The AI Delivery Contract
Taking AI into production is not about picking a clever model. Value is created or destroyed at every stage, from data to switch-off, and governance is the thread that runs through all of it. The model is the easy, replaceable part. The contract around it is the product.
No code required. Click around. Everything on this page runs in your browser.
A good architecture states what exists, what does not, and who is responsible. The whole journey is four stages, with a governance "spine" running across all of them. Click any stage to see what has to be true before work can move on.
Most "AI governance" is a document nobody checks. This project ships a small program that checks the rules automatically, the same way a test blocks a broken build. The headline rule is fairness. Below is the exact check the project runs, made visual. Flip the switch and watch what happens.
Who gets advanced, by group
The four automatic checks
What you are looking at. The dashed line is the fairness bar (a long-standing legal rule of thumb: the lower group must reach at least 80% of the higher group). When a change quietly makes the assistant favour one group, Group B drops below the line, the fairness check fails, and the change is refused before it can ship. The rule was decided in advance and is enforced by the machine, not by good intentions.
The same contract applies whether AI advises a human or acts on its own, but the risk lands in different places. Switch between the two and see where the attention goes.
A reference earns trust by naming the failures and the control that owns each one. None of these are model problems. They are lifecycle problems.
| What goes wrong | What catches it | Stage |
|---|
This page is the doorway. The repository has the full reference, the reusable templates, two worked examples, and the runnable check itself.
Run the fairness check yourself: clone the repo and run make gate, then ADC_INJECT_BIAS=1 make gate to watch it fail.