Cogniverr is a RAG system that runs entirely on your own hardware. Your documents are indexed locally, your AI model runs locally, and nothing ever leaves your network.
Most AI tools require sending your documents to a third-party server. Cogniverr does the opposite — it installs on your own server, loads a local language model, and answers questions about your documents without a single byte leaving your network.
Documents are tagged by project and classification level. Users only retrieve content they are explicitly authorized to see — enforced at the database layer, not just the app.
Drop documents in a folder. Cogniverr detects what changed, re-indexes only the new files, and keeps the vector store current without manual intervention.
Works with any GGUF-format model. Run it on a workstation, a server, or a fully air-gapped machine. No API keys, no subscriptions, no external inference calls.
Clone the repo, drop in a GGUF model, and run. Works on Linux bare metal, Docker, and Kubernetes. No cloud account required.
Organize files by project and access level. Cogniverr auto-discovers the structure and builds a local vector index. Only changed files are ever re-processed.
Query via the CLI or the REST API. Responses are grounded in your documents and scoped to the permissions of the user making the request.
Query case files and contracts without exposing privileged material to a cloud provider.
Keep deal documents and internal research on-premises in line with data residency requirements.
Run document intelligence on clinical data within your existing HIPAA-compliant infrastructure.
Deploy on isolated networks with no internet access using a self-contained model bundle.
If your team needs to run AI on sensitive documents without cloud exposure, we’d like to hear about your use case. Early access users shape the roadmap.