Visit ibl.ai/contact to get started.