Legal practitioners in Thailand must navigate fragmented government websites to research over 3,800 active laws and 87,000 Supreme Court decisions, with no unified tool for cross-referencing, version tracking, or structural navigation. We present FourCorners, a deployed platform that addresses five practitioner pain points through three modules built on a temporal legal knowledge graph covering 552K nodes and 6.3M edges: (1) an AI legal assistant that performs grounded generation via structured graph retrieval, streaming verified source content inline with responses; (2) an interactive law reader that translates graph structure into navigation and comparison features; and (3) a court decision explorer with version-aware citations produced by temporal entity resolution across 87,394 rulings. The system discovers implicit cross-corpus relationships through co-citation analysis of Supreme Court decisions. Interviews with 20 legal professionals reveal that inline source verification fundamentally changes how practitioners interact with AI-generated legal content, and that cross-corpus enrichment surfaces legal relationships that existing tools leave invisible.

Large language models (LLMs) show promise in legal question answering (QA), yet Thai legal QA systems face challenges due to limited data and complex legal structures. We introduce NitiBench, a novel benchmark featuring two datasets: (1) NitiBench-CCL, covering Thai financial laws, and (2) NitiBench-Tax, containing Thailand’s official tax rulings. Our benchmark also consists of specialized evaluation metrics suited for Thai legal QA. We evaluate retrieval-augmented generation (RAG) and long-context LLM (LCLM) approaches across three key dimensions: (1) the benefits of domain-specific techniques like hierarchy-aware chunking and cross-referencing, (2) comparative performance of RAG components, e.g., retrievers and LLMs, and (3) the potential of long-context LLMs to replace traditional RAG systems. Our results reveal that domain-specific components slightly improve over naive methods. At the same time, existing retrieval models still struggle with complex legal queries, and long-context LLMs have limitations in consistent legal reasoning. Our study highlights current limitations in Thai legal NLP and lays a foundation for future research in this emerging domain.