Solution

AI-Ready Cloud Infrastructure

Secure, cost-predictable cloud foundations to run AI and analytics — on AWS, Azure, Google Cloud or your own data centre.

CloudBuilt to run AI“Will it stay up, stay secure, and not surprise me on the bill?”

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Is this you?

If two of these sound familiar, this solution is worth a conversation.

“AI pilots run on someone’s laptop or a one-off server”
“Cloud bills are a monthly surprise”
“Security wants to know where the AI runs and what it can reach”

What you get

What changes once it is live

Production-grade environments for AI from the first use case
Predictable running costs with built-in guardrails
Compliance-ready logging and access control

We design and operate the cloud environments AI workloads need — private networking, compute that scales with demand, encryption and access controls, and budgets with alerts — so pilots graduate to production without a rebuild.

In detail

What’s included — and the impact you should expect

What an engagement actually covers, and what should be different for your business once it is live.

What’s included

Private, compliant environments on AWS, Azure, Google Cloud or on-premise
Compute that scales with demand, including specialised AI hardware
Encryption, access control and audit logging as standard
Budgets, alerts and right-sizing so costs stay predictable

Who it’s for

The people who usually sponsor this

If one of these is in the room, this solution is worth the first conversation.

01
Security and compliance leaders who will not let AI run on someone’s laptop
02
Finance partners surprised by last month’s cloud bill
03
Platform teams asked to ‘just make it run in production’ after a demo

How it works

Four steps from the problem you have to the change you want

A path your marketing team can walk a client through — no architecture diagram required.

01
Set the boundaries first
Where it may run, which regions, who may reach it, what must be logged. Security answers these before a model is chosen.
02
Build an environment that can grow and shrink
Busy when the business is busy, quiet when it is not. Specialised AI hardware only when the workload needs it.
03
Put a budget on it
Alerts, limits and right-sizing so a successful pilot cannot become an unbounded bill.
04
Make production the default path
The same environment pattern for the next use case. Demos that cannot run here do not start.

A typical engagement

What the first 90 days look like

One real problem, built on real work, measured before we talk about the second use case.

1Weeks 1–3
Map and agree the outcome
Security, finance and the first AI owner agree region, access, logging and a monthly cost envelope.
2Weeks 4–10
Build it on real work
A production-shaped environment running the first real workload — not a rebuilt demo server.
3Go-live and after
Run, measure, expand
The next team clones the pattern. Bills match the envelope. Auditors see the logs they asked for.

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