Control Patterns
AI decision systems work when they're governed. These are the operational control patterns Aurevity builds into every workflow — across Finance, HR, and Procurement.
Defined checkpoints where a human reviews, edits, or approves AI-generated outputs before they move forward in the workflow.
Structured sequences of sign-offs required before a decision is finalized, with each approver adding authority based on the decision's scope, risk, or value.
Ensuring every AI-generated recommendation or answer is explicitly grounded in documented organizational policies, with citations to the specific policy section that supports the guidance.
Maintaining a complete, immutable record of every AI input, output, human decision, and override — so any action can be reconstructed and reviewed after the fact.
Detecting when a situation falls outside the AI system's confidence or authority, and automatically routing it to a qualified human with the right context — rather than guessing or failing silently.
Attaching a quantified confidence score to every AI output and defining clear thresholds that determine whether the output is auto-accepted, flagged for review, or escalated — so teams know how much to trust each result.
Tracking the complete chain from source data through AI processing to final output — so any result can be traced back to its origins and any data quality issue can be identified at its root.
Ensuring that AI system inputs, outputs, and controls are visible only to people with the appropriate role and authority — preventing information leakage while maintaining operational efficiency.
Building structured mechanisms for capturing human corrections, measuring AI accuracy over time, and systematically improving system performance — turning every human override into a learning opportunity.
Designing AI-assisted workflows so that when the AI component fails, slows, or produces unusable outputs, the workflow continues operating through a defined manual fallback — rather than stopping entirely.
Automated checks that validate AI outputs against defined quality criteria — format compliance, numerical consistency, completeness, and policy alignment — before the output reaches a human reviewer.
