Genda Phool Full-Stack Delivery Engine
End-to-end flower delivery platform with mobile apps, subscriptions, logistics, and admin analytics - built for peak days (festivals) without collapsing dispatch.
01 — The problem
What we were solving
India hyperlocal market: volatile demand, cash and UPI payments, and third-party rider fleets with unpredictable availability.
- Peak days spiked 10× normal orders; monolithic APIs timed out during checkout.
- Dispatchers needed live maps and batch reassignment when riders dropped orders.
- Finance needed per-hub settlement and promo liability tracking.
02 — The approach
What we built
- Split checkout, catalogue, and dispatch into independently scalable services.
- Built rider assignment with SLA timers and fallback pools.
- Admin RBAC with audit logs for pricing and refunds.
Architecture notes for your engineers
- Read replicas for catalogue browsing; write path isolated for orders.
05 — Outcomes
Key results
- iOS, Android, Web & Delivery Partner apps
- Subscription + instant delivery models
- Location-based pricing & availability
- Full RBAC admin & analytics
06 — Stack
What it was built on
Representative tools and patterns — exact vendors vary per client environment.
Clients
React Native / native appsResponsive webRider app
Backend
Node or Python servicesPostgreSQLRedis geo queries
Ops
Kubernetes or managed containersCI/CDError budgets
07 — Learnings
What we'd tell the next team
- Hyperlocal is an operations product - maps and dispatch UX beat clever algorithms alone.
- Design for festival load tests early; caching strategies differ for gifts vs staples.
- Expose operational metrics (rider wait, prep delay) to product teams, not only engineering.
FAQ
Questions this engagement anticipated
How did the architecture survive 10× peak days?
By isolating write-heavy order paths from catalogue reads and adding geo-aware dispatch controls so timeouts during checkout did not cascade across hubs.
What finance controls were built in?
Per-hub settlement views and promo liability tracking so operations and finance could reconcile high-volume promotional windows.
Compare your situation to this case.
Bring your constraints - environment, timeline, and budget. We scope before we quote.