Use case

Model risk management for AI that changes.

Traditional model risk assumes a model that sits still. Modern AI does not. Trinitite gives your model-risk team living evidence, change control, and proof that every version behaved, the way SR 11-7 expects.

trinitite / rails

Without a Guard

What goes wrong on its own.

Left alone, AI does not just say the wrong thing. It does the wrong thing, at the worst possible time.

!

Language models change behavior in ways a spreadsheet cannot capture.

!

Validating a model once and filing it away no longer reflects reality.

!

Regulators expect the same rigor for AI as for any other model.

The save

Watch the Guard make the catch.

A risky move comes in. The Guard catches it before it runs, then writes down what it did and why. That is the whole job, in one picture.

the catchlive
?the ask
the Guard
block
ship an untested model
the recordreversible anytime
ship an untested modelblocked · held until it passes

How Trinitite helps

Put a Guard on it.

01

Living validation

Every version is tested and graded, and the results are kept, so you can show how each one behaved.

02

Change control built in

A new model does not go live unless it passes your bar first. Regressions get blocked at the door.

03

Evidence on tap

Model documentation and results assemble themselves for your validators and examiners.

The payoff

Let AI act. Keep the wheel.

Every
version tested and graded
0
regressions promoted by mistake
SR 11-7
rigor, built for AI that changes

Questions

Straight answers.

  • Does this satisfy SR 11-7?

    It gives your model-risk function the ongoing validation, change control, and documentation the guidance expects, tailored to AI that changes.

  • Can I compare two models?

    Yes. Grade a challenger against your champion on the same tests before you promote it.

Ready when you are

Let's put a Guard on your AI.

Bring the workflow that worries you most. We will show you the risky move getting caught before it runs.