See exactly what changed and which authority, data, tool or control boundaries moved.
Assurance that moves
with the system.
AURELIS evaluates the consequences of AI-system changes before they become production failures.
Not a report. A decision system.
Every material change becomes a first-class object. AURELIS computes impact, selects relevant validations, executes them, records findings, and produces a bounded release decision.
Change the system. Watch the assurance path react.
Controlled visitor environment. The demonstration uses the same change-assurance logic as the engine; it does not access visitor infrastructure.
Every decision has a trace.
System version, observed change, affected control boundary, test definition, execution result, remediation, retest and decision are persisted as an evidence chain.
A control plane for changing AI systems.
The browser is a control surface. The actual work occurs in the server-side engine and persistent infrastructure.
What the infrastructure gives an engineering team.
Run validations selected from the observed change instead of treating every release as a blind full test.
Produce an explicit APPROVED, BLOCKED or review-required outcome backed by recorded evidence.
Turn confirmed failures into durable regression cases so the same class of failure is not silently reintroduced.
Keep the version, test conditions, results and integrity hash together for later review.
Each paid client operates in an isolated tenant with server-side entitlement enforcement.