TLDR: Canner has launched WrenAI, an open-source Generative Business Intelligence (GenBI) agent that allows users to interact with structured data using natural language. It translates plain language queries into accurate SQL, generates charts, and provides AI-driven insights, making data analytics accessible to both technical and non-technical users without requiring SQL knowledge. WrenAI supports a wide array of large language models and various data sources, aiming to streamline data exploration and decision-making.
Canner has officially introduced WrenAI, an innovative open-source Generative Business Intelligence (GenBI) agent designed to transform how users interact with structured data. Launched on July 21, 2025, WrenAI empowers both technical and non-technical professionals to query, analyze, and visualize data through natural language, eliminating the need for traditional SQL coding. This development marks a significant step towards democratizing data analytics.
WrenAI functions as a conversational AI agent, capable of understanding data questions posed in plain language, including multiple languages. It then translates these queries into precise, production-grade SQL, a process referred to as Text-to-SQL. Beyond just generating SQL, the platform offers multi-modal outputs, including charts, summary reports, dashboards, and spreadsheets, providing immediate data presentation and operational reporting. This capability ensures that users receive decision-ready analysis directly from AI-generated summaries and context-aware visualizations.
A core strength of WrenAI lies in its flexibility regarding Large Language Models (LLMs). It supports a broad spectrum of LLMs, including OpenAI GPT series, Azure OpenAI, Google Gemini and Vertex AI, DeepSeek, Databricks, AWS Bedrock (Anthropic Claude, Cohere), Groq, and Ollama for local or custom LLMs. This extensive compatibility allows organizations to leverage their preferred or existing AI infrastructure. The performance of WrenAI is noted to be significantly dependent on the capabilities of the chosen LLM, with recommendations for using the most powerful models for optimal results.
Underpinning WrenAI’s accuracy and contextual understanding is its semantic layer, which utilizes a Modeling Definition Language (MDL). This MDL allows users to encode schema, metrics, and join relationships, providing LLMs with the necessary business context to generate precise SQL that aligns with the data model and intent. This feature is crucial for ensuring trustworthy and explainable business intelligence outputs.
Furthermore, WrenAI is designed for extensibility and integration. Its APIs enable developers to embed AI-generated SQL and charts into custom applications, dashboards, or workflows, facilitating the creation of custom agents and chatbots. The platform also boasts support for various popular data sources, including Athena (Trino), Redshift, BigQuery, DuckDB, PostgreSQL, MySQL, Microsoft SQL Server, ClickHouse, Oracle, Trino, and Snowflake.
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Canner emphasizes that WrenAI is a verified, open-source GenBI solution that bridges the gap between business teams and databases through conversational, context-aware, AI-powered analytics. It is engineered with a strong semantic backbone to ensure trustworthy, explainable, and easily integrated business intelligence, fostering a culture where data becomes a collaborative tool across teams.
