Technical problem
As users and queries grow, GPU and API token costs in live AI systems get away from you.
Compute, token and infrastructure cost optimisation
Architectural solution
Semantic caching answers similar questions from cache in milliseconds without running the model at all. Dynamic model routing sends simple queries to small, cheap models and complex analytical ones to large models. Prompt compression and token-trimming algorithms bring the cost of data transfer down.
Operational outcome
A direct saving of 60% to 80% on AI infrastructure and running costs, with no compromise on model quality or system speed.
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.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.
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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.