Company knowledge AI
Your documents, answered with citations — and never indexed off your instance.
A company knowledge AI is a retrieval-augmented system: your documents are chunked, embedded into a vector store, and searched at question time so the model answers from your material with citations. Hosted here, both the documents and the vector index stay on a single-tenant instance you control.
The failure mode of most internal AI projects is not the model — it is that the documents never make it in. The tools in this stack are the ones that solve ingestion first: AnythingLLM for a workspace anyone can drop files into, Dify when the pipeline needs configuration and an API, Onyx when the knowledge lives in the tools you already use rather than in files, RAGFlow when the source material is complex layouts and scans.
Retrieval quality is where the money is. That means chunking you can inspect, embeddings you can change, and a vector store you can query directly — which is why we offer Qdrant, Weaviate and pgvector as instances in their own right rather than hiding them inside an application.
The applications in this stack
AnythingLLM
A private document workspace — drop files in, ask questions, get cited answers.
from $18.99/moDetails →
Dify
A visual studio for building LLM apps, agents and RAG pipelines.
from $18.99/moDetails →
Onyx
Search and chat across every tool your company already uses.
from $27.99/moDetails →
Qdrant
A fast vector database with real filtering, written in Rust.
from $5.99/moDetails →Straight answers
Do I need a separate vector database?
Where do embeddings get computed?
How much storage do documents need?
Put your AI stack on your own box
Pick an app, pick a size, and have it running today. Month to month, cancel whenever.