Cloud AI assistants process your PHI on the vendor's infrastructure under a BAA. ibl.ai runs entirely on your own infrastructure — air-gapped or on-premise — so protected health information never leaves your environment.
Cloud-hosted SaaS AI assistants are genuinely capable. They run frontier models, ship polished interfaces, deploy fast, and most vendors now offer HIPAA Business Associate Agreements and SOC 2 attestations with no-training-on-your-data options.
But for health systems with strict data-residency and audit requirements, every one of those tools still processes ePHI on someone else's cloud. You depend on a BAA and the vendor's controls rather than your own perimeter — with no air-gapped, owned alternative.
ibl.ai is built for healthcare organizations that need to own their AI stack. Deploy on your own infrastructure or fully air-gapped so PHI never leaves your environment. Own the code, run any model, and ship autonomous agents — proven across 400+ organizations.
Cloud AI assistants are the general category of cloud-hosted SaaS AI tools that healthcare teams adopt for clinical and administrative support — including offerings such as ChatGPT Enterprise, Microsoft Copilot, and Gemini. They run on the vendor's managed infrastructure, expose a polished chat interface, and most now offer a HIPAA Business Associate Agreement and SOC 2 attestation. They are fast to adopt and broadly familiar to staff, but ePHI is processed on the vendor's cloud rather than inside your own perimeter.
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Where PHI Is Processed | ePHI transits and is processed on the vendor's cloud infrastructure | PHI is processed entirely within your own infrastructure and never leaves it | ibl.ai |
| Data-Residency Control | Limited — residency is defined by the vendor's regions and controls | Complete — you define the perimeter; data stays where your infrastructure sits | ibl.ai |
| Telemetry & Egress | Vendor receives usage telemetry and metadata even with training opt-out | Zero outbound telemetry — no PHI or metadata leaves your environment | ibl.ai |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| On-Premise Deployment | Not available — cloud-hosted on the vendor's infrastructure only | Full on-premise deployment on your own data center or private cloud | ibl.ai |
| Air-Gapped Operation | Not supported — requires connectivity to the vendor's cloud endpoints | Fully supported — runs disconnected with no external dependencies | ibl.ai |
| Time to Deploy | Fast — admin setup in hours with no infrastructure work required | Structured onboarding; production deployment typically within 4–6 weeks | competitor |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Source Code Ownership | None — SaaS subscription; the vendor owns and controls the platform | Full source code delivered to your organization; you own it permanently | ibl.ai |
| Model Flexibility | Model lock-in to that vendor's models — no cross-provider choice | Model-agnostic — Claude, GPT, Gemini, Llama, Mistral, or fine-tuned models | ibl.ai |
| Frontier Model Quality | Direct, day-one access to the vendor's latest frontier models | Routes to any frontier model you license, including the latest releases | Tie |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| BAA Dependency | Requires a signed BAA — compliance leans on the vendor's controls | No third-party BAA needed — PHI never reaches an external processor | ibl.ai |
| Audit Trail Ownership | Audit logs and telemetry are controlled and retained by the vendor | Complete audit trail stored in and owned by your environment | ibl.ai |
| Out-of-Box Compliance Posture | Pre-attested SOC 2 and HIPAA BAA available immediately at signup | Inherits your controls; HIPAA/HITECH posture is yours to evidence | Tie |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Pricing Model | Typically per-seat subscription — costs scale with every user added | Flat-fee licensing — one price regardless of clinical or admin user count | ibl.ai |
| Cost at Scale | Per-seat pricing compounds as adoption spreads across the health system | Flat-fee model delivers roughly 85% lower cost versus per-seat SaaS at scale | ibl.ai |
| Long-Term TCO | Perpetual subscription — costs never decrease and are subject to changes | Source code ownership means no perpetual licensing after initial investment | ibl.ai |
With cloud assistants, every clinical prompt sends ePHI to the vendor's infrastructure, and you depend on their controls plus a BAA. ibl.ai runs entirely on your infrastructure — air-gapped or on-premise — so PHI never leaves the environment you control.
A BAA shifts liability but still puts PHI in someone else's cloud. Because ibl.ai never sends PHI to an external processor, you don't need a third-party BAA for the AI layer — your HIPAA posture is inherited from your own existing controls.
Cloud assistants tie you to one vendor's models. If a different model is better, cheaper, or more compliant for a clinical or coding workflow, you can't switch. ibl.ai is model-agnostic, so you route each workload to the best-fit model.
Cloud assistants are chat interfaces. ibl.ai deploys autonomous agents for clinical support, patient education, medical coding, prior authorization, compliance training, and quality improvement — agents that reason, plan, and execute multi-step workflows.
With SaaS assistants, the vendor controls your audit logs and the platform itself. With ibl.ai, you own the complete source code and every audit record lives in your environment — available for HIPAA, HITECH, and Joint Commission evidence.
Per-seat pricing compounds as nurses, physicians, coders, and administrators adopt AI. ibl.ai's flat-fee licensing means one price regardless of how many users you onboard across the health system.
ibl.ai is deployed on the health system's own infrastructure — on-premise, private cloud, or fully air-gapped. Protected health information is processed where your perimeter sits and never transits an external vendor's cloud, so you don't rely on a third party's controls for PHI handling.
Because PHI never reaches an external processor, there's no need to negotiate or rely on a Business Associate Agreement for the AI layer. Your HIPAA and HITECH posture is inherited directly from the controls you already operate around your own infrastructure.
ibl.ai delivers the full platform codebase to your organization. You inspect it, modify it, extend it, and run it forever — with or without an ongoing vendor relationship. Your clinical AI platform becomes an owned asset, not a rented subscription.
ibl.ai is not tied to any single LLM vendor. Run Claude, GPT, Gemini, Llama, Mistral, or fine-tuned models, and route each clinical, coding, or administrative workload to the best-fit model — swapping providers as the landscape evolves without re-architecting.
ibl.ai ships autonomous agents for clinical support, patient education, medical coding, prior authorization, compliance training, and quality improvement. Agents reason over context, integrate with your systems, and execute multi-step workflows — not just generate chat replies.
Every action taken by every agent is logged at the infrastructure level, stored in your environment, and owned by your compliance team. The complete audit trail supports HIPAA, HITECH, and Joint Commission reporting without depending on a vendor's logging.
ibl.ai integrates with Epic, Cerner/Oracle Health, Allscripts, athenahealth, and Meditech via an MCP and API-first architecture — embedding agents directly into clinical and revenue-cycle systems rather than living in a standalone chat window.
Audit current cloud-assistant usage across clinical and administrative teams — identify use cases, EHR integration points, user groups, and data-residency requirements. Map these to ibl.ai's agent architecture and define your target environment (on-premise, private cloud, or air-gapped).
Provision your target environment and deploy the ibl.ai platform inside your perimeter. Configure your chosen LLM provider(s) and establish SSO, RBAC, and data isolation aligned to your organizational and HIPAA control structure — all within your own infrastructure.
Build priority use cases as autonomous agents — clinical support, patient education, medical coding, prior authorization, compliance training, and quality improvement. Configure MCP and API integrations with Epic, Cerner/Oracle Health, Allscripts, athenahealth, or Meditech.
Deploy to a defined pilot group such as a single department or clinic. Validate agent behavior, EHR integration reliability, audit-trail completeness, and PHI containment. Confirm no data egress and gather structured clinician feedback before broader rollout.
Execute health-system-wide rollout with change management. Decommission cloud-assistant subscriptions where they handled PHI. Establish internal governance using ibl.ai's owned audit trail and admin controls, and transition to ongoing platform ownership.
Cloud assistants process ePHI on vendor infrastructure under a BAA, and per-seat pricing compounds across thousands of clinicians, nurses, and administrators — creating both data-residency exposure and unpredictable budget growth.
On-premise or air-gapped deployment keeps PHI inside the health system's perimeter while flat-fee licensing controls cost across every department and facility.
Smaller practices often lack the leverage to negotiate strong BAAs and still carry full HIPAA liability for any PHI sent to a cloud assistant, while per-seat costs strain limited budgets as adoption grows.
ibl.ai keeps patient data on practice-controlled infrastructure and removes the third-party BAA dependency, with flat-fee pricing that fits smaller-organization economics.
Payers handle member PHI, claims, and prior-authorization data at scale; routing that through a vendor's cloud raises data-residency and auditability concerns alongside compounding per-seat costs for large claims and clinical-review teams.
Autonomous agents for prior authorization and claims review run on owned infrastructure with complete, payer-owned audit trails — keeping member PHI inside the perimeter.
Clinical-trial data, patient records under IRB protocols, and proprietary research IP cannot be exposed to third-party cloud infrastructure, and model lock-in limits the ability to use specialized models for research workflows.
Air-gapped, model-agnostic deployment keeps trial and research data on controlled infrastructure while letting teams route workloads to the best-fit model per task.
Behavioral and telehealth data is among the most sensitive PHI, and sending session content or clinical notes to a vendor's cloud heightens privacy risk under HIPAA and state-level protections beyond what a BAA alone addresses.
ibl.ai processes sensitive session and patient-education content entirely within the provider's environment, with owned audit trails and zero external telemetry.
AMCs blend patient care, research, and education, each with distinct data-governance and IRB requirements that cloud assistants handle uniformly, while audit logs controlled by the vendor complicate institutional compliance oversight.
Owned, on-premise deployment with multi-tenant isolation supports care, research, and teaching workloads under one platform — with audit trails the institution fully controls.
Schedule an assessment to see how ibl.ai can replace your current platform with a solution you fully own and control.