All Case Studies
Logistics
ShipSwift
99.9% uptime achieved
99.9%
System uptime
3x
Throughput
68%
Infra cost saved
ShipSwift's package-tracking platform ran on an aging monolith that handled roughly 50 million monthly events across pickup, transit, and delivery. Traffic spikes during peak season regularly pushed the system past its limits, and outages were typically discovered only after customers started reporting missing tracking updates — by which point the root cause had already cascaded across multiple services.
Rather than migrating service-by-service and hoping for the best, we treated observability as part of the architecture from day one. Every new microservice shipped with structured metrics and logging built in, giving us a real-time stream of system behavior to validate against before decommissioning any piece of the old monolith.
On top of that telemetry, we trained a predictive-monitoring model on historical incident data to recognize the early signatures of failure — rising queue depths, memory pressure, latency drift on specific service pairs — well before they escalated into customer-facing outages. Instead of paging an on-call engineer after something breaks, the system flags the pattern early enough for the team to intervene during the migration itself, before traffic ever hit a broken path.
The migration to microservices completed with zero incidents and full SLA compliance throughout, something VP of Engineering Marcus Williams called out directly. Post-migration, ShipSwift runs at 99.9% uptime and 3x the throughput of the old monolith, while infrastructure costs dropped 68% thanks to right-sized, independently scaling services.
Rather than migrating service-by-service and hoping for the best, we treated observability as part of the architecture from day one. Every new microservice shipped with structured metrics and logging built in, giving us a real-time stream of system behavior to validate against before decommissioning any piece of the old monolith.
On top of that telemetry, we trained a predictive-monitoring model on historical incident data to recognize the early signatures of failure — rising queue depths, memory pressure, latency drift on specific service pairs — well before they escalated into customer-facing outages. Instead of paging an on-call engineer after something breaks, the system flags the pattern early enough for the team to intervene during the migration itself, before traffic ever hit a broken path.
The migration to microservices completed with zero incidents and full SLA compliance throughout, something VP of Engineering Marcus Williams called out directly. Post-migration, ShipSwift runs at 99.9% uptime and 3x the throughput of the old monolith, while infrastructure costs dropped 68% thanks to right-sized, independently scaling services.
Tech Stack
Python
TensorFlow
Prometheus
Kubernetes
GCP