The production-grade OS for AI agents — Kubernetes-native, model-agnostic, and built to scale across your entire organization from day one.
Most organizations don't have an AI problem — they have an infrastructure problem. Deploying AI at scale requires more than a model API key. It demands a complete, hardened infrastructure layer that can orchestrate agents, manage models, enforce security, and integrate with every system in your stack.
ibl.ai is that infrastructure layer. Like Linux for servers or Kubernetes for containers, ibl.ai is the operating system that your AI agents run on — not a single app, but the platform that all your AI applications are built upon.
With 1.6M+ users across 400+ organizations — including powering learn.nvidia.com — ibl.ai delivers Kubernetes-native, Docker-containerized, Terraform-provisioned AI infrastructure that is production-ready from day one.
Enterprises attempting to deploy AI at scale quickly discover that stitching together individual LLM APIs, custom agent scripts, and ad-hoc integrations creates a fragile, unmanageable mess. There is no unified runtime, no policy enforcement, no audit trail, and no way to scale — just technical debt accumulating faster than business value.
Without a proper AI infrastructure layer, every team reinvents the wheel. Security gaps emerge between systems. Models can't be swapped without rewriting applications. Costs spiral as there is no intelligent routing or resource management. What should be a strategic platform becomes a collection of disconnected experiments that never reach production.
Teams build one-off agent scripts with no shared execution environment, no reasoning loop standardization, and no way to reuse logic across projects.
Duplicated engineering effort, inconsistent agent behavior, and zero visibility into what agents are actually doing in production.Applications hardcoded to a single LLM provider cannot adapt to new models, pricing changes, or task-specific routing without full rewrites.
Vendor dependency, inflated inference costs, and inability to leverage best-in-class models as the landscape evolves.Without a centralized security layer, AI agents access sensitive data without RBAC, leave no audit trails, and execute code in unsandboxed environments.
Regulatory exposure under HIPAA, FERPA, SOX, and FedRAMP — and the real risk of data leakage across tenant boundaries.Point-to-point integrations between AI tools and enterprise systems — SIS, LMS, CRM, HRIS — break under load and require constant maintenance.
Engineering teams spend more time maintaining glue code than building AI capabilities, slowing time-to-value to a crawl.Homegrown AI deployments lack health monitoring, auto-scaling, rolling update pipelines, and blue-green deployment support.
Outages during peak load, no safe path to update models or agents, and no SLA guarantees for production AI workloads.Deploy the entire ibl.ai stack onto your cloud or on-premise environment using battle-tested Terraform modules. Full source code ownership means your infrastructure, your rules — no black-box SaaS dependencies.
Every ibl.ai component — Agent Runtime, Model Router, Memory Layer, Orchestrator — runs as Docker containers managed by Kubernetes. Auto-scaling, pod health checks, and namespace isolation are built in from the start.
The Integration Bus connects your SIS, LMS, CRM, HRIS, and any REST or webhook endpoint through MCP servers and LTI adapters. The Memory Layer federates this data with policy-aware access controls so agents only see what they're authorized to see.
Pull from 5,700+ community skills in the Skill Registry or publish custom enterprise skills. The Agent Runtime executes autonomous agents with full reasoning loops, tool use, and sandboxed code execution — all managed by the Orchestrator.
The Model Router analyzes each request and routes it to the optimal LLM — Claude, GPT-4, Gemini, Llama, Mistral, or your private model — based on task complexity, latency requirements, and cost targets. No application rewrites needed to swap models.
Built-in health monitoring, rolling updates, and blue-green deployment pipelines ensure zero-downtime operations. The Security Layer maintains RBAC enforcement, credential management, and full audit trails across every agent interaction.
Horizontal pod autoscaling and cluster autoprovisioning ensure your AI workloads scale to meet demand — from 10 users to 1.6M — without manual intervention or over-provisioning.
Route requests across any LLM — GPT-4, Claude, Gemini, Llama, Mistral — based on real-time cost, latency, and capability scoring. Swap models without touching application code.
The Agent Runtime executes autonomous agents in isolated, resource-constrained environments with full reasoning loop support, tool use, and code execution — safely and auditably.
A policy-aware data federation layer connects SIS, LMS, CRM, and HRIS systems. Agents access contextually relevant data without violating tenant boundaries or compliance policies.
Ship updates to agents, models, and skills with zero downtime using built-in blue-green and canary deployment support. Roll back instantly if health checks fail.
Serve hundreds of organizations from a single infrastructure deployment with cryptographic tenant isolation, namespace separation, and per-tenant audit trails — all without sacrificing performance.
RBAC, credential vaulting, sandboxed execution, and immutable audit logs are built into the infrastructure layer — not bolted on. Designed to satisfy HIPAA, FERPA, SOX, and FedRAMP requirements.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Deployment Model | Ad-hoc scripts and API calls deployed manually with no repeatable provisioning process | Terraform IaC + Helm charts provision the full stack in hours with version-controlled, repeatable infrastructure |
| Scaling | Fixed-capacity servers that over-provision for peak load or fail under unexpected traffic spikes | Kubernetes horizontal pod autoscaling dynamically matches capacity to demand — proven at 1.6M+ user scale |
| Model Flexibility | Applications hardcoded to one LLM provider — any model change requires significant engineering rework | Model Router intelligently routes to any LLM (GPT-4, Claude, Gemini, Llama, Mistral) with zero application changes |
| Security and Compliance | Security bolted on after the fact — inconsistent RBAC, no audit trails, unsandboxed execution, compliance gaps | RBAC, sandboxing, credential vaulting, and immutable audit logs are architectural — HIPAA, FERPA, SOX, FedRAMP by design |
| Operational Visibility | No unified monitoring — teams discover failures from user complaints, with no tracing or cost attribution | Prometheus metrics, OpenTelemetry tracing, Grafana dashboards, and per-tenant cost reporting out of the box |
| Deployment Safety | Big-bang deployments with manual rollback procedures — every update is a production risk event | Blue-green and canary pipelines with automated health gates enable zero-downtime updates and instant rollback |
| Time to Production | 12-18 months to build a custom agent platform with comparable capabilities — if the team has the expertise | Production-ready AI infrastructure from day one — full source code ownership, deploy on your infrastructure in weeks |
Ad-hoc scripts and API calls deployed manually with no repeatable provisioning process
Terraform IaC + Helm charts provision the full stack in hours with version-controlled, repeatable infrastructure
Fixed-capacity servers that over-provision for peak load or fail under unexpected traffic spikes
Kubernetes horizontal pod autoscaling dynamically matches capacity to demand — proven at 1.6M+ user scale
Applications hardcoded to one LLM provider — any model change requires significant engineering rework
Model Router intelligently routes to any LLM (GPT-4, Claude, Gemini, Llama, Mistral) with zero application changes
Security bolted on after the fact — inconsistent RBAC, no audit trails, unsandboxed execution, compliance gaps
RBAC, sandboxing, credential vaulting, and immutable audit logs are architectural — HIPAA, FERPA, SOX, FedRAMP by design
No unified monitoring — teams discover failures from user complaints, with no tracing or cost attribution
Prometheus metrics, OpenTelemetry tracing, Grafana dashboards, and per-tenant cost reporting out of the box
Big-bang deployments with manual rollback procedures — every update is a production risk event
Blue-green and canary pipelines with automated health gates enable zero-downtime updates and instant rollback
12-18 months to build a custom agent platform with comparable capabilities — if the team has the expertise
Production-ready AI infrastructure from day one — full source code ownership, deploy on your infrastructure in weeks
Eliminates shadow AI sprawl, reduces infrastructure costs by 60%+, and gives platform teams a single control plane for all AI workloads.
Meets federal security mandates without sacrificing AI capability — enabling agencies to deploy autonomous agents on sensitive workloads.
Enables clinical AI automation without compliance risk — audit trails and data isolation satisfy HIPAA Security Rule requirements by design.
Supports internal audit requirements and regulatory examinations with complete, tamper-proof records of every AI decision and data access.
Proven at 1.6M+ user scale with FERPA-compliant data handling, LTI integration for LMS systems, and per-institution tenant isolation.
Reduces compliance audit preparation time by providing pre-built evidence artifacts: audit logs, access controls, and data lineage documentation.
Compresses 12-18 months of infrastructure engineering into weeks, letting product teams focus on differentiated AI experiences rather than plumbing.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.