Description
Wren AI is an agentic GenBI (Generative Business Intelligence) platform designed to provide a unified and governed context layer for both humans and AI agents. It allows users to query the same trusted data across multiple databases, including BigQuery, PostgreSQL, MySQL, and Snowflake. The platform's agentic design enables sandboxed, multi-step reasoning, allowing agents to query, chart, extract from PDFs, build dashboards, and save skills within an isolated environment. Every step is traceable and replayable.
Wren AI's architecture is Git-native, with a file system where agents read and write directly using MDL (Model Definition Language). Skills, memory, semantic models, and instructions are all versioned, branched, and PR-reviewed. The open MDL approach ensures a single definition, enabling agents to reason over it instead of hallucinating joins. The platform supports over 20 data sources and offers role-based access control with full activity logs, ensuring data governance and auditability. It provides a feedback loop where past interactions contribute to institutional knowledge.
Wren AI caters to a wide range of users, including data leaders, operational users, C-suite executives, data engineers, and agent developers. It offers self-service analytics in Slack and Teams, enabling users to get instant insights through natural language. The platform's use cases span various industries, including media & entertainment, healthcare, manufacturing, banking & finance, and retail & e-commerce. Wren AI helps companies ship governed answers to both humans and agents, improving decision-making speed and transforming data culture. It offers multi-tenant cloud, private cloud, and air-gapped on-premise deployment options.
Wren AI's value proposition includes faster time-to-answer, reduced waiting time, and the ability to perform advanced analytics. It empowers teams to answer data questions instantly and automates recurring reporting. The platform integrates with various architectures, delivering high query hit rates and secure, on-prem AI operations. Wren AI is more than just analytics; it revolutionizes data culture by making analytics more accessible and actionable without requiring technical expertise. It enables natural language data interaction across multiple databases without ETL or migration.
Wren AI's Core Features
Agentic design with sandboxed, multi-step reasoning
Git-native architecture for version control and collaboration
Support for 20+ data sources
Role-based access control and full activity logs
Feedback loop for institutional knowledge
Self-service analytics in Slack and Teams
Governed context layer for trusted data
Automated dashboard creation
Natural language data interaction
Auditable by design
Multi-tenant cloud, private cloud, and on-premise deployment options
Reusable workflows and context that compounds
Vibe-coded dashboards in one prompt
How to use Wren AI?
Explore the platform: Visit the Wren AI website to learn more about its features and capabilities.
Choose your deployment: Select from multi-tenant cloud, private cloud, or on-premise options based on your needs.
Connect your data sources: Integrate Wren AI with your existing databases, including BigQuery, PostgreSQL, MySQL, and Snowflake.
Define your data model: Utilize MDL to define your data models and ensure consistency across your organization.
Build and deploy agents: Create AI agents to automate tasks, generate insights, and answer data questions.
Use natural language: Interact with your data using natural language queries in Slack or Teams.
Monitor and optimize: Track agent performance and refine your data models and queries for optimal results.
Wren AI's Use Cases
- Executive Reports
- Governed Automation
- Self-service Analytics
- Dashboards
- Data Analysis
- Healthcare Insights
- Manufacturing Analysis
- Banking & Finance
- Retail & E-commerce
