Code analysis ensures software quality, readability, and maintenance. Traditional methods such as Static Analysis Tools (SAT) and SZZ algorithms recognize errors and analyze contributions based on predefined rules and historical trends. However, complex code semantics are difficult to understand and often lead to overlooked issues and limited feedback. Despite its benefits, LLM is not practical in some business environments, facing challenges such as high computing costs, slower processing, and data protection concerns. Hybrid models may provide scalable and inexpensive solutions for large enterprise projects. This study contrasts SATs and LLMs, focusing on capabilities to detect bugs and maintain code quality, by investigating hybrid models that fuse the contextual strength of LLMs with the efficiency of SATs. In evaluations with 30 participants, the hybrid model consistently received the highest scores across accuracy, clarity, relevance, and usability (average ratings >8.5/10), outperforming both SATs and AI-alone approaches. Hybrid models may thus provide a scalable and cost-effective solution for problems arising in large-scale corporate enterprise projects. This study supports the use of case studies and benchmarks to assess the effectiveness, cost, and scalability of these various approaches. The findings aim to give practical suggestions for improving traditional code review processes, helping balance rule-based tools with AI-powered insights to enhance software development workflows.