Title: Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL Show Chinese title

Title: 为PostNL创建基于Gen-AI的跟踪和追溯助理MVP（SuperTracy）

Abstract: The developments in the field of generative AI has brought a lot of opportunities for companies, for instance to improve efficiency in customer service and automating tasks. PostNL, the biggest parcel and E-commerce corporation of the Netherlands wants to use generative AI to enhance the communication around track and trace of parcels. During the internship a Minimal Viable Product (MVP) is created to showcase the value of using generative AI technologies, to enhance parcel tracking, analyzing the parcel's journey and being able to communicate about it in an easy to understand manner. The primary goal was to develop an in-house LLM-based system, reducing dependency on external platforms and establishing the feasibility of a dedicated generative AI team within the company. This multi-agent LLM based system aimed to construct parcel journey stories and identify logistical disruptions with heightened efficiency and accuracy. The research involved deploying a sophisticated AI-driven communication system, employing Retrieval-Augmented Generation (RAG) for enhanced response precision, and optimizing large language models (LLMs) tailored to domain specific tasks. The MVP successfully implemented a multi-agent open-source LLM system, called SuperTracy. SuperTracy is capable of autonomously managing a broad spectrum of user inquiries and improving internal knowledge handling. Results and evaluation demonstrated technological innovation and feasibility, notably in communication about the track and trace of a parcel, which exceeded initial expectations. These advancements highlight the potential of AI-driven solutions in logistics, suggesting many opportunities for further refinement and broader implementation within PostNL operational framework. Show Chinese abstract

Abstract: 生成式人工智能领域的进展为公司带来了许多机遇，例如改善客户服务效率和自动化任务。 荷兰最大的包裹和电子商务公司 PostNL 希望利用生成式人工智能来提升包裹追踪沟通的质量。 在实习期间，创建了一个最小可行产品（MVP），以展示使用生成式人工智能技术的价值，增强包裹追踪功能，分析包裹的运输过程，并能够以易于理解的方式进行沟通。 主要目标是开发一个基于内部大语言模型（LLM）的系统，减少对外部平台的依赖，并在公司内部建立专门的生成式人工智能团队的可行性。 该多代理大语言模型系统旨在高效且准确地构建包裹运输故事并识别物流中断。 研究涉及部署一个先进的 AI 驱动通信系统，采用检索增强生成（RAG）提高响应精度，并优化针对特定领域任务的大语言模型（LLMs）。 MVP 成功实施了一个名为 SuperTracy 的多代理开源 LLM 系统。 SuperTracy 能够自主管理广泛的用户查询并改进内部知识处理。 结果与评估展示了技术的创新性和可行性，特别是在包裹追踪沟通方面，超出了最初的预期。 这些进展突显了人工智能驱动解决方案在物流领域的潜力，表明在 PostNL 运营框架内有许多进一步完善和广泛实施的机会。