The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.

The rapid evolution of intelligent chatbots has been largely driven by the advent of Large Language Models (LLMs), which have greatly enhanced natural language understanding and generation. However, the fast-paced advancements in generative Artificial Intelligence (AI) and LLM technologies present challenges for developers to stay up-to-date and to select optimal architectures or approaches from a wide range of available options. This survey article addresses these challenges by providing a overview of cutting-edge techniques and architectural application choices in modern generative chatbot development. We explore various approaches involving retrieval strategies, chunking methods, context management, embeddings, and the utilization of LLMs. Furthermore, we analyze paradigms such as naive Retrieval-Augmented Generation (RAG) compared to Graph-Based RAG, as well as single-agent versus multi-agent systems. We examine agent-based methodologies, comparing single-agent systems with multi-agent architectures, and analyze how multi-agent systems can proficiently handle intricate tasks, enhance scalability, and mitigate faults such as hallucinations through collaborative efforts. Additionally, we review tools and frameworks such as LangGraph that facilitate the implementation of stateful, multi-agent LLM applications. By categorizing and analyzing these modern techniques, this survey aims to present the current landscape and future directions in chatbot development.

Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and Object Detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for the sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.