docsagent/docsagent

A local-first document intelligence layer that sits entirely on your machine. You point it at your folders, it indexes everything, and you query the resulting knowledge base through a chat interface. Nothing leaves your environment, no API calls home, no file uploads to a third-party server. The architecture is built around local embedding and retrieval, so even the vector search stays on-device. For founders handling sensitive contracts, client research, internal specs, or competitive analysis, this removes the awkward calculation of what is safe to upload to a hosted AI tool. It also works at scale, handling thousands of documents without performance collapsing. Who benefits most: solo researchers sitting on years of PDFs, legal or compliance-adjacent teams who cannot afford cloud exposure, and technical founders who want a private second brain over their entire codebase and documentation archive. The honest reservation is setup friction. Local AI tooling still demands more configuration tolerance than most non-technical users will accept, and model quality depends heavily on what you can run locally. -> Best for: privacy-conscious indie founders and small teams managing large sensitive document libraries who refuse to trade confidentiality for convenience.