To feed legal data into an AI agent, you first have to parse XML from public institutions. Table structures break, article numbering systems get tangled, and preprocessing alone eats half a day. Beopmang is a service that has solved this upfront. What Changes When You Receive JSON Legal data from public institutions is not machine-ready out of the box. Beopmang parses even complex table structures into a consistent JSON format. It covers most Korean statutes and updates weekly. Token consumption drops. Without the need for preprocessing to extract text from XML or HTML, the pipeline simplifies. Numerical values inside tables come out as clean arrays, reducing the chance of the model misreading context. Vector Search Is Built In Keyword matching alone struggles to capture the context of legal articles. Beopmang has converted key articles into pgvector-based vector data. A single API call enables semantic search. The core point is that no separate infrastructure needs to be set up. You can immediately run a RAG structure where the model directly references articles to generate answers. Integration Is Simple There is no authentication process. It works without API key issuance. Rate limits are generous, and no user logs are kept, making it frictionless for prototyping. Revision history comparison is also available via API. Even non-experts can track how articles have changed over time. Key Takeaways Pre-cleaned JSON legal data means virtually no preprocessing effort Built-in pgvector-based semantic search plugs directly into RAG pipelines No authentication required, generous rate limits – fast to get started Related Posts Why DataNexus — Structuring domain knowledge determines AI accuracy Legal Data Beyond Pipes – Gaining a Brain — Synergy between korean-law-mcp and DataNexus ontology Source: https://news.hada.io/topic?id=28050