Wren Engine

Wren Engine is the Semantic Engine for MCP Clients and AI Agents. Wren AI GenBI AI Agent is based on Wren Engine.

Challenge Today

At the enterprise level, the stakes - and the complexity - are much higher. Businesses run on structured data stored in cloud warehouses, relational databases, and secure filesystems. From BI dashboards to CRM updates and compliance workflows, AI must not only execute commands but also understand and retrieve the right data, with precision and in context.

While many community and official MCP servers already support connections to major databases like PostgreSQL, MySQL, SQL Server, and more, there's a problem: raw access to data isn't enough.

Enterprises need:

Accurate semantic understanding of their data models
Trusted calculations and aggregations in reporting
Clarity on business terms, like "active customer," "net revenue," or "churn rate"
User-based permissions and access control

Natural language alone isn't enough to drive complex workflows across enterprise data systems. You need a layer that interprets intent, maps it to the correct data, applies calculations accurately, and ensures security.

Our Mission

Wren Engine is on a mission to power the future of MCP clients and AI agents through the Model Context Protocol (MCP) — a new open standard that connects LLMs with tools, databases, and enterprise systems.

As part of the MCP ecosystem, Wren Engine provides a semantic engine powered the next generation semantic layer that enables AI agents to access business data with accuracy, context, and governance.

By building the semantic layer directly into MCP clients, such as Claude, Cline, Cursor, etc. Wren Engine empowers AI Agents with precise business context and ensures accurate data interactions across diverse enterprise environments.

We believe the future of enterprise AI lies in context-aware, composable systems. That’s why Wren Engine is designed to be:

Embeddable into any MCP client or AI agentic workflow
Interoperable with modern data stacks (PostgreSQL, MySQL, Snowflake, etc.)
Semantic-first, enabling AI to “understand” your data model and business logic
Governance-ready, respecting roles, access controls, and definitions