# AI Data Engineering - Forward-Deployed Engineers (FDEs)

> Source: https://ibl.ai/service/data-engineering/higher-education

Forward-Deployed Engineers build your MCP-powered campus memory layer for AI agents — with your data, in your environment.

Build your campus "memory layer" for AI agents — powered by the Model Context Protocol (MCP) — with your data, in your environment.

## What This Is

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ibl.ai's Forward-Deployed Engineers embed with your team to connect SIS, LMS, CRM, ERP, identity, storage, and content systems into a secure, policy-aware memory layer built on the Model Context Protocol (MCP). That memory becomes the backbone for AI agents — student tutors, instructor TAs, and administrative assistants — running privately in your infrastructure.

This is professional services, billed by the hour (ultra-competitive rates), with clear milestones and artifacts you own.

## Built on the Model Context Protocol (MCP)

### What is MCP?

The Model Context Protocol is an open standard — originally developed by Anthropic — that defines how AI models connect to external data sources, tools, and services. MCP provides a universal interface between AI agents and your institutional systems, replacing brittle custom integrations with standardized, secure connectors.

Think of MCP as USB-C for AI: one protocol, every system. Instead of building a custom integration for each SIS, LMS, or CRM, MCP gives agents a single, consistent way to read data, call tools, and respect permissions across your entire campus stack.

### Why MCP Matters for Higher Education

Campus IT teams maintain dozens of systems — Banner, Canvas, Salesforce, Workday, SharePoint, and more. Traditional integration approaches require point-to-point connectors that break with every vendor update. MCP eliminates this fragility by providing a protocol-level contract between AI agents and data sources.

With MCP, your institution gets portable agents that work across any LLM provider (OpenAI, Anthropic, Google, Meta, or local models), interchangeable connectors that can be swapped without rewriting agent logic, and built-in security boundaries where every data access goes through policy-aware middleware with FERPA-grade controls.

### MCP Architecture at ibl.ai

Every ibl.ai deployment uses MCP as the core integration protocol. Our Forward-Deployed Engineers build MCP servers for each campus system — SIS, LMS, CRM, ERP, identity providers, and document stores. These MCP servers expose structured tools and resources that agents can discover and invoke at runtime.

The result is a composable agent architecture: a student tutor agent can query enrollment data from Banner, fetch course materials from Canvas, check deadlines from the academic calendar, and retrieve advising notes from Salesforce — all through MCP — without any custom glue code between systems.

## MCP Servers We Build

### SIS MCP Server (Banner, PeopleSoft, Colleague)

Exposes enrollment records, degree audits, registration holds, transfer credits, and academic standing as MCP resources. Agents can query real-time student data without direct database access. Field-level FERPA controls enforce who sees what based on role and consent.

### LMS MCP Server (Canvas, Blackboard, Brightspace, Moodle)

Provides course content, assignment metadata, grades, discussion threads, and rubrics as MCP tools. Agents can retrieve specific lecture materials, check submission deadlines, and access course-level analytics — all scoped to the requesting user's permissions.

### CRM MCP Server (Salesforce, Slate, HubSpot)

Connects enrollment pipelines, prospect records, donor relationships, and advancement data. Agents can look up application status, pull advising case histories, and surface engagement metrics for retention interventions.

### Identity & Directory MCP Server (Entra ID, Okta, Google Workspace)

Provides role resolution, group memberships, and authentication context. MCP-level RBAC ensures agents only access data appropriate for the authenticated user's institutional role — student, faculty, advisor, or admin.

### Document & Storage MCP Server (SharePoint, OneDrive, S3, GCS)

Indexes institutional documents — policies, handbooks, catalogs, syllabi — and makes them retrievable via semantic search through MCP. Agents can cite specific documents with page-level provenance rather than generating answers from training data alone.

### Custom MCP Servers

We build MCP servers for any system with an API or database: ERP/HR (Workday, Oracle), scheduling (25Live, Ad Astra), housing, dining, parking, library systems, research databases, and more. If your institution has it, we can connect it.

## MCP Security and Governance

### Protocol-Level Access Control

Every MCP request carries authentication context — who is asking, what role they hold, and what consent has been granted. Our MCP middleware enforces field-level permissions before data ever reaches the agent.

A student asking about their own grades sees their records; an advisor querying the same system sees their advisee cohort; an admin sees aggregate analytics. Same MCP server, different views.

### PII Masking and Data Minimization

MCP responses pass through a policy engine that redacts sensitive fields based on configurable rules. Social security numbers, financial aid details, and disability accommodations are masked or excluded from agent context unless explicitly authorized.

Every redaction decision is logged for audit.

### Audit Trails and Compliance

Every MCP tool invocation is logged with timestamp, requesting agent, authenticated user, data accessed, and response summary.

These audit trails support FERPA compliance reviews, internal security audits, and incident response. Logs are stored in your infrastructure and retained per your institutional policy.

### Sandboxed Execution

MCP servers run in isolated containers within your VPC or on-premises infrastructure. No campus data leaves your environment.

Agents interact with MCP servers over internal networks with mTLS encryption. External LLM providers receive only the agent's synthesized prompts — never raw institutional data.

## Who We Work With

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IT & Enterprise Architecture

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Institutional Research

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Student Success / Advising

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Teaching & Learning Centers

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Program/Department Leads

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Security, Privacy, Legal

## What We Do (Scope at a Glance)

### Systems & Data Mapping

Inventory: SIS (e.g., Banner, PeopleSoft, Colleague), LMS (Canvas, Blackboard, Brightspace, Moodle), CRM (Salesforce, Slate), ERP/HR (Workday, Oracle), Identity (Entra/Okta/Google), Storage (SharePoint/OneDrive/S3/GCS).

Schemas & Contracts: enrollment, rosters, outcomes, accommodations, deadlines, advising notes, policies, course materials metadata. Policy & Governance: FERPA fields, role scopes, retention rules, redaction maps, consent flows.

### MCP Server Development

We build production-grade MCP servers for every campus system in your stack. Each server exposes typed tools and resources following the MCP specification, with built-in schema validation, error handling, rate limiting, and observability.

Servers are containerized and deployed via Terraform or Kubernetes manifests you own.

### Memory Layer Engineering

MCP-based Connectors: secure adapters that normalize read/write paths across systems. Per-Learner Memory Graph: knowledge graph + vector index for contextual retrieval (course content, outcomes, deadlines, interventions).

Guardrails Engine: RBAC, field-level permissions, PII masking, consent receipts, audit trails. Sync & Freshness: event bus/CDC, backfills, idempotent jobs, conflict resolution, replay.

### Agent Enablement (Optional)

Student Tutor: citable Q&A grounded in course content, deadlines, and policies via MCP. Teaching Assistant: study guides, FAQ triage, formative feedback, discussion support.

Digital Assistant (Admin): policy lookups, enrollment snapshots, risk roll-ups, "what's changed?" digests. Model Hub: OpenAI, Gemini, Anthropic, Llama, or local/NPU — hot-swappable per policy/cost.

### Workflow Automation (Partner Institutions)

Proactive nudges (risk, deadlines), case routing, degree milestones. Content pipelines (ingest → chunk → cite), assessment item generation with human review. Approval gates for academic control (human-in-the-loop).

## Deliverables You Keep (No Lock-In)

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MCP server source code for every connected campus system

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Connector code & IaC (Terraform/K8s manifests) to deploy in your VPC/on-prem

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Data dictionaries, MCP tool schemas, and contract tests

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Policy configs (RBAC matrices, redaction rules, retention/expiry)

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ETL/ELT jobs, sync runbooks, and observability dashboards

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Agent starter kits (prompts, MCP tool definitions, evaluation harnesses)

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Security & Compliance packet (threat model, MCP data flows, audit checklist)

## Engagement Model (Hours-Based, Transparent)

### Discovery & Design (1–3 weeks):

workshops, MCP architecture, system inventory, backlog, estimates

### MCP Server Sprints (2–6 weeks):

build and test MCP servers for each campus system, memory layer, policy engine

### Pilot & Hardening (2–4 weeks):

limited cohorts, telemetry, MCP performance tuning, handover

### Handoff or Co-Manage:

your team runs it; we stay on a light retainer if desired

### Billing:

hourly, ultra-competitive rates; weekly timesheets; milestone demos; you can pause/rescope anytime

## Security, Privacy, and Compliance

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All MCP servers run in your environment (AWS/Azure/GCP or on-prem), with your IAM/KMS

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FERPA/GDPR support, SOC 2–aligned practices, least-privilege MCP access

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MCP-level data minimization, field-level masking, consent receipts, audit logs

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Red-team prompts, safety filters, and replay evaluation for agents

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mTLS between agents and MCP servers; no raw data sent to external LLM providers

## Reference Architecture (MCP-Powered)

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MCP Server Layer → Typed connectors to SIS/LMS/CRM/ERP/Identity/Storage

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MCP Gateway → Authentication, rate limiting, request routing, and observability

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Event Bus + CDC → Reliable syncs, backfills, and change capture

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Student Memory Layer → Graph + vector store with MCP-aware policy retrieval

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Policy/Guardrails Engine → RBAC, PII redaction, consent, rate limits

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Agent Interfaces → Tutor (student), TA (instructor), Digital Assistant (admin)

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Observability → MCP request traces, latency metrics, cost monitors, evaluation harnesses

## Common Use Cases We Deliver

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"Single pane of glass" tutor with deadlines, materials, and accommodations context — powered by MCP connections to SIS, LMS, and advising systems

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TA that drafts study guides and triages repetitive questions (cited answers from MCP-connected course materials)

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Admin assistant that surfaces risk cohorts and policy answers with provenance via MCP queries across enrollment, grades, and intervention systems

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Cross-system automations: enrollment triggers, nudges, degree-progress alerts — orchestrated through MCP tool chains

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Content ingestion pipelines with citations and IP safeguards

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Multi-agent workflows where specialized agents collaborate through shared MCP servers — one agent handles advising, another handles scheduling, a third handles financial aid — all sharing the same secure data layer

## Why ibl.ai FDEs

### MCP-native architecture:

every integration we build follows the open MCP standard — no proprietary lock-in

### Higher-ed native:

LTI 1.3, SIS/LMS nuances, academic governance baked in

### Ownership by design:

you get the MCP server code, configs, and deployment scripts

### Model-agnostic and cost-aware:

MCP works with any LLM provider; swap models and optimize for accuracy and spend

### Speed + rigor:

we ship working MCP integrations quickly, with tests and runbooks

## Get Started

### Architecture Review (hours):

map systems, goals, risks, and design your MCP server topology

### Fixed-Scope Pilot (optional):

cap hours for MCP servers covering a specific cohort or program

### Ongoing Hours (as needed):

new MCP servers, additional connectors, and workflow builds

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*[View on ibl.ai](https://ibl.ai/service/data-engineering/higher-education)*