Staging environment

Build a Text-to-SQL Agent Blueprint

Hosted by Viren Shah

In this video

What you'll learn

Frame a Text-to-SQL Question

Turn a natural-language request into clear entities, measures, filters, time periods, and expected results.

Map the Required Data

Identify the approved tables, columns, keys, and relationships the agent needs to generate the SQL.

Add Business Meaning

Document metric definitions, filters, exclusions, time rules, and usage guidance so the SQL matches the intended answer.

Build a Knowledge Graph Map

Connect business entities and relationships so the agent can identify the correct path from a question to the data.

Validate the Generated SQL

Check the selected data, joins, calculations, permissions, and evidence before presenting the answer.

Why this topic matters

Organizations already hold valuable answers in databases, warehouses, and operational systems. Text-to-SQL lets people explore that information through natural-language questions. By combining approved data, business definitions, relationships, permissions, and validation rules, teams can produce answers that are useful, traceable, and ready for responsible business use.

You'll learn from

Viren Shah

Founder and CEO of Vayom AI and author of Building Enterprise AI Agents

Viren Shah is the founder and CEO of Vayom AI and the author of Building Enterprise AI Agents. He has more than twenty years of experience in pharmaceutical revenue management, enterprise data, revenue intelligence, and advanced analytics. He has held leadership roles at Model N and consulting roles at Accenture and Cognizant, working with pharmaceutical and biotechnology manufacturers across contract-to-cash, market access, Medicaid rebates, 340B, and gross-to-net programs. Viren has also taught financial accounting, financial management, and database courses as an adjunct faculty member. He holds an MBA from the University of Mumbai and helps professionals connect business knowledge, structured data, and AI.

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The Enterprise AI - Builder Program
VIREN SHAH
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