RAG Infrastructure
Private RAG and document AI infrastructure
Dedicated hardware for embeddings, vector search and retrieval-augmented generation over your own documents. Everything stays on your private server.
Why private RAG
Search and reason over your own knowledge
RAG lets a model answer using your documents instead of guessing. On private infrastructure, your knowledge base, embeddings and index never leave your server, which matters for sensitive internal content.
- Embeddings on your hardwareGenerate and store vectors privately, with no external calls.
- Vector search included in your stackRun your vector database alongside the model on the same server.
- Document pipelinesIngest, chunk and index your own content on dedicated GPUs.
- Fully privateKnowledge base and index stay inside your infrastructure.
FAQ
Common questions
Retrieval-augmented generation combines a language model with search over your own documents. The infrastructure is the GPU server, embeddings and vector store that power it, all on private hardware.
Yes. Your documents, embeddings and vector index live on your dedicated server. Nothing is sent to an external AI platform.
Embedding models, a vector database, the LLM runtime and your retrieval pipeline. You run the pipeline; we provide and host the hardware.
It depends on the embedding and generation models you choose. We help size GPU and VRAM during workload analysis.
Build private RAG on your own server
Tell us about your documents and goals. We will design the right GPU server for retrieval and generation, host it and hand it over.