Healthcare

AI for Healthcare — Private and Reliable

Privacy-First AI

Run AI models entirely on
Premise or at the edge, ensuring patient data never leaves your environment.
Data Confidentiality
No external access to patient inputs or model outputs.
Regulatory Compliance
Align with healthcare data protection standards.
On-Premise and Edge AI Deployment
Bring AI closer to where healthcare data is generated.
Low Latency Diagnosis
Real-time insights for imaging, triage, and clinical decision support.
Localized Intelligence
Deploy models in hospitals, labs, or diagnostic devices.

Efficient AI Infrastructure

Achieve high performance without specialized hardware.
CPU-Optimized Runtime
Run large medical models efficiently on existing compute.
Cost-Effective Scalability
Expand AI use across departments with minimal infrastructure change.
Sustainable Compute
Lower energy use without compromising output quality.

AI for Clinical Innovation

Enable hospitals and research centres to build domain-specific models.
Customizable LLMs
Adapt models for medical records, radiology reports, and patient communication.
Faster Experimentation
Deploy, test, and refine securely within your clinical environment.

Auditability & Governance

Built for clinical accountability and regulatory scrutiny.
End-to-End Audit Trails

Log every model interaction, input, and output for review and compliance.

Model Traceability

Track which model and version generated each clinical insight.

Controlled Access

Role-based permissions aligned to clinical and administrative workflows.

Auditability & Governance

Built for clinical accountability and regulatory scrutiny.
End-to-End Audit Trails
Log every model interaction, input, and output for review and compliance.
Model Traceability
Track which model and version generated each clinical insight.
Controlled Access
Role-based permissions aligned to clinical and administrative workflows.

Data Ownership & IP Protection

Ensure patient data and clinical intelligence remain fully protected.
Full Data Ownership
Hospitals retain complete control over data and outputs.
No External Training
Patient data is never reused or shared for model training.

Frequently Asked Questions

How can we keep patient data fully within our hospital or lab infrastructure?

Kompact AI runtime runs entirely on your on-premise servers. All data—inputs, outputs, and logs—stay inside your environment. Nothing is sent to external clouds. For more information, please contact us at contact@ziroh.com.

Can AI models run without sending any patient information to external clouds?

Yes. All processing happens locally. The models operate without any external data transfer or third-party access.

  • The runtime to execute the Models.
  • Remote REST‑based server for serving model inferences remotely.
  • Observability to track model and system performance.
  • Client-Side SDKs in Go, Python, Java, .NET, and JavaScript, which are OpenAI Compatible for writing downstream applications that use the Kompact AI models.
How does your solution help us meet healthcare data protection and compliance requirements?

By keeping all AI workloads within your controlled network, we help you align with healthcare data protection frameworks such as HIPAA-equivalent or regional medical data standards. We do not store or process patient data on our side.

Can we deploy AI models directly in our hospitals, diagnostic centers, or medical devices?

Yes. You can deploy models where your data is generated—inside hospitals, labs, or edge devices—without relying on external infrastructure.

How quickly can the system deliver insights for imaging, triage, or clinical support?

Kompact AI runtime delivers low-latency inference, suitable for real-time or near-real-time workflows such as radiology or triage support.

How do we scale AI usage across departments without major hardware upgrades?

Since the system works efficiently on CPUs, departments can adopt AI tools without investing in GPUs or new server architectures. For more information, please contact us at contact@ziroh.com.

What energy or cost efficiencies can we expect from a CPU-optimised runtime?

CPU-based deployments reduce both energy consumption and operational costs compared to GPU-heavy setups, while maintaining strong performance.For detailed benchmarks, please write to contact@ziroh.com.

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