The Short Answer
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Google demonstrated AI running real-time video medical consultations. The clinical capability is real.
But the infrastructure question decides whether it deploys: whose servers process that live patient video?
What did Google demonstrate with real-time video medical AI?
Google showed AI capable of processing live video medical consultations. A cardiologist watching the demo called it "a turning point."
The system processes live video, understands medical context, and assists physicians in real time. It represents a genuine advance in clinical AI capability.
But capability has never been the bottleneck in healthcare AI. According to the Ponemon Institute, 83% of healthcare organizations have experienced at least one data breach. HIPAA violations carry fines up to $2.1M per violation category per year.
The constraint is always the same: who controls the infrastructure processing patient data?
Why does infrastructure matter more than model capability in healthcare AI?
HIPAA compliance, patient data residency, and institutional control over sensitive medical data determine whether impressive demos become deployed reality.
A 2025 AMA survey found that 65% of physicians expressed concern about patient data leaving institutional control when using AI tools. The concern is not theoretical.
When patient video streams through a third-party API, the healthcare system has handed control of its most sensitive data to an external party. That creates compliance risk, audit complexity, and a dependency that regulators will scrutinize.
What is the infrastructure decision that determines deployment?
The turning point is not the model's capability. It is the infrastructure decision.
Whether that real-time patient video streams through a third-party API or runs on infrastructure the healthcare system actually controls — that is what separates a demo from a deployment.
Healthcare systems processing over 2.3 billion patient records annually in the U.S. alone need infrastructure they own and audit. Capability without sovereignty is a demo. Capability with sovereignty is a deployment.
Who owns the AI infrastructure once the system reaches production?
With ibl.ai, you own all the code and the data. The full source runs under a perpetual license on your infrastructure — your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network.
It is model-agnostic by design: run Claude, GPT, Gemini, Llama, Command, or your own fine-tune, and switch providers without rewriting the platform. Billing is usage-based against a cap you set, with no per-seat pricing.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.