The Deal Analytics group of PricewaterhouseCoopers Amsterdam has requested a tool for automatising the business-to-business customer analysis. This analysis was performed manually, which left room for performance improvement. This report discusses how the a product was developed which automates the analyses After two weeks of initial research, a complete system was designed and implemented in the subsequent nine weeks. The tool consists of two distinct parts: a front-end and a back-end. The front-end allows the user to customise the analysis to its own preferences, and communicates with the back-end to efficiently perform the analysis. With the help of user evaluations, the front-end has been designed such that it is usable by any PwC employee within the Deals branch.The back-end uses data analysis techniques and machine learning to analyse customer behaviour. Strong points and growth opportunities of a company are found using techniques such as customer segmentation, regression analysis, and cross-sell analysis. The product has been tested using a variety of techniques to ensure that the software does not crash on unexpected input. The final product is evaluated based on the requirements, design goals and success criteria set at the start of the project and can be considered successful.

Language is an intuitive and effective way for humans to communicate. Large Language Models (LLMs) can interpret and respond well to language. However, their use in deep reinforcement learning is limited as they are sample inefficient. State-of-the-art deep reinforcement learning algorithms are more sample efficient but cannot understand language well. This research aims to study whether RL agents can improve learning by utilizing language assistance and how LLMs can help them. A sentence describing the agent's environment is fed into an LLM to create a semantic embedding, which is consumed by a recurrent Soft Actor-Critic (SAC) agent to create an agent that can listen to natural language. This research shows that the best method for the agent to consume the embedding is concatenating it to each observation. Also, LLM-based embeddings lead to faster and more stable learning than non-LLM-based embeddings. The agent is sensitive to noise in the embedding but not to the embedding's dimensionality. The agent can generalize well across sentences that have a similar meaning to sentences seen during training but are formulated differently, but it can not generalize as well across sentences with unknown subjects and needs the subjects of the sentences to be grounded in training. Lastly, this research shows that the proposed architecture supports scaling language assistance to more complex environments.