This study evaluates an AI post-editing (AIPE) system in a professional translation setting, covering translation from English into ten target languages across five domains. We evaluate the system using automatic metrics on 71,262 production segments and human evaluation on a stratified sample of 6,618 segments (approximately 600 segments per target language) assessed by 60 professional translators. AIPE refines machine translation output using a secure publicly available LLM, retrieving language-specific style guides and high-quality bilingual examples to guide edits. We compare it with direct LLM translation (LLMT), Google Translate, and DeepL. The two AIPE configurations evaluated consistently outperform the generic translation baselines in terms of quality. LLMT does not match this quality, though it may suit less quality-sensitive domains. We observe how AIPE’s gains vary according to pre-translation type, with fuzzy translation memory matches over-represented among severe errors, and discuss deployment implications.