Technical problem
In regulated sectors — finance, healthcare, defence, law — sending sensitive data to third-party APIs creates legal exposure under data protection law.
On-premise and private-cloud LLM deployment with data security layers
Architectural solution
We deploy every model and vector database entirely on the organisation's own physical servers or inside an isolated private cloud, and we design air-gapped infrastructure where the requirement calls for it. At retrieval time, row-level security and role-based access control decide which document fragments a given user is allowed to see.
Operational outcome
High-security AI use in which not one byte of data leaves the company, in full compliance with the regulation that applies to it.
IV
Security, data privacy and LLMOps
Real-time LLMOps: monitoring, guardrails and rollbackModels in production lose performance over time as the data drifts, produce harmful or inappropriate answers under prompt injection, and there is no way to intervene the moment it happens.Compute, token and infrastructure cost optimisationAs users and queries grow, GPU and API token costs in live AI systems get away from you.
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Which one shall we start with?
See it on your own data in one session, or write first and ask what we do.