Start with approved state
A governance decision should identify the exact system and assurance context it accepted.
Company
Enterprise AI changes faster than its approval process.
Steerlane is being built around a specific problem: an AI workflow can change after it is approved, but the reasoning behind that approval often lives in disconnected documents, evaluation runs and review systems.
What we're solving
Reconnect the decision to the system it governs.
A production AI system is not static. Models, prompts, tools, data sources and permissions evolve. The right question after each change is not simply whether the code differs. It is what that change means for previously accepted controls and evidence, and what the next reviewer needs to know.
We want assurance to become a usable, continuously explainable operating state rather than a series of disconnected approval exercises.
How we think
Four principles guide the product.
Make change explainable
Technical diffs alone do not show which obligations or evidence must be reconsidered.
Preserve valid work
Do not make teams repeat an unaffected assessment simply because some other component changed.
Keep humans accountable
Missing relationships must remain visible and formal approval must stay an explicit decision.
Help shape the first practical deployments.
We're seeking specific production AI governance problems to evaluate with design partners.