Client Experiences

The client operated retail outlets across multiple locations in the Klang Valley. Their existing inventory management process required staff to manually count and record products during stocktakes, a process taking several hours per location and producing inconsistent results.

After the discovery phase identified product recognition as a high-value opportunity, we developed a custom model trained to identify products specific to their catalog. Implementation included a mobile application that staff could use to photograph shelves, with the system automatically identifying and counting visible products.

Within three months of deployment, inventory counting time decreased substantially while accuracy improved. The client expanded the system to additional locations and is now exploring extensions for planogram compliance monitoring.

The healthcare organization had identified several AI applications that could improve patient care and operational efficiency, including appointment scheduling optimization and medical imaging assistance. However, they lacked the infrastructure to deploy and operate these systems reliably.

We designed and deployed a cloud-native platform using containerization and orchestration to provide a foundation for multiple AI applications. The architecture included automated scaling to handle varying patient volumes, comprehensive monitoring for both performance and compliance, and cost management controls to prevent unexpected expenses.

The platform is now supporting four distinct AI applications across their facilities. The monitoring systems have helped identify optimization opportunities that reduced operational costs substantially below initial projections. Their IT team manages the platform independently after our structured handover process.

This manufacturing company was aware that competitors were implementing AI but struggled to identify where to begin. They had limited in-house technical expertise and needed guidance on which AI applications would provide meaningful returns without requiring unrealistic resource investments.

The discovery process involved workshops with stakeholders across departments to understand pain points and opportunities. We assessed their data landscape, evaluated technical requirements for potential applications, and developed a prioritized roadmap based on feasibility and expected impact.

The engagement identified quality control automation as the highest-value opportunity, along with two additional applications for later implementation. The client has begun the first project and reports early results tracking above projections. The roadmap we developed provides clear direction for their AI initiatives over the coming year.