MigrateyourEdTechplatformwithoutlosingproduction.
Staged migrations with dual-write, feature-flag cutovers, and rollback-ready deploys at every stage. No big-bang rewrites. No multi-hour maintenance windows. Every phase ships user-facing value.
Daily active users
Peak requests per minute
Products shipped
Downtime through migrations
Platforms at three inflection points.
Stuck on a monolith
- Who
- Platforms built as a single codebase that is now too slow to deploy, too coupled to refactor, and too scary to touch.
- Problem
- Every deploy risks the whole platform. New engineers take months to ramp. Features slip because changes cascade everywhere.
- What we do
- Staged monolith-to-services migration starting with the highest-value extraction.
Changing stacks
- Who
- Teams moving from a legacy stack (PHP monolith, old Rails, early Angular) to a modern one.
- Problem
- The hiring market for the old stack is shrinking. The new stack would deliver faster, but the existing product is live and the old code cannot just be thrown away.
- What we do
- Parallel-run architecture that lets new features ship in the new stack while old features continue serving production.
Database rewrite
- Who
- Platforms hitting hard limits on their current database technology, typically a MySQL monolith needing sharding or a MongoDB store needing relational integrity.
- Problem
- The database is the single biggest scaling bottleneck and vertical scaling has stopped helping.
- What we do
- Staged data migration with dual-write, validation tooling, and zero-downtime cutover.
Where platforms break. And how we rebuild them.
The cutover window problem
The pain: Traditional migrations need a multi-hour maintenance window. For an EdTech platform with global users, there is no window, someone is always in class.
Our approach: Strangler-fig migration pattern. Dual-write phase where both old and new systems receive every write. Gradual read traffic shift with feature flags. Old system stays hot as rollback until we have confidence.
Data integrity during the transition
The pain: Data gets written to the old system and the new system. They diverge. Now the rollback path is corrupted.
Our approach: Tooling that runs continuously during migration, comparing every write between old and new, flagging divergence within seconds. Automated reconciliation for common cases, human review for edge cases.
Losing institutional knowledge
The pain: The engineers who understand the old system are leaving. The migration has to complete before they do.
Our approach: Early migration phases focus on knowledge capture, extracting domain logic into documented services so the logic outlives the original implementation. The new system does not just replicate the old; it makes the logic legible.
Product velocity drop during migration
The pain: The team fears that 9 months of migration will mean 9 months of no new features.
Our approach: Every phase of the migration ships user-facing value. New services typically get new functionality that was hard to build in the old system, better performance, better UX, or new features that were blocked by the old architecture.
Methodology tuned for platforms at scale.
- 01
Migration spec (weeks 1-3)
Define the target architecture, the exact sequence of extractions, the data migration strategy, and the cutover plan. Includes rollback criteria for every stage. This spec is the contract for the rest of the work.
- 02
Extract the first service (weeks 4-10)
Start with the highest-value, lowest-risk extraction, usually authentication. Dual-write from day one. Gradual traffic shift. By end of phase, the old system has stopped handling that workload entirely.
- 03
Parallel extractions (weeks 11-30)
With the first service proven, we run 2-3 extractions in parallel. Each is staged, dual-written, and gradually cutover. The old monolith shrinks. The new architecture takes on more of the platform.
- 04
Final cutover and decommission (weeks 31-40)
Last workloads move off the old system. Old system goes cold, then gets decommissioned. Infrastructure cost drops. Product velocity returns to pre-migration levels, then exceeds it.
250,000+ daily users. Multi-tenant by design.
Our multi-tenant EdTech platform powers white-label brands including Your CA Buddy and Youth Pathshala. It holds 250,000+ daily active users, 10 million requests per minute at peak, and has sustained zero downtime through three major scaling migrations. Every pattern on this page, the architecture, the decisions, the approach, has been battle-tested there first.
READ THE PLATFORM STORYHow the platform scaled from 20K to 250K daily active users over 3 years.Read case study →Questions founders ask about this.
When is a rewrite actually the right answer?+
When the current architecture cannot support the next 2-3 years of product direction, not because the code is ugly, but because the architectural model is wrong. Signs: every feature requires changes across 5+ files in 3+ services, the team is scared to touch core paths, and the hiring market for the current stack is shrinking.
How do you avoid the classic 18-month big-bang rewrite?+
We do not do big-bang. Every migration is staged: strangler-fig pattern where new functionality lands in the new system while the old system keeps serving production. Cutover happens feature-by-feature over months, not a single weekend.
What about zero-downtime during the migration?+
Required. Our cutover pattern uses dual-write for a transition period, feature flags for gradual traffic shift, and rollback-ready deploys at every stage. We have migrated production databases serving 250K+ daily users with zero visible user-facing downtime.
How long does a typical EdTech migration take?+
Small migrations (single service extraction): 2-3 months. Medium migrations (monolith to 5-10 services): 6-9 months. Full platform rewrites: 12-18 months with staged feature cutover. Migrations shorter than this usually fail, the compressed timeline forces big-bang cutover risk.
Can we migrate stacks at the same time as scaling work?+
Yes, and usually the migration is how you scale. Moving authentication to a dedicated service is both a rewrite AND a scaling fix. We plan migrations so each step delivers production value, not just architecture-is-prettier value.