Federated AI for Healthcare.
Your Data Never Leaves.
Single Source · Federated Truth
HIPAA-compliant AI infrastructure delivered as a service — edge intelligence deployed at your facility, continuously improving from the collective without ever exposing protected health information.
The Problem
Healthcare AI is broken.
The three forces holding back clinical AI — and why the current approach is fundamentally unsustainable.
PHI Exposure Risk
Cloud-based AI requires patient imaging data to leave your facility. Every transmission is a compliance liability and a breach waiting to happen.
Crushing CapEx Burden
Owning and maintaining AI-grade hardware demands millions in upfront capital, specialized staff, and continuous refresh cycles — resources most health systems don't have.
Siloed Models That Stagnate
AI models trained on a single institution's data hit a performance ceiling fast. Without cross-institutional learning, diagnostic accuracy stops improving.
How It Works
Intelligence without exposure.
Federated learning lets every institution contribute to — and benefit from — a shared AI model without sharing a single byte of patient data.
Edge Node Deployed
NVIDIA IGX Thor
An IGX Thor unit is installed directly inside your facility. Your DICOM imaging data never moves — it stays on your hardware, in your building.
Local Training Only
On-Premise Compute
The AI model trains exclusively on your local data using the IGX Thor's onboard FP4 compute. No raw patient data is ever transmitted anywhere.
Encrypted Weights Transmitted
Mathematical Gradients Only
Only encrypted model weight updates — pure mathematics, zero patient information — are sent securely to the Solosift DGX hub.
Global Model Improves
NVIDIA DGX GB300 Hub
The DGX hub aggregates encrypted updates from all participating institutions, producing a continuously improving global model — redistributed back to every edge node.
Live Network Simulation
Hospital A
IGX Thor Edge Node
Solosift Hub
DGX GB300 · NVIDIA FLARE
Hospital B
IGX Thor Edge Node
Raw imaging data never leaves the facility. Only encrypted mathematical weight updates traverse the network.
For Clinicians
AI surfaces the findings.
You make the call.
Solosift is a decision support tool — not a replacement for clinical judgment. The interface is designed for radiologists and clinicians, not engineers. If you've read a scan before, you already know how to use it.
Interactive Preview — Confirm, dispute, or mark your own findings
Confirm Findings
One click validates an AI finding and logs it to the patient record. Your confirmation becomes a verified training signal.
Dispute Findings
Flag an incorrect detection with a reason. Your correction is fed back into the federated model — your expertise makes it smarter for every institution.
Mark Potential Findings
Draw your own region of interest directly on the scan. Provider-marked findings are tracked separately, fed into training with your attribution.
Contrast & Window Controls
Standard DICOM window/level controls. No new interface to learn — if you've read a scan before, you already know how to use this.
Prior Comparison
Side-by-side view of current and prior studies with AI overlay toggle. See exactly what changed — and what the AI sees differently.
Inline Annotation
Add clinical notes directly to flagged regions. Notes are stored with the finding, not buried in a separate report field.
Every confirmation, dispute, and provider-marked finding is a training signal. Your clinical expertise — built over years of practice — feeds directly back into the federated model, making AI diagnostics more accurate for every institution in the network. Your judgment makes it smarter for everyone.
Architecture
The full stack, layer by layer.
From raw DICOM input to a globally improved model — every step of the federated pipeline, with nothing leaving your facility but math.
DICOM Ingestion
At the Edge NodeRaw medical imaging data (DICOM, HL7 FHIR) is received directly from your imaging equipment — MRI, CT, fluoroscopy — by the IGX Thor. Data never touches a network.
Local Pre-processing
IGX Thor · On-PremiseThe edge node normalizes, de-identifies for training purposes, and prepares image tensors. All computation runs on the IGX Thor's onboard GPU — zero cloud dependency.
Federated Local Training
NVIDIA Clara · FLARE ClientThe FLARE client trains the local model for N rounds using only your facility's data. Gradient updates (model weights) are computed — not the underlying images.
Weight Encryption
AES-256 · TLS 1.3Model weight gradients are encrypted using AES-256 before any transmission. The encrypted payload contains zero patient information — it is pure mathematics.
Secure Transmission
Encrypted Gradients OnlyEncrypted weight tensors are transmitted to the Solosift DGX hub over a secured channel. Packet inspection reveals only ciphertext — no recoverable patient data.
Federated Aggregation
DGX GB300 · FLARE ServerThe DGX hub runs the FedAvg aggregation algorithm across all participating edge node weight updates, producing an improved global model without ever seeing raw data.
Global Model Distribution
Back to All Edge NodesThe improved global model is distributed back to all participating edge nodes. Every hospital benefits from collective intelligence without sharing any patient records.
The Platform
Built on NVIDIA.
Delivered as a service.
Enterprise-grade AI hardware without the enterprise CapEx. You pay a monthly OpEx subscription. We own, manage, and maintain the hardware.
Edge Node · At Your Facility
NVIDIA IGX Thor
Deployed directly inside your facility. Runs inference and local training on your data — no cloud dependency, no data egress.
- ▸ FP4 Tensor Core compute
- ▸ Integrated security enclave
- ▸ Hardened real-time OS
- ▸ Medical-grade certifications
Central Hub · Solosift Infrastructure
NVIDIA DGX GB300
The Solosift aggregation hub. Receives only encrypted model weight updates, produces the global federated model, and redistributes improvements to all edge nodes.
- ▸ GB300 NVL72 architecture
- ▸ Dual 400GbE ConnectX-8 SuperNICs
- ▸ 800 Gbps aggregate fabric
- ▸ NVIDIA FLARE runtime
Edge Inference · AI Annotation
NVIDIA FLARE · Live Training Log
Compliance
HIPAA compliance isn't a feature.
It's the architecture.
Most AI vendors bolt compliance on after the fact. Solosift is built from the ground up so that exposing PHI is architecturally impossible — not just against policy.
PHI Never Leaves Your Facility
Raw patient imaging data — DICOM, HL7, or otherwise — is processed exclusively on the on-premise edge node. Nothing is transmitted to any external system.
Only Encrypted Gradients Transmitted
The federated learning protocol transmits only encrypted mathematical model weight updates. These contain zero patient information — they are pure mathematical abstractions.
Compliant by Architecture
HIPAA compliance is not a policy overlay — it is a structural property of the system. The architecture makes non-compliant behavior technically impossible, not just discouraged.
Business Associate Agreement
Solosift executes a full HIPAA Business Associate Agreement (BAA) with every partner institution prior to deployment, establishing clear legal accountability.
Why Solosift
We can service it.
You can trust the results.
Two questions every health system asks before signing. Here are the answers.
We can service it.
Operational Reliability
Managed Hardware Lifecycle
We own the edge hardware. Installation, commissioning, maintenance, and refresh cycles are on us — not your IT department.
24/7 Remote Node Monitoring
Every IGX Thor edge node is continuously monitored. We detect and respond to hardware or software issues before they affect your operations.
Named Technical Contacts
No ticket queues. You have a named Solosift engineer and a direct line — with SLA-backed response times for critical issues.
Zero IT Burden
Model updates, security patches, and firmware upgrades are pushed and managed remotely. Your team touches nothing.
"Who do I call at 2am?" — You call us. Not a support queue. A person who knows your deployment.
You can trust the results.
Clinical Trustworthiness
Full Inference Audit Trail
Every AI inference is logged with timestamp, model version, input hash, and output confidence. Complete chain of custody for clinical accountability.
Explainable Outputs
Clinicians see confidence scores, contributing factors, and attention maps — not just a prediction. The AI shows its work.
Per-Round Performance Reporting
After every federated training round, accuracy deltas are reported to your team. You always know if the model improved, by how much, and why.
Version Control & Instant Rollback
Every model version is snapshotted. If a round produces unexpected behavior, you roll back to the previous validated version in one action.
"Can I stake a clinical decision on this?" — Yes. Every output is traceable, explainable, and auditable.
Get In Touch
Ready to pilot?
Whether you're a health system exploring federated AI or a research institution looking for a compute partner, we'd like to talk.