This study introduces the Next Generation Quality Accelerator (NGQA) pipeline, a comprehensive framework that automates software quality assurance by intelligently orchestrating static analysis and large language models (LLMs). The NGQA methodology addresses the critical challenge of scalable quality assurance by establishing a systematic, six-step pipeline that progressively refines code quality while minimizing human intervention. The framework integrates SonarQube-based issue detection, retrieval-augmented generation (RAG)-driven false-positive mitigation, LLM-based code remediation, structural dependency analysis, comprehensive test suite generation via a novel Local Chain-of-Thought framework that employs four sequential AI agents, and multi-metric quality validation. Experimental evaluation across 70 repositories spanning seven programming languages demonstrates significant effectiveness: PassRatio improved by 16.5%, CodeBLEU increased by 28.8%, and CodeScore improved by 24.0%. The false-positive mitigation agent achieved an 89.0% F1-score, NGQA successfully resolved 83.5% of validated issues, and achieved a 32.6-fold mean acceleration over the estimated manual QA effort, representing a significant step toward largely automated software QA with minimal human intervention.