AI Implementation Guides
Practical guides to implementing AI inside owned, compliant infrastructure — built from real institutional rollouts.
26 pages
- FAR 16.601: Time & Materials for AI Services
- Government AI Contract Vehicles: MAS, OASIS+
- GSA's Draft AI Clause 552.239-7001: What It Means
- How to Automate Accreditation Reporting with AI
- How to Automate Compliance Training with AI
- How to Automate Financial Aid with AI
- How to Automate Transfer Credit Evaluation with AI
- How to Build an AI Early Warning System for Student Retention
- How to Build an AI Governance Program
- How to Build an AI Knowledge Base for Your Institution
- How to Deploy AI for Career Services
- How to Deploy Air-Gapped AI: A Guide
- How to Deploy an AI-Powered IT Help Desk
- How to Deploy FERPA-Compliant AI Systems
- How to Evaluate AI Vendors for Higher Education
- How to Implement AI Academic Advising
- How to Implement AI Enrollment Management
- How to Integrate AI Agents with Your LMS
- How to Migrate Off Per-Seat AI Pricing
- How to Secure AI Agents in Production
- How to Self-Host an LLM in Production
- How to Use AI for Course Design and Development
- How to Use AI for Donor and Alumni Engagement
- T&M Ceiling Price: Why AI Programs Reach It
- Time & Materials for AI Infrastructure: A Guide
- Writing a D&F for AI Services Acquisitions
What's in the AI Implementation Guides Hub
Practical implementation guides for AI inside owned, compliant infrastructure — what to do in week one, what the staged rollout looks like, what the integration touch points are, and what the governance framework needs to cover. Each guide is written for the operator who's been told to ship the AI initiative — the CIO, the dean of advising, the head of compliance, the IT director — not the executive who's been told to fund it.
The guides cluster by lifecycle: assessment (AI readiness, current-state audit, gap analysis), architecture (FERPA-by-design, HIPAA-aligned, FedRAMP-ready), implementation (LMS-integration steps, agent configuration, model routing), and operations (monitoring, evaluation, governance, change management). Pair them with the reference architectures (linked from each guide) and the calculators (for the TCO and ROI inputs the board will ask about).
Each guide assumes the deployment model ibl.ai recommends: orchestration managed by ibl.ai, compute and data inside the customer's perimeter, any LLM with automatic fallbacks, agents from the open source claws library. Steps are concrete; checklists are real; the timeline assumes a competent operator and a willing IT department.
Related on ibl.ai
What do the ibl.ai implementation guides cover?
These 26 guides walk through implementing something end to end — deploying agents, integrating an LMS or SIS, standing up governance, evaluating models, or running an AI program across an institution. They are sequenced as work, with the decisions, prerequisites, and failure modes at each step.
Are these guides specific to ibl.ai?
The sequences are general — the order in which you unify data, define governance, and then deploy agents is the same regardless of vendor, and that ordering is where most AI programs go wrong. The implementation detail is written against the ibl.ai platform because that is the stack we can document precisely, including the parts you would self-host.
What makes the ibl.ai platform different?
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.