01
Readiness
Define what production requires.
Use reusable controls, evaluations, evidence requirements, and review requirements to determine whether the AI system is ready for approval.
The control layer for production AI
One control model for AI that keeps changing.
Bring approved state, meaningful change, assurance impact and human decisions into one traceable operating context. Each capability answers a different question; together they support the next production decision.
Five connected capabilities
Start with the question your team needs answered.
01
AI state
What state are we actually governing?
Keep intended, declared, deployed, observed and approved representations distinct, with source and time context rather than false certainty.
State reconciliation
02
Change intelligence
What changed, and where does it reach?
Compare meaningful differences across prompts, models, tools, permissions and data sources; follow their dependent controls.
Semantic change + impact
03
Assurance & evidence
What remains valid?
Connect controls, evaluations and evidence to the approved baseline; preserve unaffected proof and target revalidation.
Evidence validity + next actions
04
Simulation
What could a proposed change require?
Explore potential impact before committing a new system state. A scenario preview never substitutes for an authoritative decision.
Non-authoritative impact preview
05
Governed decisions
Who decides whether change can proceed?
Keep reviewer actions, unresolved mappings, approval history and audit lineage explicit.
Review + decision record
Operating model
An approval is a reference state, not the end of governance.
When a workflow changes, previously accepted evidence should not be discarded without knowing what the change affects. Equally, missing mappings must never be treated as permission to reuse evidence.
Follow the six-step process →Steerlane control loop · production assurance lifecycle
Current product model
Steerlane maintains continuity between the approved AI system state and the proposed version, so assurance state does not need to restart from zero after every update.
Pre-approval
02
Approved baseline
Freeze the accepted assurance state.
Capture the approved version together with the evidence and assurance state that justified the production decision.
03
Change intelligence
Compare the proposed version against the baseline.
Detect semantic changes across prompts, models, tools, permissions, data sources, and configuration rather than relying on raw file diffs.
04
Assurance impact
Trace what the change can affect.
Follow changed AI system components into controls, evaluations, evidence, and unresolved dependency paths.
05
Revalidation
Reopen only what needs attention.
Preserve unaffected assurance state, rerun impacted evaluation paths, and escalate uncertain relationships for explicit review.
Evaluate one system, then expand with evidence.
Integration scope, source access and deployment requirements are confirmed during evaluation.