Private RAG and LLM. Nothing Leaves Your Network.
A search-and-answer platform built on Retrieval-Augmented Generation (RAG) and a private LLM — a large language model that runs entirely on your own infrastructure. Ask a question in plain language, get a cited answer pulled from your manuals, compliance records, and engineering documentation — no document, query, or model call ever reaches a third-party API.
Why organizations choose private RAG and a private LLM
If your documents include compliance credentials, controlled technical data, or anything you're contractually or legally required to keep in-house, a public AI API is a non-starter no matter how good the answers are. We build the alternative: retrieval, embeddings, and the language model all run on infrastructure you control, so nothing ever leaves your environment to get an answer.
Nothing leaves your environment.
Embeddings, retrieval, reranking, and the language model itself all run on infrastructure you control. No document, query, or generated answer ever touches a third-party API.
Answers your team can verify.
Every response comes with citations back to the source document, a confidence indicator, and a warning when a document is a draft or has been superseded so nobody has to take an AI's word for it.
Built for documents that don't play nice with plain keyword search.
Part numbers, drawing numbers, serial numbers, and compliance codes get exact-match search running alongside semantic search, so precision-critical lookups don't get buried under "close enough" results.
A fixed price and a fixed timeline.
You get a defined first release, delivered in phases with a review point every one to two weeks, for one upfront price not an open-ended AI experiment with no delivery date.
AI model choice you can defend.
Every engagement includes a documented review of where the embedding, retrieval, and generation models actually come from recorded in writing before anything goes into production. If your organization needs to justify its AI stack to a security team, a customer, or an auditor, that answer already exists.
Discovery : We map your documents, your access rules, and your acceptance criteria before writing a line of code.
Infrastructure : Your self-hosted platform goes live inside your approved environment, with single sign-on and role-based access from day one.
Ingestion : Your manuals, drawings, and records are processed, indexed, and made searchable — both by meaning and by exact identifier.
Search & AI workflow : Your team gets a purpose-built research portal: ask a question in plain language, get a cited, ranked answer.
Testing : Real users, real questions, measured against benchmarks you helped define.
Launch & handover : Your team is trained, your documentation is delivered, and the platform is yours to run.
The Stack
Every layer of this platform is open, self-hosted, and swappable — no proprietary runtime, no vendor lock-in, and nothing that has to leave your infrastructure to work.
Interface & Identity
Next.js
React
TypeScript
Node.js
Entra IDAI, Search & Retrieval
Dify
Hugging Face
Qdrant
PostgreSQL
Mistral AIInfrastructure & Operations
Docker
NGINX
Ubuntu
Redis
MinIODocument Sources
SharePoint
Microsoft GraphGeneration and embedding models are selected per engagement from an approved, allied-origin shortlist — Mistral AI is shown above as the representative default candidate, not a fixed commitment.
Let's Connect /
Ready to see what private RAG and a private LLM can do with your data?
Private RAG on a private LLM for sensitive technical documents. Your data, your infrastructure, cited answers
No. Every component document storage, search, and the AI model itself runs inside your approved environment. Nothing is sent to a third-party AI API at any point.
We deploy self-hosted, openly licensed models and document exactly where each one (generation, embedding, and reranking) comes from before it goes live, so your organization can sign off on the choice rather than take it on faith.
A first release typically takes 8–11 weeks from kickoff, depending on document volume, infrastructure access, and how quickly your team can review and approve each phase.
Every engagement starts with a fixed-fee proposal scoped to a defined first release, so you know the full cost before you commit not a per-seat or per-query bill that grows with usage.
