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Company knowledge AI

Your documents, answered with citations — and never indexed off your instance.

from $16.99/mo single tenantbring your own key
In short

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 logo

AnythingLLM

A private document workspace — drop files in, ask questions, get cited answers.

from $18.99/moDetails →Dify logo

Dify

A visual studio for building LLM apps, agents and RAG pipelines.

from $18.99/moDetails →Onyx logo

Onyx

Search and chat across every tool your company already uses.

from $18.99/moDetails →Qdrant logo

Qdrant

A fast vector database with real filtering, written in Rust.

from $5.99/moDetails →
How it works

Three steps, no Docker knowledge required

Pick the app and the size

Choose memory and storage from the plan table. Every plan is a single-tenant instance with its own volume, its own configuration and its own admin account.

We deploy and harden it

TLS on your domain or ours, firewall, a version pinned to a reviewed release, nightly off-box backups and isolated secrets.

You log in and build

The admin account is yours. Add your API keys, invite your team, export your data whenever you want. Patching stays with us.

Questions

Straight answers

Do I need a separate vector database?
Not to start. AnythingLLM and Dify both ship with an embedded store that is included in the plan. Move to a dedicated Qdrant or Weaviate instance when the corpus grows or several applications need to share one index.
Where do embeddings get computed?
Either at a provider you configure with your own key, or on a local model plan. In the second case no part of your document set leaves our network.
How much storage do documents need?
Text is small; the index is the part that grows. As a rough guide, a million 768-dimension vectors sit around 3 GB before quantization. Tell us the corpus size and we will size it with you.
Is this a shared account or my own instance?
Your own. Every plan is a single-tenant instance with its own storage, its own configuration and its own admin account. Nobody else’s workload runs inside it, and nothing you store is pooled with other customers.
Do I have to know Docker?
No. We install the application, put it behind TLS on your domain or a subdomain of ours, and hand you the login. If you do want shell-level access to your data we provide SFTP; you are never required to use it.
What is included in the monthly price?
The instance, the storage, the bandwidth, nightly off-box backups, monitoring, security patching and human support. Model tokens are not included — you bring your own provider key, so there is no markup on usage.
Can I move to a bigger plan later?
Yes, resizing is an in-place upgrade and your data stays where it is. If you outgrow the catalogue entirely we will help you export everything.

Put your AI stack on your own box

Pick an app, pick a size, and have it running today. Month to month, cancel whenever.

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