Higher-ed shouldn’t have to bet everything on a single AI vendor. Model ecosystems evolve monthly, and different providers shine at different tasks. That’s why ibl.ai’s mentorAI runs as an abstraction layer across multiple LLM SDKs—so universities can align each workflow with the most suitable model without rebuilding tools, retraining users, or renegotiating licenses.

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One Layer, Many Choices (With Clear Capability Boundaries)

Under the hood, our application layer connects to leading providers (e.g., OpenAI, Google, Anthropic) through a unified API/SDK. Practically, this lets your teams:

• Choose the right model per workflow (or per assistant) and change later with minimal disruption.

• Adopt provider-specific features where appropriate—for example, a data-analysis workflow may be configured to use a model that supports code execution; a separate vision or collaboration scenario may use a model that excels at those tasks.

• Plan with a published capability matrix so faculty and admins know which features are available with which provider before they deploy.

Why This Matters For Teaching & Learning

• Feature breadth without platform churn: Instructors can build assistants that cite course materials, analyze data/code, or support multimodal interaction—using whichever provider best fits that workflow—while the UX stays consistent for students.

• Simplicity + control: Default configurations work “out of the box,” and advanced prompt/pedagogy settings let faculty tune behavior when they want to.

• Future-proofing: When new models or features arrive, you can adopt them in targeted workflows instead of ripping and replacing campus tools.

Centralized Governance and Safety

Each vendor ships its own alignment policies; we layer institution-level guardrails on top so governance travels with you across models:

• Per-tenant / per-course policies and logging.

• Domain scoping (e.g., “answer only from this course’s corpus”).

• Flexible deployment: host with ibl.ai or run in your environment, with full code and data ownership and multi-tenant controls.

Budget Flexibility, No Lock-In

A model-agnostic application layer lets you:

• Route tasks to cost-effective models for routine work and reserve premium options for harder problems.

• Use API pricing under your terms instead of paying per-seat for closed assistants.

• Switch providers as prices/capabilities change—without a campus-wide refactor.

Meet Learners Where They Are

The same core powers LTI 1.3 embeds for Canvas/Brightspace/Blackboard, as well as standalone web and mobile apps. Whether you start in the LMS sidebar or a departmental tool, admins control which provider backs each assistant template.

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In Conclusion

If you want assistants that keep pace with the model landscape—without tying your campus to one vendor—let’s talk. We’ll show how a multi-LLM application layer delivers broader capabilities, clearer governance, and lower total cost while respecting your security model. Visit ibl.ai/contact to get started.