Stride's legacy database was the anchor dragging their entire engineering org. Queries ran for minutes. Environment syncs failed routinely. Feature releases stalled sprint after sprint. RTS Labs used AI-assisted schema analysis and automated migration tooling to complete what would have been a 4-month migration in 3 weeks — with zero data integrity issues and an 80% improvement in query performance.
SaaS
AI-Assisted Database Migration & Modernization
PostgreSQL
Python
dbt
AWS RDS
Our engineers will map your workflow and define a ship date in a 2-week Discovery Sprint.
Stride’s engineering team had been living with a performance problem they couldn’t outrun. The company’s legacy database — built fast in the early days and never properly restructured — had become the bottleneck for everything. Queries that should return in seconds were routinely taking four minutes or more. Complex analytics reports couldn’t run during business hours without impacting production stability. And with the schema undocumented and inconsistently structured after years of ad hoc changes, no one on the team fully understood what they were working with.
The downstream effects were compounding. Three out of four engineering sprints in the prior quarter had been delayed by database-related issues — either performance problems that required hotfixes, or sync failures between environments that forced manual rollbacks. The team was firefighting instead of building. New feature development had effectively stalled, and with competitors shipping faster, leadership knew the database issue had crossed the line from technical debt to strategic risk.
A traditional migration project was estimated at 16+ weeks — months of engineer time spent on manual schema analysis, script writing, and data validation before a single line of production data moved. Stride needed a faster path.
Core business queries were averaging 4+ minutes — slow enough that reports couldn’t run during business hours without degrading production performance. Engineers were scheduling complex queries for off-hours just to avoid the impact.
Data syncing between production, staging, and development environments was failing 23% of the time — requiring manual intervention, rollback procedures, and hours of engineer time to reconcile. Every failed sync was a tax on velocity.
Database performance issues delayed 3 of 4 engineering sprints in the prior quarter, forcing the engineering team into constant, daily firefighting. The backlog of features waiting on a stable, performant data layer actively stalled product velocity.
RTS Labs came in with a different frame: instead of treating this as a migration project, they treated it as an AI co-development problem. Rather than assigning engineers to manually reverse-engineer the schema, document relationships, and write migration scripts by hand, the RTS team used LLM-assisted analysis to do in 3 days what would have traditionally taken 3 weeks — producing a complete schema map, normalization recommendations, and a draft PostgreSQL migration plan before a single human script was written. That pace set the tone for everything that followed.
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