Tursio for Credit Unions: Powering Structured Data Search with Automated Context Graph

2026-03-07

AI Summary

This work proposes a secure, localized natural language database querying platform tailored for highly regulated industries such as credit unions, where complex database schemas and stringent data governance policies impede business users’ effective access to structured data. The platform uniquely integrates large language models (LLMs) deeply into the entire query processing pipeline: it automatically constructs a semantic knowledge graph to align user intent, generates compliant and context-aware query plans, and accurately translates natural language into executable SQL. Evaluated in real-world settings, the approach substantially lowers the barrier to data access while ensuring regulatory compliance, effectively bridging the semantic gap between intricate data structures and evolving business needs.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Extracting actionable insights from structured databases in regulated industries, such as credit unions, is often hindered by complex schemas, legacy systems, and stringent data governance requirements. We present Tursio, a secure, on-premises, context-aware database search platform that enables business users to query enterprise databases using natural language. Tursio automatically infers a semantic knowledge graph from existing schemas, contextualizes user intent, and systematically generates accurate and compliant query plans by integrating Large Language Models (LLMs) throughout the query processing stack. We demonstrate Tursio's capabilities through realistic scenarios in the credit union domain, highlighting its effectiveness in bridging the gap between complex data structures and user intent.

Problem

• structured data search
• credit unions
• data governance
• complex schemas
• legacy systems

Innovation

• context-aware search
• semantic knowledge graph
• natural language query
• large language models
• on-premises data platform