Large language model (LLM)-based conversational assistants are designed for general-purpose conversation tasks and are primarily optimized for high-resource languages. Although these systems support some low-resource languages (LRLs), their responses often fall short of user expectations. Consequently, speakers of LRLs remain marginalized and unable to fully benefit from advances in LLMs. These challenges underscore the need for targeted, language-specific solutions that can effectively serve underrepresented language communities. This study presents Labadain Chat, a conversational agent for Tetun, a low-resource language spoken by over 932,000 people in Timor-Leste. We adapt existing LLMs to Tetun using language-specific prompting strategies and report on the system's architecture, features, applications, and utility for the Tetun-speaking community. Results from the user study show a high task success rate for Labadain Chat (91%, with substantial inter-annotator agreement, Cohen's κ=0.67) and high user satisfaction (4.30 out of 5, with Cohen's weighted κ=0.75), demonstrating the effectiveness of language-specific LLM customization for Tetun. Overall, this study provides a practical pathway toward promoting equitable access to AI-powered information services for the Tetun-speaking community and suggests an adaptable methodology that can be applied to other under-resourced languages in similar contexts. The system is publicly available at https://www.labadain.com, with mobile applications for both iOS and Android.

This paper proposes Labadain Crawler, a data collection pipeline tailored to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. The system is built on top of Nutch, an open-source web crawler and data extraction framework, and incorporates language processing components such as a tokenizer and a language identification model. The pipeline efficacy is demonstrated through successful testing with Tetun, one of Timor-Leste’s official languages, resulting in the construction of a high-quality Tetun text corpus comprising 321.7k sentences extracted from over 22k web pages. The contributions of this paper include the development of a Tetun tokenizer, a Tetun language identification model, and a Tetun text corpus, marking an important milestone in Tetun text information retrieval.