Regulated and confidential work

For a whole class of professions, local AI is not a preference, it is the only legal option. These are the cases where the method pays off most, because the stakes are highest and the argument is the clearest.

The professions

• Law — client documents, privileged material, matters that cannot leave the firm.
• Healthcare — patient records, governed by strict rules.
• Finance and M&A — deals, valuations and non-public information.
• Any business with client data, trade secrets, or contractual confidentiality.

For these, "can it go to the cloud?" is not a cost question, it is a compliance question, and the answer is often no, or yes-with-a-mountain-of-caveats.

Why the method matters more here

In regulated work you cannot wave away a wrong answer. The evaluation is not optional polish, it is the thing that keeps you out of trouble:

• Grounding — answers must be traceable to the source document.
• Anonymization — your evalbench itself must never leak, so the data discipline is doubly important.
• Human review — the model drafts, a qualified person signs off. The pipeline must make that review easy, not decorative.

The honest pitch

For these clients, the argument is almost self-made:

1. The data cannot leave, so cloud is out.
2. Local keeps it in the building, under your control.
3. The evaluation proves it hits the bar on your documents.
4. The human review is the final guarantee, and it is built in.

That is a stronger, more honest pitch than any benchmark, because it is about control and compliance, not about beating the cloud on raw intelligence.

The line you do not cross

Never claim a model is compliant, or that it replaces professional judgement. You provide a tool, measured and grounded, that a professional reviews. That humility is exactly what builds trust with a regulated client.

This is the highest-value application of the whole method. Do it carefully, and it is also the most defensible.