Private AI Infrastructure • On-Premise & Air-Gapped Solutions

Your AI. Your Infrastructure.
Your Data Never Leaves.

Deploy powerful large language models on your own servers — fully private, fully compliant, fully under your control. No data flows to OpenAI, Google, or any third-party cloud. Petronella builds, deploys, and manages private AI infrastructure purpose-built for regulated industries where data sovereignty isn’t optional.

HIPAA • CMMC • SOX • FERPA • PCI DSS Compliant Deployments

0
Data Breaches Among
Compliant Clients
100%
On-Premise
Data Retention
23+
Years Cybersecurity
Experience
2,500+
Businesses
Protected
The Problem

Why Public Cloud AI Creates Compliance Risk

Every API call to ChatGPT, Claude, or Gemini sends your data through third-party servers — servers you don’t control, in jurisdictions you didn’t choose.

Data Leaves Your Control

Prompts containing patient records, CUI, financial data, or legal documents flow through OpenAI, Google, or Anthropic infrastructure. You have no visibility into how that data is stored, processed, or retained.

Compliance Violations

HIPAA, CMMC, SOX, and FERPA all require demonstrable control over sensitive data processing. Sending regulated data to a third-party AI provider without a proper BAA or data handling agreement is a compliance violation waiting to happen.

No Audit Trail

When regulators ask where patient data was processed, “OpenAI’s servers” is not an acceptable answer. Private deployment gives you full logging, audit trails, and data lineage that auditors and compliance officers require.

Our Solution

Private AI Deployment — Built for Compliance

Private LLM Deployment — AI That Never Phones Home

We deploy state-of-the-art large language models directly on your infrastructure — whether that’s your on-premise data center, a private cloud enclave, or an air-gapped network with zero internet connectivity.

What You Get

  • On-premise GPU servers running enterprise-grade NVIDIA hardware, configured and hardened by our cybersecurity team
  • Open-source LLMs (Llama, Mistral, Qwen, DeepSeek, Phi) that rival GPT-4 for domain-specific tasks — with no usage fees or API rate limits
  • Custom fine-tuning on your proprietary data to create AI models that understand your industry, terminology, and workflows
  • Air-gapped deployment options for CMMC Level 3, classified environments, and organizations requiring zero internet exposure
  • Full data sovereignty — every prompt, response, and model weight stays within your security boundary
How It Works — From Assessment to Production in Weeks
AI Readiness Assessment
We audit your current infrastructure, data workflows, compliance requirements, and AI use cases. You receive a detailed report with hardware specs, model recommendations, and a deployment roadmap.
Model Selection & Testing
We evaluate open-source models against your specific use cases — document processing, code generation, customer support, compliance analysis, or custom workflows. You choose the model that performs best on your actual data.
Infrastructure Setup & Hardening
GPU servers are provisioned, hardened per NIST 800-53 / CIS benchmarks, and deployed to your environment. Network segmentation, encryption at rest and in transit, and access controls are configured from day one.
Custom Fine-Tuning
Using your proprietary data (with full data handling agreements), we fine-tune the base model to understand your domain. Medical terminology, legal language, defense acronyms, financial regulations — the model learns your world.
Deployment & Integration
The fine-tuned model goes live with API endpoints your applications can consume. We integrate with your existing tools — EHR systems, document management, ticketing, CRM — through secure internal APIs.
Monitoring & Optimization
Continuous monitoring of model performance, hardware health, and security posture. We handle updates, model refreshes, and scaling as your AI usage grows — all within your security boundary.
Industry Use Cases — Who Needs Private AI?

Any organization handling sensitive, regulated, or proprietary data benefits from private AI deployment. Here are the industries where it’s not just beneficial — it’s mandatory.

Healthcare & Life Sciences
HIPAA / HITECH
Clinical documentation, patient communication, medical coding, drug interaction analysis — all processed on HIPAA-compliant infrastructure with full BAA coverage.
Defense Contractors
CMMC / ITAR / NIST 800-171
CUI processing, proposal generation, technical documentation, and supply chain analysis on air-gapped or FedRAMP-equivalent infrastructure.
Financial Services
SOX / GLBA / PCI DSS
Fraud detection, regulatory document analysis, compliance monitoring, and customer communication — with full audit trail and data lineage.
Legal & Law Firms
Attorney-Client Privilege
Document review, contract analysis, case research, and brief drafting. Attorney-client privilege requires that no third party processes confidential communications.
Government Agencies
FedRAMP / FISMA
Citizen services automation, internal document processing, and policy analysis — deployed within government-controlled infrastructure boundaries.
Education & Research
FERPA
Student data analysis, research assistance, administrative automation, and grant writing — with student records never leaving campus infrastructure.
Technology Stack — Enterprise-Grade Open Source AI

We use battle-tested open-source AI infrastructure — the same tools powering AI deployments at major tech companies, but configured and hardened for regulated environments.

vLLM
High-throughput inference engine with PagedAttention for maximum GPU utilization
Ollama
Simplified model management and deployment for rapid prototyping and production
llama.cpp
Optimized inference across NVIDIA, AMD, and Apple Silicon hardware
Unsloth
2x faster fine-tuning with 60% less memory for custom model training
NVIDIA Enterprise GPUs
RTX 5090, A100, H100 — right-sized for your workload and budget
RAG Pipelines
Retrieval-augmented generation connecting AI to your documents and databases

All infrastructure is hardened per NIST 800-53 controls, encrypted at rest (AES-256) and in transit (TLS 1.3), with role-based access control and comprehensive audit logging.

Why Petronella for Private AI?

Most AI consultants are software developers who learned some machine learning. We’re a cybersecurity firm that built AI infrastructure — and that difference matters when compliance is on the line.

  • 23+ years in cybersecurity and compliance — we understand the regulatory landscape (HIPAA, CMMC, SOX, PCI DSS) at a depth that pure AI firms simply don’t
  • Licensed digital forensics examiners — our data handling expertise comes from investigating breaches, not just preventing them
  • Own GPU infrastructure — we operate our own NVIDIA-powered inference clusters, giving us hands-on expertise with the exact hardware we deploy for clients
  • Zero breaches among compliant clients — security isn’t an add-on to our AI practice; it’s the foundation everything is built on
  • BBB A+ accredited since 2003 — sustained excellence in client service, not a startup that might not exist next year
  • CMMC Certified Registered Practitioner — we hold the same certifications required to assess defense contractors, so we build to that standard from day one
FAQ

Frequently Asked Questions

How does private AI compare to ChatGPT or Claude in terms of capability?
Modern open-source models like Llama 3.3, Qwen 2.5, and Mistral Large perform comparably to GPT-4 on most business tasks — especially when fine-tuned on your domain-specific data. For specialized tasks like medical coding, legal document review, or compliance analysis, a fine-tuned private model often outperforms general-purpose cloud AI because it understands your specific terminology and workflows.
What hardware do I need to run private AI?
It depends on your use case and user count. A single NVIDIA RTX 4090 or 5090 can serve a small team of 10–20 concurrent users running a 70B parameter model. Larger deployments use multi-GPU servers or clusters with A100 or H100 GPUs. We right-size the hardware to your needs and budget — you don’t need a data center to get started.
Can private AI be deployed in an air-gapped environment?
Yes. All model weights are downloaded and transferred offline. The inference server runs entirely locally with no internet dependency. We have deployed air-gapped AI systems for defense contractors and government agencies where zero internet exposure is a hard requirement. Updates and model refreshes are handled via secure physical media transfer.
How long does deployment take?
A basic deployment with an off-the-shelf model can be operational in 1–2 weeks. Deployments with custom fine-tuning typically take 4–6 weeks, including data preparation, training, evaluation, and hardening. Air-gapped deployments may require additional time for security certification and physical infrastructure setup.
What does private AI cost compared to cloud AI APIs?
Private AI has higher upfront costs (hardware + setup) but dramatically lower ongoing costs. Organizations processing more than 1 million tokens per day typically break even within 3–6 months compared to API pricing. Beyond cost, you eliminate per-token fees, rate limits, API dependency risk, and — most importantly — compliance risk. The total cost of a cloud AI compliance violation far exceeds any infrastructure investment.
Is my data used to train the AI model?
Only if you explicitly choose fine-tuning — and even then, training happens exclusively on your hardware with your data, under your control. Unlike cloud AI providers that may use your prompts to improve their models, private deployment means your data is never seen by anyone outside your organization. You own the model weights, the training data, and every inference log.
Can Petronella manage the AI infrastructure ongoing?
Yes. We offer fully managed AI infrastructure as part of our managed IT and cybersecurity services. This includes model updates, hardware maintenance, security patching, performance monitoring, and user support. You get the benefits of private AI without needing an in-house AI operations team.
What open-source models do you recommend?
Model selection depends on your use case, hardware, and performance requirements. For general business tasks, Llama 3.3 70B and Qwen 2.5 72B are excellent choices. For code generation, DeepSeek Coder V2 excels. For smaller hardware or edge deployment, Phi-4 and Mistral 7B deliver strong performance in compact packages. We benchmark multiple models against your actual workload before making a recommendation.

Related Services

Ready to Deploy AI on Your Terms?

Get a free AI readiness assessment. We’ll evaluate your infrastructure, compliance requirements, and use cases — and deliver a deployment plan within one week.

No obligation • No data leaves your environment • Results in one week