Privacy-Aware Local RAG
Built a local Knowledge Retrieval system for sensitive internal documents without cloud exposure.
Context
A boutique consultative firm needed a way to query their vast archive of past project proposals and strategic memos. However, strict NDAs and internal compliance meant they could absolutely not upload documents to OpenAI, Anthropic, or any public cloud provider. They required an on-premise "AI assistant" that could reason over their secure data.
Approach
I architected a local RAG (Retrieval-Augmented Generation) pipeline running entirely on their internal hardware. Using Ollama for local LLM inference and a fast edge vector database, the pipeline ingests marked-down versions of their PDFs. I structured a clean, minimal UI allowing partners to converse with their data, citing specific past proposals without any API calls leaving the local network.
Impact
Provided accurate, context-aware retrieval across hundreds of documents. Maintained 100% GDPR compliance. The firm now has an intellectual property compounding engine that respects data boundaries.