We updated the firmware of the client's sensor over the air, fixed software bugs, and conducted full testing on iOS and Android platforms. A key feature of the app was its ability to analyze car vibrations in motion to detect wear in undercarriage components. By combining IoT data, NVH (noise, vibration, harshness) technology, and machine learning, the system monitored vibration behavior in real time. Our work focused on ensuring sensor accuracy and maintaining stable, bidirectional communication between mobile devices and backend servers throughout the driving process.

During manufacturing, each diesel engine part passes strict multi-level quality control. After final inspection, a unique identifier from the quality control service is printed on the packaging. We developed a mobile app that allows clients and end users to scan QR codes and validate these identifiers, confirming the authenticity of the parts. The app also connects users to product details and additional information via the company website. With this tool, the client strengthens customer confidence, ensures transparency in part validation, and adds a digital touchpoint that supports marketing and customer engagement.

We developed an iPad application that empowered sales representatives to configure and visualize new vehicles in real time, both within and outside the dealership. Users could build a custom car from scratch by selecting the model, engine type, and optional features. The app provided immediate 360-degree views of the car in 2D/3D, along with immersive interior panoramas under varied lighting conditions. It also integrated financing calculators to present final prices and was connected to the dealership’s presentation system. The solution enhanced mobility for staff, improved the customer experience, and supported seamless interaction between dealers and buyers.

The client aimed to launch a centralized admin tool to support the real-time analysis and adjustment of rental services. We developed a robust web application that allowed admins to view user behavior, track performance metrics, and modify settings—such as pricing—on the fly. A key feature was the integration of predictive analytics to forecast rental demand for up to six weeks. Machine learning models processed parameters like booking lead time, rental duration, and meta-rent to calculate accurate projections. The platform helped the client respond to changing market needs, optimize fleet usage, and improve decision-making efficiency.

We built a streamlined mobile web solution to bring the full rental journey to travelers’ smartphones. Designed as a responsive counterpart to the desktop site, the platform optimized features for mobile use while maintaining consistent branding and functionality. Users could plan trips with integrated maps and turn-by-turn route guidance, instantly reserve vehicles with saved preferences, and access emergency roadside support within a tap. The platform prioritized accessibility, delivering smooth performance across devices and operating systems. It helped transform the rental process into a fast, intuitive, and user-centric experience that supports travelers at every stage.

We created an intelligent mobile app that simplifies vehicle servicing by using advanced license plate recognition. When a user scans a license plate, the app connects to an automotive database and retrieves detailed vehicle specifications, such as engine type, transmission, brake system, and cooling configuration. Based on this data, it generates personalized lubrication recommendations for each component. Keeneo designed and trained a neural network optimized to recognize plates in real-world conditions. A lean development approach allowed for rapid prototyping, ensuring the concept was practical and user-friendly while maintaining high recognition accuracy and service precision.