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AI and data engineering
Systems that reach production and earn their keep
Plenty of AI work stalls at the demo. We build data and AI capability designed to survive production: costed before it is built, monitored once it is live, and governed so it holds up to scrutiny from your auditors and your board.
You may recognise
- Promising pilots that never reached production
- AI spend rising without a matching return
- No clear view of what a model costs to run
- Compliance is asking questions you cannot answer
How the work runs
- 01
Cost it before you build it
Inference, storage and data movement are the line items that surprise people six months in. We model them upfront so the business case is honest and the system is designed against a budget rather than discovered to exceed one.
- 02
Build pipelines for production load
A pipeline that works on a sample and a pipeline that runs daily against real volume are different systems. We build the second kind, with the failure handling that implies.
- 03
Optimise inference for speed and spend
Model serving is where cost and latency both concentrate. Batching, caching, right-sized hardware and honest evaluation of whether the largest model is actually required.
- 04
Monitor quality, not just uptime
A model that is up but drifting is worse than one that is down, because nobody notices. We instrument output quality alongside system health.
- 05
Govern it so it can be defended
Audit trails, data lineage, access controls and documented decisions — the things that determine whether a system survives its first serious question from compliance.
What you get
Cost model produced before build
Production-grade data pipelines
Optimised model serving and inference cost
Production ML pipelines
Quality and drift monitoring
Governance, lineage and audit trails
Documentation and team handover
Common questions
Our AI pilot never made it to production. Why does that happen?
Usually because the pilot optimised for a demo rather than for operation — no cost model, no monitoring, no failure handling, and no owner once the excitement passed. Production readiness is a different engineering problem from proving the idea, and it is the one we work on.
How do you control AI running costs?
By modelling them before the build, then attacking the three places cost concentrates: inference volume, model size relative to the task, and data movement. Frequently a smaller model with better retrieval outperforms a larger one at a fraction of the spend.
Do we need AI at all?
Often not, and we will say so. A good deal of what is scoped as an AI project is a data quality or automation problem wearing a fashionable label, and solving it directly is cheaper and more reliable.
Can you work with our existing data team?
Yes, and that is the usual arrangement. We work alongside internal teams and hand over documented systems rather than leaving a dependency on us.
Start with the free two-week assessment.
You get a written report on where your budget goes and what it would take to reduce it. Yours to keep either way.
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