What makes AI Transformation different from buying an AI chatbot?

AI Transformation is not a product—it is a consulting engagement. We analyze your actual workflows, build knowledge bases from your institutional content, and deploy agents designed for your specific processes. Generic chatbots give generic answers. Our agents know your policies, your systems, and your escalation paths.

What is the knowledge base read/write separation approach?

Agents that answer questions read from curated, validated knowledge stores. Agents that update knowledge write through approval workflows with human review. This prevents hallucinated or incorrect content from contaminating your institutional knowledge base. Updates go through your governance process before agents surface them.

How are agent roles defined?

Each agent is designed like a new hire with a specific job description—defined responsibilities, access to specific systems, knowledge boundaries, and escalation protocols. An admissions agent handles admissions workflows. A financial aid agent handles financial aid. They do not bleed into each other's domains. Skills are composable: agents chain discrete capabilities to handle multi-step workflows.

How long does an AI Transformation engagement take?

A typical engagement runs 10-18 weeks: 2-3 weeks for workflow analysis, 2-4 weeks for knowledge base construction, 4-8 weeks for agent development and integration, and 2-3 weeks for deployment and training. Phased rollout starts with staff-facing agents before expanding to student-facing use cases.

What do we own after the engagement ends?

Everything. All agent code, knowledge bases, integration adapters, infrastructure configurations, monitoring dashboards, and documentation belong to your institution. Your team receives comprehensive training to build new agents, update knowledge bases, and manage the system independently. No ongoing dependency.

Interested in an on-premise deployment or AI transformation? Call or text 📞 (571) 293-0242

AI Transformation

We work with your agency to analyze workflows, build classified knowledge bases, and deploy federal security-ready AI agents you own and control.

Sector:

Your Code. Your Data.Full ownership, zero vendor lock-in

Any LLM, Your ChoiceWorks with all major providers

85% Lower CostSignificantly cheaper at scale

AI TransformationPre-existing and custom agents

AI Transformation - Own Your Agency's Intelligent Workflows

ibl.ai works directly with your agency to analyze existing workflows, build structured knowledge bases, and deploy purpose-built AI agents that run on your infrastructure—GovCloud, on-prem, or IL4/IL5 enclaves—with your full ownership and federal compliance controls. No black boxes, no vendor lock-in—agents that operate like cleared hires within your team.

What This Is

AI Transformation is a hands-on engagement where ibl.ai partners with your agency to understand how work actually gets done—then builds AI agents tailored to your specific processes. We do not sell generic chatbots. We analyze your workflows, document your institutional knowledge, and create agents with defined roles, skills, classification boundaries, and escalation protocols. Every agent, knowledge base, and integration runs on your infrastructure within your ATO boundary. You own the code, the data, and the configurations. When the engagement ends, your team operates and extends everything independently.

Knowledge Base Architecture

Read/Write SeparationYour knowledge base is architected with strict read/write separation. Agents that answer questions read from curated, validated knowledge stores. Agents that update knowledge write through approval workflows with human review. This separation prevents hallucinated content from contaminating your agency's authoritative knowledge.

Structured Knowledge IngestionWe work with your subject-matter experts to catalog and ingest agency knowledge—policy directives, regulatory guidance, SOPs, training materials, and institutional decisions. Each knowledge source is tagged with provenance, classification level, freshness dates, and authority levels.

Version-Controlled KnowledgeKnowledge bases are version-controlled like code. Every update is tracked, reversible, and auditable. When directives change, OMB guidance updates, or new regulations take effect, knowledge updates flow through your existing governance process before agents surface them.

Multi-Source RetrievalAgents retrieve from multiple knowledge stores simultaneously—your training content, HR policies, regulatory databases, and program documentation—ranked by relevance, authority, and classification level. Need-to-know enforcement at every layer.

Agent Roles - AI Hires with Defined Skills

Role-Based Agent DesignEach agent is designed like a new hire with a specific position description. It has defined responsibilities, access to specific systems, knowledge boundaries, clearance-aware access controls, and escalation protocols. An acquisition agent knows procurement workflows. A HR agent knows OPM qualification standards. They do not bleed into each other's domains.

Skills as CapabilitiesAgents are equipped with discrete skills—query DCPDS, draft a correspondence, generate a status report, schedule a review, look up a regulation. Skills are composable: agents chain them to handle multi-step workflows that previously required multiple staff and manual handoffs.

Escalation ProtocolsEvery agent knows its limits. When a question falls outside its defined competence—especially anything involving classification, legal authority, or congressional sensitivity—it escalates to the right human. Escalation paths are configured per role, per office, per classification level.

Performance ReviewsJust like human hires, agents get reviewed. We build evaluation frameworks that measure accuracy, response quality, escalation appropriateness, and mission impact. Underperforming agents get retrained or restructured.

Workflow Analysis Process

Process MappingWe embed with your teams to map how work actually flows—not how org charts say it should. Every handoff, approval step, data lookup, and decision point is documented. This reveals automation opportunities that generic AI tools miss.

Bottleneck IdentificationWe identify where staff spend time on repetitive, rule-based tasks that agents can handle. Common findings: answering the same policy questions, manual data entry across HRIS and financial systems, routing FOIA requests, and generating routine compliance reports.

Agent Opportunity ScoringEach potential automation is scored on impact (time saved, error reduction), feasibility (data availability, system access), and risk (classification sensitivity, NIST 800-53 compliance requirements). High-impact, low-risk workflows deploy first.

Phased Rollout PlanningWe plan deployment in phases—starting with internal-facing agents that assist program staff, then expanding to interagency and citizen-facing agents as confidence builds. Each phase has defined success criteria and security reviews before proceeding.

Full Agency Ownership

Your InfrastructureAgents run on your GovCloud accounts, your on-prem servers, or your IL4/IL5 enclaves. No ibl.ai infrastructure in the critical path. When you scale, you scale your own systems. When you audit for NIST 800-53 or IG review, you audit your own logs.

Your CodeEvery agent definition, skill implementation, knowledge pipeline, and integration adapter is delivered as source code in your repositories. Your engineering team can modify, extend, or replace any component.

Your DataKnowledge bases, conversation logs, analytics, and operational data stay entirely within your ATO boundary. Nothing is sent to ibl.ai or third-party services unless you explicitly configure it. Classification-aware data handling throughout.

Your Team's CapabilityWe do not create dependency. Knowledge transfer is built into every engagement. Your team learns to build new agents, update knowledge bases, and manage the system independently—within your security posture.

What You Receive

Workflow analysis documentation with automation opportunity map

Knowledge base architecture with read/write separation, classification tagging, and ingestion pipelines

Agent role definitions with skills, boundaries, clearance-aware access, and escalation protocols

Deployed agents on your infrastructure with full source code within your ATO boundary

Integration adapters for your agency systems (HRIS, financial, case management, grants)

Monitoring dashboards and agent performance evaluation frameworks

Operations runbooks, SSP documentation contributions, and training for your team

Engagement Model

Discovery & Workflow Analysis (2-3 weeks):Embed with your teams, map processes across program offices, identify agent opportunities, and define the transformation roadmap within your ATO boundary.

Knowledge Base Build (2-4 weeks):Ingest agency knowledge from policy directives, SOPs, and regulatory guidance. Build retrieval pipelines with classification-aware access, establish governance workflows, and validate with subject-matter experts.

Agent Development (4-8 weeks):Design agent roles, implement skills, integrate agency systems (USA Staffing, FPDS, ServiceNow Gov), and build evaluation frameworks. Iterative development with your stakeholders and ISSO review.

Deployment & Training (2-3 weeks):Phased rollout starting with internal-facing agents. Comprehensive knowledge transfer and ATO documentation support so your team owns ongoing operations and development.

Get Started

Workflow Assessment:Free 30-minute session to discuss your agency workflows and identify high-impact automation opportunities.

Pilot Program:Transform one office's workflows—acquisition, HR, or citizen services—with 2-3 agents to demonstrate value before broader investment.

Agency Transformation:Full-scale AI transformation across program offices with comprehensive knowledge bases, agent teams, and ATO-ready documentation.