Chatting with organisational data

Md Abul Bashar, Richi Nayak

Published: 01 Nov 2025, Last Modified: 21 Jan 2026Knowledge and Information SystemsEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The world of data is vast and complex, harbouring valuable insights and patterns that can drive decision-making in various fields. However, accessing useful information from raw data (e.g. mining project reports) can be a formidable task, often requiring specialised skills and tools. The emergence of generative artificial intelligence (AI) has opened up an intriguing and novel means of engaging with data conversation. This article delves into the novel concept of chatting with organisational data using generative AI. We present an innovative solution that combines a generative AI chatbot (e.g. ChatGPT-QAM) with a language model (e.g. BERT) based extractive question-answer model (BERT-QAM) to generate responses based on given contexts. We use an answer verification model to resolve any disagreements between the responses of ChatGPT-QAM and BERT-QAM. We use the context filtering model to enhance responses by considering valid contexts. Our solution is tested on mining project reports made available by the Geological Survey of Queensland. Through this case study, we highlight several challenges that must be addressed to utilise this approach effectively. The case study shows that the concept of chatting with organisational data can revolutionise how we interact with complex scientific reports, which contain a mix of tables, text and images, to find valuable insights.