Your data. Your models. Your control.
Build and orchestrate dedicated on-premise AI infrastructure using Mac machines. Keep your proprietary data, Excel models, and PDFs completely private—never used to train closed LLMs.
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The Hidden Cost of Cloud LLMs
When you use closed LLMs like GPT-4, Claude, or Gemini, your proprietary data doesn't just get processed—it gets ingested and learned from. Your Excel models, financial spreadsheets, internal PDFs, and confidential documents become part of the model's training data.
No Control
You have zero control over how your data is used or who can access insights derived from it.
No Visibility
No audit trail of what data was accessed, when, and by which model versions.
Compliance Risk
HIPAA, SOC 2, GDPR, and other compliance requirements may be violated when data is processed by third-party LLMs.
Full control over your AI infrastructure
Dedicated Mac hardware, optimized for AI workloads.
Data Never Leaves Your Premises
Your proprietary data, documents, and models stay within your infrastructure. No third-party access, no training on your data.
Complete Control & Compliance
Meet strict regulatory requirements with full audit trails, access controls, and compliance certifications.
Dedicated Mac Infrastructure
We build and orchestrate dedicated Mac Studio and Mac Pro clusters optimized for AI workloads.
Apple Silicon Performance
Leverage the power of M2 Ultra and M3 Max chips for efficient local model inference with up to 192GB unified memory.
Full-Stack On-Premise Solution
See how these deployment patterns map to specific tooling in our integrations catalog.
Local Model Hosting
Run Llama, Mistral, and other open-source models entirely on your hardware.
Private Network Isolation
Air-gapped or VPN-secured networks with no external dependencies.
End-to-End Encryption
All data encrypted at rest and in transit with your own keys.
Automated Orchestration
Kubernetes-based deployment with automatic scaling and failover.
Model Management
Version control, A/B testing, and rollback capabilities for all models.
24/7 Monitoring
Real-time performance monitoring with proactive alerting.
Cloud LLMs vs. On-Premise
See why enterprises are choosing on-premise infrastructure.
| Feature | Cloud LLMs | On-Premise |
|---|---|---|
| Data is not used to train foundation models | ✗ | |
| Sensitive data remains inside your trust boundary | ✗ | |
| Full audit trail & compliance control | ✗ | |
| Air-gapped network capability | ✗ | |
| Custom hardware optimization | ✗ | |
| Predictable costs at scale | ✗ |
Note: many mainstream providers offer no-training enterprise modes, but policy and data-handling defaults vary by product, plan, and user behavior (e.g. accidental uploads of sensitive files).
Purpose-built for local inference
We specialize in building on-premise infrastructure using Apple's Mac Studio and Mac Pro machines. The M2 Ultra and M3 Max chips deliver exceptional performance for local model inference.
Ready to own your AI infrastructure?
Let's discuss your on-premise requirements. We'll design, build, and orchestrate a dedicated AI infrastructure that keeps your data completely under your control.