Recovery Objective Optimisation uses AI to model and optimise recovery time and recovery point objectives across the application portfolio, enabling cost-effective DR architecture decisions, by simulating recovery scenarios under different infrastructure and data replication configurations, across IT disaster recovery planning cycles.
IT DR planners assign RTO and RPO targets manually without modelling the cost-effort curve of achieving different recovery tiers, leading to over-provisioned DR for low-criticality systems and under-provisioned DR for critical ones.
A simulation model maps application dependencies, backup schedules, and replication configurations, runs failure scenarios across defined threat types, calculates achievable RTO/RPO under each configuration, and surfaces the cost-optimal DR architecture for each business criticality tier.
Reduction in DR infrastructure spend and improvement in tested RTO compliance rate for tier-one applications.
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