On-premise deployment
All of it, inside your walls. Nothing leaves.
The problem it removes
For banks, clinics, and public institutions the question is not whether AI helps; it is whether a single customer record may leave the building. Often the honest answer must be no.
Uzbek data legislation localizes personal data processing. A cloud endpoint in another jurisdiction is not a compliance strategy.
What gets built
The full stack, self-hosted
Language models, the knowledge base, integrations, and logs run on your hardware or your private cloud. No external inference calls.
Sized to your load
We spec the hardware honestly: a support assistant for two hundred dialogs a day does not need a GPU cluster.
Your security model, respected
SSO, network segmentation, audit logging, and role-based access wired to your existing policies. Your security team gets documentation, not surprises.
SLA and handover
Response times in the contract, updates on your schedule, and complete documentation so your team can run it without us if you choose.
What it plugs into
- Your servers or private cloud
- Internal CRMs, ERPs, and databases
- Corporate SSO and access control
- Telephony inside your perimeter
What typically changes
- Data residency requirements are met by architecture, not by contract clause.
- Security review passes on documentation instead of stalling the project.
- The organization gets modern assistants without a single external API call.
Framed from industry benchmarks and typical deployments, not from named clients.
Where it lives in pricing
Enterprise is the on-premise package: from 65 000 000 UZS, scoped per project after a technical audit. All packages and details
Are self-hosted models weaker than cloud ones?
For open-ended creative work, somewhat. For grounded business tasks answering from your documents, well-chosen open models perform close to cloud quality, and we benchmark on your real questions before you commit.
What hardware do we need?
It depends on load and latency targets. Typical support deployments run on one GPU server; we provide the exact spec and can supply through your preferred vendor.
Who maintains it?
Your choice: our SLA covers updates and monitoring, or we train your team and hand over. The documentation is written for the second case either way.