Model observability &AI architecture done right.

We design the platform your models run on: compute, storage, feature access, orchestration, and the environments between a laptop and production. Sized for the team you have and the workloads you're actually running, with the cost envelope agreed up front rather than discovered on the invoice.

• —ML platform and reference architecture
• —GPU, compute and storage planning
• —Feature stores and model registries
• —Cloud cost modelling and optimisation

Getting a model out of a notebook and into a service your product can call, reliably, at the latency your users will tolerate. Reproducible training runs, versioned artefacts, containerised inference, and automated release paths so a new model version is a routine deployment instead of an event.

• —Real-time and batch inference services
• —CI/CD and CT pipelines for models
• —Containerisation, autoscaling and GPU serving
• —Canary, shadow and blue-green rollouts

Models degrade quietly. We instrument yours so drift, data quality breaks, latency creep, and quality regressions surface as alerts with an owner, not as a customer complaint months later. Every prediction traceable back to the data and the model version that produced it.

• —Drift, skew and data-quality monitoring
• —Evaluation harnesses and quality scoring
• —Tracing, logging and latency observability
• —Automated retraining and rollback triggers

The operational layer that generative workloads need on top of classic MLOps: prompt and model versioning, offline and online evals, guardrails, caching, and routing across providers. Plus the access controls, audit trail, and per-feature cost visibility your finance and security teams will ask for.

• —Prompt, model and RAG pipeline versioning
• —Eval suites, guardrails and safety checks
• —Token cost tracking and provider routing
• —Access control, audit trails and compliance