Getting a model working in a prototype is one thing. Running AI reliably, securely and affordably in production is another. We build the platforms and pipelines that close that gap.
What we deliver
AI infrastructure
We design AI platforms on Amazon Bedrock and SageMaker, Azure OpenAI and Azure Machine Learning, Google Vertex AI, or your own Kubernetes clusters. This includes private hosting of open-weight models and vector databases for retrieval-augmented generation.
MLOps pipelines
- Automated, reproducible training and evaluation pipelines
- Versioning of data, code and models, with a model registry
- CI/CD for models, so new versions are tested and promoted like any other software. See DevOps and CI/CD
Model serving and scaling
Inference endpoints that scale with demand, efficient GPU scheduling on Kubernetes, and request batching and caching to keep latency low.
Cost and footprint control
AI workloads can become expensive quickly. We keep them efficient by right-sizing GPU capacity and using spot capacity for training, choosing smaller models where they meet the need, applying caching and quantisation, and setting usage budgets and per-team reporting. The same approach underpins our cloud optimisation service.
Monitoring and quality
We track latency, cost per request, model drift and output quality, with guardrails and alerts so that issues are caught before your users notice them.
Security and data residency
Network isolation and private endpoints, managed secrets, fine-grained access control, full audit logging, and hosting in UK or EU regions where your data must stay.
Want to explore what AI could do for your organisation? Book a free initial consultation.