Tanmai Gopal

Tanmai Gopal is the co-founder and chief executive of PromptQL and a co-founder of Hasura, where he helped create the Hasura GraphQL Engine. His work focuses on making enterprise data usable by applications and AI agents without sacrificing security, organizational context, or accountability.

Gopal studied computer science at the Indian Institute of Technology Madras. While developing a food-delivery venture in Chennai with Rajoshi Ghosh, he built procrust.es, a database platform intended to simplify application development while maintaining transactional consistency. Interest from other developers prompted the pair to pivot toward infrastructure and found Hasura. Gopal initially served as chief technology officer and later became chief executive.

He also helped create and teach IIT Madras’s Introduction to Modern Application Development, covering web applications, databases, security, and mobile development. At Hasura, the open-source GraphQL Engine generated real-time APIs over existing data with fine-grained permissions; its Data Delivery Network subsequently extended secure data access across distributed enterprise systems.

In 2025, Gopal introduced PromptQL as the next stage of that infrastructure: enabling AI systems to turn natural-language requests into executable programs operating on business data and workflows.

• Live enterprise data as AI infrastructure. Gopal argues that assistants become useful when they can securely query operational information and business logic across databases, documents, and APIs. His AI Engineer demonstration combined customer records and movie-rental histories to generate a personalized message, illustrating how a shared SQL-like interface gives models consistent semantics across disparate systems.

• Executable code for dependable agents. Instead of relying exclusively on conversational reasoning, Gopal uses generated Python to plan retrieval, combine information from multiple sources, and perform calculations. Executable operations make multistep requests more inspectable and repeatable.

• Shared organizational context. Gopal treats institutional knowledge as an evolving product of collaboration: customer history, operational exceptions, and colleagues’ corrections must remain available to future interactions. His case for context over token consumption prioritizes continuously maintained team knowledge over simply increasing model usage.

• Human-accountable agent authorization. His authorization model for multiplayer AI gives shared agents the permissions of the person requesting an action, preserving existing access controls, auditability, and explicit approval for sensitive operations.

With PromptQL Tag, Gopal is applying these ideas to shared, model-independent workspaces that combine organizational context, collaboration, and secure execution.