Custom logic was developed to optimize Large Language Models (LLMs) using domain-specific data, ensuring accurate outputs. This approach enhances the generation of opinions, graphical outputs, and depreciation rates.

Retrieval-Augmented Generation (RAG) models were integrated to combine data retrieval with LLM-generated insights, ensuring contextually relevant, data-driven outputs for research assistance and opinion generation.

The solution's performance was benchmarked against industry standards to ensure accuracy and reliability. This step validated features such as tax calculations and depreciation rates, maintaining the system's competitive edge.