Machine translation (MT) systems have achieved remarkable progress in recent years, especially with the emergence of deep learning and Transformer-based architectures. Nonetheless, the application of a general neural machine translation approach to domain-specific scenarios remains limited. Firstly, the presence of domain-specific terminology may reduce the accuracy of general translation models, which typically lack fine-tuning for the target domain. Secondly, fine-tuning such models necessitates domain-specific training data, which is frequently limited in availability. Finally, general translation tools like Google Translate do not provide a unified pipeline for seamless integration into industry workflows. This paper introduces a unified translation workflow developed to address these limitations, offering adaptability and extensibility for domain-specific applications in industry. Our proposed approach delivers a complete pipeline for end-users, incorporating customizable domain-specific terminology and supporting translation across multiple document formats, thereby enhancing both usability and output quality in real-world applications. We evaluate the proposed approach in an industrial setting, focusing on the textile and garment domain. Using a dataset of textile and apparel domain-specific documents in various formats, along with human evaluation, the experiments demonstrate the effectiveness and practicality of our approach for domain-adaptive machine translation.