ARCHITECTURE
AI-first, quantum-ready healthcare architecture.
Ardia Health sits between data sources and front-line care teams, transforming raw signals into clinical-grade insight using healthcare-tuned AI today and quantum acceleration tomorrow.
HIGH-LEVEL FLOW
From data to intelligence to action.
THREE-PART CARE INTELLIGENCE FLOW
Data sources connect in.
EHR / EMR, imaging systems, labs, claims, wearables and patient apps stream data into Ardia through secure connectors.
The Ardia Intelligence Core transforms it.
Ingestion, normalization, encryption and healthcare-tuned AI engines (LLMs, vision models, signal AI) convert raw signals into risk scores, predictions and recommendations.
Care experiences & quantum deliver impact.
Clinical AI, diagnostics copilots, RPM dashboards, precision health programs and device cloud analytics surface intelligence today — while future quantum workloads plug into the same core.
A visual view of how Ardia connects data sources, the intelligence core and care experiences.
HIGH-LEVEL FLOW
From raw signals to agents and experiences.
Data flows from EHRs, devices and documents into Ardia’s secure platform, where foundation models and agents operate over an MCP tool layer. SRL continuously improves behaviour from real-world feedback, while experiences stay simple for clinicians and patients.
1Data sources
Where clinical signals originate.
- • EHR / EMR (FHIR, HL7: encounters, meds, notes)
- • Imaging & labs (DICOM, LIS/HL7)
- • RPM & wearables (REST / streaming APIs)
- • Documents: visit summaries, letters, forms
- • Patient-reported outcomes & questionnaires
- • Payer rules, benefits and prior auth criteria
2Ardia intelligence fabric
Secure data platform
- • Encrypted PHI store (S3/Blob) for raw docs, audio and transcripts.
- • Relational DB (Postgres) for structured clinical entities.
- • Vector store (pgvector / Pinecone / Weaviate) for notes, guidelines and pathways.
- • Audit/event log for every agent action and tool call.
Models, tools & MCP layer
- • Foundation LLMs: GPT-4.1 / GPT-4o plus Ardia-tuned Llama / Mistral.
- • Specialised models: Whisper, OCR and optional imaging models.
- • MCP tools: FHIR read/write, knowledge retrieval, calculators, document generator, scheduling, billing rules.
- • Agents (documentation, prior-auth, patient explainer) call MCP tools instead of hard-coded APIs.
Supervised reinforcement learning (SRL)
- • Capture de-identified transcripts, tool traces, outputs and human edits.
- • Train reward models on accuracy, safety, edit distance and guideline compliance.
- • Fine-tune models and tool-selection policies to reduce clinician edits over time.
- • Governance layer monitors for drift, bias and policy violations.
Future-ready for more general models
Agents are model-agnostic. As more powerful “near-AGI” models arrive, Ardia can plug them into the same MCP tool layer without changing integrations or experiences.
3Experiences & integrations
Where patients, providers and partners feel the value.
- • Clinical AI in the EHR: ambient notes, coding suggestions, prior auth drafts, discharge summaries.
- • Patient experiences: upload a document and get simple explanations, visit prep and after-visit summaries.
- • RPM dashboards: combine device, symptom and narrative data for chronic programs.
- • Payer/employer views: structured justifications, review support and trend analytics (with PHI controls).
- • API & MCP endpoints for partners and future quantum workloads.
MODELS
Foundation models for Ardia.
- • GPT-4.1 / GPT-4o for complex reasoning and drafting.
- • Llama / Mistral variants fine-tuned with Ardia SRL.
- • Whisper and speech models for ambient documentation in clinic and telehealth.
- • OCR & layout models for scanned forms and PDFs.
TOOLS & MCP
Tool-centric agent layer.
- • MCP server exposing FHIR, search, calculators, templates.
- • Agents choose tools via policies trained with SRL instead of hard-coded flows.
- • Web, mobile and EHR plugins act as MCP clients.
- • All tool calls logged for audit and improvements.
DATA & RESIDENCY
Built for global regulations.
- • Region-locked PHI storage and compute (US, EU, etc.).
- • Configurable routing to external vs in-house models.
- • De-identification pipelines for SRL training data and analytics.
- • Fine-grained permissions by organisation, group and role.