Air cargo operations still rely heavily on email for booking requests—but as volumes grow and complexity increases, this manual process creates bottlenecks that slow response times, introduce errors, and limit scalability. Expanding into multilingual environments only compounds the challenge, making traditional approaches increasingly unsustainable.

To address this, Naviq reimagined its AI-powered cargo mailbot solution by evaluating next-generation LLMs through a pragmatic, ROI-driven lens. Rather than chasing peak accuracy, the focus shifted to identifying the optimal balance between performance, cost, and scalability—ultimately achieving 88% accuracy with significantly lower total cost while delivering near-complete business value.

This whitepaper explores the evaluation framework, model selection strategy, and prompt engineering approach that enabled this transformation—highlighting how organizations can unlock measurable efficiency gains without overinvesting in diminishing returns.

• A practical framework to evaluate AI models beyond just accuracy
• Insights into balancing cost, latency, and performance for real-world ROI
• How prompt fine-tuning can significantly boost model effectiveness
• Key lessons on avoiding diminishing returns in AI investments
• Real-world impact metrics, including faster processing and reduced manual effort

This whitepaper explores how to build cost-effective, scalable AI automation for cargo operations—download to read more.