Before you let an AI
do a real job, read this.

The question you're actually asking

It isn't “how smart is this AI?” It's simpler and harder than that:

That is a question about proof, not about technology. Everything below is about getting you that proof.

The number you were shown doesn't answer it

The AI arrives with an impressive score. The score is real. But look at where it came from: someone else's test, built from someone else's tasks, graded by a judge nobody checked — on a day that has already passed.

You would never hire a person because they aced a different company's interview. This is the same thing. The number isn't wrong. It's just not about your job.

A test built from your own work — and a judge that passes its own exam first

Gradia builds the test out of your real work: your files, your systems, the standards your own people wrote down. Vague standards like “good tone” are turned away — they can't be checked. Then every task must prove it can be graded fairly before it counts. In one worked example, 4 of 12 tasks failed that proof. None of them shipped.

And the judge doing the grading? It is not trusted by default. It takes its own exam first: it may only grade your work after it agrees with your own experts often enough. In that same example, the first judge fell short — so it was not allowed to run. It was rebuilt until it agreed with the experts about 8 times in 10 on work it had never seen before.

Then it gets harder: people try to fool the judge on purpose, before it is trusted. When Gradia ran that trick-test against a popular public grading system, the grader was fooled 89 times in 1,800 tries — each one caught and recorded. Your judge gets the same treatment, and its result goes on your report.

You walk away with a sealed result

When the test finishes, the result is sealed. Change even one line afterward and the seal visibly breaks — there is no way to quietly edit history. Your auditor, your regulator, your board: any of them can check the seal themselves, without calling Gradia. Nobody has to take our word for anything.

Try it yourself:

Why this matters now: evidence for a real decision

When AI helps make a lending decision, reviewers need to understand the system, its limitations, and the evidence behind its use.

Colorado replaced its earlier AI framework with SB26-189, signed May 14, 2026. The enacted summary places developer technical-documentation duties at January 1, 2027 and describes record retention, notices and human review, subject to scope and exemptions. It does not support the annual testing mandate previously described here.

Gradia can support a buyer-approved assessment and review cadence. An evidence package does not by itself establish legal compliance. Colorado General Assembly, enacted summary · checked September 6, 2026.