Apex Foundry/Case Studies/Ultralight Hospital AI
Healthcare · Mid-Size Hospital

Ultralight Sovereign AI Micro-Data Center for Clinical Operations

On-Prem Multimodal AI Infrastructure for Radiology, Documentation & Hospital Operations

50–100
Concurrent Clinicians
90 Days
Go-Live Target
70B
Max Model Size
$2.5M+
Annual Value Created
The Strategic Problem

Mid-Size Hospitals Face the Same Risks, Without Enterprise Budgets

✗

Radiology backlog

Slower diagnosis, reduced imaging throughput, and physician strain.

✗

Administrative overload

Physician burnout and productivity loss from documentation burden.

✗

AI SaaS pricing volatility

Escalating, unpredictable recurring OpEx with no exit path.

✗

Data sent to cloud inference

HIPAA/GDPR compliance exposure and loss of institutional data control.

Hardware Architecture

Compact 3-Node Cluster

A rack-level deployment designed for seamless integration within the existing hospital network — minimal footprint, maximum sovereignty.

Management Node

1× Server

Cluster control, centralized logging, and identity management.

CPU Node

1× Server

EMR integration, secure databases, and orchestration layer.

GPU Inference Node

🔒 Confidential
144GB VRAM total

All AI model inference — LLM, radiology, voice, and agents.

Software Stack

34B–70B Quantized Medical LLM
Multimodal radiology model (natively DICOM capable)
Voice-to-structured clinical notes pipeline
Advanced agent orchestration framework
Role-based access control (RBAC)
Immutable audit logging
AI Agent Capabilities

Four Agents. One Compact Node.

🔬

Radiology Co-Pilot

DICOM interpretation, urgency scoring, structured findings generation, and comparative scan analysis — all on-premise.

📋

Clinical Documentation Agent

Voice-to-SOAP note conversion, discharge draft automation, and EMR-ready structured output to reduce physician burden.

💊

Drug & Risk Agent

Contraindication detection, real-time lab anomaly alerts, and medication interaction screening.

📊

Operations Optimization Agent

Bed allocation modeling, OR scheduling support, and predictive patient flow forecasting.

Capacity & Performance

Deployment Scope

Concurrent Clinicians50–100
Maximum Model SizeUp to 70B (quantized)
Context Window16k tokens
Radiology Inference LatencyNear real-time (sub-second)
Daily AI Interactions1,000–5,000
Data LocationFully On-Premise
Financial Impact

Mid-Size Hospital Estimate

$1.2M–$2.5M
Annual physician productivity improvement
15–25%
Increase in radiology throughput
$500k–$1M
Reduction in recurring AI SaaS / cloud GPU spend
75%
CAPEX financing available
2×
5-year savings vs. equivalent cloud costs
Implementation

Live in 90 Days

Procurement4–6 weeks
Installation2–3 weeks
Integration & Testing2–4 weeks
Go-LiveWithin 90 Days
Phase 2 Expansion

Future-Ready Architecture

ICU anomaly prediction and patient deterioration modeling
Autonomous triage scoring and dynamic assignment
Predictive admissions forecasting and resource allocation
Multi-site hospital federation and shared model learning
Robotics-assisted logistics and inventory management
Add GPU nodes incrementally as demand grows

Infrastructure Ownership, Not AI Subscription Dependency

Deploy a sovereign AI micro-data center at your hospital in 90 days with 75% CAPEX financed.

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