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This paper introduces a conversational AI system that allows users to query 1.7 million digitized specimen records from the Australian Museum using natural language. The system combines an interactive map with a conversational agent powered by LLM function calling to retrieve specimen data and answer collection-specific questions. By enabling natural language queries against large, frequently updated datasets, the system improves public access to and understanding of natural history collections.
Unlock millions of natural history specimens with a conversational AI that understands complex queries and dynamically retrieves data from live museum APIs.
Recent digitisation efforts in natural history museums have produced large volumes of collection data, yet their scale and scientific complexity often hinder public access and understanding. Conventional data management tools, such as databases, restrict exploration through keyword-based search or require specialised schema knowledge. This paper presents a system design that uses conversational AI to query nearly 1.7 million digitised specimen records from the life-science collections of the Australian Museum. Designed and developed through a human-centred design process, the system contains an interactive map for visual-spatial exploration and a natural-language conversational agent that retrieves detailed specimen data and answers collection-specific questions. The system leverages function-calling capabilities of contemporary large language models to dynamically retrieve structured data from external APIs, enabling fast, real-time interaction with extensive yet frequently updated datasets. Our work provides a new approach of connecting large museum collections with natural language-based queries and informs future designs of scientific AI agents for natural history museums.
