DocMind — RAG Frontend for Private Data
DocMind makes private document collections — PDFs, notes, internal wikis, contract archives — semantically searchable, fully offline. Embeddings are computed locally, Qdrant runs as a sidecar process inside the Electron app. User data does not leave the computer. Built for lawyers, fiduciaries, research teams, and SME leadership who need AI on confidential material without sending it to the cloud.
Architecture highlight — Qdrant as sidecar in Electron
Classic RAG solutions need a vector database in the backend — meaning server hardware, hosting, network. DocMind starts a Qdrant instance as a child process inside the Electron app, tied to the user window's lifecycle. Embeddings are computed locally with Sentence-Transformers via CPU or Apple's Neural Engine. Result: zero servers, zero external dependency, zero outbound network traffic for research. The index is encryptable and can live on an encrypted volume.
What local RAG can deliver — and where cloud still wins
In 2026 local embedding models are good enough for almost every German-language use case we see in advisory mandates. What does not scale locally: very large corpora (millions of documents), cross-lingual search with rare language pairs, and state-of-the-art reranking. For 90 percent of SME use cases (internal documentation, contract archives, knowledge bases) a local setup on a modern Mac or Windows laptop is sufficient. DocMind lets us answer the 'local or cloud' question empirically in advisory engagements — not from marketing claims.
Practice proof for RAG strategy workshops
Anyone who runs DocMind learns quickly: the hardest RAG problems are not model problems. They are chunking problems, metadata problems, reranking problems. We bring this experience into our RAG strategy workshops for law firms, fiduciary offices, and public administrations — including concrete architecture sketches, vendor evaluation local vs. cloud, and a realistic cost/effort estimate.