Chapter Seven / Step Four
Prove
Before any output reaches your team or your clients, it is checked against your firm’s rules.
Pass or fail. With the rule named.
Why it matters
A confidence score is not proof.
Unverified, the firm carries liability for every output. When something goes wrong the cost is not the error itself. It is every hour spent establishing whether the error happened, plus every client who now checks your work before paying. A confidence score is a property of the model, not of the answer. What survives the question is the rule, the input, the output and the link between them.
What good looks like
A separate verification layer, not the same AI grading its own homework, working from your written-down policies and returning a definitive pass or fail. On a failure you see the rule that broke and the data that caused it.
In PitCrew Factory, this layer is powered by AWS Automated Reasoning, a formal verification system that checks the rules as written. It is not a second model checking the first.
R 3.4 was applied to this input and produced this output. Pass.
The model reported 94% confidence.
The same output, two answers
Output consistent.
Pass.
Source not cited.
No rule named.
Figure 7.1
The rules check. A different system does the checking.
The gate is separate from the thing it is checking. That separation is what makes the result proof rather than an opinion.
What travels with a pass
In your firm
The agent reports that the wire matches standing instructions. The routing number is one digit off from the file.
Pattern matching found a near-fit and called it a match. A rules check compares the field exactly, and catches it before $200K moves to the wrong account.
The number was there. Nothing recorded which rule had produced it.