As AI becomes more prevalent, edge devices face challenges due to limited resources and the high demands of deep learning (DL) applications. In such cases, quality scalability can offer significant benefits by adjusting computational load based on available resources. Traditional Image-Signal-Processor (ISP) tuning methods prioritize maximizing intelligence performance, such as classification accuracy, while neglecting critical system constraints like latency and power dissipation. To address this gap, we introduce FlexEye, an application-specific, quality-scalable ISP tuning framework that leverages ISP parameters as a control knob for quality of service (QoS), enabling trade-off between quality and performance. Experimental results demonstrate up to 6% improvement in Object Detection accuracy and a 22.5% reduction in ISP latency compared to state of the art. In addition, we also evaluate Instance Segmentation task, where 1.2% accuracy improvement is attained with a 73% latency reduction.