AI Data Engineering

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.