Abstract: A system and method for iteratively populating, querying, and ranking governmental data across heterogeneous databases may include iteratively populating at least one database with governmental data elements by obtaining source data from governmental data sources at predefined times, transforming the source data using at least one database schema, and populating the database, which may be a vector, relational, or graph database. A user query is received, comprising a query string, and processed using a routing agent to determine the data type or semantic scope and select at least one agent from a plurality of agents, including structured data agents, unstructured data agents, graph data agents, semantic search agents, validation agents, bias mitigation agents, or fallback agents. The query is modified using metadata, executed to retrieve responses, and input into a relevance machine learning model to determine relevance scores and rank responses. Ranked query responses are outputted to the user.

Abstract: A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface.