01
Pilots that never ship
A demo that works on a laptop and a data extract. No evaluation anyone trusts, and no owner for the route to production — so there isn't one.
Approach
Foundation-first delivery. Four phases, one team, no handoffs.
The same engineers who scope the work build it and hand it over. Nothing is sold by one team and delivered by another, which is the single most common reason enterprise AI work goes wrong.
01 — The real problem
Most enterprises don't have an AI problem. They have a foundation problem.
The budget was approved. The pilots ran. Almost none of it reached production — and not because the models were wrong. There were no pipelines that held under real load, no evaluation anyone trusted, no route from notebook to system that survived a security review. The model is consistently the smallest part of the problem, and the last thing that needs fixing.
02
Nothing underneath it
Every answer needs four systems, three teams and a fortnight. The model is fine. The infrastructure it stands on isn't.
03
Advice without engineers
A strategy deck arrives. It is not wrong. It is also not a system, and nobody in the room can build it.
04
Governance bolted on last
Lineage, access and audit treated as a launch blocker rather than an architecture decision. So launch blocks.
02 — How we work
Four phases. The same people from end to end.
We deliberately don't publish fixed timelines — the honest answer depends entirely on the state of your data and the shape of the decision. What doesn't change is the sequence, and who is in the room.
01
Diagnose
We go into the data, the stack and the constraints. You get an honest map of what's ready, what isn't, and the shortest route to value — including the parts you won't want to hear.
02
Ground
We build only the foundation the first use case actually needs — pipelines, infrastructure, evaluation, governance — and no more. Nothing speculative.
03
Build
The system itself, in your environment, against your data, reviewed by your engineers as it goes. In production, not in a sandbox.
04
Hand over
Documentation, runbooks, and your team trained to run and extend it. We are structured to leave, not to embed.
03 — Already building?
Already working with AI, and hitting the wall?
Most of the work we're brought in for starts here. You don't need to be at the beginning to talk to us — we join at whatever point the sequence has stalled.
"The pilot worked. Production is nine months late."
Usually a foundations problem wearing a delivery problem's clothes. We find the actual blocker in weeks, then build the path.
"Risk and compliance won't sign it off."
We design lineage, access control and evaluation as architecture, not as a remediation exercise, and we've done it under FCA, GDPR and NHS IG.
"We inherited a system nobody can maintain."
We audit it, stabilise it, document it, and train your team to own it — or tell you plainly if it should be replaced.
Not ready for a full engagement?
Start with a fixed-fee AI & Data Readiness Assessment: a candid map of your data estate and infrastructure, and a costed route to the first system in production. It stands alone — no obligation to continue.