The Health AI Orchestrator
We’re not here to digitize healthcare. We’re here to help it think.
Orchestral is the platform health systems use to unify their data, deploy AI agents, and govern everything that touches a patient - all in one place, built for global health systems.
One platform. Two layers. Everything connected.
Most health organizations have data. Most have AI ambitions. What they’re missing is the layer between the two - something that makes the data trustworthy and the AI governable. Orchestral provides both.
The Health Information Platform (HIP) standardizes and activates your data. The Health Agent Library (HAL) assembles, governs, and deploys AI into your workflows. Together, connected via an MCP server, they give every clinician, analyst, and system a single coherent view of every patient.
HIP - Health Information Platform
The data foundation your AI actually needs.
Orchestral’s platform is the connective tissue of your health enterprise - centralizing, standardizing, and activating data to power a 360-degree view of every patient and every workflow.
Health Data Model
One model. Every patient. Complete.
All data is centralized and standardized into a single canonical model - giving every clinician, analyst, and AI agent a consistent, complete 360-degree view of the patient. No more reconciling across systems. No more gaps in the record.
The Health Data Model covers provenance, party, business interaction, findings, service delivery, finance, and substance data. It ingests any format - FHIR, CCDA, HL7, XML, CSV - and outputs in any format your downstream systems need.
MCP server - AI-agent ready
Built for the agent era, today.
Orchestral is natively compatible with modern AI agent frameworks via our MCP server. That means your AI agents can access clean, governed health data without custom integration work. No bespoke pipelines. No months of pre-work. Just AI that connects to trusted data from day one.
Works within your existing stack
An orchestrator, not a replacement.
Orchestral is an operational platform that integrates with your existing investments - CWS, CRM, Snowflake, and more. It extends the value of what you already have rather than asking you to rip and replace. Your tools get smarter. Your team spends less time wrangling data and more time acting on it.
HAL - Health Agent Library
The intelligence layer for your health enterprise.
The Health Agent Library assembles, governs, and deploys AI agents into clinical and operational workflows - so every algorithm your organization runs is trusted, auditable, and built to be reused.
Agent library
Pre-built. Reusable. Ready.
A growing library of pre-built AI agents for healthcare workflows — from theater optimization to pre-admission screening, medication safety to discharge planning. Every agent is assembled from shared building blocks, so your team isn’t starting from scratch every time.
Use what exists. Extend what you need. Build once, deploy everywhere.
Works across your ecosystem
One library. Your entire stack, smarter.
Agents plug into any application in your ecosystem - PAS, CWS, CRM, PMS, third-party tools. HAL doesn’t lock you into a single interface. It makes every system you already use more intelligent, without replacing any of them.
AI governance
The system of record for every AI asset in your organization.
Who approved this model? What risk does it carry? Is it still performing?
HAL gives you answers. It is the central governance layer for every AI asset your organization runs - vendor-supplied or homegrown, clinical or operational. Risk scoring, audit trails, performance monitoring, model cards, and compliance documentation, all in one place.
When something goes wrong with an AI system, you will know immediately. When a regulator asks, you will have the answer.
The six foundational layers
What’s under the hood.
HAL is built on six layers, each essential for AI governance at scale.
AI tooling - Create, configure and deploy AI assets. Enable reuse via shared building blocks across every use case and workflow.
Registry - A governed registry of every AI asset in your organization - agents, algorithms, data connectors, skills - regardless of origin or vendor.
Risk framework - An assisted approach to assessing AI-specific risks and compliance requirements before any model goes near a patient.
Decision capture and collaboration - Audit-ready capture of discussions and decisions throughout the AI development and deployment lifecycle.
Monitoring and health check - Proactive monitoring of performance, usage, and risk across every model and agent in production.
Browsable library - A searchable, filterable view of every AI asset and its building blocks - so teams can find, evaluate, and reuse what already exists.