10M+
Daily Transactions
99.99%
Uptime SLA
40%
Cost Reduction
8x
Peak Handling
Overview
LogiFlow processes shipping transactions for 200+ enterprise clients. During peak seasons, volume spikes 8x — and their monolithic platform couldn't keep up. Dropped transactions meant lost shipments, SLA penalties, and eroding client trust. They needed elastic scale without elastic costs.
Challenge
Existing infrastructure buckled under peak loads, causing dropped transactions and SLA breaches during holiday seasons.
Solution
Architected a cloud-native event-driven platform with AI-powered auto-scaling and predictive load balancing.
Result
Platform now handles 10M+ daily transactions with 99.99% uptime and 40% lower infrastructure costs.
Implementation
Monolith Decomposition
Identified 7 bounded contexts within the monolith and designed a migration strategy that allowed incremental extraction without downtime.
Event-Driven Core
Built the transaction processing core on an event-sourced architecture with Kafka, enabling replay, audit trails, and exactly-once semantics.
AI Auto-Scaling
Trained a predictive model on 2 years of traffic patterns to pre-scale infrastructure 15 minutes before demand spikes, eliminating reactive scaling lag.
Observability & Resilience
Implemented distributed tracing, circuit breakers, and chaos engineering practices. The system now self-heals from 94% of incidents without human intervention.
Technology Stack
"Last Black Friday we processed 14M transactions without a single dropped request. That would have been unthinkable a year ago."
Marcus Chen
CTO, LogiFlow
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