10,000+ families stayed live while the platform was rebuilt underneath them.
EdPlace's legacy platform could no longer accommodate new subjects, richer assessments, or the mobile experience customers expected, but taking it offline was not an option. The new platform was built alongside the live system and cut over in controlled stages.
Phased parallel rebuild · Rehearsed migration · Verified data integrity · Deliberate legacy retirement
Signs the application has become a business constraint
Releases keep getting slower
Small changes need disproportionate regression cycles, coordination, and operational caution.
Too much knowledge sits with too few people
When only a handful of engineers understand the system, technical debt becomes operating risk.
Data is trapped inside the system
Analytics, automation and AI initiatives keep running into the same access and integration limitations.
The team is afraid to touch critical areas
When avoiding change becomes the engineering strategy, modernization is overdue.
The application can take six different paths
Not every legacy application should be rebuilt. The business case should determine how much changes, and how quickly.
The commerce core changed while catalog complexity kept working
A 400K-SKU automotive catalog, vehicle-fitment data, inventory feeds and search continued operating as the architecture moved to Adobe Commerce Cloud with a headless React storefront, Contentful and Akeneo.
The modernization changed the architecture without simplifying away the requirements that made the legacy platform complex in the first place.
Modernization across architecture, data, and delivery
Application architecture and legacy code
Assessment, decomposition, refactoring, and progressive replacement where coupled systems make change slow or risky.
APIs, integrations, and data
Cleaner boundaries, accessible data, and rehearsed migration so systems evolve without breaking each other.
Cloud, DevOps, and delivery
Infrastructure change where it creates operating benefit, plus CI/CD, testing, and observability for safer releases.
AI-ready foundations
Clean APIs and accessible data that remove the same constraints many future AI initiatives will otherwise expose.
Assess. Prioritize. Modernize. Retire.
The whole system rarely needs to change at once. The sequence should reduce business risk while producing working outcomes early.
