Technical Problem
In regulated sectors such as finance, healthcare, defence and law, sending sensitive data to third-party APIs creates legal risk in terms of data security and GDPR/KVKK.
ServicesIII — Process Automation, Autonomous Agents and Workflows
LLMDeployment
Closed-Circuit (On-Premise / Private Cloud) LLM Deployment and Data Security Layers
Architectural solution
We deploy all AI models and vector databases entirely on your own physical servers (on-premise) or in an isolated private cloud. We design air-gapped infrastructure, cut off from the outside world. At the retrieval stage we build row-level security and role-based access control layers, so the model is served only the document fragments the user is cleared to see.
Operational outcome
A deployment where the model and the vector database run inside the organisation's boundary, outbound traffic is limited to an allowlist, and that limit can be demonstrated by measurement. Legal compliance itself requires a legal assessment; what is produced here is the technical evidence such an assessment rests on.
Starting Conditions
This service applies when data must not leave the organisation's boundary. Three things have to be on your side: hardware or an isolated cloud resource for the model to run on, access rights to it, and a classification saying which data is genuinely sensitive. The third is the one most often skipped: treating everything as sensitive makes a closed deployment needlessly expensive and slow.
How We Work
Classification and model selection happen together first: which work a model of which size can carry — in a closed deployment every model has a hardware price. The installation comes up inside an isolated segment with no outbound path. Measurement and logging are on from day one, because logging added later cannot answer questions about the past. At handover, the procedures for running and updating it are delivered in writing.
Out of Scope
A closed deployment is not on its own a compliance certificate; the network, access and logging layer is separate work and is described under On-Premise / Private Cloud. We do not take on hardware procurement. Installation also does not determine model quality: expecting the answer of the largest hosted model while running a smaller one in a closed environment is a trade-off that has to be discussed in advance.