LOGISTICS / 14 WEEKS
CASE STUDY
LogiFlow

Scalable Transaction Processing Platform

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.

10M+
Daily Transactions
99.99%
Uptime SLA
40%
Cost Reduction
8x
Peak Handling
01  THE CHALLENGE

What wasn't working.

Existing infrastructure buckled under peak loads, causing dropped transactions and SLA breaches during holiday seasons.

02  THE SOLUTION

What we built.

Architected a cloud-native event-driven platform with AI-powered auto-scaling and predictive load balancing.

03  THE OUTCOME

What it delivered.

Platform now handles 10M+ daily transactions with 99.99% uptime and 40% lower infrastructure costs.

04  EXECUTION

From kickoff to production.

01

Monolith Decomposition

Identified 7 bounded contexts within the monolith and designed a migration strategy that allowed incremental extraction without downtime.

02

Event-Driven Core

Built the transaction processing core on an event-sourced architecture with Kafka, enabling replay, audit trails, and exactly-once semantics.

03

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.

04

Observability & Resilience

Implemented distributed tracing, circuit breakers, and chaos engineering practices. The system now self-heals from 94% of incidents without human intervention.

05  TECHNOLOGY

The stack.

GoApache KafkaKubernetesTerraformPrometheusCockroachDB
06  FROM THE CLIENT
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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