Healthcare Private LLMs
Compliant AI for clinical and operational workflows.
Built for the controls regulators expect.
Private LLMs run inside your existing security perimeter — so the model fits inside the audit, residency, and access controls your compliance team already operates.
- Data residency + retention you control
- Audit log on every prompt, retrieval, and response
- Role-based + document-level access enforcement
At LLM.co, we build private, domain-specific language models designed exclusively for healthcare environments. Whether deployed on-prem or in your VPC, our solutions help automate clinical documentation, support medical research, and streamline operations—without ever risking patient data privacy.
Domain-Specific Artificial Intelligence (AI) Solutions
LLM.co provides secure, domain-specific large language models built for healthcare environments. From clinical documentation and patient intake to compliance and analytics, our platform enables providers and organizations to use the power of AI—without compromising on privacy, accuracy, or regulatory compliance.
Why Healthcare Organizations Trust LLM.co
Private Deployments for Full Data Control: Unlike public AI tools that process data in third-party servers, our healthcare LLMs can be deployed on-premise or in your virtual private cloud.
Tuned for Medical Language and Terminology: Our models are pre-trained on medical literature, clinical documentation, patient records (when permitted), and diagnostic language.
HIPAA, HITECH, and GDPR Compliance: All interactions are logged, encrypted, and auditable. Data stays under your control, and no PHI is ever used for training or inference outside of your secure environment.
Bring Your Own Data (BYOD): Feed the system with de-identified patient records, policies, clinical trial reports, standard operating procedures, and more.
Custom Workflows for Clinical and Administrative Use Cases: Our system adapts to your environment. It works with your staff, not against them, and integrates seamlessly into existing processes.
Grounded Outputs, No Hallucinations: LLM.co models use retrieval-augmented generation (RAG) to ensure every answer is backed by real, verifiable documents.
Key Use Cases
Clinical Documentation and Note Drafting
Patient Intake and Communication
Medical Knowledge Search and Decision Support
Claims Review and Coding Assistance
Research and Clinical Trial Support
Your Data, Your Model
With LLM.co, healthcare organizations never have to upload sensitive information to public clouds or third-party APIs. We enable custom model deployments where the data, model weights, and vector storage remain entirely within your infrastructure.
Query internal SOPs, de-identified case files, or physician notes
Build organization-specific AI workflows for documentation, analysis, and research
Maintain full ownership and control over data governance
Designed for Healthcare Compliance and Security
LLM.co deployments are designed from the ground up to support healthcare's strict regulatory needs.
HIPAA-compliant architecture
Support for air-gapped environments
Role-based access controls and usage auditing
Full encryption of data in transit and at rest
Model Context Protocol (MCP) to ensure explainability and traceability
Who Uses LLM.co for Healthcare
Health systems and hospital networks implementing AI for clinical efficiency
Research institutions and clinical trial teams accelerating insight extraction
Payers and insurance companies improving claims review and risk stratification
Digital health startups developing AI-first patient tools
Public health organizations supporting care coordination and analysis
Prior Authorization and Medical Coding at Scale
Prior authorization is one of the most document-intensive, time-consuming workflows in healthcare administration. A private LLM deployed inside your environment can parse unstructured clinical notes, cross-reference payer criteria, and draft authorization narratives for clinician review—without routing a single PHI token through a third-party API. Because the model runs entirely within your infrastructure, every inference stays inside your data privacy boundary and satisfies BAA obligations by design.
Medical coding presents a similar opportunity. ICD-10, CPT, and HCPCS assignment from free-text encounter notes demands both clinical vocabulary depth and institutional policy awareness. An on-prem LLM fine-tuned on your charge-master and coding guidelines surfaces candidate codes with rationale, shortening coder review cycles while maintaining a complete audit trail required for payer disputes and compliance audits.
EHR-Integrated Clinical Documentation and Patient Communications
Ambient and post-encounter documentation is the highest-volume AI use case across health systems today. A private LLM for healthcare connects directly to your EHR or EMR via secure APIs, drafting SOAP notes, discharge summaries, and referral letters grounded in the actual visit record. Because the model queries your internal documents through retrieval-augmented generation, outputs cite real source material rather than hallucinating clinical detail—a non-negotiable requirement when documentation feeds billing, quality reporting, and care continuity.
Patient-facing communications—appointment reminders, care-plan explanations, medication instructions—can be drafted and personalized at scale without exposing PHI to consumer AI services. Role-based access controls limit which staff can trigger generation for which patient cohorts, and every interaction is logged for HITECH-compliant audit purposes. The result is reduced administrative burden on care teams while keeping all sensitive data within your governance perimeter.
Agentic Workflows for Revenue Cycle and Clinical Operations
Beyond single-turn generation, healthcare organizations are deploying agentic AI pipelines that chain multiple reasoning steps: ingest a referral, verify eligibility, draft a prior-auth letter, and flag missing clinical evidence—all without human hand-offs at each stage. These workflows operate entirely inside your VPC or on-prem environment, so PHI never crosses a trust boundary even as the agent orchestrates across internal systems.
Revenue cycle teams benefit from automation of denial management, claim scrubbing, and remittance reconciliation. Each agent action is auditable, explainable, and scoped to least-privilege data access—critical for organizations subject to CMS, OIG, and state-level oversight. LLM.co's architecture supports air-gapped deployment for environments where no external network egress is permitted.
Common questions
01What does a Business Associate Agreement (BAA) cover for a private LLM deployment?
A BAA is a legally required contract under HIPAA that establishes a vendor's obligations to protect PHI on your behalf. With a private LLM deployed on-prem or in your VPC, LLM.co executes a BAA covering model inference, data storage, and any vector indexes built from your clinical data. Because PHI never leaves your controlled environment, your compliance exposure is substantially narrower than with cloud-hosted AI services.
02How does retrieval-augmented generation (RAG) work with protected health information?
In a HIPAA-compliant RAG setup, your clinical documents—policies, de-identified case files, formularies, EHR excerpts—are chunked, embedded, and stored in a vector database that sits entirely within your infrastructure. At query time, the LLM retrieves relevant passages locally and grounds its response in those documents. No PHI is transmitted to external embedding APIs or inference endpoints, and retrieval logs are captured for audit purposes.
03Can a private LLM integrate with our existing EHR or EMR system?
Yes. LLM.co deployments expose secure internal APIs that connect to EHR platforms via HL7 FHIR, proprietary SDKs, or direct database integrations, depending on your vendor and architecture. The integration is scoped to read and write permissions you define, and all data flow is encrypted in transit. Clinical documentation drafted by the model is returned as a draft for clinician review before it is committed to the patient record.
04What governance controls apply to model outputs in a regulated healthcare environment?
Every inference passes through configurable output filters, role-based access policies, and a complete interaction log retained according to your data retention schedule. Outputs are traceable to source documents via citation, supporting both internal quality review and external audits. Administrators can restrict model use by department, workflow type, or data classification, and usage reports can be exported in formats compatible with compliance reporting. See our governance capabilities for detail.
05Is an air-gapped deployment possible for environments with strict network egress controls?
Yes. LLM.co supports fully air-gapped on-premises deployments where neither model weights nor inference traffic touches any external network. This is common in government-affiliated health systems, VA facilities, and high-security research environments. See our on-prem and cybersecurity pages for architecture details on air-gapped and private-cloud configurations.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
Book a Call