Logistics & Last-Mile Delivery
"A growing delivery platform moved from a single crashing server to a fully auto-scaling cloud-native architecture — eliminating peak-hour downtime entirely."
↓ Scroll to reveal the cure
A fast-growing logistics platform was experiencing complete service outages every time a promotional campaign ran. The application ran on a single under-provisioned server with no auto-scaling strategy, no database read replicas, and no CDN. The engineering team had no infrastructure expertise in-house.
The team was manually monitoring alerts late at night and rebooting servers to survive traffic spikes — a short-term fix that was unsustainable as order volumes grew. Every outage directly correlated to missed delivery confirmations and customer churn.
The entire application and database ran on one instance. Any hardware fault or traffic surge caused a complete business outage with no failover.
All read and write queries hit the same database instance. Under peak load, read queries were timing out and blocking write operations.
Client apps polled the server every 3 seconds for status updates. At scale, this created thousands of redundant requests per minute with no data value.
Images, fonts, and JS bundles were served directly from the application server, consuming bandwidth that should have been reserved for business logic.
Code deployments happened manually via direct server access. There was no rollback mechanism and no staging validation before production releases.
The team learned about outages from customer complaints, not automated alerting. Mean time to detection was measured in hours, not minutes.
We performed a two-week architecture audit, mapping every API endpoint, database query pattern, and external service dependency. We identified four critical hotspots causing cascading failures under load: a polling-heavy status endpoint, synchronous write operations on the critical path, missing database connection pooling, and a complete absence of static asset offloading.
The audit produced a phased remediation blueprint: containerisation into isolated services, introduction of a read replica for query load distribution, a Redis caching layer for high-frequency read patterns, and a CDN for all static assets. The blueprint was handed to the client's engineering team with full implementation guidance.
99.99%
Uptime
From 94%
10x
Peak Load Handled
Without scaling events
110ms
P95 Response Time
−68%
Full CI/CD
Deployment Confidence
Zero-downtime deploys
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