Rollout Gates are the governance mechanism that controls how new AI capabilities, updated models, and configuration changes reach production. Every change passes through defined gates — automated tests, human approvals, canary deployments, and performance monitoring — before full rollout. This prevents the common enterprise AI failure mode of deploying a capability that works in testing but fails at scale.
New capabilities roll out in stages — internal testing, limited pilot, expanded rollout, full deployment. Each stage has criteria that must be met before advancement.
Before full deployment, changes are tested on a subset of traffic. Performance is compared against the baseline — any degradation automatically halts the rollout.
Significant changes require human approval at defined gates. Approval workflows route to the right stakeholders with relevant context and evaluation data.
If a deployed change causes performance degradation, the system automatically reverts to the previous known-good state — minimizing the impact window.
Rollout Gates works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Eliminate the fear of AI deployment failures. Rollout gates provide the safety net that allows organizations to iterate quickly on AI capabilities while maintaining the reliability that enterprise operations demand.
Discover how Rollout Gates fits into your enterprise intelligence strategy.
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