Objective
This project focuses on the development of an advanced ChatBot designed to improve bill explainability and transform customer service through the use of large-scale language modeling (LLM) and state-of-the-art technology tools. This project seeks to demonstrate how the implementation of an automated system can streamline and reduce the cost of customer service processes, providing accurate and quick responses to invoice-related queries.
In today’s context, customer service is a crucial component for the success of any company. Invoice queries are often recurring and, in many cases, complex, requiring specialized attention and consuming a considerable amount of resources. This project proposes an innovative solution through the implementation of a ChatBot that not only explains in detail the charges and concepts of the invoices, but also optimizes the company’s resources, reducing costs and improving the customer experience. The use of large-scale language models (LLM) allows the ChatBot to generate consistent and accurate responses based on a vast amount of previous data and examples.
The main objective of this project is to develop a ChatBot that uses generative artificial intelligence and large-scale language modeling (LLM) to provide detailed explanations of invoices. In addition, we seek to integrate advanced tools that allow access to and extraction of relevant information from various databases. Another crucial objective is to create a system based on LLM agents capable of efficiently handling user queries, improving the effectiveness and accuracy of explanations.
IRENE FERNÁNDEZ ROBLEDO
Academic Experience
Double Degree in Computer Engineering and Business Administration, Universidad Carlos III de Madrid (September 2017 – July 2023)
Work Experience
Banco Santander SCIB – Credit Risk: 01/2024 – now
Accenture AGBG department (Accenture and Google Business Group): 10/2023 – 01/2024
Intern Grupo MásMóvil (MásOrange).
Technical skills
Programming languages: Python, Java, C++ y JavaScript.
Tools: Pandas, Numpy, Tensorflow, Keras y Sci-kit Learn.
Databases: Oracle y BigQuery.