The foundational infrastructure layer that runs, routes, and orchestrates every AI agent, tool, and workflow across your organization — not an app, the platform apps run on.
Most organizations don't have an AI strategy problem. They have an AI infrastructure problem. Individual AI tools get deployed in silos, models get swapped manually, agents have no shared memory, and security is bolted on as an afterthought.
ibl.ai is the AI Operating System — the production-grade infrastructure layer that sits beneath every AI agent, workflow, and tool in your organization. Like Windows or Linux for software, it is the platform that other AI applications run on.
With 1.6M+ users across 400+ organizations — including powering learn.nvidia.com — ibl.ai delivers a complete Agentic OS: agent runtime, model routing, federated memory, skill registry, security, and integrations, all managed as one unified system.
Deploying a single AI chatbot is easy. Deploying AI as organizational infrastructure — reliably, securely, at scale, across dozens of teams and systems — is an entirely different engineering challenge. Without a proper AI OS, every team builds their own fragile stack: hardcoded model calls, no shared memory, duplicated integrations, and zero governance.
The result is AI sprawl. Costs spiral as redundant models run in parallel. Security gaps emerge as credentials and data access go unaudited. Agents can't collaborate because there is no shared runtime. And when a model provider changes an API or pricing, the entire house of cards collapses. Organizations need infrastructure, not more apps.
Teams independently deploy AI tools with no shared infrastructure, creating duplicated integrations, inconsistent behavior, and uncontrolled costs.
No organizational visibility, no cost control, and no ability to enforce governance or security policies across AI usage.Hardcoding to a single LLM provider means any pricing change, outage, or capability gap breaks production systems and forces expensive re-engineering.
Vendor dependency increases risk and prevents organizations from adopting better or cheaper models as the landscape evolves.AI agents built in isolation have no access to organizational data — SIS, LMS, CRM, HRIS — and cannot share context or state with other agents.
Agents give generic, uninformed responses and cannot complete multi-step workflows that require cross-system knowledge.Without a centralized security layer, AI agents execute with unchecked permissions, no audit trails, and no sandboxing — a critical liability in regulated industries.
Exposure to data breaches, compliance violations under HIPAA, FERPA, SOX, or FedRAMP, and inability to pass enterprise security reviews.Individual agents cannot be scheduled, scaled, or coordinated. There is no runtime to manage agent lifecycles, handle failures, or enable inter-agent communication.
Complex, multi-step AI workflows are impossible to build reliably, limiting AI to simple single-turn interactions with no real organizational impact.ibl.ai ships as full source code you own and deploy on your cloud, on-premise, or hybrid environment. No SaaS lock-in. Your data never leaves your infrastructure boundary.
The Integration Bus connects to your existing enterprise systems — LMS, SIS, CRM, HRIS, databases — via MCP servers, REST APIs, webhooks, and LTI. The Memory Layer federates this data with policy-aware access controls.
Connect any LLM — Claude, GPT-4, Gemini, Llama, Mistral — to the Model Router. Define routing rules based on task complexity, cost thresholds, latency requirements, and compliance constraints.
Browse 5,700+ community skills or build custom enterprise skills. The Agent Runtime executes agents with full reasoning loops, tool use, and sandboxed code execution managed by the Orchestrator.
The multi-channel Gateway delivers AI interactions across web, mobile, Slack, Teams, WhatsApp, email, and SMS — with consistent behavior and identity resolution across all surfaces.
The Security Layer enforces RBAC, maintains full audit trails, and manages credentials. The Orchestrator handles agent scaling and scheduling. Multi-tenant isolation ensures hundreds of organizations share infrastructure safely.
Executes autonomous AI agents with full reasoning loops, tool use, and sandboxed code execution. Supports multi-step, multi-agent workflows with state management and failure recovery built in.
Routes every LLM request to the optimal model based on task complexity, cost, latency, and compliance requirements. Model-agnostic — supports Claude, GPT, Gemini, Llama, Mistral, and any future model.
Connects SIS, LMS, CRM, and HRIS systems into a unified, policy-aware data layer. Agents access the right organizational context without violating data boundaries or access controls.
A marketplace of agent capabilities spanning community-contributed and custom enterprise skills. Teams deploy, version, and share skills across the organization without rebuilding from scratch.
Single integration point for delivering AI across web, mobile, Slack, Teams, WhatsApp, email, and SMS. Consistent agent behavior and identity resolution regardless of channel.
RBAC, full audit trails, sandboxed execution, and centralized credential management. Designed for HIPAA, FERPA, SOX, and FedRAMP compliance from the ground up — not retrofitted.
Manages agent lifecycles, scheduling, scaling, and inter-agent communication. Serves hundreds of organizations from a single deployment with strict data isolation between tenants.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Model Flexibility | Hardcoded to one LLM provider — any pricing change or outage breaks production and requires re-engineering. | Model Router intelligently routes to any LLM — Claude, GPT, Gemini, Llama, Mistral — based on cost, task, and compliance rules. |
| Agent Orchestration | Agents are isolated scripts with no shared runtime, no scheduling, and no ability to coordinate on multi-step workflows. | The Orchestrator manages full agent lifecycles, DAG-based workflows, inter-agent communication, and autoscaling in production. |
| Organizational Memory | Each AI tool has its own disconnected context. Agents cannot access SIS, LMS, CRM, or HRIS data in a governed way. | The federated Memory Layer connects all enterprise systems with policy-aware access so every agent has the right context. |
| Security and Compliance | Security is bolted on per-tool. No centralized audit trail, no sandboxing, no RBAC — a liability in regulated industries. | Centralized RBAC, immutable audit trails, sandboxed execution, and HIPAA/FERPA/SOX/FedRAMP compliance by design. |
| Multi-Channel Delivery | Each channel (Slack, web, Teams) requires a separate integration built and maintained by engineering teams. | The Gateway handles all channels — web, mobile, Slack, Teams, WhatsApp, email, SMS — from a single integration point. |
| AI Governance and Cost Control | No visibility into which teams are using which models, at what cost, or whether agents are behaving within policy. | Full observability across every agent, model call, and data access — with cost tracking, usage quotas, and policy enforcement. |
| Infrastructure Ownership | SaaS AI tools mean your data, models, and agent logic live on a vendor's infrastructure you don't control. | Full source code ownership. Deploy on your cloud, on-premise, or hybrid. Your data and agent logic never leave your boundary. |
Hardcoded to one LLM provider — any pricing change or outage breaks production and requires re-engineering.
Model Router intelligently routes to any LLM — Claude, GPT, Gemini, Llama, Mistral — based on cost, task, and compliance rules.
Agents are isolated scripts with no shared runtime, no scheduling, and no ability to coordinate on multi-step workflows.
The Orchestrator manages full agent lifecycles, DAG-based workflows, inter-agent communication, and autoscaling in production.
Each AI tool has its own disconnected context. Agents cannot access SIS, LMS, CRM, or HRIS data in a governed way.
The federated Memory Layer connects all enterprise systems with policy-aware access so every agent has the right context.
Security is bolted on per-tool. No centralized audit trail, no sandboxing, no RBAC — a liability in regulated industries.
Centralized RBAC, immutable audit trails, sandboxed execution, and HIPAA/FERPA/SOX/FedRAMP compliance by design.
Each channel (Slack, web, Teams) requires a separate integration built and maintained by engineering teams.
The Gateway handles all channels — web, mobile, Slack, Teams, WhatsApp, email, SMS — from a single integration point.
No visibility into which teams are using which models, at what cost, or whether agents are behaving within policy.
Full observability across every agent, model call, and data access — with cost tracking, usage quotas, and policy enforcement.
SaaS AI tools mean your data, models, and agent logic live on a vendor's infrastructure you don't control.
Full source code ownership. Deploy on your cloud, on-premise, or hybrid. Your data and agent logic never leave your boundary.
Consistent, personalized AI experiences for every stakeholder without duplicating infrastructure or violating FERPA data boundaries.
Eliminate AI sprawl, enforce security policy across all AI usage, and reduce total cost of ownership through shared model routing and infrastructure.
Deploy AI in regulated clinical environments with confidence — every agent action is logged, permissioned, and auditable.
Meet federal security requirements while delivering modern AI capabilities to agencies and constituents without SaaS data exposure.
Satisfy regulatory requirements while accelerating AI adoption — every model call, agent action, and data access is logged and attributable.
Deliver adaptive, context-aware learning experiences at massive scale with the infrastructure reliability required for mission-critical training programs.
Compress years of AI infrastructure engineering into weeks. Ship production-grade AI features with enterprise security and multi-tenancy from day one.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.