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This master thesis explores the application of local Large Language Models (LLMs) in the consultancy industry, specifically focusing on the challenge of matching consultants to client assignments. The study develops and evaluates a structured pipeline integrating an LLM to automate the consultantassignment matching process. The research encompasses a comprehensive methodology, and culminating in a sophisticated LLM application.

The core of the thesis is an in-depth analysis of how the LLM, along with its constituent components like nodes, embedding models, and vector store indexes, contributes to the matching process. Special emphasis is placed on the role of temperature settings in the LLM and their impact on match accuracy and quality. Through methodical experimentation and evaluation, the study sheds light on the effectiveness of the LLM in accurately matching consultants to assignments and generating coherent motivations.

This master thesis establishes a foundational framework for the utilization of LLMs in consultancy matching, presenting a significant step towards the integration of AI in the field. The thesis opens avenues for future research, aiming to enhance the efficiency and precision of AI-driven consultant matching in the consulting industry.