Tursio for Credit Unions: Powering Structured Data Search with Automated Context Graph
This work addresses the challenge of enabling business users in regulated industries, specifically credit unions, to access and query complex structured data using natural language, overcoming issues like complex schemas and data governance.
This paper introduces Tursio, a secure, on-premises platform designed to enable business users in regulated industries like credit unions to query complex structured databases using natural language. Tursio achieves this by automatically inferring a semantic knowledge graph from existing schemas and integrating Large Language Models to contextualize user intent and generate accurate, compliant query plans.
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.