How we think about responsible deployment

Every capability we ship goes through the same questions: is it useful, is it reliable enough for the way people will actually use it, and what could go wrong at scale?

Evaluate before release

New features are tested against our internal evaluations for accuracy, misuse and failure modes before they reach users.

Monitor after release

We watch for abuse and unexpected behaviour in production, and we roll back or restrict features when the evidence calls for it.

Be clear about limits

AI makes mistakes. We tell people where our products are strong, where they are weak, and how to report problems.