Own the source code. Deploy autonomous agents on your infrastructure. Zero PHI exposure, zero vendor dependency, full clinical control.
ibl.ai is a production-grade AI platform — not a pilot, not a proof of concept. It is deployed across 400+ organizations serving 1.6M+ users, delivered as complete source code that your team owns, operates, and controls entirely.
For healthcare and life sciences organizations, that distinction is everything. PHI never leaves your perimeter. Agents run on your infrastructure — on-premise, private cloud, or air-gapped — with no telemetry, no external API calls, and no dependency on ibl.ai remaining in business or in contract.
From clinical training and credentialing to research automation and EHR-integrated decision support, ibl.ai deploys autonomous AI agents that reason, execute, and coordinate across your systems — not chatbots that answer questions, but agents that get work done.
1.6M+ active users across 400+ organizations including NVIDIA, Kaplan, and Syracuse University. This is not experimental software — it is a hardened, enterprise-grade platform with a live track record.
You receive the complete codebase at contract close. No SaaS subscription, no black-box runtime. Your legal, security, and engineering teams can audit, modify, and extend every line.
Run on your on-premise servers, private cloud, or fully air-gapped environment. Zero external dependencies. PHI and clinical data never leave your infrastructure boundary.
Integrate Claude, GPT-4, Gemini, Llama, Mistral, or your own fine-tuned clinical models. Swap models without rebuilding workflows. No lock-in to any single AI provider.
If you never call ibl.ai again after delivery, the platform keeps running. No license checks, no usage metering, no forced upgrades. You own it outright.
Every capability is accessible via RESTful APIs. MCP (Model Context Protocol) connects agents to EHR systems, clinical databases, research repositories, and internal APIs without custom middleware.
Autonomous agents continuously monitor clinician training progress across departments, identify competency gaps in real time, trigger remediation pathways, and generate compliance-ready reports — without manual intervention from L&D teams.
Agents ingest provider credentials, cross-reference primary source verification databases, flag expiring certifications, and route privileging workflows to the correct approvers — executing the full credentialing lifecycle autonomously.
Agents continuously query PubMed, internal trial databases, and proprietary research repositories, synthesize findings against active study protocols, and surface relevant signals to research teams — running 24/7 without analyst hours.
Agents connect directly to EHR APIs, monitor patient records against clinical protocols, identify care gaps or contraindications, and push structured alerts to care teams — acting on live data, not static rule sets.
For biotech and pharma, agents autonomously compile regulatory submission packages, cross-check data against FDA or EMA requirements, flag discrepancies, and maintain audit-ready version histories across the submission lifecycle.
Agents personalize post-discharge education content based on patient diagnosis, literacy level, and language preference, then coordinate follow-up scheduling and monitor engagement — closing care gaps autonomously after discharge.
Traditional chatbots answer questions. Autonomous AI agents take action, reason over context, and deliver measurable outcomes.
ibl.ai deploys autonomous AI agents that go beyond simple Q&A. Our agents reason, plan, and execute multi-step workflows while you retain full code ownership and infrastructure control.
The entire platform runs on your infrastructure with zero external network dependencies. No calls to ibl.ai servers, no cloud routing of PHI, no third-party data processors in the chain. Suitable for the most sensitive clinical and research environments.
No usage data, no query logs, no model inputs or outputs leave your perimeter. ibl.ai has no visibility into your deployment after delivery. Your patient data and clinical workflows remain entirely within your control.
Every autonomous agent action is logged with full fidelity — what data was accessed, what decision was made, what system was called, and what output was produced. Audit logs are stored locally and exportable for compliance review.
Granular RBAC ensures clinicians, researchers, administrators, and IT teams access only the data and agent capabilities appropriate to their role. Multi-tenant architecture enforces strict isolation between departments, facilities, or business units.
Because you own the source code and run the platform on your own infrastructure, PHI sovereignty is structural — not contractual. There is no technical mechanism by which patient data can be exfiltrated to a third party.
Your security team receives the complete codebase and can conduct full static and dynamic analysis before deployment. No black-box components, no obfuscated logic, no hidden dependencies. What you audit is what runs.
Receive the full source code prior to go-live. Your InfoSec, legal, and compliance teams can conduct thorough code review, penetration testing, and dependency audits — satisfying the most rigorous healthcare security requirements.
Adapt the platform to your specific EHR environment, clinical protocols, and institutional workflows. No feature request queues, no waiting for a vendor roadmap. Your engineers make the changes your clinicians need.
Run on-premise in your data center, in your private cloud account, or in a fully air-gapped environment. The deployment model is your decision — not constrained by vendor infrastructure requirements.
The platform operates without any connection to ibl.ai post-delivery. No license validation, no usage metering, no forced updates. If ibl.ai ceased to exist tomorrow, your clinical AI operations continue uninterrupted.
Any modifications, fine-tuned models, or custom agents your team builds on the platform belong to your institution. You are not contributing to a vendor's product — you are building your own.
ibl.ai delivers the complete source code and deploys the platform within your infrastructure — on-premise, private cloud, or air-gapped. Your team receives full access to the codebase, documentation, and architecture. Security review and compliance validation occur at this stage.
ibl.ai engineers work alongside your clinical informatics, IT, and operations teams to configure agents for your specific use cases — EHR integrations, credentialing workflows, training programs, or research pipelines. MCP connectors are established for your data sources and internal APIs.
Your engineers and clinical operations staff take the platform to production and own it entirely from that point forward. ibl.ai provides knowledge transfer, documentation, and optional ongoing support — but the system runs independently of any continued vendor relationship.
Autonomous credentialing agents reduce provider onboarding timelines from 4–6 weeks to under 2 weeks, recovering $7,000–$10,000 per provider per day in delayed revenue.
Automated competency monitoring, gap identification, and remediation routing eliminate the majority of manual L&D administration work across nursing, physician, and allied health staff.
Continuous autonomous literature monitoring and synthesis compresses weeks of analyst time into hours, accelerating clinical trial design and drug discovery timelines.
Automated regulatory submission compilation and compliance cross-checking reduces consultant and staff hours on FDA and EMA submission packages, with measurable reductions in submission errors.
Source code ownership eliminates perpetual per-seat or usage-based SaaS fees. Large health systems and research institutions typically recover platform investment within 12–18 months through licensing cost elimination alone.
The Health Insurance Portability and Accountability Act requires covered entities and business associates to implement technical safeguards protecting PHI, including access controls, audit controls, integrity controls, and transmission security.
Air-gapped deployment ensures PHI never leaves your infrastructure. Zero telemetry eliminates third-party data processor risk. Complete audit trails satisfy HIPAA audit control requirements. Role-based access enforces minimum necessary access standards. Because ibl.ai has no access to your deployment post-delivery, BAA scope is structurally minimized.
For life sciences organizations, FDA regulations governing electronic records and electronic signatures require audit trails, access controls, and system validation for records used in regulated research and manufacturing.
Every agent action is logged with immutable audit records. Source code ownership enables full system validation documentation. Role-based access and authentication controls satisfy Part 11 access requirements. Your team controls the validation lifecycle without vendor dependency.
Health systems and biotech companies increasingly require SOC 2 compliance from technology vendors and internal systems handling sensitive data, covering security, availability, processing integrity, confidentiality, and privacy.
Source code auditability supports your internal SOC 2 audit processes. Comprehensive logging, RBAC, and air-gapped architecture align with SOC 2 security and confidentiality criteria. Your security team can validate controls directly against the codebase.
Accreditation and CMS Conditions of Participation require documented staff competency, training records, and quality improvement processes — all subject to survey and audit.
Autonomous training and credentialing agents generate compliance-ready documentation automatically. Competency records, training completions, and remediation pathways are logged and exportable in formats suitable for Joint Commission surveys and CMS reporting.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.