Sovereign AI infrastructure
Your models. Your perimeter.
Your data stays put.
ZuBrIQ is not a chat subscription. It is a complete LLM stack — gateway, private models, retrieval over your documents, agents with tools — deployed inside infrastructure you own and operate. Nothing is sent to a foreign cloud, because there is no foreign cloud in the picture.
Built and operated by ArtCloud, an engineering company running this stack in production since 2016.
Where the boundary runs
Your perimeter
Servers, GPUs, network segments and access policy — all yours. The stack runs where you put it, air-gapped if that is the requirement.
Our stack
Gateway, models, retrieval, agents and tooling. Delivered, installed and supported by us; operated by you.
Your data
Documents, prompts and answers never leave the boundary. Nothing is used for training, because nothing is sent anywhere to train on.
What gets deployed
Six components, installed together or in the order that matches your priorities.
One OpenAI-compatible endpoint
Gateway
Quotas, per-team budgets and usage accounting in front of every model you run. Applications change one base URL and keep working.
Open weights on your hardware
Private models
Your GPUs, your racks. Requests never leave the network segment you put them in, and nothing is sent to a foreign vendor.
RAG with your own embedder
Knowledge base
Documents are chunked, embedded and indexed inside the perimeter. Answers cite the source document, and updating knowledge means replacing a file.
Documents, drive, mail, calendar, search, MCP
Agent with tools
The assistant does work rather than describing it: fills documents, reads files, queries internal systems through MCP tools you allow.
Names and numbers never reach the model
PII masking
Identifiers, cards, accounts and personal names are replaced with synthetic values before the request leaves, and restored in the answer — streaming included. Events go to your SIEM.
Engineering work inside the perimeter
Coding agent and sandbox
A terminal agent that edits files, runs tests and prepares commits against a private model, with execution confined to a sandbox you control.
Who this is for
Organisations that cannot put their data in someone else's cloud — whether because of regulation, contract or plain risk appetite.
- Banks and financial institutions under data-residency rules
- Government and public-sector bodies
- Telecom operators with subscriber data they cannot export
- Industrial and holding groups with proprietary engineering data
How a deployment goes
Scoping
We map the use cases worth automating first, the data they touch and the boundary that data must not cross.
Sizing
Model selection drives the hardware, not the other way round. We size GPUs, storage and network against your concurrency, then agree what the pilot runs on.
Pilot
The stack is deployed in your perimeter and evaluated on your real documents and your real questions — not on a benchmark.
Handover
Operations, monitoring and update procedure handed to your team, with our engineers reachable directly.
Hardware requirements depend on which models you run and how many people run them at once, so we size them with you rather than publishing a number that would be wrong for most readers.
Talk to the engineers who deploy it
Tell us what you want to automate and what the data boundary looks like. You will get an engineer, not a sales sequence.