Code review is essential for maintaining software quality but remains time-consuming and cognitively demanding, especially in industrial environments. While language models (LMs) have shown promise in automating code review tasks, most prior work targets dominant programming languages such as Java and Python, leaving industrially relevant languages like C# underexplored. This study presents an empirical evaluation of monolingual C#-specific fine-tuning of three architecturally diverse open-source LMs — a review-specialized pretrained model, a code-pretrained large language model, and a general-purpose instruction-tuned model — across three core automated code review tasks: Code Change Quality Estimation, Review Comment Generation, and Code Refinement. The review-specialized model was fine-tuned across all three tasks, while the two larger models were fine-tuned exclusively for comment generation due to computational constraints, using a dataset combining public benchmarks with proprietary industrial repositories. We systematically examined the effects of programming-language scope (C#-only vs. multilingual baselines) and natural-language composition (English-only vs. English–German), using both automated metrics and expert-based human evaluations. Our results reveal that the benefits of monolingual fine-tuning are task-dependent: C#-specific adaptation improves review-worthiness detection and enhances lexical alignment and human-perceived quality in comment generation, yet it reduces performance in code refinement, where broader multilingual pretraining proves more effective. English-only fine-tuning consistently outperforms bilingual configurations in comment quality. Across all tasks, fine-tuned LMs offer substantial efficiency gains over human reviewers, but human judgment remains superior for semantically complex and context-sensitive assessments, suggesting that LMs are best deployed as assistive tools within hybrid review workflows.