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Why local RAG pipelines win on data privacy

Running LLMs on your own infrastructure, paired with a RAG architecture, gives you full sovereignty over your data.

  • Local AI
  • Architecture

The rise of generative AI pushed many companies toward cloud solutions. But sending sensitive data outside your walls carries major security and compliance risks.

What is local AI?

Deploy LLMs on dedicated infrastructure, pair them with a RAG (Retrieval-Augmented Generation) architecture, and your documents never leave your perimeter. The model reasons over your data without ever absorbing it.

const ragPipeline = new LocalRAG({
  model: "llama-3-8b-instruct",
  vectorDB: "pgvector",
  embedding: "bge-m3",
});

await ragPipeline.query("Analyze internal audit logs");

What does a local RAG pipeline change for a company?

You get the reasoning power of modern models with the security of an isolated network: controlled latency, predictable cost, and no third-party dependency for your critical data.