In recent times Large Language Models have exhibited tremendous capabilities, especially in the areas of mathematics, code generation, and general-purpose reasoning. However, for specialized domains especially in applications that require parsing and analyzing large chunks of numeric or tabular data, even state-of-the-art (SOTA) models struggle. In this paper, we introduce a new approach to solving domain-specific tabular data analysis tasks by presenting a unique RAG workflow that mitigates the scalability issues of existing tabular LLM solutions. Specifically, we present Tabular Embedding Model (TEM), a novel approach to fine-tune embedding models for tabular Retrieval-Augmentation Generation (RAG) applications. Embedding models form a crucial component in the RAG workflow, and even current SOTA embedding models struggle as they are predominantly trained on textual datasets and thus underperform in scenarios involving complex tabular data. We choose the domain of financial markets for model evaluation to demonstrate TEM’s ability to handle intricate and high-dimensional datasets, an area where existing models typically falter. The evaluation results showcase that our approach not only outperforms current SOTA embedding models in this domain, but also does so with a notably smaller and more efficient model structure.