Private AI is fully self-hosted artificial intelligence that runs entirely in your own infrastructure or dedicated private cloud. Unlike cloud AI (OpenAI, Claude, Gemini), your data never leaves your systems. Syntora builds Private AI using open-source models like LLaMA 3 and Mistral, providing complete data sovereignty with zero third-party API calls.

How much does Private AI cost?

Private AI systems are project-based, scoped by complexity and infrastructure requirements. Every implementation includes full system build, deployment, training, and handoff. Book a discovery call to discuss your compliance requirements and get a custom scope.

Who needs Private AI instead of cloud AI?

Private AI is essential for regulated industries: Law firms need it to maintain attorney-client privilege, healthcare providers for HIPAA compliance, financial services for SEC compliance, and manufacturers protecting trade secrets. If your industry has strict data compliance requirements, you need Private AI.

How long does Private AI implementation take?

Typical Private AI implementation takes 12-14 weeks: Weeks 1-2 for discovery and architecture design, Weeks 3-4 for GPU provisioning and LLM deployment, Weeks 5-8 for document processing pipeline and RAG system development, and Weeks 9-14 for UI development, integration, testing, training, and deployment.

What technology does Syntora use for Private AI?

Syntora uses open-source models like LLaMA 3.1 (70B/405B), Mistral, or Kimi selected for each use case. Infrastructure includes vLLM or Ollama for local inference, ChromaDB/Weaviate/Qdrant for vector storage, LlamaIndex or LangChain for RAG pipelines, Pydantic for structured output validation, and Python for document ingestion. All systems include full encryption, JWT authentication, audit logging, and role-based access control.

Is Private AI as capable as ChatGPT or Claude?

Open-source models like LLaMA 3 405B approach GPT-4 capabilities for most business applications. While cloud AI may have slight edges in general knowledge, Private AI can be fine-tuned for your specific domain, often achieving better accuracy on your actual use cases. Syntora targets 90%+ accuracy before production deployment.

Enterprise-Grade | Compliance-Ready

Private AI Systems

Fully private, self-hosted AI that never leaves your infrastructure. For regulated industries that can't use cloud AI.

Book a Discovery Call See Industries

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 3.1 (70B/405B), Mistral, or Kimi selected for your use case
→vLLM or Ollama for accelerated local inference serving
→Custom fine-tuning when domain-specific accuracy is critical
→Open-source only. No data leaves your infrastructure.

Infrastructure & RAG

→ChromaDB, Weaviate, or Qdrant for vector storage
→LlamaIndex or LangChain for RAG pipelines
→Pydantic for structured output validation and data contracts
→Python 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

Side by Side

Private AI vs Cloud AI

Feature	Private AI (Syntora)	Cloud AI (OpenAI, Claude)
Data Sovereignty	All data stays on your infrastructure. Zero third-party calls	Data sent to provider servers
Regulatory Compliance	HIPAA, SEC, attorney-client privilege compliant by architecture	BAAs available but data leaves network
Fine-Tuning	Full model fine-tuning on your domain data	Limited fine-tuning. Shared model
Uptime Control	No dependency on provider availability	Subject to provider outages
Model Quality	Open-source approaching GPT-4 for domain tasks	Frontier models lead on general benchmarks
Infrastructure Cost	GPU hosting $1-3K/month fixed	Pay-per-token. Lower at low volume

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

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.

Book a Discovery Call Our Security Practices

See our tech stack or browse industry-specific private AI applications.