Personal LLM Deployment

Overview

A defense contractor needed AI capabilities but couldn't send any data to external APIs due to ITAR compliance. We deployed a fully private LLM instance on their infrastructure, connected to their internal knowledge bases via a RAG (Retrieval-Augmented Generation) pipeline. Employees can ask questions about SOPs, engineering specs, project history, and compliance requirements — getting instant, accurate answers sourced from their own documents.

The Challenge

Running LLMs on-premise requires significant infrastructure. The knowledge base spanned 50,000+ documents across multiple systems (SharePoint, Confluence, network drives) in various formats. Answers needed to cite sources for verification. The system needed role-based access — not everyone should be able to query all documents.

Our Approach

We deployed an open-source LLM on their GPU cluster with a custom RAG pipeline. Documents are chunked, embedded, and stored in a vector database with permission metadata. When a user asks a question, the system retrieves relevant chunks based on the user's access level, feeds them as context to the LLM, and generates an answer with source citations. We fine-tuned the model on domain-specific terminology.

Key Features

• Fully on-premise deployment (no external API calls)
• RAG pipeline across 50K+ documents
• Role-based access control for document queries
• Source citation for every answer
• Multi-format document ingestion
• Conversational memory for follow-up questions
• Admin dashboard for usage analytics and document management

Results

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