Connecting Decision Makers to data through Agentic AI for a National Distributor

At a leading large-scale distribution company managing complex inventory flows and operation performance, leadership teams depend on rapid access to reliable data to answer critical business questions. However, insights were constrained by a reporting model built around Microsoft SQL Server and manual query development. Business questions required analyst intervention, limiting leaderships’ ability to explore data independently or iterate in real time.

The organization did not lack data; it lacked accessible, self-service insight. 

As the organization evaluated modernization of its analytics platform, the central question became whether generative AI could bridge this accessibility gap while maintaining the accuracy, governance, and scalability required for enterprise decision-making.

Aimpoint Digital designed and deployed a production-ready multi-step agentic table-augmented generation (TAG) architecture that enables executives to interact with structured enterprise data conversationally, without compromising accuracy or governance.

Rather than relying on basic text-to-SQL generation, we implemented a multi-step reasoning architecture that plans queries before execution, intelligently identifies the right tables, understands schema relationships, and validates SQL prior to running it. This structured approach ensures reliable, explainable responses grounded in governed enterprise data.

The agent automatically interprets business questions, determines required data sources, constructs optimized SQL queries, validates them for errors, executes them against Delta Lake tables, and returns clear, executive-ready insights in natural language.

To accelerate development and ensure enterprise readiness from day one, we leveraged Aimpoint’s AgentOps Accelerator. By embedding standardized evaluation workflows, model validation, lifecycle governance, and controlled deployment processes into the solution, we reduced implementation risk and improved reliability while moving from proof of concept to production more efficiently. Built on Databricks with integrated GenAI capabilities, the result is a governed, scalable foundation for conversational decision intelligence — accelerating Lakehouse modernization while reducing reliance on manual SQL reporting workflows.