Intelligent Automation for Global Logistics Platform

While the client’s platform processes vast real-time data flows throughout their network, the objective was very clear: they wanted to use intelligent automation to reduce the cost per shipment, speed up decision-making, and eliminate manual tasks.

This was the next cycle of our collaboration. We have already updated a 30-year-old monolith platform, boosted the performance and user experience. So the client is ensured in stable and transparent shipments around the globe.

The next step was to introduce AI-powered logic to automate operations and analyze vast datasets, reducing maintenance costs and optimizing workflows.

A Technical Delivery Manager led the AI automation initiative, supported by a cross-functional squad of 7 specialists: AI Solution Architect, Data Engineer, Data Scientist, MLOps Engineer, Full-stack Developer, DevOps Engineer, and QA Automation Engineer.

The revamped ecosystem presented multiple obstacles:

• Labor expenses and delays grew because of manual quoting and carrier selection,
• Real-time data (traffic, capacity, and rates) could not be accommodated by static routing rules,
• Operations personnel were overburdened by high ticket volumes, which put Service Level Agreement (SLA) violations at risk.

These elements worked together to raise labor costs, cause shipment delays, and erode consumer trust. It was our responsibility to implement intelligent automation logic and mechanisms, removing human handoffs, introducing automated checklists, and maximizing resource usage, while avoiding disruptions.

Front-end: React.js

Backend: .NET 7

Database: SQL

DevOps: Docker, Kubernetes, and Azure DevOps pipelines

Azure API Management is the API Gateway.

Data Ingestion: Apache Kafka

Orchestration: Azure Data Factory 

Model Development: Python (scikit-learn, TensorFlow/PyTorch) + MLflow

Monitoring: Prometheus/Grafana and Azure Monitor.

As a result of a 3-month-long cooperation, Devox Software has built a modular AI decision engine that:

• Absorbs and analyzes data in real time, such as traffic, carrier rates, and cargo details,
• Recommends the best carriers and routes, calculating and analyzing expenses,
• Automatically produces quotes, schedules shipments, and notifies users,
• Reports and signals about the vulnerabilities in the processes,
• Monitors the handoff process according to the introduced checklists.

This engine sits at the core of the transportation management system, replacing manual handoffs with event-driven, data-driven automation.

• 30% reduction in cost per shipment through optimized carrier and route selection,
• 70% fewer manual interventions, freeing operations staff for strategic tasks,
• 50% faster quote-to-booking cycle, accelerating customer response times,
• Zero unplanned downtime, thanks to containerized services and automated CI/CD.