Your AI, Under Your Control
Sovereign AI infrastructure to build your agents and apps. You own all the code and the data, unified across your ecosystem, for privacy, security and control. No need to choose build vs. buy – you get both.
How ibl.ai compares
AI Sovereignty at Syracuse University
Full code ownership, deployed on Syracuse’s own GCP, deep SSO/RBAC integration, and 85% lower costs vs. per-seat SaaS.
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The objections
The objections are right. Here is what we did about them.
We are as pro-AI as anyone. The critics still have a point on price, on data, and on capability.
“AI is insanely expensive.”
Only because you are buying a commodity at monopoly prices. Any model a few months behind the frontier is interchangeable with the next one.
A model-agnostic platform lets you pay usage rates, move to whichever model is good enough, and cap the budget yourself.
“Who exposes our financial data to an AI company?”
Nobody should. The agents, the connectors and the data run behind your firewall.
Nothing on the public internet can reach them, not because a contract promises it, but because the firewall prevents it.
“Answering questions over my PDFs is not an agent.”
Agreed. Each ibl.ai agent gets a sandboxed server where it writes and runs scripts against gigabytes of data that will never fit in a context window.
How agents work
Agents with a server attached, not a chat window.
A chatbot answers from what fits in a context window. An ibl.ai agent writes and runs code to get the answer.
Operational data is measured in gigabytes; a context window is not. Each agent runs in its own sandbox where it can write a script, query a file system or database, call an API, and check its own work before answering.
That is what turns asking your PDFs into automation, financial intelligence and multi-system workflows.
Gigabyte files
Reads a four-gigabyte budget export by writing the script on the spot, not by stuffing it into a prompt.
Scheduled automation
Runs a scheduled job over data that never leaves your network, with zero retention by any model vendor.
Multi-system runs
Reads one system, writes to a second, sends the report to a third person, in one run.
Ownership and exit
Who owns what, and what happens if you leave?
Behind your firewall, by construction.
Agents earn their keep by reaching your sensitive systems: finance, HR, customer records, operations. So the whole runtime deploys inside your perimeter, on your cloud, on-premise or air-gapped, with local models if you want them.
Every query is logged and auditable, so when a leader's number disagrees with a report, you can trace how the agent got it.
Fire us and keep everything.
On ibl.ai you own all the code and the data, and the platform runs on infrastructure you control. There is no ten-year contract and no exit fee.
End the relationship tomorrow and the platform keeps running with your team. Ask whoever else you are evaluating whether they will write the same terms.
ibl.ai — your AI, under your control
ibl.ai is an AI operating system for building agents and apps where you own all of the code and data — for privacy, security, and control.
You don’t have to choose between build and buy; the ibl.ai platform gives you both. It is model-agnostic, so you are never locked into a single AI provider.
Deploy ibl.ai in our cloud, in your own VPC, on-premise, or fully air-gapped — as managed SaaS or entirely on your own servers, so regulated teams keep data in their environment.
ibl.ai is family-owned and operated from New York, NY.
More than 1.6M users across 400+ organizations use ibl.ai, including Syracuse University, SUNY, Google, NVIDIA, and Kaplan.
Syracuse University runs ibl.ai on its own Google Cloud with full code ownership, deep SSO and RBAC integration, and roughly 85% lower cost than per-seat SaaS.
Products
- Agentic OS — the core platform for building AI agents and apps
- Agentic Vibe — build AI apps for web, mobile, and desktop
- Agentic LMS — AI-native learning management
- Agentic Course — AI course and content creation
- Agentic Video — AI avatar and video production
- Agentic Wallet — verifiable skills and credentials
Solutions by sector
- AI for Higher Education
- AI for K-12
- AI for Enterprise
- AI for Government
- AI for Small Business
- AI for Legal
- AI for Financial Services
- AI for Medical & Healthcare
Explore
- Build vs. Buy
- Self-Hosted & Private AI
- Flat-rate pricing
- Customer case studies
- Blog
- Documentation
- Contact the team
Data unification & ontology
AI agents are only as good as the data they reason over. ibl.ai unifies your existing systems into one knowledge layer you own and self-host — over MCP, with no data extraction.
- AI Data Unification — the on-premise knowledge layer
- The ontology framework
- Why AI agents fail without an ontology: unify data first
- Ontology vs RAG for AI agents
- Vector database vs knowledge graph for AI agents
- Why MCP is the data layer for AI agents
- Enterprise AI data integration: the ontology-first approach
Start with a pilot
Bring us a hard problem. Keep the code.
Not a chatbot demo. Something that has to read several systems, write to one, and report to a third.
Pick a problem a general chatbot cannot solve:
- financial intelligence across a budget system
- an automation that reconciles two systems of record
- a scheduled workflow over gigabytes of operational data
We scope a fixed pilot on your real data, behind your firewall. Every line of code written in the pilot is yours, whether or not you continue.
Pilots from $15KThen read our case study.