Empowered by AWS JIC, eNeural redefines edge AI with dual solutions to accelerate global expansion
Empowered by the Startup Terrace Kaohsiung AWS Joint Innovation Center (JIC), eNeural is redefining the landscape of edge AI with two flagship innovations - AI-Craft, an automated AI development platform, and an edge AI self-learning solution. These breakthroughs aim to make AI truly self-learning and self-evolving, helping industries overcome long-standing barriers in deploying AI at the edge.As AI applications rapidly expand across industries, enterprises have realized that beyond computing power, the biggest barriers to AI implementation lie in the high cost and lengthy cycle of model training. For system integrators, success increasingly depends on the ability to integrate optimized AI models into lightweight edge devices to deliver reliable inference services in real time.A spin-off startup from National Yang Ming Chiao Tung University (NYCU), eNeural combines strong academic research foundations with practical AI R&D experience. The company has already forged partnerships with major electronics manufacturers including Quanta, Compal, and Lite-On, and is collaborating with a North American e-commerce logistics fleet on developing an AI-based collision warning system.According to eNeural CEO Eric Huang, edge devices are typically designed to be small, lightweight, and energy-efficient, which makes AI model optimization particularly challenging. "Our AI-Craft platform provides a one-stop, modular, and standardized AI development workflow," said Huang. "It allows developers to automatically generate optimized models as efficiently as products rolling off an intelligent production line."Edge AI self-learning accelerates model optimization and reduces costsFounded in 2022, eNeural focuses on proprietary AI technology R&D, with AI-Craft serving as the backbone of its innovation. The platform integrates four key technology modules - automated annotation, model pruning, model quantization, and generative AI data augmentation - to make AI development faster, more scalable, and more energy-efficient.Using pre-trained AI models and advanced image analysis algorithms, AI-Craft boosts data labeling efficiency by 10 to 100 times, significantly shortening AI project launch time. Model pruning removes redundant parameters to enhance computational efficiency by up to 90% without compromising accuracy, while reducing the memory and processor load.Model quantization converts floating-point operations into 8-bit, 4-bit, or even 2-bit precision, dramatically lowering power consumption and enabling smooth deployment of AI models on compact edge devices. Meanwhile, generative AI data augmentation automatically creates training data for rare yet critical conditions - such as night, rain, snow, or backlight - improving AI model robustness and generalization.Traditionally, AI models deployed on edge devices must send large amounts of data back to the cloud for retraining - a process that consumes time and resources. eNeural's Edge AI Self-Learning technology allows models to learn and recalibrate autonomously in real time, reducing the need for cloud data transfer."For example," Huang explained, "when fleet vehicles operate under different weather, lighting, or road conditions, the AI model can automatically adjust its decision logic to maintain accuracy. With federated learning, training results are securely shared across thousands of vehicles." This innovation reduces retraining cycles from months to weeks and significantly lowers AI operation and maintenance costs.AWS JIC Accelerates cloud transformation and strategic collaborationSince joining the AWS JIC in 2025, eNeural has leveraged the expertise of AWS specialists to upgrade its AI-Craft platform using Amazon SageMaker, a fully managed machine learning (ML) service, and Amazon Elastic Compute Cloud (Amazon EC2) GPU computing power. This transformation has turned AI-Craft into a cloud-based development solution, enabling developers to perform automated annotation, model pruning, and quantization in the cloud. With dynamic AWS resource allocation, eNeural can efficiently run multiple AI projects in parallel with greater scalability and cost efficiency.Participation in the AWS JIC program has also opened doors for new business collaborations. Huang shared that while earlier partnership talks with Advantech did not progress, joining the AWS JIC program led to the successful signing of a Memorandum of Understanding (MOU) between the two companies in 2025. Under this collaboration, eNeural will feature its two flagship AI solutions on Advantech's Marketplace, paving the way for deeper engagement with system integrators worldwide.Beyond Taiwan, eNeural is actively expanding into the U.S., Japan, and India. Leveraging AWS's global cloud infrastructure and innovation ecosystem, the company is pursuing a "software-driven core + hardware collaboration" strategy, collaborating with global players to unlock new opportunities in the rapidly growing edge AI market.
