Waydev measures engineering delivery, and has repositioned around a question every engineering organisation now faces: whether AI coding tools are actually improving outcomes. It tracks AI adoption across a team, follows its impact from code through to production, and quantifies return down to token spend.
That is a sharper question than it sounds. Most organisations have bought AI coding tools on the assumption of a productivity gain and have no measurement to confirm it, which means they cannot tell whether they are buying speed, more review burden, or more production incidents.
It builds on the recognised delivery frameworks, DORA, SPACE and DX, rather than inventing proprietary metrics, which matters because engineering measurement has a long history of tracking things that are easy to count and harmful to optimise. Line counts and commit volume make developers game the metric; DORA-based measures are more defensible. Any such tool should still be used to inform decisions rather than to evaluate individuals.
