Infrastructure built for the day after launch.
We design and run the cloud platforms AI systems live on: secure, observable, cost-controlled and compliant with European data residency — on Google Cloud, AWS or Azure, as code your team can maintain.
The problems this solves.
The pilot can't be hosted
It runs on a laptop or a vendor sandbox, and nobody has designed where it lives, who can reach it or what it costs at scale.
Cloud bills nobody predicted
GPU time, token spend and storage grow unobserved until finance notices.
Residency and security as afterthoughts
Data crosses regions it should not, secrets live in config files, and the audit question has no answer.
Capabilities, in the order they're usually needed.
Not a menu to pick from — a sequence. Most engagements start at the top and stop when the business number moves.
Cloud architecture
Landing zones, networking, identity and environments designed for AI workloads on GCP, AWS or Azure — or across them, where that is the honest answer.
Infrastructure as code
Everything reproducible from Terraform or the cloud's native tooling, reviewed and deployed through CI, so environments are consistent and recoverable.
AI deployment infrastructure
Serving for models and agents — managed APIs, self-hosted models, GPU scheduling, caching and routing — sized for your traffic rather than for a benchmark.
MLOps and LLMOps
Pipelines for training, evaluation and release; prompt, model and dataset versioning; rollback and canary strategies.
Cost optimisation
Budgets, tagging and alerts; right-sizing; model and token routing; the reporting that tells you what each use case costs to run.
Monitoring and observability
Logs, traces and metrics across infrastructure and models, with the dashboards and alerts your on-call team needs.
Cloud security
Least-privilege IAM, secrets management, network controls, vulnerability management and the evidence trail for audits.
European data residency
Region pinning, data-flow mapping and provider configuration so personal and regulated data stays where your obligations say it must.
How this fits the orchestration.
Infrastructure is the hall the orchestra plays in: acoustics, lighting, exits. Our orchestration model treats it as a shared platform — one secure, observable place where every AI use case runs, instead of a new stage for each one.
Orchestration is the layer between your business goals and your AI capabilities — the part that decides what to build, how it fits together and how you know it's working. See how orchestration works.
Deliverables you can use.
Every engagement ends with something that works without us: documents your board can read, systems your engineers can run.
- Platform architectureReference design with environments, identity, networking and the AI serving layer.
- Infrastructure as codeRepository, CI pipeline and documentation, deployed to your accounts.
- MLOps/LLMOps pipelineRelease process for models, prompts and agents with evaluation gates.
- Cost and observability setupBudgets, dashboards, alerts and cost reporting per use case.
- Security and residency reviewFindings, remediation and the controls evidence for your auditors.
Usually played together.
Ready to bring your AI into tune?
Tell us the business goal. In a 30-minute call we'll show you how we'd score it — which instruments, in which order, measured how.