A growing e-commerce retailer sought to increase its online conversion rates and improve the user shopping experience. Their catalog was large, but users often struggled to find relevant items. We implemented an AI-powered product recommendation engine that personalized suggestions in real-time based on customer behavior and preferences. The result was a seamless shopping experience that drove higher sales and improved customer engagement.
Static product listings made it difficult for users to find what they wanted
Low conversion rates and high cart abandonment
No personalization in product offerings
We integrated a machine learning-based recommendation engine into the client’s e-commerce platform. The engine analyzed user behavior — browsing patterns, search queries, and purchase history — to generate personalized product recommendations shown on home, category, and product pages.
increase in sales
increase in session time.
A national logistics company operating multiple distribution centers was struggling with inefficient inventory tracking. Manual box counting on pallets was time-consuming, prone to human error, and required significant labor. We introduced an AI-powered object detection system that uses camera images to automatically count boxes on pallets with high accuracy, streamlining the inventory process.
Inventory miscounts due to manual error
Time delays in inventory tracking
Increased labor cost and shipping delays
Using Azure Custom Vision, we built a computer vision model trained to detect and count box types on various pallet configurations. The system used existing surveillance or mobile device cameras, and results were displayed in a simple dashboard with real-time reporting.
increase in sales
faster inventory cycles
labor hours and errors
shipment scheduling and logistics accuracy
A leading manufacturer experienced frequent equipment failures and production interruptions. Traditional maintenance scheduling either wasted resources or failed to prevent downtime. We deployed an AI-powered predictive maintenance solution that identified early signs of wear, allowing for proactive repairs and optimized resource use.
High maintenance costs and inefficient scheduling
Unplanned equipment failures
Productivity losses due to downtime
We integrated real-time sensors and AI algorithms that monitored machine performance metrics such as vibration, temperature, and cycle frequency. Predictive models alerted operators before failures occurred, allowing for timely and targeted maintenance.
in maintenance costs
in equipment uptime
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