Why do most AI pilots never graduate?
Because they were built to impress, not to survive. Demos optimise for the best output; production is judged on the worst. Our build lifecycle is designed around that single asymmetry.
Five stages, in an order that rarely gets followed
Value case
What decision or task does this take on, and what is that worth per month? If we can't answer, we don't build.
Ground truth & evals
A scored evaluation harness against real data — before the first agent exists. Accuracy becomes a number, not a feeling.
Models read, code decides
The line is drawn explicitly: LLMs interpret unstructured input; deterministic code owns arithmetic, state and side-effects, checked by invariants.
Assurance by design
Human-in-the-loop by exception, tool scoping, egress control, confidence thresholds — architecture, not prompt hygiene.
Production ops
Cost per unit of work, drift monitoring, versioned deployments, and honest dashboards of eval scores over time.
The questions we ask before a single agent exists
- What does 'good' look like — and how will it be measured in production, not in a notebook?
- Where is the ground truth, and who maintains it as the business changes?
- What happens when the model is wrong — who reviews, who overrides, who is accountable?
- Which numbers, records or actions must never be produced by a model directly?
- What does a unit of work cost — and at what volume does that break the business case?
- What data feeds the system, and can it be trusted, traced and refreshed?
The organisational versions of these questions — who may use what, on which data, at what cost — live on the consulting side: AI readiness & operating model.