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.

AI Transformation

We work with your organization to analyze workflows, build knowledge bases, and deploy AI agents you own and control.

AI Transformation - Own Your Intelligent Workflows

ibl.ai works directly with your organization to analyze existing workflows, build structured knowledge bases, and deploy purpose-built AI agents that run on your infrastructure with your full ownership. No black boxes, no vendor lock-in—agents that operate like skilled hires within your team.

What This Is

AI Transformation is a hands-on engagement where ibl.ai partners with your institution 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, and boundaries. Every agent, knowledge base, and integration runs on your infrastructure. 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 institutional knowledge.

Structured Knowledge IngestionWe work with your subject-matter experts to catalog and ingest institutional knowledge—policy documents, process guides, training materials, historical decisions, and domain expertise. Each knowledge source is tagged with provenance, freshness dates, and authority levels.

Version-Controlled KnowledgeKnowledge bases are version-controlled like code. Every update is tracked, reversible, and auditable. When policies change, knowledge updates flow through your existing governance process before agents surface them.

Multi-Source RetrievalAgents retrieve from multiple knowledge stores simultaneously—your LMS content, HR policies, IT documentation, and departmental procedures—ranked by relevance and authority. No single point of knowledge failure.

Agent Roles - AI Hires with Defined Skills

Role-Based Agent DesignEach agent is designed like a new hire with a specific job description. It has defined responsibilities, access to specific systems, knowledge boundaries, and escalation protocols. An admissions agent knows admissions workflows. A financial aid agent knows financial aid. They do not bleed into each other's domains.

Skills as CapabilitiesAgents are equipped with discrete skills—query the SIS, draft an email, generate a report, schedule a meeting, look up a policy. Skills are composable: agents chain them to handle multi-step workflows that previously required multiple people and manual handoffs.

Escalation ProtocolsEvery agent knows its limits. When a question falls outside its defined competence, it escalates to the right human—not a generic support queue, but the specific person or team responsible. Escalation paths are configured per role, per department.

Performance ReviewsJust like human hires, agents get reviewed. We build evaluation frameworks that measure accuracy, response quality, escalation appropriateness, and user satisfaction. 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 50 questions, manual data entry across systems, routing requests to the right department, and generating routine reports.

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

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

Full Institutional Ownership

Your InfrastructureAgents run on your servers, your cloud accounts, your network. No ibl.ai infrastructure in the critical path. When you scale, you scale your own systems. When you audit, 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 perimeter. Nothing is sent to ibl.ai or third-party services unless you explicitly configure it.

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.

What You Receive

Workflow analysis documentation with automation opportunity map

Knowledge base architecture with read/write separation and ingestion pipelines

Agent role definitions with skills, boundaries, and escalation protocols

Deployed agents on your infrastructure with full source code

Integration adapters for your campus systems (LMS, SIS, CRM, HR)

Monitoring dashboards and agent performance evaluation frameworks

Operations runbooks and training for your team

Engagement Model

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

Knowledge Base Build (2-4 weeks):Ingest institutional knowledge, build retrieval pipelines, establish governance workflows, and validate with subject-matter experts.

Agent Development (4-8 weeks):Design agent roles, implement skills, integrate campus systems, and build evaluation frameworks. Iterative development with your stakeholders.

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