Privacy-first document chat using fully local LLMs - no data leaves the machine. Built for enterprises that can't compromise on data security.
The client needed to query internal documents - contracts, technical specs, compliance records - using natural language. But sending this data to cloud-based AI APIs like OpenAI or Google was a non-starter due to strict data privacy requirements.
Existing solutions either required cloud connectivity, lacked the accuracy needed for technical documents, or were too complex for non-technical staff to use. They needed a system that was as easy as chatting but kept everything local.
I built a complete Retrieval-Augmented Generation (RAG) system that runs entirely on-premise. The architecture uses three core components:
Local LLM inference engine. No API calls, no cloud. Supports multiple models for different accuracy/speed tradeoffs.
Vector database for semantic document search. Documents are chunked, embedded, and indexed locally for instant retrieval.
Custom ingestion, retrieval, and response generation pipeline. Handles PDF, DOCX, TXT, and Markdown formats.
Architecture diagram: Document → Chunking → Embedding → ChromaDB → Query → Ollama → Response
The system enabled non-technical staff to query complex documents in natural language, getting accurate answers in seconds - all without any data leaving the building. Cross-platform support (macOS and Windows) ensured the entire team could use it.