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yves
Government and regulationNational mandate

National standards regulator

The deployment that taught Yves to publish its own uncertainty.

The first Yves installation, and the one that set the rules every deployment since has been built to. A regulator publishes figures it will have to defend in front of a committee.

±4.2

Points, on every headline share

12

Detection rules, all published

100%

Tenant isolation at the data layer

100%

AI generations archived verbatim

Deployment model

Tenant on the shared multi-tenant backend

Live since

2026

Languages

English · Français · Español · العربية · 中文 · हिन्दी · Русский · Português

The challenge

A published satisfaction figure is a claim a regulator has to stand behind. A point estimate drawn from a corpus that may have been brigaded, presented with no stated uncertainty, gives a committee a very easy afternoon.

What we did

Wilson 95% confidence intervals went onto every share in the console, printed alongside the sample size in the same type size as the number, with thin cells marked as thin.

Every AI generation is archived verbatim with what was asked and what came back, so any figure in any board pack resolves to the exact computation that produced it.

The detection-rule catalogue was documented inside the product: twelve rules, each mapped to recognised techniques with the analyst response beside it. The console generates that page from the running system, so the documentation cannot drift from the code.

What happened

The board moved on from litigating the numbers and started arguing about what to do with them, which is the only outcome worth having.

Every rule set here became a standard for every deployment afterwards: quarantine and keep, archive every generation, state the interval, and hold a finding until the data supports it.

“Every share arrives with a confidence interval attached. Our board now spends its whole meeting deciding what to do.”

Head of quality assurance, National standards regulator

Illustrative composite, attributed to a role. Full disclosure on the stories index.

Next step

Run the same engine
on your own corpus.

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