Understanding and effectively responding to email communication remains a critical yet complex challenge for current AI techniques, especially in corporate environments. These tasks are further complicated by the need for domain-specific knowledge, accurate entity recognition, and high precision to prevent costly errors. While recent advances in AI, specifically Large Language Models (LLMs), have made strides in natural language understanding, they often lack business-specific expertise required in such settings. In this work, we present Advanced Messaging Platform (AMP), a production-grade AI pipeline that automates email response generation at scale in real-world enterprise settings. AMP has been in production for more than a year, processing thousands of emails daily while maintaining high accuracy and adaptability to evolving business needs.

Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models’ challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs’ performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts.