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.
@article{arxiv.2603.07304,
title = {Tursio for Credit Unions: Structured Data Search with Automated Context Graphs},
author = {Shivani Tripathi and Ravi Shetye and Shi Qiao and Alekh Jindal},
journal= {arXiv preprint arXiv:2603.07304},
year = {2026}
}