This paper introduces Rebound Reasoning, an architecture leveraging iterative prompting and mixture of agents to improve the reasoning capabilities of quantized language models for efficient deployment as services in production systems. Our approach enables quantized models to narrow the performance gap with unquantized counterparts while requiring only a fraction of computational resources, making them suitable for manufacturing environments. We demonstrate the effectiveness of Rebound Reasoning through comprehensive experiments using the Alpaca Eval benchmark. The architecture integrates with vLLM and ollama infrastructure, generating multiple perspectives through iterations to reduce biases and synthesize more comprehensive answers. This approach enhances output diversity and quality while improving error tolerance and consistency—particularly valuable for decision support in production environments. Our experiments show Rebound Reasoning achieves comparable performance to unquantized models while offering significant improvements with quantized models. The approach's flexibility allows adaptation to various model configurations, making it scalable for different production applications. This work contributes to making large language models more efficient and accessible as services for production systems, broadening the application of AI reasoning capabilities in manufacturing environments.