# Forward-Deployed Engineering (FDEs)

> Source: https://ibl.ai/service/forward-deployed-engineering

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

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

## What This Is

ibl.ai's Forward-Deployed Engineers embed with your team to connect HRIS, LMS, CRM, ERP, identity, storage, and knowledge-base systems into a secure, policy-aware memory layer built on the Model Context Protocol (MCP). That memory becomes the backbone for AI agents — employee coaches, L&D assistants, and compliance monitors — 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 enterprise 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 HRIS, LMS, or CRM, MCP gives agents a single, consistent way to read data, call tools, and respect permissions across your entire enterprise stack.

### Why MCP Matters for Enterprise L&D

Enterprise IT teams maintain dozens of systems — Workday, SuccessFactors, Cornerstone, Salesforce, 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 organization 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 SOC 2/SOX/HIPAA-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 enterprise system — HRIS, 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: an employee coaching agent can query role history from Workday, fetch training completions from Cornerstone, check compliance deadlines, and retrieve competency frameworks — all through MCP — without any custom glue code between systems.

## MCP Servers We Build

### HRIS MCP Server (Workday, SuccessFactors, BambooHR, ADP)

Exposes employee records, role histories, org charts, compensation bands, and performance reviews as MCP resources. Agents can query real-time workforce data without direct database access. Field-level permissions enforce who sees what based on role and authorization.

### LMS MCP Server (Cornerstone, Docebo, Absorb, Degreed)

Provides training catalogs, completion records, certification status, skill assessments, and learning paths as MCP tools. Agents can retrieve specific training materials, check compliance deadlines, and access team-level analytics — all scoped to the requesting user's permissions.

### CRM MCP Server (Salesforce, HubSpot, Microsoft Dynamics 365)

Connects sales pipelines, customer records, support tickets, and partner data. Agents can look up deal status, pull onboarding case histories, and surface engagement metrics for enablement 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 organizational role — employee, manager, L&D admin, or CISO.

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

Indexes enterprise documents — policies, handbooks, SOPs, compliance guides — 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/Finance (SAP, Oracle, NetSuite), ticketing (ServiceNow, Jira), communication (Slack, Teams), BI platforms, and more. If your organization 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 authorization has been granted. Our MCP middleware enforces field-level permissions before data ever reaches the agent.

An employee asking about their own training sees their records; a manager querying the same system sees their team; an L&D admin sees department 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, compensation details, and health information 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 SOC 2/SOX/HIPAA compliance reviews, internal security audits, and incident response. Logs are stored in your infrastructure and retained per your organizational policy.

### Sandboxed Execution

MCP servers run in isolated containers within your VPC or on-premises infrastructure. No enterprise 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 employee data.

## Who We Work With

IT & Enterprise Architecture

Learning & Development

Human Resources / People Operations

Compliance & Risk

Department / Business Unit Leads

CISO / Security, Privacy, Legal

## What We Do (Scope at a Glance)

### Systems & Data Mapping

Inventory: HRIS (e.g., Workday, SuccessFactors, BambooHR), LMS (Cornerstone, Docebo, Absorb), CRM (Salesforce, HubSpot), ERP/Finance (SAP, Oracle, NetSuite), Identity (Entra/Okta/Google), Storage (SharePoint/OneDrive/S3/GCS).

Schemas & Contracts: employee profiles, training completions, certifications, competency frameworks, compliance deadlines, knowledge-base metadata. Policy & Governance: PII fields, role scopes, retention rules, redaction maps, authorization flows.

### MCP Server Development

We build production-grade MCP servers for every enterprise 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-Employee Memory Graph: knowledge graph + vector index for contextual retrieval (training content, competencies, compliance deadlines, career goals).

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

### Agent Enablement (Optional)

Employee Coach: citable Q&A grounded in training content, policies, and competency frameworks via MCP. L&D Assistant: learning paths, skills-gap analysis, compliance tracking, program ROI reporting.

Compliance Monitor: policy lookups, certification status, audit roll-ups, "what's changed?" digests. Model Hub: OpenAI, Gemini, Anthropic, Llama, or local/NPU — hot-swappable per policy/cost.

### Workflow Automation (Enterprise Partners)

Proactive nudges (compliance deadlines, certification renewals), onboarding sequences, skills-gap alerts. Content pipelines (ingest → chunk → cite), assessment generation with human review. Approval gates for governance control (human-in-the-loop).

## Deliverables You Keep (No Lock-In)

MCP server source code for every connected enterprise system

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

Data dictionaries, MCP tool schemas, and contract tests

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

ETL/ELT jobs, sync runbooks, and observability dashboards

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

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 enterprise system, memory layer, policy engine

### Pilot & Hardening (2–4 weeks):

limited teams, 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

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

SOC 2/SOX/HIPAA support, least-privilege MCP access, data residency options

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

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

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

## Reference Architecture (MCP-Powered)

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

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

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

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

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

Agent Interfaces → Coach (employee), L&D Assistant (manager), Compliance Monitor (CISO)

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

## Common Use Cases We Deliver

"Single pane of glass" coach with training history, certifications, and competency context — powered by MCP connections to HRIS, LMS, and performance systems

L&D assistant that builds learning paths and triages skills-gap questions (cited answers from MCP-connected training materials)

Compliance monitor that surfaces certification expirations and policy answers with provenance via MCP queries across HR, training, and audit systems

Cross-system automations: onboarding triggers, compliance nudges, certification-renewal alerts — orchestrated through MCP tool chains

Knowledge-base ingestion pipelines with citations and IP safeguards

Multi-agent workflows where specialized agents collaborate through shared MCP servers — one agent handles onboarding, another handles compliance, a third handles skills development — 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

### Enterprise L&D native:

HRIS/LMS nuances, compliance governance, SOC 2/SOX/HIPAA readiness 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 team or business unit

### Ongoing Hours (as needed):

new MCP servers, additional connectors, and workflow builds

## By Sector

This page has a sector selector. Every sector is listed below with its own URL; where a sector's body differs from the one above, it is reproduced in full — that is the content a visitor sees after choosing that sector.

### Higher Education

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

Forward-Deployed Engineers build your MCP-powered campus memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

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

#### What This Is

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

IT & Enterprise Architecture

Institutional Research

Student Success / Advising

Teaching & Learning Centers

Program/Department Leads

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-User 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)

MCP server source code for every connected campus system

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

Data dictionaries, MCP tool schemas, and contract tests

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

ETL/ELT jobs, sync runbooks, and observability dashboards

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

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

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

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

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

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

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

#### Reference Architecture (MCP-Powered)

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

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

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

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

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

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

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

#### Common Use Cases We Deliver

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

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

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

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

Content ingestion pipelines with citations and IP safeguards

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

### Corporate

> Source: https://ibl.ai/service/forward-deployed-engineering/corporate

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

This sector's body is the one shown above.

### Small Business

> Source: https://ibl.ai/service/forward-deployed-engineering/small-business

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

This sector's body is the one shown above.

### K-12

> Source: https://ibl.ai/service/forward-deployed-engineering/k12

Forward-Deployed Engineers build your MCP-powered district memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

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

#### What This Is

ibl.ai's Forward-Deployed Engineers embed with your team to connect SIS, LMS, rostering, assessment, identity, storage, and curriculum 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, teacher assistants, and district admin tools — 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 district 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 rostering tool, MCP gives agents a single, consistent way to read data, call tools, and respect permissions across your entire district stack.

##### Why MCP Matters for K-12 Districts

District IT teams maintain dozens of systems — PowerSchool, Google Classroom, Clever, NWEA MAP, 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 district 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 COPPA/CIPA/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 district system — SIS, LMS, rostering, assessments, 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 PowerSchool, fetch assignments from Google Classroom, check assessment scores from NWEA MAP, and retrieve IEP accommodations — all through MCP — without any custom glue code between systems.

#### MCP Servers We Build

##### SIS MCP Server (PowerSchool, Infinite Campus, Skyward, Aeries)

Exposes enrollment records, grade histories, attendance data, IEP/504 plans, and demographic information as MCP resources. Agents can query real-time student data without direct database access. Field-level FERPA/COPPA controls enforce who sees what based on role and parental consent.

##### LMS MCP Server (Google Classroom, Canvas, Schoology, Brightspace)

Provides assignments, grades, class materials, discussion posts, and rubrics as MCP tools. Agents can retrieve specific lesson materials, check submission deadlines, and access classroom-level analytics — all scoped to the requesting user's permissions and age-appropriate filters.

##### Rostering MCP Server (Clever, ClassLink, OneRoster)

Connects class rosters, teacher assignments, school schedules, and organizational hierarchies. Agents can resolve student-teacher relationships, pull section enrollments, and maintain accurate class groupings across semester changes.

##### Identity & Directory MCP Server (Google Workspace for Education, Microsoft 365, Clever SSO)

Provides role resolution, group memberships, and authentication context. MCP-level RBAC ensures agents only access data appropriate for the authenticated user's role — student, teacher, counselor, or administrator — with parental consent enforcement for minors.

##### Assessment MCP Server (NWEA MAP, Renaissance Star, iReady)

Indexes assessment scores, growth metrics, grade-level proficiency, and diagnostic data — and makes them retrievable via secure MCP queries. Agents can cite specific performance benchmarks to personalize tutoring and flag students for intervention.

##### Custom MCP Servers

We build MCP servers for any system with an API or database: behavior tracking (PBIS platforms), transportation, food services, special education case management, state reporting systems, and more. If your district 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 parental 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; a teacher querying the same system sees their classroom; a principal sees school-wide 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, disability classifications, disciplinary records, and family information are masked or excluded from agent context unless explicitly authorized by policy.

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 COPPA/CIPA/FERPA compliance reviews, state student privacy law audits, and incident response. Logs are stored in your infrastructure and retained per your district policy.

##### Sandboxed Execution

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

#### Who We Work With

District IT & Technology Directors

Curriculum & Instruction Leaders

Student Services / Counseling

Special Education Coordinators

School Principals / Building Leaders

Privacy Officers, Legal, Board

#### What We Do (Scope at a Glance)

##### Systems & Data Mapping

Inventory: SIS (e.g., PowerSchool, Infinite Campus, Skyward), LMS (Google Classroom, Canvas, Schoology), Rostering (Clever, ClassLink, OneRoster), Assessments (NWEA MAP, Renaissance Star, iReady), Identity (Google Workspace/Microsoft 365/Clever SSO), Storage (Google Drive/OneDrive/S3).

Schemas & Contracts: enrollment, rosters, grades, IEP/504 plans, attendance, assessment scores, curriculum metadata. Policy & Governance: COPPA/CIPA/FERPA fields, parental consent, role scopes, retention rules, redaction maps.

##### MCP Server Development

We build production-grade MCP servers for every district 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-Student Memory Graph: knowledge graph + vector index for contextual retrieval (curriculum content, assessment data, accommodations, interventions).

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

##### Agent Enablement (Optional)

Student Tutor: age-appropriate Q&A grounded in curriculum content, standards, and accommodations via MCP. Teacher Assistant: lesson support, differentiation ideas, formative assessment hints, progress report drafts.

Admin Assistant: attendance patterns, intervention tracking, compliance roll-ups, "what's changed?" digests. Model Hub: OpenAI, Gemini, Anthropic, Llama, or local/NPU — hot-swappable per policy/cost.

##### Workflow Automation (Partner Districts)

Proactive nudges (attendance alerts, grade drops), intervention routing, parent communication drafts. Content pipelines (ingest → chunk → cite), formative assessment generation with teacher review. Approval gates for instructional control (human-in-the-loop).

#### Deliverables You Keep (No Lock-In)

MCP server source code for every connected district system

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

Data dictionaries, MCP tool schemas, and contract tests

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

ETL/ELT jobs, sync runbooks, and observability dashboards

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

Security & Compliance packet (threat model, MCP data flows, COPPA/CIPA/FERPA 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 district system, memory layer, policy engine

##### Pilot & Hardening (2–4 weeks):

limited schools or grade levels, 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

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

COPPA/CIPA/FERPA support, state student privacy laws, least-privilege MCP access

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

Age-appropriate content filters, safety controls, and replay evaluation for agents

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

#### Reference Architecture (MCP-Powered)

MCP Server Layer → Typed connectors to SIS/LMS/Rostering/Assessments/Identity/Storage

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

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

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

Policy/Guardrails Engine → RBAC, PII redaction, parental consent, content-safety filters

Agent Interfaces → Tutor (student), Teacher Assistant (teacher), Admin Assistant (district staff)

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

#### Common Use Cases We Deliver

"Single pane of glass" tutor with assignments, accommodations, and reading-level context — powered by MCP connections to SIS, LMS, and assessment systems

Teacher assistant that drafts differentiated activities and triages parent questions (cited answers from MCP-connected curriculum materials)

Admin assistant that surfaces attendance patterns and intervention needs with provenance via MCP queries across enrollment, grades, and behavior systems

Cross-system automations: attendance triggers, grade-drop alerts, parent notification workflows — orchestrated through MCP tool chains

Curriculum content ingestion pipelines with standards alignment and citations

Multi-agent workflows where specialized agents collaborate through shared MCP servers — one agent handles tutoring, another handles intervention tracking, a third handles parent communication — 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

##### K-12 native:

OneRoster, Clever, SIS/LMS nuances, COPPA/CIPA/FERPA 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 school or grade band

##### Ongoing Hours (as needed):

new MCP servers, additional connectors, and workflow builds

### Government

> Source: https://ibl.ai/service/forward-deployed-engineering/government

Forward-Deployed Engineers build your MCP-powered agency memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

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

#### What This Is

ibl.ai's Forward-Deployed Engineers embed with your team to connect HRIS, LMS, case management, ERP, identity, storage, and regulatory systems into a secure, policy-aware memory layer built on the Model Context Protocol (MCP). That memory becomes the backbone for AI agents — workforce coaches, program assistants, and citizen-service tools — 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 agency 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 HRIS, LMS, or case management system, MCP gives agents a single, consistent way to read data, call tools, and respect permissions across your entire agency stack.

##### Why MCP Matters for Government Agencies

Agency IT teams maintain dozens of systems — USA Staffing, DCPDS, Cornerstone for Government, ServiceNow, 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 agency gets portable agents that work across any LLM provider (OpenAI, Anthropic, Google, Meta, or air-gapped 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 federal-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 agency system — HRIS, LMS, case management, 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 workforce coaching agent can query personnel records from DCPDS, fetch training completions from FedVTE, check certification expirations, and retrieve regulatory guidance — all through MCP — without any custom glue code between systems.

#### MCP Servers We Build

##### HRIS MCP Server (USA Staffing, DCPDS, Workday Government)

Exposes personnel records, position classifications, clearance levels, service history, and performance ratings as MCP resources.

Agents can query real-time workforce data without direct database access. Field-level classification controls and need-to-know enforcement determine who sees what based on role and clearance.

##### LMS MCP Server (Cornerstone for Government, Percipio, FedVTE, AgLearn)

Provides training catalogs, completion records, mandatory training status, certification tracking, and competency assessments as MCP tools.

Agents can retrieve specific training materials, check compliance deadlines, and access agency-level analytics — all scoped to the requesting user's permissions and clearance level.

##### Case Management MCP Server (ServiceNow Gov, Salesforce Government Cloud)

Connects citizen service requests, program cases, inter-agency referrals, and compliance tracking. Agents can look up case status, pull program eligibility data, and surface performance metrics for mission reporting.

##### Identity & Directory MCP Server (PIV/CAC via Entra ID, Okta for Government)

Provides role resolution, clearance verification, group memberships, and authentication context. MCP-level RBAC ensures agents only access data appropriate for the authenticated user's role and clearance — analyst, program manager, contracting officer, or agency CISO.

##### Document & Storage MCP Server (GovCloud S3, DISA Storage, SharePoint Gov)

Indexes agency documents — directives, regulations, standard operating procedures, policy memoranda — 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. Classification-aware retrieval ensures CUI/FOUO handling.

##### Custom MCP Servers

We build MCP servers for any system with an API or database: ERP/Finance (SAP S/4HANA Public Sector, Oracle Federal Financials), grants management, procurement systems, GIS platforms, and more. If your agency 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 and clearance they hold, and what need-to-know has been established. Our MCP middleware enforces field-level permissions before data ever reaches the agent.

An employee asking about their own training sees their records; a supervisor querying the same system sees their directorate; an agency admin sees aggregate analytics. Same MCP server, different views.

##### PII Masking and Data Classification

MCP responses pass through a policy engine that redacts sensitive fields based on configurable rules and data classification levels. Social security numbers, clearance details, and CUI-marked content are masked or excluded from agent context unless explicitly authorized by policy.

Every redaction and classification 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 NIST 800-53 compliance reviews, NIST 800-53 control assessments, IG audits, and incident response. Logs are stored in your infrastructure and retained per your agency records schedule.

##### Sandboxed Execution

MCP servers run in isolated containers within your GovCloud VPC, on-premises infrastructure, or IL4/IL5 enclaves. No agency data leaves your environment.

Agents interact with MCP servers over internal networks with mTLS encryption. Air-gapped deployment options ensure LLM inference stays within your security boundary.

#### Who We Work With

Agency CIO / IT & Enterprise Architecture

Chief Learning Officer / Workforce Development

Program Managers / Mission Owners

Contracting & Procurement Officers

Agency CISO / Security & Compliance

Privacy Officers, General Counsel, IG

#### What We Do (Scope at a Glance)

##### Systems & Data Mapping

Inventory: HRIS (e.g., USA Staffing, DCPDS, Workday Government), LMS (Cornerstone for Government, Percipio, FedVTE, AgLearn), Case Management (ServiceNow Gov, Salesforce Government Cloud), ERP/Finance (SAP S/4HANA Public Sector, Oracle Federal Financials), Identity (PIV/CAC via Entra/Okta for Government), Storage (GovCloud S3/DISA/SharePoint Gov).

Schemas & Contracts: personnel records, training completions, certifications, clearance levels, compliance status, regulatory metadata. Policy & Governance: classification fields, need-to-know scopes, retention schedules, redaction maps, authorization flows.

##### MCP Server Development

We build production-grade MCP servers for every agency 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 — in GovCloud, on-prem, or IL4/IL5 enclaves.

##### Memory Layer Engineering

MCP-based Connectors: secure adapters that normalize read/write paths across systems. Per-User Memory Graph: knowledge graph + vector index for contextual retrieval (training content, certifications, compliance deadlines, regulatory guidance).

Guardrails Engine: RBAC, field-level permissions, data classification enforcement, need-to-know controls, audit trails. Sync & Freshness: event bus/CDC, backfills, idempotent jobs, conflict resolution, replay.

##### Agent Enablement (Optional)

Workforce Coach: citable Q&A grounded in training content, regulations, and agency policies via MCP. Program Assistant: compliance tracking, reporting roll-ups, mandate deadline monitoring, status briefings.

Citizen Service Agent: eligibility lookups, case status, program guidance with provenance. Model Hub: OpenAI, Gemini, Anthropic, Llama, or local/NPU — hot-swappable per policy/cost, air-gap compatible.

##### Workflow Automation (Agency Partners)

Proactive nudges (certification renewals, mandate deadlines), case routing, compliance milestones. Content pipelines (ingest → chunk → cite), assessment generation with human review. Approval gates for mission control (human-in-the-loop).

#### Deliverables You Keep (No Lock-In)

MCP server source code for every connected agency system

Connector code & IaC (Terraform/K8s manifests) to deploy in GovCloud/on-prem/IL4/IL5

Data dictionaries, MCP tool schemas, and contract tests

Policy configs (RBAC matrices, classification rules, need-to-know controls, retention/expiry)

ETL/ELT jobs, sync runbooks, and observability dashboards

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

Security & Compliance packet (threat model, MCP data flows, NIST 800-53 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 agency system, memory layer, policy engine

##### Pilot & Hardening (2–4 weeks):

limited directorates or programs, 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

All MCP servers run in your environment (GovCloud, on-prem, or IL4/IL5 enclaves), with your IAM/KMS

NIST 800-53 support, NIST 800-53 controls, least-privilege MCP access

MCP-level data minimization, classification enforcement, need-to-know controls, audit logs

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

mTLS between agents and MCP servers; air-gapped deployment options for sensitive workloads

#### Reference Architecture (MCP-Powered)

MCP Server Layer → Typed connectors to HRIS/LMS/Case-Management/ERP/Identity/Storage

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

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

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

Policy/Guardrails Engine → RBAC, data classification, need-to-know enforcement, rate limits

Agent Interfaces → Coach (workforce), Program Assistant (manager), Citizen Service Agent (public)

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

#### Common Use Cases We Deliver

"Single pane of glass" coach with training history, certifications, and clearance context — powered by MCP connections to HRIS, LMS, and personnel systems

Program assistant that tracks compliance mandates and triages reporting questions (cited answers from MCP-connected regulatory materials)

Citizen service agent that surfaces eligibility and case status with provenance via MCP queries across program, identity, and case management systems

Cross-system automations: certification-renewal triggers, mandate deadline alerts, onboarding workflows — orchestrated through MCP tool chains

Regulatory content ingestion pipelines with citations and classification safeguards

Multi-agent workflows where specialized agents collaborate through shared MCP servers — one agent handles workforce development, another handles compliance, a third handles citizen services — 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

##### Government native:

NIST 800-53 controls, PIV/CAC authentication, GovCloud/IL4/IL5 deployment readiness 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 including air-gapped models; swap and optimize freely

##### 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 program or directorate

##### Ongoing Hours (as needed):

new MCP servers, additional connectors, and workflow builds

### Legal

> Source: https://ibl.ai/service/forward-deployed-engineering/legal

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

This sector's body is the one shown above.

### Financial Services

> Source: https://ibl.ai/service/forward-deployed-engineering/financial-services

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

This sector's body is the one shown above.

### Healthcare

> Source: https://ibl.ai/service/forward-deployed-engineering/healthcare

Forward-Deployed Engineers build your MCP-powered workforce memory layer for AI agents — with your data, in your environment. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

This sector's body is the one shown above.

---

*[View on ibl.ai](https://ibl.ai/service/forward-deployed-engineering)*