Air-Gapped Document Intelligence: AI Answers That Never Leave Your Network
Overview
The client needed AI answers over a sensitive document corpus, but that data legally could not be sent to OpenAI or Anthropic. The usual 'chat with your docs' build was off the table — not on cost, but on jurisdiction.
The Challenge
The client needed AI answers over a sensitive document corpus, but that data legally could not be sent to OpenAI or Anthropic. The usual 'chat with your docs' build was off the table — not on cost, but on jurisdiction. A second problem was subtler: a retrieval system that invents a citation is worse than one that stays silent, and they had no way to measure which they would get.
Our Solution
We built a self-hosted, provider-agnostic RAG platform — an engine for document-grounded assistants rather than a single chatbot. Retrieval is hybrid: vector search over pgvector/HNSW fused with Postgres full-text via reciprocal rank fusion, so exact-keyword and semantic matches both surface. Answers are citation-grounded with fabricated-citation rejection, so the system refuses when the answer is not in the corpus instead of improvising. A RAGAS-style evaluation harness measures faithfulness, answer relevance, context precision and context recall via CLI, UI and CSV export. Every model provider sits behind a lint-enforced adapter boundary, so switching between a hosted model and a local one is a config change.
Results
Runs fully air-gapped on a local model — zero outbound calls, proven end-to-end
Delivered in 18 days across 53 commits, with 11 handover guides
Answer quality measured continuously by a 4-metric evaluation harness
Pre-delivery security audit found and fixed a path-traversal flaw before handover
Tech Stack
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