AI-Native Health Architecture
The AI-native architecture powering the next generation of clinical intelligence.
Explore how Model Context Protocol, HL7 FHIR schemas, graph bio-models, and zero-trust security construct our medical engine.
Architectural Pillars
AI Agents
Autonomous multi-agent swarms executing parallel clinical triage, drug safety checks, and diagnostic recommendations.
Foundation Models & LLMs
Domain-adapted medical LLMs fine-tuned on PubMed, pharmacology, and HL7 FHIR clinical datasets for zero-hallucination outputs.
Model Context Protocol (MCP)
Standardized open protocol enabling LLM agents to securely query EMR databases and FHIR servers with granular scopes.
HL7 FHIR Integration
Native FHIR R4 and R5 schemas guaranteeing instant interoperability with Epic, Cerner, and global hospital repositories.
Knowledge Graphs
Structured graph databases mapping 10B+ connections across genes, proteins, diseases, and chemical interactions.
LLM Wiki (Google OKF)
Organized medical knowledge representations aligning open knowledge schemas (Google OKF) with dynamic EMR updates.
Retrieval Augmented Generation (RAG)
Hybrid vector + graph search querying clinical guidelines and patient charts with source citation back-references.
Healthcare Standards
Strict adherence to ICD-10/11 coding, CPT, LOINC, and RxNorm medical terminologies.
Enterprise Security
HIPAA database isolation, SOC-2 Type II controls, TLS 1.3 encryption tunnels, and zero-knowledge data masking.
Cloud Architecture
Zero-trust VPC multi-tenant topology with sub-10ms edge latency and failover availability across regions.
Agentic RAG & MCP Context Flow
How user prompts interact with Knowledge Graphs, MCP servers, and FHIR data stores.
Zero-Trust Enterprise Cloud Security
Multi-tenant database isolation, TLS 1.3 tunnels, and HIPAA privacy boundaries.
Deploy AI-native clinical architecture.
Review our technical documentation or schedule an enterprise architectural briefing with our team.