Global Logistics Solution Manufacturer
A global leader in cargo protection solutions, serving industrial sectors with innovative systems designed to secure goods in transit.
The challenge.
Establishing a Strategic Foundation for AI in Microsoft Fabric
The client had begun exploring Microsoft Fabric’s AI capabilities, including Data Science features such as AI functions and services. However, their efforts lacked clear direction, established patterns, and a strategic framework. Without in-house expertise to guide their adoption, they struggled to translate experimentation into meaningful, scalable outcomes.
The solution.
1
AI-Driven Cargo Analysis
We developed Fabric Data Agents capable of analysing the effectiveness of cargo protection systems. These agents surfaced actionable insights and provided recommendations for improvement, helping the client move from reactive reporting to proactive optimisation.
2
End-to-End MLOps Framework
To support sustainable AI development, we implemented an MLOps framework within Fabric. This included model versioning, automated retraining, and deployment pipelines—ensuring that AI solutions could evolve with the business and remain production-ready.
3
Strategic Data Foundation
We delivered a comprehensive data strategy aligned with the client’s operational goals. This included guidance on data governance, architecture, and integration patterns to support future AI initiatives and ensure long-term scalability.
The results.
What did we achieve in 6 months?
Accelerated AI Adoption
With a clear framework and working examples, the client rapidly transitioned from experimentation to production-grade AI solutions. Fabric’s capabilities are now being used to drive operational improvements and inform strategic decisions.
Operational Efficiency Gains
AI agents now provide real-time feedback on cargo protection performance, allowing teams to identify inefficiencies and implement improvements faster than ever before.
Scalable AI Infrastructure
The MLOps framework enables the client to deploy and maintain multiple AI models across use cases, with minimal manual intervention—laying the groundwork for future innovation.
Data-Driven Culture Shift
The engagement helped foster a stronger data and AI culture within the organisation. Teams are now more confident in leveraging Fabric’s capabilities and are actively exploring new opportunities for intelligent automation.
