Orchestral’s AI Platform manages the full lifecycle of health AI. Registration, validation, deployment, monitoring, and explainability through one framework built for regulated health systems.
Orchestral AI Platform
Every patient has a risk profile. AI flags the high-risk ones. Governance is what makes that flag trustworthy.
30,000+
algorithm executions per month
2019
started running production AI in healthcare
4 countries
with production deployments
You don’t start by deploying AI. You start by seeing it.
AI is already in your organisation: embedded in the products you buy, trialled by your departments, built by your own teams. The starting point is not choosing your first algorithm; it is knowing what is already there.
Register every AI asset, risk-assess it, and bring it under governance. From that footing, adoption gets faster, not slower: a governed catalogue makes the safe path the easy path, and saying yes to the next AI proposition takes days, not committees’ worth of months, because the framework already exists.
Visibility first. Velocity follows.
One system of record for every AI asset.
Vendor-supplied, internally built, open source, or embedded in a product you bought: if it makes or shapes decisions about patients, it belongs in the registry. Orchestral governs and monitors AI wherever it runs, on our runtime, on yours, or in a third party’s cloud, and holds no commercial stake in any AI vendor, so the governance is neutral by construction.
Built for health AI governance. Not adapted for it.
Six layers of governance. One framework every agent operates under.
1. Model registration
Every AI asset in your organization is registered, regardless of origin. Vendor-supplied, internally built, open source, or commercial. Risk profile, intended use, training data, and clinical validation evidence all captured at registration.
2. Clinical assessment
Structured assessment of clinical safety, efficacy, and appropriateness before deployment. Validation evidence packaged for governance review.
3. Deployment and regional control
Activation is environment-specific. Each facility, region, or organization controls which agents are active in their workflow. No silent global rollouts.
4. Performance monitoring
Continuous monitoring of model accuracy and clinical impact. Alerts when performance degrades.
5. Bias detection
Equity monitoring across protected and locally relevant population groups. Identifies differences in how a model performs across demographics, geographies, and programs.
6. Explainability
Every decision includes a reasoning trail. The clinician sees why the model surfaced the recommendation. Your leadership team sees why the model was approved. The regulator sees why the decision was defensible.
Governed AI, ready to adopt. Part of the platform.
The platform doesn’t just govern the AI you bring: it comes with a library of pre-built, health-specific agents, offered as part of the platform and deployable through the same six-layer framework as everything else.
Each agent is independently deployable, and you control which agents are active in your environment. Every agent inherits the same governance, monitoring and assurance framework, so adopting one is proof the governance works, not an exception to it. Start with one. Expand when ready.
Eight pre-built agents. Clinically validated. Ready to deploy.
Each agent is independently deployable and governed through the same framework. Start with one. Expand when ready.
Care gap detection
Continuous identification of patients missing screenings, medication reviews, or care management enrolment.
Readmission and deterioration prediction
Identifies patients at risk of readmission or deterioration, with explainable reasoning integrated into discharge and care management workflows.
Program enrollment intelligence
Identifies eligible patients not yet enrolled in funded programs. Relevant for primary care, Medicaid, and value-based contract operators.
Prior authorization triage
Automated authorization review against medical policy, with full oversight, traceability, and exception handling.
Prescription safety
Drug interaction, contraindication, and dosing checks at the point of prescribing.
Clinical risk stratification
Population-level risk assessment using clinical, claims, and social determinant data.
Triage and case routing
Referral and case management triage with explainable urgency and complexity recommendations.
Equity monitoring
Continuous surveillance of program participation, outcomes, and access by demographic group. Surfaces inequity, then targets the agent that closes it.
The AI lives where the work happens. Not in a separate tool.
Connects to the applications your organization already uses. PMS and EMR systems. Case management and workflow tools. CRMs and member portals. Provider portals. Third-party clinical applications.
The clinician sees the recommendation in the workflow they already use. The case manager sees the risk score in the queue they already manage. The patient sees the right message in the channel they already use.
What teams ask first.
What happens when a regulator asks why the model made a recommendation?
What if a model's performance degrades in production?
What if a vendor updates their algorithm after deployment?
Does the same governance apply to models we build internally?
What about AI tools that expose no telemetry?
Are you neutral about which AI we adopt?
Production deployments. National scale. Not pilots.
Ontario Health 811
Population-scale digital triage operating under the requirements of a publicly funded health system.
NZ Government Algorithm Hub
The world’s first national AI orchestration layer for health, running on Orchestral. Over 30,000 algorithm executions per month. During COVID-19, new predictive models were deployed within days, not months.
Regional prescription safety audit for 1M people (New Zealand)
Connected primary care prescribing data, applied clinical safety rules, and identified prescription risks including drug interactions, contraindications, and dosing concerns. Clinicians received actionable alerts. NZIER’s economic analysis estimated significant value from adverse events prevented.
Runs on your existing stack. Goes even further with the Data Platform.
The AI Platform connects to your existing data environment. Whatever you have built, it works on top of it.
Pair it with Orchestral’s Data Platform and both layers run on the same data model, the same governance framework, and the same source of truth. Every agent operates on data that is already connected, normalized, and governed. Nothing duplicated. Nothing rebuilt.
Or alternatively see how the AI platform solves specific problem? Explore our solutions - from readmission prediction and medication safety to rural health and prior authorization.
Bring us a use case. We will show you what governed deployment looks like end to end.
Choose one use case from your environment. We will run it through the platform, show you the six-layer framework in action at each stage, and walk you through every decision the platform makes.
No commitment. Just a working session on your use case.