Private AI Systems
Fully private, self-hosted AI that never leaves your infrastructure. For regulated industries that can't use cloud AI.
Why Private AI?
Most AI automation consultants only offer cloud-based solutions using OpenAI, Claude, or Gemini APIs. This works great for most companies, but it's impossible for regulated industries.
Law firms can't send client files to OpenAI without violating attorney-client privilege. Healthcare providers can't use cloud AI without HIPAA violations. Financial services can't share portfolio data with third parties without SEC compliance issues.
That's where Private AI Systems come in. We build fully self-hosted AI using open-source models (LLaMA 3, Mistral) that run entirely in your infrastructure or dedicated private cloud. Complete data sovereignty. Zero third-party API calls.
Who Needs This
Ideal industries
Private AI is essential for regulated industries with strict data compliance requirements.
Legal Firms
Build private legal research assistants, contract analysis systems, and case file intelligence. Maintain attorney-client privilege while gaining AI capabilities. Typical use case: 60% reduction in research time for 20-attorney firms.
Healthcare Practices
HIPAA-compliant medical records intelligence, patient history summarization, clinical decision support. Typical use case: 2-3 more patients per provider per day through faster chart review.
Financial Services
SEC-compliant portfolio analysis, client financial data Q&A, investment report generation, compliance monitoring. Typical use case: advisors serve more clients with same team size.
Manufacturing
Quality control analysis with trade secret protection, production data monitoring, supplier quality tracking. Typical use case: 40% defect reduction through early pattern detection.
Our Stack
Technology we use
Core AI Models
- →LLaMA 3 (70B/405B) or Mistral 8x7B (quantized for efficiency)
- →vLLM for accelerated inference serving
- →Custom fine-tuning when domain-specific accuracy is critical
Infrastructure & RAG
- →ChromaDB, Weaviate, or Qdrant for vector storage
- →LlamaIndex or LangChain for RAG pipelines
- →n8n for document ingestion workflows
- →CoreWeave GPU hosting (~$1,200-2,000/month) or your on-premise servers
- →Full encryption at rest and in transit
- →JWT authentication, audit logging, role-based access
Quality Assurance
- →Ground truth evaluation with test questions and known answers
- →Retrieval quality measurement (relevant document retrieval rate)
- →Response quality assessment (hallucination rate tracking)
- →90%+ accuracy target before production deployment
- →Security penetration testing and compliance audits
The Process
Implementation timeline
Typical Private AI system implementation takes 12-14 weeks.
Weeks 1-2
Discovery, architecture design, infrastructure selection
Weeks 3-4
GPU provisioning, LLM deployment, security setup
Weeks 5-8
Document processing pipeline, RAG system development
Weeks 9-14
UI development, integration, testing, training, deployment
Investment
Pricing structure
Private AI systems are project-based, scoped by complexity. Reference point: mid-sized law firm implementation starts around $35,000. This includes full system build, deployment, training, and handoff.
Infrastructure costs (GPU hosting) are separate and typically $1,000-3,000/month depending on usage. You can host on your own servers or use our recommended private cloud providers.
We offer optional managed services (monthly retainer) for monitoring, optimization, and support. This gives you peace of mind that your system continues to perform.
Payment terms: 30% upfront, 30% at infrastructure setup, 20% at completion, 20% after UAT.
Need AI but can't use the cloud?
Book a discovery call. We'll discuss your compliance requirements and show you how Private AI can give you AI capabilities without compromising data sovereignty.