Private & On-Premise AI Solutions

We help you deploy AI models and supporting infrastructure locally so inference and (where needed) training run in your environment. We support model selection and sizing, deployment onto your hardware (servers, GPU nodes), containerisation and orchestration, and integration with your applications and data pipelines so you have an end-to-end local AI capability. We work with open-source and commercial models and can support ongoing updates and model refresh so your deployment stays current and secure.

We help you design on-premise AI infrastructure so your environment can support the AI workloads you need. We support requirements analysis (throughput, latency, model size, users), architecture design (compute, storage, network), technology selection (GPU types, servers, software stack) and capacity planning so you invest in the right scale. We consider power, cooling and physical constraints and can produce design documents and implementation plans so your team or partners can build and operate the infrastructure. We can also support hybrid designs where some workloads run on-premise and others in a private or public cloud under your control.

We help you optimise AI infrastructure and GPU/compute environments so you get the best performance and efficiency from your investment. We support GPU and hardware selection, cluster design and workload placement, inference and training tuning (batch size, precision, parallelism), and monitoring and capacity management so you identify bottlenecks and scale appropriately. We can assess your current setup, recommend optimisation (e.g. model quantisation, better utilisation) and support implementation so your private AI runs faster and more cost-effectively.

We help you deploy and manage private large language models (LLMs) so you can use generative AI without sending data to external services. We support selection of open-source or licensable LLMs, deployment onto your infrastructure (on-premise or private cloud), configuration and tuning, and ongoing management (updates, monitoring, access control) so your private LLM is available, secure and aligned with your use cases. We can integrate private LLMs with your applications and workflows and support governance so usage is auditable and compliant.

We help you implement offline AI systems for secure environments where connectivity to the internet or external APIs is not allowed or desired. We support deployment of models and inference stack so they run entirely within your network, design of update and model refresh procedures (e.g. via secure transfer or on-site media) and integration with your applications so users can access AI capabilities offline. We consider data residency, classification and access control so your offline AI meets your security and compliance requirements.

We help you design and implement secure AI systems for air-gapped or high-security networks where isolation from external systems is required. We support architecture that keeps AI workloads and data within the secure boundary, secure procedures for introducing models and updates (e.g. one-way transfer, validation), and hardening of the AI stack (access control, audit, encryption) so the system meets your security and accreditation requirements. We work with your security and compliance teams to align with policies and can support documentation and assurance for regulated or classified environments.