ML-Driven Support Ticket Classification for Transportation Management

Industry: Logistics & SaaS

Region: North America

Company Size: Mid-market

Use Case: NLP-powered automation for support operations

Products Used: Amazon SageMaker, S3, Athena, SageMaker Model Monitor, HuggingFace, AWS IAM, CloudWatch

Company Overview

A transportation management platform faced growing pressure on its customer support operations as inbound communication volume surged. Thousands of emails, forms, and unstructured messages flooded support queues every day—many of them irrelevant. The organisation partnered with VeUP to develop an ML-powered classification engine that could automatically detect genuine support requests and reduce manual triage effort.

Challenge

Support agents were overwhelmed by a constant influx of non-support messages, promotional emails, and general inquiries mixed with critical customer issues. Manual filtering slowed response times and created SLA breaches, directly affecting customer satisfaction. The company needed a scalable, accurate classification system that could process large volumes of unstructured text while maintaining compliance and auditability.

Solution

VeUP designed an end-to-end NLP classification pipeline using Amazon SageMaker and a fine-tuned RoBERTa transformer model. A labelled dataset—curated using a blend of LLM-assisted tagging and human review—provided the foundation for high-precision binary classification. The system was built to handle real-time and batch workloads, with SageMaker autoscaling inference endpoints responding dynamically to spikes in message volume. S3 and Athena enabled cost-efficient data storage and analytics, while SageMaker Model Monitor tracked drift, accuracy, and operational performance. All predictions were logged for audit and compliance, ensuring transparent MLOps governance.

• Fine-Tuned RoBERTa Model: Customised transformer model trained on historical ticket data, achieving high precision and recall for distinguishing real support requests.
• AWS-Native ML Pipeline: SageMaker handled training, deployment, and autoscaling inference, with S3 and Athena enabling efficient storage and analytics.
• Real-Time & Batch Classification: System processed over 10,000 daily messages with consistent latency and high reliability during peak activity.
• Continuous Monitoring: Model Monitor tracked drift, performance, and anomalies, ensuring predictable and auditable behaviour across all predictions.
• Governance & Auditability: All classification outputs, confidence scores, and metadata were logged for transparent MLOps oversight.

Results

• 91% reduction in irrelevant tickets entering the support queue
• 60% increase in agent productivity, redirecting efforts to genuine customer needs
• Improved SLA performance through faster triage of legitimate requests
• Highly scalable architecture handling thousands of messages per day

Looking Ahead

With an ML-driven support pipeline in place, the company is now exploring broader automation opportunities across customer insight generation and workflow prioritisation. The foundation also enables future expansion into multilingual ticket analysis and additional classification layers for routing and escalation.

Thinking About Automating Support at Scale?

If manual triage is slowing down your support teams, VeUP can help you build and deploy scalable NLP and ML solutions on AWS—improving response times, accuracy, and customer satisfaction while reducing operational burden.