Digital governance increasingly relies on artificial intelligence (AI) to deliver citizen services efficiently, yet concerns around algorithmic opacity, regulatory compliance, and equitable access persist. Service delivery mechanisms are often hindered by limited transparency, fragmented communication channels, and inadequate trust between citizens and governing institutions. To address these challenges, retrieval-augmented generation (RAG) architectures integrated with conversational AI offer a pathway to enhance accessibility and accountability in digital governance frameworks. RAG-based chatbots combine knowledge retrieval with generative reasoning, enabling real-time, context-aware responses grounded in authoritative data sources. Unlike traditional rule-based systems, these chatbots not only provide accurate information but also justify their outputs through transparent citation of underlying documents. This ensures interpretability and aligns decision support with legal and regulatory requirements. By embedding transparency into the conversational process, citizens gain clearer insight into how information is processed and delivered, which strengthens trust in digital governance platforms. Furthermore, such chatbots streamline compliance monitoring by dynamically cross-referencing policies, regulations, and service protocols. They can guide users through complex procedures such as licensing, benefits application, or regulatory reporting while ensuring adherence to mandated standards. The approach fosters inclusive service delivery by bridging gaps in literacy, digital skills, and accessibility through natural language interaction. This article examines how RAG-powered conversational systems can operationalize algorithmic transparency, improve compliance assurance, and strengthen citizen engagement. It positions AI-enabled information transparency as a cornerstone for enhancing trust, accountability, and inclusivity in the next generation of digital governance ecosystems.