Most organisations do not have an AI problem or a cloud problem — they have an architecture problem that both expose. Workloads were provisioned for a peak that never came, data sits in stores no model can reach safely, and every new initiative adds spend without retiring anything. We advise on the parts that are expensive to get wrong: what to build, what to buy, what to decommission, and how to run it without a surprise invoice.
Where engagements usually start
Rarely with a model. Usually with a bill nobody can fully explain, or a proof of concept that works on a laptop and cannot be put in front of customers. Both trace back to the same root causes:
- Infrastructure sized for imagined peak load rather than observed demand
- Data spread across systems with no consistent access, lineage or governance layer
- No environment parity, so what works in staging behaves differently in production
- Security and compliance treated as a release gate instead of a design input
How we work
We audit what is actually running — not what the architecture diagram claims — and produce a costed roadmap that sequences changes by payback period. Quick wins that reduce spend immediately come first, because they tend to fund the structural work that follows.
For AI specifically, we are deliberately conservative about where models belong. A retrieval system over well-governed internal data delivers more value than a fine-tuned model over data nobody trusts. We help you tell those cases apart before budget is committed, and we will tell you when the answer is that a workflow does not need a model at all.
What you get
How we run it
Frequently asked
See How AI Search Describes Your Brand Today
We run your domain through the same visibility checks we use on client accounts — AI answer coverage, technical SEO and content gaps — and send you the findings. No obligation.
