Local RAG
Search and answer over your own documents, running entirely on hardware you own. No cloud calls, no per-token cost, nothing leaves the building.
Most RAG offerings are a wrapper around someone else's API. Ask a question, and your documents and the question both travel to a cloud model before an answer comes back. For a law firm, an accounting practice, or anyone else bound by client confidentiality, that path is closed before the pitch even starts.
This build runs entirely on hardware you own. The documents never leave your network, the model runs locally, and there is no per-token bill for a client to notice on your invoice.
Wiring a vector database to a model takes an afternoon. Making it answer correctly is the actual work: a passage can sit right there in your documents and still never surface, because the question and the source use different words for the same thing, because one large document family crowds out a smaller one, or because the ranking step scores the wrong passage higher than the right one. That tuning is what turns a demo into something you would hand a client.
What you get
- Retrieval and generation running entirely on your own hardware
- No cloud API calls, no per-token cost, no data leaving your network
- Tuning pass to catch and fix retrieval misses before launch
- Source by source verification against real questions, not a demo script
- Written documentation of what was tested and what is a known limitation