The decline of proficient practitioners poses an existential threat to numerous ancient writing systems, risking the loss of the cultural heritage they encode. Among these systems is Sawndip, a logographic script whose computational preservation faces severe challenges due to the scarcity of standardized corpora. In this paper, we present the first systematic framework for low-resource Sawndip-Chinese translation and bilingual conversational modeling. We introduce a newly curated dataset comprising sentence-level Sawndip-Chinese parallel data and character-level lexical definitions. Building upon this foundation, we propose a knowledge-enhanced supervised fine-tuning paradigm that incorporates lexical knowledge during training. To address the additional challenges of Chinese-to-Sawndip generation, we further develop a manually curated template-based substructure substitution strategy for synthesizing reverse-direction data. Bilingual conversational data is then constructed through a dimension-conditioned dialogue generation and multi-candidate back-translation pipeline, enabling controllable and diverse dialogue synthesis. Experimental results show that our final unified model effectively supports both Sawndip-Chinese translation and bilingual conversational abilities. Ultimately, this work provides a foundational reference for the digital preservation of Sawndip, offering broader insights into the computational documentation of globally-endangered languages.