Your SQL AI Agent

Indexing with Semantics

Wren AI has implemented a semantic engine architecture to provide the LLM context of your business; you can easily establish a logical presentation layer on your data schema that helps LLM learn more about your business context.

TEXT2SQL

Generate SQL Queries with Context

With Wren AI, you can process metadata, schema, terminology, data relationships, and the logic behind calculations and aggregations with “Modeling Definition Language”, to generate accurate SQL queries with semantic context.

conversational interface

get Insights without writing code

When starting a new conversation in Wren AI, your question is used to find the most relevant tables. From these, LLM generates three relevant questions for the user to choose from. You can also ask follow-up questions to get deeper insights.

AUTOMATE WORKFLOW

Query feedback loop

The AI self-learning feedback loop is designed to refine SQL augmentation and generation by collecting data from various sources. These include user query history, revision intentions, feedback, schema patterns, semantics enhancement, and query frequency.

Coming Soon

SQL AI Agent on your data

Our user-friendly interface, coupled with a powerful semantic architecture offers extensive data connectivity and semantic context to all data sources, empowering you to unlock the full potential of your data.

GitHub Get Started

secure and reliable

Why wren AI?

Turnkey Solution

Wren AI makes it easy to onboard your data. Discover and analyze your data with our user interface. Effortlessly generate results without needing to code.

Secure By Design

Your database contents will never be transmitted to the LLM. Only metadata, like schemas, documentation, and queries, will be used in semantic search.