Technical problem
Models 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.
Real-time LLMOps: monitoring, guardrails and rollback
Architectural solution
We build LLMOps architectures that inspect all live AI traffic in real time. Guardrails on the input and output layers automatically mask sensitive data — personal information, card numbers, credentials — and block malicious instructions. When the system detects a deviation, automatic rollback moves traffic straight back to the last stable model version.
Operational outcome
Zero security exposure in production, answer quality that holds, and a guarantee of uninterrupted operation when a model fails.
IV
Security, data privacy and LLMOps
On-premise and private-cloud LLM deployment with data security layersIn regulated sectors — finance, healthcare, defence, law — sending sensitive data to third-party APIs creates legal exposure under data protection law.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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