Public LLMs are a Liability for Proprietary Data
Standard AI tools help your employees, but they train on your data, leak confidentiality, and hallucinate when analyzing complex records. If you are a law firm, an architecture setup, or an operations-heavy business, "close enough" is not good enough.
The Risk
- ✕Data Training Liability: Public APIs may silently use your proprietary inputs to train future public models.
- ✕Hallucinations: Generic models invent facts when they lack specific context, destroying trust in critical workflows.
- ✕Compliance Violations: Sending PII or financial data to 3rd-party servers instantly breaks GDPR, HIPAA, and SOC2 rules.
The PatnAI Engine
We build deterministic AI systems that operate under strict boundaries:
- ✓Zero External Leakage: Your data stays strictly within your local servers or private cloud tenant.
- ✓Source-Grounded Answers: The system only answers using your verified context. If it’s not in the data, it won’t invent facts.
- ✓On-Premise Capability: Run fully open-source quantized models (Llama, Phi-3, Qwen) locally with zero dependency on third-party APIs.
What's Included in the Box?
A production-ready RAG system designed for strict data privacy. We deploy it locally on your infrastructure so your data never leaves your walls.
Local Vector DB (Qdrant)
High-performance vector search engine deployed via Docker.
Orchestration API (FastAPI)
Python backend ensuring deterministic routing and guardrails.
Local LLM Integration
Plug in Phi-3 or Llama 3.3 directly on your local hardware.
Adaptive Chat UI
Premium React frontend for your team to query documents.
100% Private & Secure
Zero external API calls. No data leakage or third-party tracking.
Infrastructure Requirements
Lean hardware footprint. Runs on standard CPUs with 16GB+ RAM for Phi-3 models, or GPU instances for heavier Llama 3.3 deployments.