AI Driven Migration for Predictability, Precision, and Low-Disruption Transformation 

Legacy schedulers accumulate years of embedded logic, undocumented dependencies, and environment-specific behaviour. Without a rigorous migration approach, even a well-planned cutover can result in service disruptions.

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Why modernize now

Legacy schedulers accumulate risk you can no longer afford

Enterprises running legacy workload schedulers cannot risk service interruptions, SLA violations, or operational surprises. Multiple workload schedulers compounds the risk and operational costs you are already paying.

Our approach eliminates that risk by using a proven playbook enhanced with AI-powered assessment, translation, and validation to ensure absolute schedule fidelity and operational continuity.

Image of ai predictive pulse.
Our AI Predictive Pulse can provide a Business Service hyper-view across workload tools as air-cover while migrating and removing automation siloes beneath.

Our AI-Driven Migration Method

A phased approach to low-risk workload automation modernization

We make modernization safe by following a structured, phased approach—where each phase is sequenced to surface and resolve risk before it reaches the next stage, so your operations stay uninterrupted throughout.

Phase 1:
Readiness & Governance

We establish full governance and readiness by identifying and validating every rule, schedule, and dependency upfront—ensuring controlled, predictable migration with no downstream surprises.

  • Define scope and KPIs to align migration goals
  • Establish a secure identity model and policy baselines

Phase 2:
Discovery & Assessment

We use AI to classify your entire workload landscape by complexity and SLA sensitivity—so nothing is missed before translation begins.

  • Build a complete job inventory with AI-assisted analysis
  • Map dependencies to surface hidden relationships between workloads
  • Score complexity to prioritise and sequence the migration

Phase 3:
Universal translation

We don't copy your scheduler, we rebuild it on the target platform using best practices.

  • Map calendars and holiday rules to the target system
  • Execute migration routines mapping jobs, schedules, script and orchestration
  • Apply layered definitions to eliminate redundant rules and orphans

Phase 4:
Validation & Comparison

Before any cutover, we use automated tools and AI simulation to prove with data that the new system behaves like the old one.

  • Automated comparison workbooks validate schedules, dependencies, and calendars across both systems side-by-side
  • AI simulates up to 24 months of forecasts to guarantee matching run times and prevent edge-case mismatches
  • Use historical data to make projected runs of any workload across schedulers offline

Phase 5:
Pilot & Parallel Run

Where suitable environments are available, execute a controlled pilot and parallel validation to build confidence, compare outcomes, and refine configurations prior to full cutover. This phase is adapted based on customer infrastructure and constraints.

  • Publish to both systems simultaneously to validate translation
  • Track KPIs including ≥99% on-time start rate and zero weekday/holiday deviations
  • Run acceptance testing to confirm SLA performance meets or exceeds baseline

Phase 6:
Cutover & Hypercare

We provide intensified support, tuning, and structured knowledge transfer during and after go-live.

  • Go live with delta capture to minimise change freezes and maintain a formal rollback plan throughout
  • Monitor performance closely and tune the new environment during and after go live
  • Transfer knowledge through structured sessions

Results of our AI-driven migration method

100 %

jobs migrated with confirmed matching outcomes

100 %

SLA adherence for batch processing

100 %

job execution traceability across environments

Why Rocket

Every step of your migration is protected, not just the last one

We make modernization safe by focusing on one outcome: your workloads run exactly as they do today—only on a more powerful, future-ready platform. Every phase includes explicit protections, so risk is managed continuously, not discovered after go-live.

What you gain
  • Service dependency mapping across schedulers
  • AI-generated inventory and complexity scoring
  • Optimized orchestration definitions
  • Automated translation and comparison workbooks
  • Post-go-live performance validation and acceptance package

Questions you may have

Have more questions? We’ve got answers.

Get answers that apply to your environment