This opportunity provides exclusice rights to a system and method for improving the reliability and accuracy of outputs from large language models (LLMs) by intelligently aggregating and extracting data from multiple model responses. The technology uses machine learning to rank and select among different LLMs based on performance, routes prompts to the most suitable models, and consolidates their outputs into refined, higher-confidence responses. It also supports ongoing model refinement through self-training and reinforcement learning feedback loops. With applications across generative AI, data extraction, and multi-model orchestration, this innovation strengthens the quality and trustworthiness of AI-generated information for enterprise, analytics, and automated decision-making use cases.