GitHub delivery evidence
Build and verify the change with GitHub context alongside it.
Read release flow, CI health, operational risk, AI usage, and estimated spend straight from the work itself.
One shared lifecycle, with the stages most relevant to this team in focus. Step through any stage to inspect the full inventory.
What is moving through delivery?
Where is the risk?
What does it cost?
Build and verify the change.
Build and verify the change with GitHub context alongside it.
Inspect delivery workflow activity in the connected release workspace.
Investigate test outcomes and flakiness evidence across runs.
Keep API contracts inspectable next to the release that changed them.
Compare public cloud compute pricing as planning evidence.
Deploy frequency, lead time, change failure rate, and MTTR fall straight out of the deploy records you already have. No survey to fill in.
Deploy frequency
14/day
Lead time
3.2h
Change failure
6%
MTTR
41min
Cycle time split by stage tells you whether delivery drags in review or in CI. It measures the work, not the people.
pull request · cycle time
18h 40mEstimated model spend, broken down by tool, model, repo, and session. So the AI bill stops being a mystery line item.
How we treat it
lube measures the work, not the people. Cycle time describes how a change moved. AI spend is an estimate of what a model run cost. Both open onto the record underneath, so you can check them.
Self-serve workspace registration.