The full discipline
Five Steps to Governed AI
The complete methodology in detail. Where ungoverned AI costs money, what each step is worth, and ten operations worked through end to end.
A discipline for AI that is grounded in your data, checked against your rules, and provable when someone asks how it works.
The complete methodology in detail. Where ungoverned AI costs money, what each step is worth, and ten operations worked through end to end.
The model is not the bottleneck. The discipline around it is.
It does not know your firm's rules, so it guesses at the edges.
Nobody tested it on your real scenarios, so failures surface in production.
And when the examiner asks "show me why," there is nothing to show.
None of them are model problems. All three are decisions nobody made before the build started.
Build it yourself or have someone build it with you. Skip a step, and the output is not trustworthy.
Agree what the AI should do before anything is built. In plain language. Signed by whoever owns the process. Skip it and rework costs more than the build.
Connect the agent to your firm's actual data, documents, and policies, so it references them instead of guessing. Skip it and the agent hallucinates.
Run it against your hardest real scenarios and compare to what your team actually did. If it cannot match your team on the hardest five, it is not ready for the other 500.
Check every output against your rules. Pass or fail, with the rule named. A separate system, not the AI grading its own work. Skip it and the investment carries liability, not return.
Your team approves what executes. The record assembles itself as a byproduct. Skip it and there is no return on the time saved.
Any job where the answer has to be right, and you have to be able to show why. Six that show up in every firm that has to prove its work.
The domain changes, the discipline does not. Banking, insurance, lending, asset management: the same five steps decide whether the output holds up.
They take a morning, cost nothing, and reveal whether your firm is ready to build reliable AI.
Pick your most painful workflow. With whoever owns it, write the trigger, the rules, the exceptions. Get sign-off. That is your spec.
List every system holding the data the agent needs. Ask which one wins when they disagree. No answer? That is your grounding problem.
Pull five real cases from the past year where the workflow got tricky. Those are your first test cases, and your bar for ready.
This discipline works with any tools. We publish it freely because the industry needs a standard for how reliable AI gets built in environments where the output has to be defensible.
Follow the discipline with any tools, or use Factory, where the five steps are the platform: spec templates, 40+ integrations, industry test scenarios, output verification, and automatic run histories.