Text-to-SQL Across Fragmented Business Data
Helping retail teams query sales, inventory, and supplier data without relying on tribal database knowledge.
- ThinkCol built an agentic text-to-SQL system for a retail group with sales, inventory, and supplier data spread across disconnected systems.
- The solution helps business users ask natural-language questions while the agent navigates schema relationships and hidden business rules.
- An evaluation engine checks generated SQL before insights are surfaced, helping make the system more reliable for enterprise use.
A retail group had valuable business data across sales, inventory, and supplier systems, but the relationships between tables were not always documented. Answering operational questions required technical support, senior staff knowledge, and manual interpretation of legacy data structures.
ThinkCol developed an agentic text-to-SQL solution that translates business questions into database queries, explores relevant tables, and identifies connections across fragmented datasets. Instead of simply generating a query, the agent is designed to reason through schema relationships, apply business context, and produce actionable insight from scattered enterprise data.
To support production-grade use, ThinkCol added an evaluation layer that reviews generated SQL for accuracy before results are returned. This gives business users a faster way to investigate performance, inventory, and supplier questions while preserving a higher level of control than a generic AI assistant.
Why customise? No general-purpose AI can reliably navigate a company's fragmented legacy databases, decide which tables to join, and evaluate its own output without being shaped around that organisation's data model and business rules.
