Tursio for Credit Unions: Structured Data Search with Automated Context Graphs

TL;DR

Tursio is presented, a secure, on-premises, database search platform that enables business users to query enterprise databases using natural language and automates this end-to-end and deploys entirely on-premises.

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, database search platform that enables business users to query enterprise databases using natural language. Tursio automatically infers a context graph -- a schema-level metadata structure that captures join paths, column semantics, and domain annotations -- and uses it to systematically generate accurate query plans through LLM-assisted compilation, grounding, and rewriting. Unlike existing AI/BI tools that require extensive manual context curation, Tursio automates this end-to-end and deploys entirely on-premises. We demonstrate Tursio through realistic scenarios in the credit union domain, and discuss its applicability to other regulated settings.

Figures (5)

• Figure 1: The journey of Symitar core banking data.
• Figure 2: Evaluating SQL structural accuracy (BIRD)
• Figure 3: Evaluating text response quality.
• Figure 4: Tursio search interface.
• Figure 5: Context graph for sample CU database, including tables, inferred joins, and associated annotations.