Platform Architecture

The logical view of the platform, top to bottom — from the agents people interact with down to the systems your data already lives in. Every layer is independently swappable; click any layer to see what it is, why it matters, and how it works.

ibl.ai Platform

Agents

Autonomous task execution across the platform

ibl.ai Switchboard Layer

Routing

Request orchestration & MCP

Security & Guardrails

Role-based access control

LLMs (Model Agnostic)

GPT, Claude, Gemini, open-source — swappable

Ontological Layer

Structured knowledge, semantic relationships

Data Lake

Unified institutional data store

Apps & Data Silos

SIS, LMS, CRM, third-party systems

How the ibl.ai platform is built

ibl.ai is an Agentic AI Operating System organizations deploy on their own infrastructure. The stack spans a data and integration layer, a per-user memory layer, an agent orchestration layer, and model-agnostic LLM routing — so you can swap providers without rebuilding.

Agents connect to your existing systems — LMS, SIS, CRM, and ERP — through MCP-based interoperability. Each agent has defined roles, access controls, and review cycles, and every interaction runs through the platform’s safety and moderation layer.

The connection point is the ontology — a knowledge layer that runs inside your network and exposes the systems you already operate, from databases and data warehouses to line-of-business SaaS, as one role-scoped MCP server queried in place, with no data extraction. It is reachable from the agent sandboxes and the application servers, and from nowhere else on the internet.

Deploy in the cloud, on-premise, or fully air-gapped. You keep full source code, full data control, and the freedom to run any LLM — OpenAI, Anthropic, Google, Meta, or self-hosted open-weight models.

Explore the platform