Engineering leaders
See how AI is really used across your repositories.
Adoption surveys tell you what people say. The ledger records what happened, with the certainty of each claim shown.
What gets hard
Adoption is a guess
Tools are adopted team by team. Nobody has a current list of what is in use, or where.
Review quality is invisible
AI-assisted changes may move faster than review. You cannot see where review thinned out.
Every request becomes a project
Each customer or board question restarts a manual data-gathering exercise.
What changes with Attesta
A live inventory
Tools and models in use by repository and team, with the evidence tier behind each entry.
Review coverage you can act on
Where human review happened, where only automation ran, and where nothing did.
Policy without friction
Test a policy against history, run it in warn mode, and enforce it when the noise is understood.
Numbers you would see
Illustrative figures from the fictional sample dataset used across this site. They are not customer results.
Changes with Attested or Declared provenance
75%
Sample data
AI-assisted changes with human review
50%
Sample data
Policy failures this period
3
Sample data
Unlisted tools seen
1
Sample data
How a leader uses it
- 1
Connect
Install the read-only git app on a few repositories.
- 2
Observe
Watch coverage and inventory build from recorded events.
- 3
Decide
Simulate a policy, then move it from warn to enforce.